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7 Artificial Intelligence-based movie characters that are now a reality

“Artificial Intelligence (AI) is the science of how to get machines to do the things they do in the movies.”- Astro Teller

Do you remember HAL 9000- the know-all machine, Baymax- the personal healthcare robot, Ava- the human looking robot, and WALL-E- the cleaning robot? I am sure you do. After all, they are famous fictional AI characters that made every sci-fi aficionado go nuts growing up.

Apperceptive, self-aware robots are closer to becoming a reality than you think.

Now, what exactly is AI?

Artificial Intelligence (AI) is defined as the ability of a machine or a computer program to think, learn, and act like a human being.The bottom-line of AI is to develop systems that exceed or at least equal human intelligence.

Sci-fi movies and TV shows have shown us multiple visions of how the future is going to be. The Jetsons, Ex Machina or Star Wars…they all had a unique take on what life would be like years later.

So, how real are these fictional characters? (Ignore the oxymoron) Where are we with the technology?

This article is sort of a brief history of AI with some fictional AI characters and their real counterparts to tell you how far we come on this amazing journey.

History of AI

We really can’t have history without some Greek bits thrown in. And unsurprisingly, the roots of AI can be traced back to Greek mythology. As the American author Pamela McCorduck writes, AI began with “an ancient wish to forge the gods.”

Greek myths aboutHephaestus, the blacksmith who manufactured mechanical servants, and the bronze man Talos, and the construction of mechanical toys and models such as those made by Archytas of Tarentum, Daedalus, and Hero are proof.

Alan Turing is widely credited for being one of the first people to come up with the idea of machines that think. He was a British mathematician and WWII code-breaker who created the Turing test to determine a machine’s ability to “think” like a human. Turing test is still used today.

His ideas were mocked at the time but they triggered an interest in theconcept, and the term “artificial intelligence” entered public consciousness in the mid- 1950s, after Alan Turing died.

The field of AI research was formally founded in a workshop conducted by IBM at Dartmouth College during 1956. AI has flourished a lot since then.

Some fictional characters that are reality

The following is a list of some fictional AI characters and their real counterparts with the features.

HAL 9000 versus IBM Watson

Remember the iconic scene of the movie, “2001: A Space Odyssey” when HAL refuses to open the pod bay doors saying, “I’m sorry, Dave. I’m afraid I can’t do that.” If you don’t remember, then take a lookthe clip below:

The movie “2001: A Space Odyssey” gave one of the world’s best representations of AI in the form of HAL 9000.

HAL stands for Heuristically Programmed Algorithmic Computer. It is a sentient computer (or artificial general intelligence) says Wikipedia. And it was the on-board computer on the spaceship called Discovery 1.

It was designed to control the systems on the Discovery 1 spaceship and to interact with the astronaut crew of the spaceship. Along with maintaining all the systems on Discovery, it is capable of many functions such as speech recognition, lip reading, emotional interpretation, facial recognition, expressing emotions, and chess.

HAL is a projection of what a future AI computer would be like from a mid-1960s perspective.

The closest real counterpart to HAL 9000 that we can think of today isIBM Watson. It is a supercomputer that combines AI and analytical software. Watson was named after IBM’s first CEO, Thomas J. Watson. Watson secured the first position in Jeopardy in 2011, after beating former winners Brad Rutter and Ken Jennings.

It is a “question answering” machine that is built on technologies such as advanced natural language processing, machine learning, automated reasoning, information retrieval, and much more.

According to IBM, “The goal is to have computers start to interact in natural human terms across a range of applications and processes, understanding the questions that humans ask and providing answers that humans can understand and justify.”

Its applications in cognitive computing technology are almost endless. It can perform text mining and complex analytics on large volumes of unstructured data.

Unlike HAL, it is working peacefullywith humans in various fields such as R&D Departments of companies as Coca-Cola and Proctor and Gamble to come with new product ideas. Apart from this, it is being used in healthcare industries where it is helping oncologists find new treatment methods for cancer. Watson is also used as a chatbot to provide the conversation inchildren’s toys.

Terminator versus Atlas robots

One of the most recognizable movie entrances of all time is attributed to the appearance of ArnoldSchwarzenegger in the movieTerminator as the killer robot, T-800.

T-800, the Terminator robot, has living tissue over a metal endoskeleton. It was programmed to kill on behalf of Skynet.

Skynet, the creator of T-800, is another interesting character in the movie. It is a neural networks-based artificially intelligent system that has taken over the world’s’ all computers to destroy the human race.

Skynet gained self-awareness and its creators tried to deactivate it after realizing the extent of its abilities. Skynet, for self-preservation, concluded that all of humanity would attempt to destroy it.

There are no AIs being developed yet which have self-awareness and all that are there are programmed to help mankind. Although, an exception to this is amilitary robot.

Atlas is a robot developed by the US military unit Darpa. It is a bipedal model developed by Boston Dynamics which is designed for various search and rescue activities.

A video of a new version of Atlas was released in Feb 2016. The new version canoperate outdoors and indoors. It is capable of walking over a wide range of terrains, including snow.

Currently, there are no killer robots but there is a campaign going on to stop them from ever being produced, and the United Nations has said that no weapon should be ever operated without human control.

C-3PO versus Pepper

Luke: “Do you understand anything they’re saying?”
C-3PO: “Oh, yes, Master Luke! Remember that I am fluent in over six million forms of communication.”

C-3PO or See-Threepio is a humanoid robot from the Star Wars series who appears in the original Star Wars films, the prequel, and sequel trilogy. It is played by Anthony Daniels in all the seven Star Wars movies. The intent of his design was to assist in etiquette, translations, and customs so that the meetings of different cultures can run smoothly. He keeps boasting about his fluency.

In real life too, companion robots are starting to take off.

Pepper is a humanoid robot designed by Aldebaran Robotics and SoftBank. It was introduced at a conference on June 5, 2014, and was first showcased in Softbank mobile phone stores in Japan.

Pepper is not designed as a functional robot for domestic use. Instead, Pepper is made with the intent of “making people happy,” to enhance their lives, facilitate relationships, and have fun with people. The creators of Pepper are optimistic that independent developers will develop new uses and content for Pepper.

Pepper is claimed to be the first humanoid robot which is “capable of recognizing the principal human emotions and adapting his behavior to the mood of his interlocutor.”

WALL-E versus Roomba

WALL-E is thetitle character of the animated science fiction movie of the same name. He is left to clean up after humanity leaves Planet Earth in a mess.

In the movie, WALL-E is the only robot of his kind who is still functioning on Earth. WALL-E stands for Waste Allocation Loader Lift: Earth Class. He is a small mobile compactor box with all-terrain treads, three-fingered shovel hands, binocular eyes, and retractable solar cells for power.

Arobot that is closely related to WALL-E is Roomba, the autonomous robotic vacuum cleaner though it is not half as cute as WALL-E.

Roomba is a series of vacuum cleaner robots sold by iRobot. It was first introduced in September 2002. It sold over 10 million units worldwide as of February 2014. Roomba has a set of basic sensors that enable it to perform tasks.

Some of its features include direction change upon encountering obstacles, detection of dirty spots on the floor, and sensing steep drops to keep it from falling down the stairs. It has two wheels that allow 360° movements.

It takes itself back to its docking station to charge once the cleaning is done.

Ava versus Geminoid

Ava is a humanoid robot with artificial intelligence shown in the movie Ex Machina. Ava has a human-looking face but a robotic body. She is an android.

Ava has the power to repair herself with parts from other androids. Atthe end of the movie, she uses their artificial skin to take on the full appearance of a human woman.

Ava gains so much intelligence that she leaves her friend, Caleb trapped inside, ignoring his screams, and escapes to the outside world. This is the kind of AI that people fear the most, but we are far away from gaining the intelligence and cleverness that Ava had.

People are experimenting with making robots that look like humans.

A geminoid is a real person-based android. It behaves and appears just like its source human. Hiroshi Ishiguro, a robotic engineer made a robotic clone of himself.

Hiroshi Ishiguro used silicon rubber to represent the skin. Recently, cosmetic company L’Oreal teamed up with a bio-engineering start-up called Organovo to 3D print human skin. This will potentially make even more lifelike androids possible.

Prof. Chetan Dube who is the chief executive of the software firm IPsoft, has also developed a virtual assistant called Amelia. He believes “Amelia will be given human form indistinguishable from the real thing at some point this decade.”

Johnny Cab versus Google self-driving car

The movie Total Recall begins in the year 2084, where a construction worker Douglas Quaid (Arnold Schwarzenegger) is having troubling dreams about the planet Mars and a mysterious woman there. In a series of events, Quaid goes to Mars where he jumps into a taxi called“Johnny Cab.”

The taxi is driver-less and to give it a feel like it has a driver, the taxi has a showy robot figure named Johnny which interacts with the commuters. Johnny ends up being reduced to a pile of wires.

Google announced in August 2012 that itsself-driving car completed over 300,000 autonomous-driving accident-free miles. In May 2014, a new prototype of its driverless car was revealed. It was fully autonomous and had no steering wheel, gas pedal, or brake pedal.

According to Google’s own accident reports, its test cars have been involved in 14 collisions, of which 13 were due to the fault of other drivers. But in 2016, the car’s software caused a crash for the first time. Alphabet announced in December 2016 that the self-driving car technology would be under a new company called Waymo.

Baymax versus RIBA II

Remember the oscar winning movie Big Hero 6? I’m sure you do.

The story begins in the futuristic city of San Fransokyo, where Hiro Hamada, a 14-year-old robotic genius, lives with his elder brother Tadashi. Tadashi builds an inflatable robot medical assistant named Baymax.

Don Hall, the co-director of the movie said, “Baymax views the world from one perspective — he just wants to help people; he sees Hiro as his patient.”

In a series of events, Baymax sacrifices himself to save Hiro’s and Abigail’s (another character in the movie) lives. Later, Hiro finds his healthcare chip and creates a new Baymax.

In Japan, the elderly population in need ofnursing care reached an astounding 5.69 million in2015. So, Japan needs new approaches to assist care-giving personnel. One of the most arduous tasks for such personnel is lifting a patient from the floor onto a wheelchair.

In 2009, the RIKEN-TRI Collaboration Center for Human-Interactive Robot Research (RTC), a joint project established in 2007 and located at the Nagoya Science Park in central Japan, displayed a robot called RIBA designed to assist carers in the above-mentioned task.

RIBA stands for Robot for Interactive Body Assistance. RIBA was capable of lifting a patient from a bed onto a wheelchair and back. Although it marked a new course in the development of such care-giving robots. Some functional limitations have prevented its direct commercialization.

RTC’s new robot, RIBA-II has overcome these limitations with added functionalities and power.

Summary

Soon a time will come when we won’t need to read a novel or watch a movie to be teleported to a world of robots. Even then, let’s keep these fictional stories in mind as we stride into the future.

AI is here already and it will only get smarter with time. The greatest myth about AI is that it will be same as our own intelligence with the same desires such as greed, hunger for power, jealousy, and much more.

Read more on How Artificial Intelligence is rapidly changing everything around you!

How to hire a full stack developer

Who is a full stack developer?

A good full stack developer is like one of those celebrities who can do it all. They can act, sing, be a DJ, host a show, even direct, and produce! They may not have won an Oscar or a Grammy, but they still have the breadth of experience.They are capable of developing full-fledged applications (Web, mobile, or desktop). They understand both the front-end and back-end and know their way around servers, databases, APIs, MVC, and hosting environments among others. (Also read - Top skills a full stack developer should have)

Layers of full stack web application, full stack developer, how to hire a full stack developer. who is a full stack developer, hire full stack developerA good full stack developer is always in demand. There are over 10,000 open positions available on Indeed alone. However, they may not be the best option in all cases.

When to hire a full stack developer

The demand for a full stack engineer is often driven by the requirements of the role. Hiring a full stack developer is a good idea in the following instances:
  1. When you need an MVP

    When your operation is lean and the company’s aim is to validate ideas by building a minimum viable product, then full stack developers are your best bet. If there is an ideal role for a full stack developer, it would be to take an idea or feature and turn into a fully-functional prototype.
  2. When you need Product Managers

    Full stack developers can make excellent product managers. They understand the business requirements and, at the same time, they are aware of the engineering capabilities. When decisions have to be made by taking all the parameters into account, they are an extremely valuable resource.
  3. When cost is a constraint

    When you cannot afford to hire a specialist for each layer of the development process, full stack developers are your saviors. That being said, good full stack developers don’t come cheap. Nevertheless, instead of spending $70,000 each for a front-end, back-end, and network engineer, it is better to opt for one $100,000 full stack developer. (Also read - How to recruit on a shoestring budget)
  4. When you need a CTO/Co-founder

    “I have an idea for a brilliant app, but I just need someone to build it”. This is a common infuriating phrase that developers often hear. When you are looking for a CTO or co-founder for a truly symbiotic relationship that involves combining their technical expertise with a shared vision for the business, full stack developers can make great CTOs or co-founders.

When not to hire a full stack developer

Do not hire a full stack developer, when you cannot see a clear value-add. For example, a full stack engineer can be a valuable asset when you are trying to optimize your application for 20,000 users. However, when you have reached a scale where you have millions of active users every day, you will definitely need a specialist or a team for each layer such as a data and infrastructure team. In such cases, a full stack developer will not add as much value as a specialist will.

How to hire a full stack developer

When hiring a full-stack developer, you should look for certain qualities and technical skills.

Qualities of a full-stack developer

With reference to qualities, look for someone who:
  • Is interested and passionate about learning new things
  • Understands not only the stacks but also different technologies
  • Can point you in the right direction for a solution even if they cannot solve it
  • Is aware of the latest trends and developments
  • Can see the big picture, the vision of the business, and understands the customer’s requirements

Technical skills to look for in a full stack developer

They should have the knowledge and skills across all the layers. For example, if you are hiring a full stack developer for a web application, then these are ideally the technical skills that you should look for:
  • HTML, CSS, and Javascript (it is pretty much mandatory!)
  • Programming languages (back end)
  • Databases
  • Version control
  • Deployment and hosting
  • Third-party APIs/services
(To read more about the top skills a full stack developer should have, go here.)

Things to look for in a resume

Reduce the dependency on a resume as much a possible. When it comes to technical skills, resumes are usually not a true indicator of the technical skills of a developer. The role of a resume ends with the sourcing of candidates. While scanning a resume don’t just look for relevant experience.Also look for other indicators of a good programmer such as contribution to open source, exposure to various technologies and previous projects. If you have an alternative mechanism for sourcing candidates like sourcing from Github, it is much better.

Technical assessment

This is the most crucial step in your hiring process. How you assess the candidates determines the quality of the hire.Conducting a generic algorithmic test as a mechanism for assessing a full stack developer is a total waste of your time.Instead, give them a real-life problem, which will allow you to assess the technical skills and knowledge across all stacks. Here is a sample problem that would give a better idea of how to use a real-life business problem for technical assessment.Sample real life problem to assess the technical skills of a full stack developer, Layers of full stack web application, full stack developer, how to hire a full stack developer. who is a full stack developer, hire full stack developer

Things to assess in the interview

Once you have a handful of candidates who you know to be technically qualified for the job, look for these two things in the interview:
  • Ability to deal with uncertainty
  • Interest and passion for learning
Apart from gauging their technical skills, give the candidates a problem that they are not familiar with. Don’t just look for a successful output, also look for candidates who are ready to try irrespective of the outcome.So when you hire your next full-stack developer, ensure that you:
  • Look for inherent qualities
  • Make technical assessment mandatory
  • Choose an appropriate mechanism to assess the technical skills
Now that you have a good idea about how to go about hiring a consummate developer, try HackerEarth developer assessment software to make candidate assessment easy, effective, and efficient.
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17 Post Graduation Courses on Machine Learning & Data Science in the US and India

Introduction

We certainly have some interesting times to look forward to. All ed tech and career forecasts for this decade talk about artificial intelligence (AI) technologies, including machine learning, deep learning, and natural language processing, enabling digital transformation in ways that are quite “out there.”

To stay relevant in this economy, the brightest minds, naturally, want to stay ahead of the pack by specialising in these exciting fields.

Going back to school may not be a feasible or attractive route when looking for new career options for people who are already equipped with degrees in computer science, engineering, math, or statistics. So, they typically get certified from edX, Coursera, and Udacity. Read more top free courses from these ed platforms here.

In the U.S., many premier universities offer offline and online graduate programs in data science and only a few in machine learning. Some universities such as Johns Hopkins, Princeton, Rutgers, and University of Wisconsin–Madison offers machine learning/AI courses designed for data science, computer science, math, or stats graduate students.

But for students who can’t wait to learn on the job, we’ve put together a list of universities that offer graduate and/or PhD programs on the campus in the US and India.

Table of Contents

  1. Universities / Colleges in the US
    • Carnegie Mellon University, Pennsylvania
    • University of Washington, Washington
    • Colombia University, New York
    • Stanford University, California
    • Texas A & M University, Texas
    • New York University, New York
    • Georgia Tech, Georgia
    • North Carolina State University, North Carolina
    • Northwestern University, Illionis
    • UC Berkley, California
  2. Universities / Colleges in India
    • Great Lakes Institute of Management, Gurgaon / Chennai / Bengaluru
    • SP Jain School of Global Management, Pune
    • Narsee Monjee Institute of Management Studies, Mumbai
    • MISB Bocconi, Mumbai
    • Indian School of Business (ISB), Bengaluru
    • IIM Bangalore
    • Institute of Finance and International Management (IFIM), Bengaluru

Universities / Colleges in the US

1. Carnegie Mellon University, Pennsylvania

Situated in Pittsburgh, CMU has seven colleges and independent schools and is among the top 25 universities in the U.S. The Machine Learning Department offers three courses to introduce students to the concept of data-driven decision making:

  • Master of Science in Machine Learning which focuses on data mining.For information about the application procedure and deadlines, go here.
  • Secondary Master’s in Machine Learning which is open only to its PhD students, faculty, and staff.For information about admission requirements and application, go here.
  • Fifth Year Master’s in Machine Learning for its undergraduate students to get an MS by earning credits in ML courses.For information about program requirements and application, go here.
  • The Language Technologies Department offers a Master of Computational Data ScienceDegree.

2. University of Washington, Washington

UW’s Master of Science in Data Science degree teaches students to manage, model, and visualize big data. Expert faculty from six of the university’s departments who teach this fee-based course expect the students to have “a solid background mathematics, computer programming and communication.” The course is designed for working professionals, with evening classes on the campus, who can enroll as part-time or full-time students.

  • For information about the application procedure and deadlines, go here.
  • For information about financial aid and cost of study, go here.

UW’s Certificate in Data Science teaches basic math, computer science, and analytics to aspiring data scientists. Professionals are expected to know some SQL, programming, and statistics. Data storage and manipulation tools (e.g. Hadoop, MapReduce), core machine learning concepts, types of databases, and real-life data science applications are part of the curriculum.

3. Columbia University, New York

Its Master of Science in Data Science is a great option for careerists who want to switch to data science. Students need to earn 30 credits, 21 by taking the core courses, including machine learning, and 9 credits by working on an elective (Foundations of Data Science, Cybersecurity, Financial and Business Analytics, Health Analytics, New Media Sense, Collect and Move Data, Smart Cities) from the Data Science Institute. The university offers both part-time and full-time options.

  • For more course information, go here.

The department also has an online Certification of Professional Achievement in Data Sciences course. The Computer Science Department has a Machine Learning Track as a part of the MS degree in CS.

4. Stanford University, California

The Department of Statistics and Institute for Computational and Mathematical Engineering (ICME) offer an M.S. in Data Science, where it is a terminal degree for the former and a specialized track for ICME. There are several electives that range from machine learning to human neuroimaging methods for students, but strong math (linear algebra, numerical methods, probabilities, PDE, stats, etc.) and programming skills (C++, R) form the core of the course. Go to the homepage for more information about prerequisites and requirements.

  • For information about admissions and financial aid, go here.
Machine learning challenge, ML challenge

5. Texas A&M University, Texas

The Houston-based university has a Master of Science in Analytics degree offered by the Department of Statistics. The course is tailored for “working professionals with strong quantitative skills.” What’s more, students can access Mays Business School courses as well. The part-time course, with evening classes, takes two years to complete. The program, which focuses on statistical modeling and predictive analysis, does have an online option.

  • For information on course requirements, go here.

6. New York University, New York

The Master of Science in Data Science is for students with a strong programming and mathematical background. The Center for Urban Science and Progress and the Center for the Promotion of Research Involving Innovative Statistical Methodology work closely with the Center for Data Science. The university offers full-time and part-time options; students have to earn 36 credits and also have six electives to choose from. Tuition scholarships are available although not for university fees.

  • For more information about the course, go here.

7. Georgia Tech, Georgia

The on-campus Master of Science in Analytics program Georgia Tech offers opportunities to strengthen your skills in statistics, computing, operations research, and business. The instructors include experts from the College of Engineering, the College of Computing, and Scheller College of Business. Applicants to this premium tuition program are expected to be proficient in basic mathematical concepts such as calculus, statistics, and high-level computing languages such as C++ and Python. Depending on what their career goals are, students can choose from one of these tracks: Analytical Tools, Business Analytics, and Computational Data Analytics.

What’s great for the students is that the college has dedicated job placement assistance and chances to network with influencers in the data science industry.

  • For more information on how to apply, go here.

The College of Computing has courses in artificial intelligence (AI) and machine learning (ML) at the undergraduate and graduate levels; they do not award degrees in these.

8. North Carolina State University

The Institute for Advanced Analytics offers a 10-month long Master of Science in Analytics degree. The program is “innovative, practical, and relevant.” The Summer session includes Statistics primer and Analytics tools and foundation. The Practicum, which last eight months in the fall and spring, teaches you a range of topics including data mining, machine learning, optimization, simulation & risk, web analytics, financial analytics, data visualization, and business concepts such as project management.

  • For information about application requirements and procedures, go here.
  • For information about the tuition and fees, go here.

9. Northwestern University, Illinois

McCormick School of Engineering and Applied Science offers a 15-month full-time MS in Analytics degree. The faculty “combines mathematical and statistical studies with instruction in advanced information technology and data management.” The course has an 8-month practicum project, 3-month summer internship, and a 10-week capstone project. Scholarships that cover up to 50% of the tuition are available on merit basis.

  • For information about admission requirements and procedures, go here.
  • For information about the tuition and funding, go here.

10. UC Berkeley, California

Although the Master of Information and Data Science is an online course, students have to attend a week on campus. The curriculum covers areas in social science, policy research, statistics, computer science, and engineering. The full-time option takes 12 to 20 months; the university lets you complete the course part time as well.

  • For more information about the course, go here.

Universities / Colleges in India

1. Great Lakes Institute of Management

Great Lakes’ Post Graduate Program in Business Analytics and Business Intelligence has been ranked the best analytics course in the country by Analytics India Magazine. The course is designed for working professionals and is offered in its Chennai, Gurgaon, and Bengaluru campuses. The curriculum combines business management skills and analytics, including case studies and hands-on training in relevant tools such as Tableau, R, and SAS. Students have to attend 230 hours of classroom sessions and 110 hours of online sessions.

  • For more information about the program, go here.

2. SP Jain School of Global Management

Students can opt for the full-time or part-time options of the Big Data & Analytics program offered by the Mumbai-based institute. People with prior work experience are given preference. The program has 10 core courses including cutting-edge topics such as machine learning, data mining, predictive modeling, natural language processing, visualization techniques, and statistics. Industry experts and academicians focus on application-based learning, teaching students how to apply current tools and technologies to extract valuable insights from big data.

  • For more information about the program, go here.

3. Narsee Monjee Institute of Management Studies

It offers a 1-year Postgraduate Certificate Program in Business Analytics in partnership with University of South Florida. The course conducted in its Mumbai campus combines classroom training with online sessions. NMIMS will take 12 hours and USF Muma College of Business faculty will take 20 hours to instruct students on the current Business Analytical tools, methodologies, and technologies. Course covers topics such as introduction to statistics, database management, business intelligence and visualization, machine learning, big data analytics, data mining, financial analytics, and optimization. Students will learn how to tackle real-world business issues through the capstone project.

  • For more information about the program, go here.

4. MISB Bocconi

The 12-month Executive Program in Business Analytics is taught by renowned faculty from SDA Bocconi (Milan) and Jigsaw Academy at the Mumbai International School of Business Bocconi (MISB) campus in Mumbai. The course content comprises web analytics, statistics, visualization, R, time series, text mining, SAS, machine learning, Big Data (Sqoop, Flume, Pig, HBASE, Hive, Oozie, and SPARK), and digital marketing. Students learn core concepts of business analytics and its application across various domains.

  • For more information about the course curriculum, go here.

5. Indian School of Business (ISB)

ISB offers a Certificate Program in Business Analytics on its Hyderabad campus. The course is designed for working professionals (with at least 3 years of work experience) who have to spend 18 days at the institute during the 12-month program; a technology-aided learning platform takes over the rest of the time. The rigorous course is chock-full with lectures, projects, and assignments. The comprehensive curriculum also includes preparatory pre-term courses and a capstone project.

  • For more information about the course curriculum, go here.

6. IIM Bangalore

The year-long Certificate Program on Business Analytics and Intelligence comprises six modules and a project. The course content includes Data Visualization and Interpretation, Data Preprocessing and Imputation, Predictive Analytics: Supervised Learning Algorithms, Optimization Analytics, Stochastic Models, Data Reduction, Advanced Forecasting and Operations Analytics, Machine Learning Algorithms, Big Data Analytics,and Analytics in Finance and Marketing. The Institute would like the applicants to have a minimum of 3 years of work experience. Online classes are open to a limited number of participants, who must attend on-campus sessions as well.

  • For information about eligibility criteria, go here.
  • For information about the program fees, go here.

7. Institute of Finance and International Management (IFIM)

The Institute of Finance and International Management, Bangalore, offers a 15-month full-time Business Analytics program for working executives. Program features include live streaming and classroom sessions, opportunity to work with relevant IBM, OpenSource, and Microsoft software, and convenient weekend classes.

  • For more information about this program, go here.

Conclusion

With the huge amounts of data pouring in and the need to apply analytical solutions to address business challenges, the future looks brighter than ever for data scientists and machine learning experts. Salaries are naturally high for these much sought-after skills.

For programmers and statisticians, getting certified is the next step. For students looking to distinguish themselves, these are great career opportunities.

In this post, we have put together a list of graduate programs offered by highly ranked institutes and universities in the US and India. On-campus courses are interactive; nothing can beat face-to-face contact with the faculty and peers, the friends you make, and the easy access to relevant resources.

Charles Babbage's computer - History of computer programming- Part 1

“What is imagination?…It is a God-like, a noble faculty. It renders earth tolerable; it teaches us to live, in the tone of the eternal.” – Ada Lovelace to Charles Babbage

When Charles Babbage, in 1837, proposed a ”Fully programmable machine” which would be later called an Analytical engine, not even the government who seed-funded his Difference Engine believed him.

Undoubtedly the most influential machine in existence in today’s modern computer.

But back in the 19th century, when the world was drooling over the industrial revolution and railway tracks and steam engines, a machine which could think and calculate looked like a distant dream.

While most see the evolution of these advanced machines such as computers and smartphones as examples of electronic innovation, what people have taken for granted had been an evolution and the hard work of transforming a mechanical device into a self-thinking smart device which would become an integral part of our lives.

Charles Babbage – The father of the computer

In the 19th century, the concept of specialization had not breached the revered halls of universities and laboratories.

Most of the geniuses were polymaths, so was the Englishman Charles Babbage. Charles Babbage was a renowned mathematician, philosopher, and mechanical engineer of his times.

During those days, mathematical tables (such as your logbook) were manually made and were used in navigation, science, and engineering.

Since most of these tables were manually updated and calculated, the values in these tables varied frequently, giving inconsistent results during studies.

While at Cambridge, Charles Babbage noticed this flaw and thought of converting this mathematical-table based calculation into a mechanical product to avoid any discrepancies.

Difference Engine

In 1822, Charles Babbage decided to make a machine to calculate the polynomial function—a machine which would calculate the value automatically.

In 1823, the British government gave Charles Babbage £1700 (probably the first ever seed funding).

He named it the Difference Engine, possibly after the finite difference method is used to calculate.

Charles Babbage invited Joseph Clement to design his ambitious massive difference engine that had about 25,000 parts, weighed around 15 tons, and was 8 feet tall.

Despite the ample funding by the government, the engine never got completed. And in the late 1840s, he planned on making an improved engine.

But that was not completed either due to lack of funds.

In 1989–1991, scientists and engineers studying Charles Babbage’s research paper built the first difference engine, which is now placed in The Museum of the History of Science, Oxford.

History of Computer Programming - Lovelace, Babbage and Engines, Ada lovelace, history of computer programming, Charles Babbage, Difference engine detail, analytical engine, how does difference engine works, working of difference engine, Ada Lovelace worlds first programmer, worlds first programmer, world's first computer, Father of computer,

How Does Charles Babbage’s Difference Engine work?

Wikipedia says: “A difference engine is an automatic mechanical calculator designed to tabulate polynomial functions.

The name derives from the method of divided differences, a way to interpolate or tabulate functions by using a small set of polynomial coefficients.”

Let’s take an example with a polynomial function R = x2 + 1

X R Difference 1 Difference 2
Step 1 0 1 1 (D11) 2 (D21)
Step 2 1 2 3 (D12) 2 (D22)
Step 3 2 5 5 (D13) 2 (D23)
Step 4 3 10 7 (D14) 2 (D24)
Step 5 4 17 9 (D15) 2 (D2)

To solve this manually, you need to solve the equation “n+1” times, where n is the polynomial. So, for the given equation, we need threesteps.

When X = 0, result of R = 1; X= 1, R =2; X=2, R= 5, and so on.

Difference 1 : D11 = R2 (Step 2) – R1 (Step 1) or D12 (Step 2) = R3 (Step 3) – R2 ( Step 2) and so on

So for the Difference 1 column in the table above,

D11 = 2 (R2) – 1(R1) = 1

D12 = 5 (R3) – 2(R2) = 3

D13 = 10 (R4) – 5(R3) = 5

Difference 2 : D21 = D12 (Difference 1 -Step 2) – D11( Difference 1- Step 1), and so on.

By subtracting two consecutive values from the Difference 1 column,

D21 = 3 (D12) – 1(D11) = 2

D22 = 5 (D13) – 3 (D12) = 2

Similarly, for a third-order equation, we can prepare a new column called Difference 3, and calculate it by subtracting two consecutive numbers from the last column.

*The values in the last column or the highest power value always remain constant in the last difference column.*

Since the engine could only add and subtract, some of the values from each column are given to the difference engine to feed the engine with information necessary for further calculations.

Working of a difference engine

Let’s take another example where you have to calculate the result for x = 3 from the above equation (R = x2 + 1), and the engine was already given the values of Step 1 and Step 2 columns (Refer to above table). The engine would follow the following steps:

Step 1: To calculate the value for D12, Step 1 difference 2 is added to Step 1 Difference 1, which is 2(D21) +1( D11)=3.

Step 2: This D12 when added with R2, which gives the result for Step 3 = 3 (D12) + 2( R2) = 5

Similarly, to calculate the result for x = 4

Step 1 – For X = 4, Step 2 – Difference 2 added to Difference 1 = 2 (D22) +1 (D12) = 5

Step 2 – Add value from Step 1 to Step 3 result R3, which is 5+5, giving the final value as 10

History of Computer Programming - Lovelace, Babbage and Engines, Ada lovelace, history of computer programming, Charles Babbage, Difference engine detail, analytical engine, how does difference engine works, working of difference engine, Ada Lovelace worlds first programmer, worlds first programmer, world's first computer, Father of computer,

A difference engine (shown above) consisted of N+1 columns, where column N could only store constants and Column 1 showed the value of the current iteration.

And the machine was only capable of adding values from column n+1 to N.

The engine is programmed by setting initial values to the columns. Column 1 is set to the value of the polynomial at the start of computation.

Column 2 is set to a value derived from the first and higher derivatives of the polynomial at the same value of X.

Each column from 3 to N is set to a value derived from the first-order derivative. To simplify what the difference engine did, here is a simple code for Polynomial Function calculation using C++ –

#include <iostream>
#include <math.h>
using namespace std;

int d; // degree of the polynomial
int i; // 
int c; // 
int value; 
int j;
int p;
int sum;



int main()
{
    cout << "Enter the degree of the polynomial: " << endl;
    cin >> d; // degree of the polynomial
    cout << "The degree of the polynomial you entered was " << d << endl;

    
    int *c = new int[i];
    
    for(i = 0; i <= d; i++)
    {
        cout << "Enter coefficients: " << endl;
        cin >> c[i];
        int c[d+1];

    }
    
        cout << "There are " << d + 1 << " coefficients";
        cout << " The coefficients are: ";
        
        for (i = 0; i < d + 1; i++)
        cout << "\n   " << c[i];
        cout << endl;
        
        cout << " Enter the value for evaluating the polynomials" << endl;
        cin >> value;
        sum = 0;
        cout << " The value is " << value << endl;
        
        cout << "First polynomial is: " << endl;
        cout << c[0] << "x^3 + " << c[1] << "x^2 + " << c[2] << "x + " << c[3] << endl;
        {

            for (i = 0; i <= d; i++)
            p = 1;
            {


                for (j = 0; j <= (d - 1); j++)
                p = p * value;
                sum = sum + p;
                sum = pow(c[0]*value,d)+pow(c[1]*value,d-1)+pow(c[2]*value,d-2)+pow(c[3]*value,d-3);
                
                cout << "The sum is " << sum << endl;
            }
            
        }
             
}

The difference engine was never finished, and during its construction, Charles Babbage had a brilliant idea of using Punch Cards for calculation.

Till then, punch cards that had been used only for the mundane job of weaving would form the basis of future computer programming.

Punch Cards

Before Joseph Jacquard came up with the idea of punch cards, the weaving was done using draw looms. A drawloom generally used a “figure harness” to control the weaving pattern.

The drawloom required two operators to control the machine.

Although till 1801, punch cards were only used for individual weaving, Jacquard decided to use perforated papers with the mechanism, because he found that though being intricate, weaving was mechanical and repetitive.

Working

In the most basic form, a weaving design is made by passing onethread over another.

History of Computer Programming - Lovelace, Babbage and Engines, Ada lovelace, history of computer programming, Charles Babbage, Difference engine detail, analytical engine, how does difference engine works, working of difference engine, Ada Lovelace worlds first programmer, worlds first programmer, world's first computer, Father of computer,

In a patterned weave, the threads crossing each other are not synchronized by equal blocks but are changed according to the required pattern.

A weaver controls the threads by pulling and releasing them.

When Joseph Jacquard came up with the idea of a loom, the fabric design in it was first copied on square papers.

This design on the square was translated into punch cards. These cards are stitched together in a continuous belt and fed into the loom.

The holes in the card controlled which threads are raised into the weaving pattern.

This automation allowed Jacquard to make designs and produce them again at lesser costs. Keeping this bunch of cards helped to reproduce the same design repeatedly with perfection on the same or another machine.

“Visualizing” the concept of using these punch cards to calculate, Charles Babbage described using them for the analytical engine.

In 1883, Charles Babbage was introduced to ayoung brilliant mathematician, Ada, who later became Countess of Lovelace, byher tutor.

He was impressed with Ada’sanalytical skills and invited her to look the difference engine, which fascinated her.

This formed the basis of a lasting friendship that continued until her death.

Ada Lovelace – The first programmer

Born to British poet Lord Byron and Annabella Milbanke, Augusta Ada Byron married William King-Noel, who was the first Earl of Lovelace.

Ada was a natural poet who found mathematics poetic.

Growing up, Ada’s education and her families’ influential presence got her in touch with a few prestigious innovators and literary figures of her time.

While studying mathematics, her tutor Mary Somerville introduced her to Charles Babbage, who, after his work on the unsuccessful Difference Engine, was working on an ambitious project of a machine which could solve any complex mathematical function (the Analytical Engine).

What you see below is a caricature image of the Analytical Engine as proposed by Charles Babbage.

The important parts of this engine still constitute our modern computers.

History of Computer Programming - Lovelace, Babbage and Engines, Ada lovelace, history of computer programming, Charles Babbage, Difference engine detail, analytical engine, how does difference engine works, working of difference engine, Ada Lovelace worlds first programmer, worlds first programmer, world's first computer, Father of computer,

Part 1 – The Store, was what we now call Hard disk or memory

Part 2 – The Mill, was what we now call Central Processing Unit (Mill where the churning or production is done)

Part 3 – Steam engine, which would be the source of energy

Ada, impressed by the theory and concept of the Analytical Engine, decided to work with Charles Babbage onthe construction of the engine.

During her study of the Analytical Engine, she wrote a series of notes which explained the difference between a Difference Engine and an Analytical Engine.

She took up Bernoulli number theory and built a detailed algorithm on the process of calculating Bernoulli numbers using an Analytical engine which was demonstrated in Note G of her article shown below.

This made her the first programmer in the world. (This is disputed.)

History of Computer Programming - Lovelace, Babbage and Engines, Ada lovelace, history of computer programming, Charles Babbage, Difference engine detail, analytical engine, how does difference engine works, working of difference engine, Ada Lovelace worlds first programmer, worlds first programmer, world's first computer, Father of computer,

Though her notes were never accepted, and as there was no funding or investment to back Charles Babbage’s fantastic idea, the analytical engine was never completed.

Here is a simple C++ program to the algorithm developed by Ada Lovelace in her lengthy notes:

// bernoulli_distribution
#include <iostream>
#include <random>

int main()
{
  const int nrolls=10000;

  std::default_random_engine generator;
  std::bernoulli_distribution distribution(0.5);

  int count=0;  // count number of trues

  for (int i=0; i<nrolls; ++i) if (distribution(generator)) ++count;

  std::cout << "bernoulli_distribution (0.5) x 10000:" << std::endl;
  std::cout << "true:  " << count << std::endl;
  std::cout << "false: " << nrolls-count << std::endl;

  return 0;

Charles Babbage declined both the title of Knighthood and baronetcy and instead asked for a life peerage, but that wish wasn’t granted in his lifetime.

He died in 1871 ate the age of 79. Ada Lovelace died at the young age of 36 in 1852.

Her contribution to computer science for having come up with the “first” algorithm still remains one of the greatest controversies in technology history.

You can read one such article here.

Irrespective of these facts, their contribution to the field of computer and programming cannot be ignored.

A super calculator which would be able to solve any mathematical problem and a device which would have the ability to think of ways to approach a problem is what Charles Babbage and Ada Lovelace thought of; this was the founding stone of the first programmable computer.

In the next article, we will discuss the use of Punch Cards and how with all technological developments in Europe, the USA got the first computer!

Simple Tutorial on SVM and Parameter Tuning in Python and R

Introduction

Data classification is a very important task in machine learning.Support Vector Machines (SVMs) are widely applied in the field of pattern classifications and nonlinear regressions. The original form of the SVM algorithm was introduced by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963. Since then, SVMs have been transformed tremendously to be used successfully in many real-world problemssuch as text (and hypertext) categorization,image classification,bioinformatics (Protein classification,Cancer classification), handwritten character recognition, etc.

Table of Contents

  1. What is a Support Vector Machine?
  2. How does it work?
  3. Derivation of SVM Equations
  4. Pros and Cons of SVMs
  5. Python and R implementation

What is a Support Vector Machine(SVM)?

A Support Vector Machine is a supervised machine learning algorithm which can be used for both classification and regression problems. It follows a technique called the kernel trick to transform the data and based on these transformations, it finds an optimal boundary between the possible outputs.

In simple words, it does some extremely complex data transformations to figure out how to separate the data based on the labels or outputs defined.We will be looking only at the SVM classification algorithm in this article.

Support Vector Machine Classification Algorithm

How does it work?

The main idea is to identify the optimal separating hyperplane which maximizes the margin of the training data. Let us understand this objective term by term.

What is a separating hyperplane?

We can see that it is possible to separate the data given in the plot above. For instance, we can draw a line in which all the points above the line are green and the ones below the line are red. Such a line is said to be a separating hyperplane.

Now the obvious confusion, why is it called a hyperplane if it is a line?

In the diagram above, we have considered the simplest of examples, i.e., the dataset lies in the 2-dimensional plane(R2). But the support vector machine can work for a general n-dimensional dataset too. And in the case of higher dimensions, thehyperplane is the generalization of a plane.

More formally, it is an n-1 dimensional subspace of an n-dimensional Euclidean space. So for a

  • 1D dataset, a single point represents the hyperplane.
  • 2D dataset, a line is a hyperplane.
  • 3D dataset, a plane is a hyperplane.
  • And in the higher dimension, it is called a hyperplane.

We have said that the objective of an SVM is to find the optimal separating hyperplane. When is a separating hyperplane said to be optimal?

The fact that there exists a hyperplane separating the dataset doesn’t mean that it is the best one.

Let us understand the optimal hyperplane through a set of diagrams.

  1. Multiple hyperplanes
    There are multiple hyperplanes, but which one of them is a separating hyperplane? It can be easily seen that line B is the one which best separates the two classes.
Support Vector Machines multiple hyperplanes
  1. Multiple separating hyperplanes
    There can be multiple separating as well. How do wefind the optimal one? Intuitively, if we select a hyperplane which is close to the data points of one class, then it might not generalize well. So the aim is to choose the hyperplane which is as far as possible from the data points of each category.
multiple separating hyperplanes SVM
  1. In the diagram above, the hyperplane that meets the specified criteria for the optimal hyperplane is B.

Therefore, maximizing the distance between the nearest points of each class and the hyperplane would result in an optimal separating hyperplane. This distance is called the margin.

The goal of SVMs is to find the optimal hyperplane because it not only classifies the existing dataset but also helps predict the class of the unseen data. And the optimal hyperplane is the one which has the biggest margin.

Optimal hyperplane SVM

Mathematical Setup

Now that we have understood the basic setup of this algorithm, let us dive straight into the mathematical technicalities of SVMs.

I will be assuming you are familiar withbasic mathematical concepts such as vectors, vector arithmetic(addition, subtraction, dot product) and the orthogonal projection. Some of these concepts can also be found in the article, Prerequisites of linear algebra for machine learning.

Equation of Hyperplane

You musthave come across the equation of a straight line as y=mx+c, where m is the slope and cis the y-intercept of the line.

The generalized equation of a hyperplane is as follows:

wTx=0

Here w and x are the vectors and wTx represents the dot product of the two vectors. The vector w is often called as the weight vector.

Consider the equation of the line as y−mx−c=0.In this case,

w=⎛⎝⎜−c−m1⎞⎠⎟ and x=⎛⎝⎜1xy⎞⎠⎟

wTx=−c×1−m×x+y=y−mx−c=0

It is just two different ways of representing the same thing. So why do we use wTx=0? Simply because it is easier to deal with this representation in thecase of higher dimensional dataset and w represents the vector which is normal to the hyperplane. This property will be useful once we start computing the distance from a point to the hyperplane.

Machine learning challenge, ML challenge

Understanding the constraints

The training data in our classification problem is of the form {(x1,y1),(x2,y2),…,(xn,yn)}∈Rn×−1,1. This means that the training dataset is a pair of xi, an n-dimensional feature vector and yi, the label of xi. When yi=1 implies that the sample with the feature vector xi belongs to class 1 and if yi=−1 implies that the sample belongs to class -1.

In a classification problem, we thus try to find out a function, y=f(x):Rn⟶{−1,1}. f(x) learns from the training data set and then applies its knowledge to classify the unseen data.

There are an infinite number of functions, f(x) that can exist, so we have to restrict the class of functions that we are dealing with. In thecase of SVM’s, this class of functions is that of the hyperplanerepresented as wTx=0.

It can also be represented as w⃗ .x⃗ +b=0;w⃗ ∈Rn and b∈R

This divides the input space into two parts, one containing vectors of class ?1 and the other containing vectors of class +1.

For the rest of this article, we will consider 2-dimensional vectors. Let H0 be a hyperplane separating the dataset and satisfying the following:

w⃗ .x⃗ +b=0

Along with H0, we can select two others hyperplanes H1 and H2 such that they also separate the data and have the following equations:

w⃗ .x⃗ +b=δ and w⃗ .x⃗ +b=-δ

This makes Ho equidistant from H1 as well as H2.

The variable ? is not necessary so we can set ?=1 to simplify the problem as w⃗ .x⃗ +b=1 and w⃗ .x⃗ +b=-1

Next, we want to ensure that there is no point between them. So for this, we will select only those hyperplanes which satisfy the following constraints:

For every vector xieither:

  1. w⃗ .x⃗ +b≤-1 for xi having the class ?1 or
  2. w⃗ .x⃗ +b≥1 for xi having the class 1
constraints_SVM

Combining the constraints

Both the constraints stated above can be combined into a single constraint.

Constraint 1:

For xi having the class -1, w⃗ .x⃗ +b≤-1
Multiplying both sides by yi (which is always -1 for this equation)
yi(w⃗ .x⃗ +b)≥yi(−1) which implies yi(w⃗ .x⃗ +b)≥1 for xi having the class?1.

Constraint 2:yi=1

yi(w⃗ .x⃗ +b)≥1 for xi having the class 1

Combining both the above equations, we get yi(w⃗ .x⃗ +b)≥1 for all 1≤i≤n

This leads to a unique constraint instead of two which are mathematically equivalent. The combined new constraint also has the same effect, i.e., no points between the two hyperplanes.

Maximize the margin

For the sake of simplicity, we will skip the derivation of the formula for calculating the margin, m which is

m=2||w⃗ ||

The only variable in this formula is w, which is indirectly proportional to m, hence to maximize the margin we will have to minimize ||w⃗ ||. This leads to the following optimization problem:

Minimize in (w⃗ ,b){||w⃗ ||22 subject to yi(w⃗ .x⃗ +b)≥1 for any i=1,…,n

The above is the case when our data is linearly separable. There are many cases where the data can not be perfectly classified through linear separation. In such cases, Support Vector Machine looks for the hyperplane that maximizes the margin and minimizes the misclassifications.

For this, we introduce the slack variable,ζi which allows some objects to fall off the margin but it penalizes them.

Slack variables SVM

In this scenario, the algorithm tries to maintain the slack variable to zero while maximizing the margin. However, it minimizes the sum of distances of the misclassification from the margin hyperplanes and not the number of misclassifications.

Constraints now changes to yi(w⃗ .xi→+b)≥1−ζi for all 1≤i≤n,ζi≥0

and the optimization problem changes to

Minimize in (w⃗ ,b){||w⃗ ||22+C∑iζi subject to yi(w⃗ .x⃗ +b)≥1−ζi for any i=1,…,n

Here, the parameter C is the regularization parameter that controls the trade-off between the slack variable penalty (misclassifications) and width of the margin.

  • Small C makes the constraints easy to ignore which leads to a large margin.
  • Large C allows the constraints hard to be ignored which leads to a small margin.
  • For C=inf, all the constraints are enforced.

The easiest way to separate two classes of data is a line in case of 2D data and a plane in case of 3D data. But it is not always possible to use lines or planes and one requires a nonlinear region to separate these classes. Support Vector Machines handle such situations by using a kernel function which maps the data to a different space where a linear hyperplane can be used to separate classes. This is known as thekernel trick where the kernel function transforms the data into the higher dimensional feature space so that a linear separation is possible.

kernel trick SVM

If ϕ is the kernel function which maps xito ϕ(xi), the constraints change toyi(w⃗ .ϕ(xi)+b)≥1−ζi for all 1≤i≤n,ζi≥0

And the optimization problem is

Minimize in (w⃗ ,b){||w⃗ ||22+C∑iζi subject to yi(w⃗ .ϕ(xi)+b)≥1−ζi  for all 1≤i≤n,ζi≥0

We will not get into the solution of these optimization problems. The most common method used to solve these optimization problems is Convex Optimization.

Pros and Cons of Support Vector Machines

Every classification algorithm has its own advantages and disadvantages that are come into play according to the dataset being analyzed. Some of the advantages of SVMs are as follows:

  • The very nature of the Convex Optimization method ensures guaranteed optimality. The solution is guaranteed to be a global minimum and not a local minimum.
  • SVMis an algorithm which is suitable for both linearly and nonlinearly separable data (using kernel trick). The only thing to do is to come up with the regularization term, C.
  • SVMswork well on small as well as high dimensional data spaces. It works effectively for high-dimensional datasets because of the fact that the complexity of the training dataset in SVM is generally characterized by the number of support vectors rather than the dimensionality. Even if all other training examples are removed and the training is repeated, we will get the same optimal separating hyperplane.
  • SVMscan work effectively on smaller training datasets as they don’trely on the entire data.

Disadvantages of SVMs are as follows:

  • Theyarenot suitable for larger datasets because the training time with SVMs can be high and much more computationally intensive.
  • They areless effective on noisier datasets that have overlapping classes.

SVM with Python and R

Let us look at the libraries and functions used to implement SVM in Python and R.

Python Implementation

The most widely used library for implementing machine learning algorithms in Python is scikit-learn. The class used for SVMclassification in scikit-learn issvm.SVC()

sklearn.svm.SVC (C=1.0, kernel=’rbf’, degree=3, gamma=’auto’)

Parameters are as follows:

  • C: It is the regularization parameter, C, of the error term.
  • kernel: It specifies the kernel type to be used in the algorithm. It can be ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’, or a callable. The default value is ‘rbf’.
  • degree: It is the degree of the polynomial kernel function (‘poly’) and is ignored by all other kernels. The default valueis 3.
  • gamma: It is the kernel coefficient for ‘rbf’, ‘poly’, and ‘sigmoid’. If gamma is ‘auto’, then 1/n_features will be used instead.

There are many advanced parameters too which I have not discussed here. You can check them outhere.

https://gist.github.com/HackerEarthBlog/07492b3da67a2eb0ee8308da60bf40d9

One can tune the SVM by changing the parameters C,γ and the kernel function. The function for tuning the parameters available in scikit-learn is called gridSearchCV().

sklearn.model_selection.GridSearchCV(estimator, param_grid)

Parameters of this function are defined as:

  • estimator: It is theestimator object which is svm.SVC() in our case.
  • param_grid: It is the dictionary or list with parameters names (string) as keys and lists of parameter settings to try as values.

To know more about other parameters of GridSearch.CV(), click here.

https://gist.github.com/HackerEarthBlog/a84a446810494d4ca0c178e864ab2391

In the above code, the parameters we have considered for tuning are kernel, C, and gamma. The values from which the best value is to be are the ones written in the bracket. Here, we have only given a few values to be considered but a whole range of values can be given for tuning but it will take a longer time for execution.

R Implementation

The package that we will use for implementing SVM algorithm in R is e1071. The function used will be svm().

https://gist.github.com/HackerEarthBlog/0336338c5d93dc3d724a8edb67ad0a05

Summary

Inthis article, Ihave gone through a very basic explanation of SVM classification algorithm. I have left outa few mathematical complications such as calculating distances and solving the optimization problem. But I hope this gives you enough know-how abouthow a machine learning algorithm, that is,SVM, can be modified based on the type of dataset provided.

Getting started with Virtual Reality

“There are two kinds of people in this world: Those who think VR will change the world. And, those who haven’t tried VR."

I read this quote somewhere not so long ago. And even while conservatively looking at the scope and scale of the technology, one can easily say that the Virtual Reality experience is exciting to say the least, whether you use a $10 Google Cardboard or a more expensive Oculus Rift or HTC Vive.

Every advancement in computing allows new form factors to emerge which allows us to use the power of computation to create new applications and experiences never seen or felt before. And Virtual Reality is one new form factor which stretches the boundaries of creativity for developers and storytellers.

Within some time any existing game developer can quickly start building applications for VR and for those who haven’t tried their hand on game development or come from non-technical background can learn to develop for VR in no time as well.

What does one need to know to start building for VR?

The Stack:Developing for VR is not much different from game development. Apart from the fact that some additional hardware and software dependencies are required, the basic tools for development do not vary much.Most VR, and even AR, applications require a gaming engine for development, such as Unity3d or Unreal Engine:While these are new tools to learn for developers who do not have much experience in game development, the learning curve is relatively steep for you to start getting comfortable and start building your first application for VR. Many free resources are now also available online to learn.*Quick Tip: Start with Unity3d as it is less complex and a lot of open source knowledge base and resources are available easily.You will also require an appropriate SDK (Software Development Kit) depending on what device you want to build for:Hardware:Although there is a necessary hardware dependency for a VR application, you don’t necessarily need to purchase an expensive device straightaway. When you’re just getting started, a cheaper Google Cardboard can do the job just fine, but it restricts your movement to just 3 DOF (Degrees of Freedom) as opposed to a 6 DOF HTC Vive or Oculus Rift, which allows an immersive room-scale experience.3 DOF means that although you will be able to see in X, Y, Z directions by the motion of your head mounted display (HMD) in the Virtual environment, you wouldn’t be able to move or touch anything. However,, 6 DOF allows for a room-scale experience. While a 6 DOF looks good on the face of it, there are downsides as well. Room-scale VR requires high computation performance with a high-end graphic card and RAM that you probably won’t get from your standard laptops and will require a desktop computer with optimal performance and also at least 6ft × 6ft free space as opposed to 3 DOF that requires just a standard smart phone with an inbuilt gyro (which is inbuilt in most modern smart phones that cost about ₹15,000 or more).Some common devices available in the market today are

Tutorials and Courses:

It can sound a little intimidating in the beginning but actually learning to develop for VR is not all that hard. Once you get a hang of the game engine, one can quickly catch on and there are multiple avenues to explore if you want to learn how to create your VR application:YouTube Tutorials

Here are a few channels that provide good “Getting Started” tutorials for VR:

NurFACEGAMES

MatthewHallberg

Fuseman

Online CoursesA lot of MOOC (massive open online course) courses have come out in the last few months, making it easier to learn and some of them are even free!Most of these courses are short (typically 2–4 weeks). Here are a few of them:
  1. Introduction to Virtual Reality | Udacity (Free)
  2. VR Scenes and Objects | Udacity (Free)
  3. VR Software Development | Udacity (Free)
  4. https://www.coursera.org/learn/augmented-reality/home (Free)
  5. Make Mobile VR Games in Unity with C# for Google Cardboard ($95, there are some discounts available reducing the price to $20–$40)
  6. Cinematic VR Crash Course - Produce Virtual Reality Films (Free)
  7. VR Developer Nanodegree | Udacity (₹9,800/month)

Additional Resources:

And these should be enough to get you started.But if you get stuck, here are some cool applications to follow if you are looking for some inspiration or just want to explore the diversity of the content being generated for VR:SUPERHOT VR

Within

Discovery VR

Job Simulator: the 2050 Archives

Roller Coaster VR attraction - Android Apps on Google Play

Cardboard - Android Apps on Google Play

Google Earth VR

Tilt Brush by Google

Jaunt - Cinematic Virtual Reality - 360° VR video

Pee World VR

To be updated with the space, there are a few media platforms that cover Mixed Reality (VR/AR) exclusively, such as

VR News, Events, and Talent | UploadVR

Road to VR - Virtual Reality News

Inside VR & AR

http://vrtalk.com/forum/

So get started and make some exciting applications.

Register at the UnitedByHCl hackathon
Happy Mixing Reality!!

Happy mixing reality!!

Author:
Pratham Sehgal
Mixed Reality Enthusiast
Previously CoFounder - Trustio Acquired by Slicepay (Exclusive: Student micro-financing startup SlicePay acquires P2P lender Trustio)
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Forecasting Tech Hiring Trends For 2023 With 6 Experts

2023 is here, and it is time to look ahead. Start planning your tech hiring needs as per your business requirements, revamp your recruiting processes, and come up with creative ways to land that perfect “unicorn candidate”!

Right? Well, jumping in blindly without heeding what this year holds for you can be a mistake. So before you put together your plans, ask yourselves this—What are the most important 2023 recruiting trends in tech hiring that you should be prepared for? What are the predictions that will shape this year?

We went around and posed three important questions to industry experts that were on our minds. And what they had to say certainly gave us some food for thought!

Before we dive in, allow me to introduce you to our expert panel of six, who had so much to say from personal experience!

Meet the Expert Panel

Radoslav Stankov

Radoslav Stankov has more than 20 years of experience working in tech. He is currently Head of Engineering at Product Hunt. Enjoys blogging, conference speaking, and solving problems.

Mike Cohen

Mike “Batman” Cohen is the Founder of Wayne Technologies, a Sourcing-as-a-Service company providing recruitment data and candidate outreach services to enhance the talent acquisition journey.

Pamela Ilieva

Pamela Ilieva is the Director of International Recruitment at Shortlister, a platform that connects employers to wellness, benefits, and HR tech vendors.

Brian H. Hough

Brian H. Hough is a Web2 and Web3 software engineer, AWS Community Builder, host of the Tech Stack Playbook™ YouTube channel/podcast, 5-time global hackathon winner, and tech content creator with 10k+ followers.

Steve O'Brien

Steve O'Brien is Senior Vice President, Talent Acquisition at Syneos Health, leading a global team of top recruiters across 30+ countries in 24+ languages, with nearly 20 years of diverse recruitment experience.

Patricia (Sonja Sky) Gatlin

Patricia (Sonja Sky) Gatlin is a New York Times featured activist, DEI Specialist, EdTechie, and Founder of Newbies in Tech. With 10+ years in Higher Education and 3+ in Tech, she now works part-time as a Diversity Lead recruiting STEM professionals to teach gifted students.

Overview of the upcoming tech industry landscape in 2024

Continued emphasis on remote work and flexibility: As we move into 2024, the tech industry is expected to continue embracing remote work and flexible schedules. This trend, accelerated by the COVID-19 pandemic, has proven to be more than a temporary shift. Companies are finding that remote work can lead to increased productivity, a broader talent pool, and better work-life balance for employees. As a result, recruiting strategies will likely focus on leveraging remote work capabilities to attract top talent globally.

Rising demand for AI and Machine Learning Skills: Artificial Intelligence (AI) and Machine Learning (ML) continue to be at the forefront of technological advancement. In 2024, these technologies are expected to become even more integrated into various business processes, driving demand for professionals skilled in AI and ML. Companies will likely prioritize candidates with expertise in these areas, and there may be an increased emphasis on upskilling existing employees to meet this demand.

Increased focus on cybersecurity: With the digital transformation of businesses, cybersecurity remains a critical concern. The tech industry in 2024 is anticipated to see a surge in the need for cybersecurity professionals. Companies will be on the lookout for talent capable of protecting against evolving cyber threats and ensuring data privacy.

Growth in cloud computing and edge computing: Cloud computing continues to grow, but there is also an increasing shift towards edge computing – processing data closer to where it is generated. This shift will likely create new job opportunities and skill requirements, influencing recruiting trends in the tech industry.

Sustainable technology and green computing: The global emphasis on sustainability is pushing the tech industry towards green computing and environmentally friendly technologies. In 2024, companies may seek professionals who can contribute to sustainable technology initiatives, adding a new dimension to tech recruiting.

Emphasis on soft skills: While technical skills remain paramount, soft skills like adaptability, communication, and problem-solving are becoming increasingly important. Companies are recognizing the value of these skills in fostering innovation and teamwork, especially in a remote or hybrid work environment.

Diversity, Equity, and Inclusion (DEI): There is an ongoing push towards more diverse and inclusive workplaces. In 2024, tech companies will likely continue to strengthen their DEI initiatives, affecting how they recruit and retain talent.

6 industry experts predict the 2023 recruiting trends

#1 We've seen many important moments in the tech industry this year...

Rado: In my opinion, a lot of those will carry over. I felt this was a preparation year for what was to come...

Mike: I wish I had the crystal ball for this, but I hope that when the market starts picking up again...

Pamela: Quiet quitting has been here way before 2022, and it is here to stay if organizations and companies...

Pamela Ilieva, Director of International Recruitment, Shortlister

Also, read: What Tech Companies Need To Know About Quiet Quitting


Brian: Yes, absolutely. In the 2022 Edelman Trust Barometer report...

Steve: Quiet quitting in the tech space will naturally face pressure as there is a redistribution of tech talent...

Patricia: Quiet quitting has been around for generations—people doing the bare minimum because they are no longer incentivized...

Patricia Gatlin, DEI Specialist and Curator, #blacklinkedin

#2 What is your pro tip for HR professionals/engineering managers...

Rado: Engineering managers should be able to do "more-with-less" in the coming year.

Radoslav Stankov, Head of Engineering, Product Hunt

Mike: Well first, (shameless plug), be in touch with me/Wayne Technologies as a stop-gap for when the time comes.

Mike “Batman” Cohen, Founder of Wayne Technologies

It's in the decrease and increase where companies find the hardest challenges...

Pamela: Remain calm – no need to “add fuel to the fire”!...

Brian: We have to build during the bear markets to thrive in the bull markets.

Companies can create internal hackathons to exercise creativity...


Also, read: Internal Hackathons - Drive Innovation And Increase Engagement In Tech Teams


Steve: HR professionals facing a hiring freeze will do well to “upgrade” processes, talent, and technology aggressively during downtime...

Steve O'Brien, Senior Vice President, Talent Acquisition at Syneos Health

Patricia: Talk to hiring managers in all your departments. Ask, what are the top 3-5 roles they are hiring for in the new year?...


Also, watch: 5 Recruiting Tips To Navigate The Hiring Freeze With Shalini Chandra, Senior TA, HackerEarth


#3 What top 3 skills would you like HR professionals/engineering managers to add to their repertoire in 2023 to deal with upcoming challenges?

6 industry experts predict the 2023 recruiting trends

Rado: Prioritization, team time, and environment management.

I think "prioritization" and "team time" management are obvious. But what do I mean by "environment management"?

A productive environment is one of the key ingredients for a productive team. Look at where your team wastes most time, which can be automated. For example, end-to-end writing tests take time because our tools are cumbersome and undocumented. So let's improve this.

Mike: Setting better metrics/KPIs, moving away from LinkedIn, and sharing more knowledge.

  1. Metrics/KPIs: Become better at setting measurable KPIs and accountable metrics. They are not the same thing—it's like the Square and Rectangle. One fits into the other but they're not the same. Hold people accountable to metrics, not KPIs. Make sure your metrics are aligned with company goals and values, and that they push employees toward excellence, not mediocrity.
  2. Freedom from LinkedIn: This is every year, and will probably continue to be. LinkedIn is a great database, but it is NOT the only way to find candidates, and oftentimes, not even the most effective/efficient. Explore other tools and methodologies!
  3. Join the conversation: I'd love to see new names of people presenting at conferences and webinars. And also, see new authors on the popular TA content websites. Everyone has things they can share—be a part of the community, not just a user of. Join FB groups, write and post articles, and comment on other people's posts with more than 'Great article'. It's a great community, but it's only great because of the people who contribute to it—be one of those people.

Pamela: Resilience, leveraging data, and self-awareness.

  1. Resilience: A “must-have” skill for the 21st century due to constant changes in the tech industry. Face and adapt to challenges. Overcome them and handle disappointments. Never give up. This will keep HR people alive in 2023.
  2. Data skills: Get some data analyst skills. The capacity to transfer numbers into data can help you be a better HR professional, prepared to improve the employee experience and show your leadership team how HR is leveraging data to drive business results.
  3. Self-awareness: Allows you to react better to upsetting situations and workplace challenges. It is a healthy skill to cultivate – especially as an HR professional.

Also, read: Diving Deep Into The World Of Data Science With Ashutosh Kumar


Brian: Agility, resourcefulness, and empathy.

  1. Agility: Allows professionals to move with market conditions. Always be as prepared as possible for any situation to come. Be flexible based on what does or does not happen.
  2. Resourcefulness: Allows professionals to do more with less. It also helps them focus on how to amplify, lift, and empower the current teams to be the best they can be.
  3. Empathy: Allows professionals to take a more proactive approach to listening and understanding where all workers are coming from. Amid stressful situations, companies need empathetic team members and leaders alike who can meet each other wherever they are and be a support.

Steve: Negotiation, data management, and talent development.

  1. Negotiation: Wage transparency laws will fundamentally change the compensation conversation. We must ensure we are still discussing compensation early in the process. And not just “assume” everyone’s on the same page because “the range is published”.
  2. Data management and predictive analytics: Looking at your organization's talent needs as a casserole of indistinguishable components and demands will not be good enough. We must upgrade the accuracy and consistency of our data and the predictions we can make from it.

Also, read: The Role of Talent Intelligence in Optimizing Recruitment


  1. Talent development: We’ve been exploring the interplay between TA and TM for years. Now is the time to integrate your internal and external talent marketplaces. To provide career experiences to people within your organization and not just those joining your organization.

Patricia: Technology, research, and relationship building.

  1. Technology: Get better at understanding the technology that’s out there. To help you speed up the process, track candidate experience, but also eliminate bias. Metrics are becoming big in HR.
  2. Research: Honestly, read more books. Many great thought leaders put out content about the “future of work”, understanding “Gen Z”, or “quiet quitting.” Dedicate work hours to understanding your ever-changing field.
  3. Relationship Building: Especially in your immediate communities. Most people don’t know who you are or what exactly it is that you do. Build your personal brand and what you are doing at your company to impact those closest to you. Create a referral funnel to get a pipeline going. When people want a job you and your company ought to be top of mind. Also, tell the stories of the people that work there.

7 Tech Recruiting Trends To Watch Out For In 2024

The last couple of years transformed how the world works and the tech industry is no exception. Remote work, a candidate-driven market, and automation are some of the tech recruiting trends born out of the pandemic.

While accepting the new reality and adapting to it is the first step, keeping up with continuously changing hiring trends in technology is the bigger challenge right now.

What does 2024 hold for recruiters across the globe? What hiring practices would work best in this post-pandemic world? How do you stay on top of the changes in this industry?

The answers to these questions will paint a clearer picture of how to set up for success while recruiting tech talent this year.

7 tech recruiting trends for 2024

6 Tech Recruiting Trends To Watch Out For In 2022

Recruiters, we’ve got you covered. Here are the tech recruiting trends that will change the way you build tech teams in 2024.

Trend #1—Leverage data-driven recruiting

Data-driven recruiting strategies are the answer to effective talent sourcing and a streamlined hiring process.

Talent acquisition leaders need to use real-time analytics like pipeline growth metrics, offer acceptance rates, quality and cost of new hires, and candidate feedback scores to reduce manual work, improve processes, and hire the best talent.

The key to capitalizing on talent market trends in 2024 is data. It enables you to analyze what’s working and what needs refinement, leaving room for experimentation.

Trend #2—Have impactful employer branding

98% of recruiters believe promoting company culture helps sourcing efforts as seen in our 2021 State Of Developer Recruitment report.

Having a strong employer brand that supports a clear Employer Value Proposition (EVP) is crucial to influencing a candidate’s decision to work with your company. Perks like upskilling opportunities, remote work, and flexible hours are top EVPs that attract qualified candidates.

A clear EVP builds a culture of balance, mental health awareness, and flexibility—strengthening your employer brand with candidate-first policies.

Trend #3—Focus on candidate-driven market

The pandemic drastically increased the skills gap, making tech recruitment more challenging. With the severe shortage of tech talent, candidates now hold more power and can afford to be selective.

Competitive pay is no longer enough. Use data to understand what candidates want—work-life balance, remote options, learning opportunities—and adapt accordingly.

Recruiters need to think creatively to attract and retain top talent.


Recommended read: What NOT To Do When Recruiting Fresh Talent


Trend #4—Have a diversity and inclusion oriented company culture

Diversity and inclusion have become central to modern recruitment. While urgent hiring can delay D&I efforts, long-term success depends on inclusive teams. Our survey shows that 25.6% of HR professionals believe a diverse leadership team helps build stronger pipelines and reduces bias.

McKinsey’s Diversity Wins report confirms this: top-quartile gender-diverse companies see 25% higher profitability, and ethnically diverse teams show 36% higher returns.

It's refreshing to see the importance of an inclusive culture increasing across all job-seeking communities, especially in tech. This reiterates that D&I is a must-have, not just a good-to-have.

—Swetha Harikrishnan, Sr. HR Director, HackerEarth

Recommended read: Diversity And Inclusion in 2022 - 5 Essential Rules To Follow


Trend #5—Embed automation and AI into your recruitment systems

With the rise of AI tools like ChatGPT, automation is being adopted across every business function—including recruiting.

Manual communication with large candidate pools is inefficient. In 2024, recruitment automation and AI-powered platforms will automate candidate nurturing and communication, providing a more personalized experience while saving time.

Trend #6—Conduct remote interviews

With 32.5% of companies planning to stay remote, remote interviewing is here to stay.

Remote interviews expand access to global talent, reduce overhead costs, and increase flexibility—making the hiring process more efficient for both recruiters and candidates.

Trend #7—Be proactive in candidate engagement

Delayed responses or lack of updates can frustrate candidates and impact your brand. Proactive communication and engagement with both active and passive candidates are key to successful recruiting.

As recruitment evolves, proactive candidate engagement will become central to attracting and retaining talent. In 2023 and beyond, companies must engage both active and passive candidates through innovative strategies and technologies like chatbots and AI-powered systems. Building pipelines and nurturing relationships will enhance employer branding and ensure long-term hiring success.

—Narayani Gurunathan, CEO, PlaceNet Consultants

Recruiting Tech Talent Just Got Easier With HackerEarth

Recruiting qualified tech talent is tough—but we’re here to help. HackerEarth for Enterprises offers an all-in-one suite that simplifies sourcing, assessing, and interviewing developers.

Our tech recruiting platform enables you to:

  • Tap into a 6 million-strong developer community
  • Host custom hackathons to engage talent and boost your employer brand
  • Create online assessments to evaluate 80+ tech skills
  • Use dev-friendly IDEs and proctoring for reliable evaluations
  • Benchmark candidates against a global community
  • Conduct live coding interviews with FaceCode, our collaborative coding interview tool
  • Guide upskilling journeys via our Learning and Development platform
  • Integrate seamlessly with all leading ATS systems
  • Access 24/7 support with a 95% satisfaction score

Recommended read: The A-Zs Of Tech Recruiting - A Guide


Staying ahead of tech recruiting trends, improving hiring processes, and adapting to change is the way forward in 2024. Take note of the tips in this article and use them to build a future-ready hiring strategy.

Ready to streamline your tech recruiting? Try HackerEarth for Enterprises today.

Code In Progress - The Life And Times Of Developers In 2021

Developers. Are they as mysterious as everyone makes them out to be? Is coding the only thing they do all day? Good coders work around the clock, right?

While developers are some of the most coveted talent out there, they also have the most myths being circulated. Most of us forget that developers too are just like us. And no, they do not code all day long.

We wanted to bust a lot of these myths and shed light on how the programming world looks through a developer’s lens in 2021—especially in the wake of a global pandemic. This year’s edition of the annual HackerEarth Developer Survey is packed with developers’ wants and needs when choosing jobs, major gripes with the WFH scenario, and the latest market trends to watch out for, among others.

Our 2021 report is bigger and better, with responses from 25,431 developers across 171 countries. Let’s find out what makes a developer tick, shall we?

Developer Survey

“Good coders work around the clock.” No, they don’t.

Busting the myth that developers spend the better part of their day coding, 52% of student developers said that they prefer to code for a maximum of 3 hours per day.

When not coding, devs swear by their walks as a way to unwind. When we asked devs the same question last year, they said they liked to indulge in indoor games like foosball. In 2021, going for walks has become the most popular method of de-stressing. We’re chalking it up to working from home and not having a chance to stretch their legs.

Staying ahead of the skills game

Following the same trend as last year, students (39%) and working professionals (44%) voted for Go as one of the most popular programming languages that they want to learn. The other programming languages that devs are interested in learning are Rust, Kotlin, and Erlang.

Programming languages that students are most skilled at are HTML/CSS, C++, and Python. Senior developers are more comfortable working with HTML/CSS, SQL, and Java.

How happy are developers

Employees from middle market organizations had the highest 'happiness index' of 7.2. Experienced developers who work at enterprises are marginally less happy in comparison to people who work at smaller companies.

However, happiness is not a binding factor for where developers work. Despite scoring the least on the happiness scale, working professionals would still like to work at enterprise companies and growth-stage startups.

What works when looking for work

Student devs (63%), who are just starting in the tech world, said a good career growth curve is a must-have. Working professionals can be wooed by offers of a good career path (69%) and compensation (68%).

One trend that has changed since last year is that at least 50% of students and working professionals alike care a lot more about ESOPs and positive Glassdoor reviews now than they did in 2020.


To know more about what developers want, download your copy of the report now!


We went a step further and organized an event with our CEO, Sachin Gupta, Radoslav Stankov, Head of Engineering at Product Hunt, and Steve O’Brien, President of Talent Solutions at Job.com to further dissect the findings of our survey.

Tips straight from the horse’s mouth

Steve highlighted how the information collated from the developer survey affects the recruiting community and how they can leverage this data to hire better and faster.

  • The insight where developer happiness is correlated to work hours didn’t find a significant difference between the cohorts. Devs working for less than 40 hours seemed marginally happier than those that clocked in more than 60 hours a week.
“This is an interesting data point, which shows that devs are passionate about what they do. You can increase their workload by 50% and still not affect their happiness. From a work perspective, as a recruiter, you have to get your hiring manager to understand that while devs never say no to more work, HMs shouldn’t overload the devs. Devs are difficult to source and burnout only leads to killing your talent pool, which is something that you do not want,” says Steve.
  • Roughly 45% of both student and professional developers learned how to code in college was another insight that was open to interpretation.
“Let’s look at it differently. Less than half of the surveyed developers learned how to code in college. There’s a major segment of the market today that is not necessarily following the ‘college degree to getting a job’ path. Developers are beginning to look at their skillsets differently and using various platforms to upskill themselves. Development is not about pedigree, it’s more about the potential to demonstrate skills. This is an interesting shift in the way we approach testing and evaluating devs in 2021.”

Rado contextualized the data from the survey to see what it means for the developer community and what trends to watch out for in 2021.

  • Node.js and AngularJS are the most popular frameworks among students and professionals.
“I was surprised by how many young students wanted to learn AngularJS, given that it’s more of an enterprise framework. Another thing that stood out to me was that the younger generation wants to learn technologies that are not necessarily cool like ExtJS (35%). This is good because people are picking technologies that they enjoy working with instead of just going along with what everyone else is doing. This also builds a more diverse technology pool.” — Rado
  • 22% of devs say ‘Zoom Fatigue’ is real and directly affects productivity.
“Especially for younger people who still haven’t figured out a routine to develop their skills, there is something I’d like you to try out. Start using noise-canceling headphones. They help keep distractions to a minimum. I find clutter-free working spaces to be an interesting concept as well.”

The last year and a half have been a doozy for developers everywhere, with a lot of things changing, and some things staying the same. With our developer survey, we wanted to shine the spotlight on skill-based hiring and market trends in 2021—plus highlight the fact that developers too have their gripes and happy hours.

Uncover many more developer trends for 2021 with Steve and Rado below:

View all

Best Pre-Employment Assessments: Optimizing Your Hiring Process for 2024

In today's competitive talent market, attracting and retaining top performers is crucial for any organization's success. However, traditional hiring methods like relying solely on resumes and interviews may not always provide a comprehensive picture of a candidate's skills and potential. This is where pre-employment assessments come into play.

What is Pre-Employement Assessment?

Pre-employment assessments are standardized tests and evaluations administered to candidates before they are hired. These assessments can help you objectively measure a candidate's knowledge, skills, abilities, and personality traits, allowing you to make data-driven hiring decisions.

By exploring and evaluating the best pre-employment assessment tools and tests available, you can:

  • Improve the accuracy and efficiency of your hiring process.
  • Identify top talent with the right skills and cultural fit.
  • Reduce the risk of bad hires.
  • Enhance the candidate experience by providing a clear and objective evaluation process.

This guide will provide you with valuable insights into the different types of pre-employment assessments available and highlight some of the best tools, to help you optimize your hiring process for 2024.

Why pre-employment assessments are key in hiring

While resumes and interviews offer valuable insights, they can be subjective and susceptible to bias. Pre-employment assessments provide a standardized and objective way to evaluate candidates, offering several key benefits:

  • Improved decision-making:

    By measuring specific skills and knowledge, assessments help you identify candidates who possess the qualifications necessary for the job.

  • Reduced bias:

    Standardized assessments mitigate the risks of unconscious bias that can creep into traditional interview processes.

  • Increased efficiency:

    Assessments can streamline the initial screening process, allowing you to focus on the most promising candidates.

  • Enhanced candidate experience:

    When used effectively, assessments can provide candidates with a clear understanding of the required skills and a fair chance to showcase their abilities.

Types of pre-employment assessments

There are various types of pre-employment assessments available, each catering to different needs and objectives. Here's an overview of some common types:

1. Skill Assessments:

  • Technical Skills: These assessments evaluate specific technical skills and knowledge relevant to the job role, such as programming languages, software proficiency, or industry-specific expertise. HackerEarth offers a wide range of validated technical skill assessments covering various programming languages, frameworks, and technologies.
  • Soft Skills: These employment assessments measure non-technical skills like communication, problem-solving, teamwork, and critical thinking, crucial for success in any role.

2. Personality Assessments:

These employment assessments can provide insights into a candidate's personality traits, work style, and cultural fit within your organization.

3. Cognitive Ability Tests:

These tests measure a candidate's general mental abilities, such as reasoning, problem-solving, and learning potential.

4. Integrity Assessments:

These employment assessments aim to identify potential risks associated with a candidate's honesty, work ethic, and compliance with company policies.

By understanding the different types of assessments and their applications, you can choose the ones that best align with your specific hiring needs and ensure you hire the most qualified and suitable candidates for your organization.

Leading employment assessment tools and tests in 2024

Choosing the right pre-employment assessment tool depends on your specific needs and budget. Here's a curated list of some of the top pre-employment assessment tools and tests available in 2024, with brief overviews:

  • HackerEarth:

    A comprehensive platform offering a wide range of validated skill assessments in various programming languages, frameworks, and technologies. It also allows for the creation of custom assessments and integrates seamlessly with various recruitment platforms.

  • SHL:

    Provides a broad selection of assessments, including skill tests, personality assessments, and cognitive ability tests. They offer customizable solutions and cater to various industries.

  • Pymetrics:

    Utilizes gamified assessments to evaluate cognitive skills, personality traits, and cultural fit. They offer a data-driven approach and emphasize candidate experience.

  • Wonderlic:

    Offers a variety of assessments, including the Wonderlic Personnel Test, which measures general cognitive ability. They also provide aptitude and personality assessments.

  • Harver:

    An assessment platform focusing on candidate experience with video interviews, gamified assessments, and skills tests. They offer pre-built assessments and customization options.

Remember: This list is not exhaustive, and further research is crucial to identify the tool that aligns best with your specific needs and budget. Consider factors like the types of assessments offered, pricing models, integrations with your existing HR systems, and user experience when making your decision.

Choosing the right pre-employment assessment tool

Instead of full individual tool reviews, consider focusing on 2–3 key platforms. For each platform, explore:

  • Target audience: Who are their assessments best suited for (e.g., technical roles, specific industries)?
  • Types of assessments offered: Briefly list the available assessment categories (e.g., technical skills, soft skills, personality).
  • Key features: Highlight unique functionalities like gamification, custom assessment creation, or seamless integrations.
  • Effectiveness: Briefly mention the platform's approach to assessment validation and reliability.
  • User experience: Consider including user reviews or ratings where available.

Comparative analysis of assessment options

Instead of a comprehensive comparison, consider focusing on specific use cases:

  • Technical skills assessment:

    Compare HackerEarth and Wonderlic based on their technical skill assessment options, focusing on the variety of languages/technologies covered and assessment formats.

  • Soft skills and personality assessment:

    Compare SHL and Pymetrics based on their approaches to evaluating soft skills and personality traits, highlighting any unique features like gamification or data-driven insights.

  • Candidate experience:

    Compare Harver and Wonderlic based on their focus on candidate experience, mentioning features like video interviews or gamified assessments.

Additional tips:

  • Encourage readers to visit the platforms' official websites for detailed features and pricing information.
  • Include links to reputable third-party review sites where users share their experiences with various tools.

Best practices for using pre-employment assessment tools

Integrating pre-employment assessments effectively requires careful planning and execution. Here are some best practices to follow:

  • Define your assessment goals:

    Clearly identify what you aim to achieve with assessments. Are you targeting specific skills, personality traits, or cultural fit?

  • Choose the right assessments:

    Select tools that align with your defined goals and the specific requirements of the open position.

  • Set clear expectations:

    Communicate the purpose and format of the assessments to candidates in advance, ensuring transparency and building trust.

  • Integrate seamlessly:

    Ensure your chosen assessment tool integrates smoothly with your existing HR systems and recruitment workflow.

  • Train your team:

    Equip your hiring managers and HR team with the knowledge and skills to interpret assessment results effectively.

Interpreting assessment results accurately

Assessment results offer valuable data points, but interpreting them accurately is crucial for making informed hiring decisions. Here are some key considerations:

  • Use results as one data point:

    Consider assessment results alongside other information, such as resumes, interviews, and references, for a holistic view of the candidate.

  • Understand score limitations:

    Don't solely rely on raw scores. Understand the assessment's validity and reliability and the potential for cultural bias or individual test anxiety.

  • Look for patterns and trends:

    Analyze results across different assessments and identify consistent patterns that align with your desired candidate profile.

  • Focus on potential, not guarantees:

    Assessments indicate potential, not guarantees of success. Use them alongside other evaluation methods to make well-rounded hiring decisions.

Choosing the right pre-employment assessment tools

Selecting the most suitable pre-employment assessment tool requires careful consideration of your organization's specific needs. Here are some key factors to guide your decision:

  • Industry and role requirements:

    Different industries and roles demand varying skill sets and qualities. Choose assessments that target the specific skills and knowledge relevant to your open positions.

  • Company culture and values:

    Align your assessments with your company culture and values. For example, if collaboration is crucial, look for assessments that evaluate teamwork and communication skills.

  • Candidate experience:

    Prioritize tools that provide a positive and smooth experience for candidates. This can enhance your employer brand and attract top talent.

Budget and accessibility considerations

Budget and accessibility are essential factors when choosing pre-employment assessments:

  • Budget:

    Assessment tools come with varying pricing models (subscriptions, pay-per-use, etc.). Choose a tool that aligns with your budget and offers the functionalities you need.

  • Accessibility:

    Ensure the chosen assessment is accessible to all candidates, considering factors like language options, disability accommodations, and internet access requirements.

Additional Tips:

  • Free trials and demos: Utilize free trials or demos offered by assessment platforms to experience their functionalities firsthand.
  • Consult with HR professionals: Seek guidance from HR professionals or recruitment specialists with expertise in pre-employment assessments.
  • Read user reviews and comparisons: Gain insights from other employers who use various assessment tools.

By carefully considering these factors, you can select the pre-employment assessment tool that best aligns with your organizational needs, budget, and commitment to an inclusive hiring process.

Remember, pre-employment assessments are valuable tools, but they should not be the sole factor in your hiring decisions. Use them alongside other evaluation methods and prioritize building a fair and inclusive hiring process that attracts and retains top talent.

Future trends in pre-employment assessments

The pre-employment assessment landscape is constantly evolving, with innovative technologies and practices emerging. Here are some potential future trends to watch:

  • Artificial intelligence (AI):

    AI-powered assessments can analyze candidate responses, written work, and even resumes, using natural language processing to extract relevant insights and identify potential candidates.

  • Adaptive testing:

    These assessments adjust the difficulty level of questions based on the candidate's performance, providing a more efficient and personalized evaluation.

  • Micro-assessments:

    Short, focused assessments delivered through mobile devices can assess specific skills or knowledge on-the-go, streamlining the screening process.

  • Gamification:

    Engaging and interactive game-based elements can make the assessment experience more engaging and assess skills in a realistic and dynamic way.

Conclusion

Pre-employment assessments, when used thoughtfully and ethically, can be a powerful tool to optimize your hiring process, identify top talent, and build a successful workforce for your organization. By understanding the different types of assessments available, exploring top-rated tools like HackerEarth, and staying informed about emerging trends, you can make informed decisions that enhance your ability to attract, evaluate, and hire the best candidates for the future.

Tech Layoffs: What To Expect In 2024

Layoffs in the IT industry are becoming more widespread as companies fight to remain competitive in a fast-changing market; many turn to layoffs as a cost-cutting measure. Last year, 1,000 companies including big tech giants and startups, laid off over two lakhs of employees. But first, what are layoffs in the tech business, and how do they impact the industry?

Tech layoffs are the termination of employment for some employees by a technology company. It might happen for various reasons, including financial challenges, market conditions, firm reorganization, or the after-effects of a pandemic. While layoffs are not unique to the IT industry, they are becoming more common as companies look for methods to cut costs while remaining competitive.

The consequences of layoffs in technology may be catastrophic for employees who lose their jobs and the firms forced to make these difficult decisions. Layoffs can result in the loss of skill and expertise and a drop in employee morale and productivity. However, they may be required for businesses to stay afloat in a fast-changing market.

This article will examine the reasons for layoffs in the technology industry, their influence on the industry, and what may be done to reduce their negative impacts. We will also look at the various methods for tracking tech layoffs.

What are tech layoffs?

The term "tech layoff" describes the termination of employees by an organization in the technology industry. A company might do this as part of a restructuring during hard economic times.

In recent times, the tech industry has witnessed a wave of significant layoffs, affecting some of the world’s leading technology companies, including Amazon, Microsoft, Meta (formerly Facebook), Apple, Cisco, SAP, and Sony. These layoffs are a reflection of the broader economic challenges and market adjustments facing the sector, including factors like slowing revenue growth, global economic uncertainties, and the need to streamline operations for efficiency.

Each of these tech giants has announced job cuts for various reasons, though common themes include restructuring efforts to stay competitive and agile, responding to over-hiring during the pandemic when demand for tech services surged, and preparing for a potentially tough economic climate ahead. Despite their dominant positions in the market, these companies are not immune to the economic cycles and technological shifts that influence operational and strategic decisions, including workforce adjustments.

This trend of layoffs in the tech industry underscores the volatile nature of the tech sector, which is often at the mercy of rapid changes in technology, consumer preferences, and the global economy. It also highlights the importance of adaptability and resilience for companies and employees alike in navigating the uncertainties of the tech landscape.

Causes for layoffs in the tech industry

Why are tech employees suffering so much?

Yes, the market is always uncertain, but why resort to tech layoffs?

Various factors cause tech layoffs, including company strategy changes, market shifts, or financial difficulties. Companies may lay off employees if they need help to generate revenue, shift their focus to new products or services, or automate certain jobs.

In addition, some common reasons could be:

Financial struggles

Currently, the state of the global market is uncertain due to economic recession, ongoing war, and other related phenomena. If a company is experiencing financial difficulties, only sticking to pay cuts may not be helpful—it may need to reduce its workforce to cut costs.


Also, read: 6 Steps To Create A Detailed Recruiting Budget (Template Included)


Changes in demand

The tech industry is constantly evolving, and companies would have to adjust their workforce to meet changing market conditions. For instance, companies are adopting remote work culture, which surely affects on-premises activity, and companies could do away with some number of tech employees at the backend.

Restructuring

Companies may also lay off employees as part of a greater restructuring effort, such as spinning off a division or consolidating operations.

Automation

With the advancement in technology and automation, some jobs previously done by human labor may be replaced by machines, resulting in layoffs.

Mergers and acquisitions

When two companies merge, there is often overlap in their operations, leading to layoffs as the new company looks to streamline its workforce.

But it's worth noting that layoffs are not exclusive to the tech industry and can happen in any industry due to uncertainty in the market.

Will layoffs increase in 2024?

It is challenging to estimate the rise or fall of layoffs. The overall state of the economy, the health of certain industries, and the performance of individual companies will play a role in deciding the degree of layoffs in any given year.

But it is also seen that, in the first 15 days of this year, 91 organizations laid off over 24,000 tech workers, and over 1,000 corporations cut down more than 150,000 workers in 2022, according to an Economic Times article.

The COVID-19 pandemic caused a huge economic slowdown and forced several businesses to downsize their employees. However, some businesses rehired or expanded their personnel when the world began to recover.

So, given the current level of economic uncertainty, predicting how the situation will unfold is difficult.


Also, read: 4 Images That Show What Developers Think Of Layoffs In Tech


What types of companies are prone to tech layoffs?

2023 Round Up Of Layoffs In Big Tech

Tech layoffs can occur in organizations of all sizes and various areas.

Following are some examples of companies that have experienced tech layoffs in the past:

Large tech firms

Companies such as IBM, Microsoft, Twitter, Better.com, Alibaba, and HP have all experienced layoffs in recent years as part of restructuring initiatives or cost-cutting measures.

Market scenarios are still being determined after Elon Musk's decision to lay off employees. Along with tech giants, some smaller companies and startups have also been affected by layoffs.

Startups

Because they frequently work with limited resources, startups may be forced to lay off staff if they cannot get further funding or need to pivot due to market downfall.

Small and medium-sized businesses

Small and medium-sized businesses face layoffs due to high competition or if the products/services they offer are no longer in demand.

Companies in certain industries

Some sectors of the technological industry, such as the semiconductor industry or automotive industry, may be more prone to layoffs than others.

Companies that lean on government funding

Companies that rely significantly on government contracts may face layoffs if the government cuts technology spending or contracts are not renewed.

How to track tech layoffs?

You can’t stop tech company layoffs, but you should be keeping track of them. We, HR professionals and recruiters, can also lend a helping hand in these tough times by circulating “layoff lists” across social media sites like LinkedIn and Twitter to help people land jobs quicker. Firefish Software put together a master list of sources to find fresh talent during the layoff period.

Because not all layoffs are publicly disclosed, tracking tech industry layoffs can be challenging, and some may go undetected. There are several ways to keep track of tech industry layoffs:

Use tech layoffs tracker

Layoff trackers like thelayoff.com and layoffs.fyi provide up-to-date information on layoffs.

In addition, they aid in identifying trends in layoffs within the tech industry. It can reveal which industries are seeing the most layoffs and which companies are the most affected.

Companies can use layoff trackers as an early warning system and compare their performance to that of other companies in their field.

News articles

Because many news sites cover tech layoffs as they happen, keeping a watch on technology sector stories can provide insight into which organizations are laying off employees and how many individuals have been affected.

Social media

Organizations and employees frequently publish information about layoffs in tech on social media platforms; thus, monitoring companies' social media accounts or following key hashtags can provide real-time updates regarding layoffs.

Online forums and communities

There are online forums and communities dedicated to discussing tech industry news, and they can be an excellent source of layoff information.

Government reports

Government agencies such as the Bureau of Labor Statistics (BLS) publish data on layoffs and unemployment, which can provide a more comprehensive picture of the technology industry's status.

How do companies reduce tech layoffs?

Layoffs in tech are hard – for the employee who is losing their job, the recruiter or HR professional who is tasked with informing them, and the company itself. So, how can we aim to avoid layoffs? Here are some ways to minimize resorting to letting people go:

Salary reductions

Instead of laying off employees, businesses can lower the salaries or wages of all employees. It can be accomplished by instituting compensation cuts or salary freezes.

Implementing a hiring freeze

Businesses can halt employing new personnel to cut costs. It can be a short-term solution until the company's financial situation improves.


Also, read: What Recruiters Can Focus On During A Tech Hiring Freeze


Non-essential expense reduction

Businesses might search for ways to cut or remove non-essential expenses such as travel, training, and office expenses.

Reducing working hours

Companies can reduce employee working hours to save money, such as implementing a four-day workweek or a shorter workday.

These options may not always be viable and may have their problems, but before laying off, a company owes it to its people to consider every other alternative, and formulate the best solution.

Tech layoffs to bleed into this year

While we do not know whether this trend will continue or subside during 2023, we do know one thing. We have to be prepared for a wave of layoffs that is still yet to hit. As of last month, Layoffs.fyi had already tracked 170+ companies conducting 55,970 layoffs in 2023.

So recruiters, let’s join arms, distribute those layoff lists like there’s no tomorrow, and help all those in need of a job! :)

What is Headhunting In Recruitment?: Types &amp; How Does It Work?

In today’s fast-paced world, recruiting talent has become increasingly complicated. Technological advancements, high workforce expectations and a highly competitive market have pushed recruitment agencies to adopt innovative strategies for recruiting various types of talent. This article aims to explore one such recruitment strategy – headhunting.

What is Headhunting in recruitment?

In headhunting, companies or recruitment agencies identify, engage and hire highly skilled professionals to fill top positions in the respective companies. It is different from the traditional process in which candidates looking for job opportunities approach companies or recruitment agencies. In headhunting, executive headhunters, as recruiters are referred to, approach prospective candidates with the hiring company’s requirements and wait for them to respond. Executive headhunters generally look for passive candidates, those who work at crucial positions and are not on the lookout for new work opportunities. Besides, executive headhunters focus on filling critical, senior-level positions indispensable to companies. Depending on the nature of the operation, headhunting has three types. They are described later in this article. Before we move on to understand the types of headhunting, here is how the traditional recruitment process and headhunting are different.

How do headhunting and traditional recruitment differ from each other?

Headhunting is a type of recruitment process in which top-level managers and executives in similar positions are hired. Since these professionals are not on the lookout for jobs, headhunters have to thoroughly understand the hiring companies’ requirements and study the work profiles of potential candidates before creating a list.

In the traditional approach, there is a long list of candidates applying for jobs online and offline. Candidates approach recruiters for jobs. Apart from this primary difference, there are other factors that define the difference between these two schools of recruitment.

AspectHeadhuntingTraditional RecruitmentCandidate TypePrimarily passive candidateActive job seekersApproachFocused on specific high-level rolesBroader; includes various levelsScopeproactive outreachReactive: candidates applyCostGenerally more expensive due to expertise requiredTypically lower costsControlManaged by headhuntersManaged internally by HR teams

All the above parameters will help you to understand how headhunting differs from traditional recruitment methods, better.

Types of headhunting in recruitment

Direct headhunting: In direct recruitment, hiring teams reach out to potential candidates through personal communication. Companies conduct direct headhunting in-house, without outsourcing the process to hiring recruitment agencies. Very few businesses conduct this type of recruitment for top jobs as it involves extensive screening across networks outside the company’s expanse.

Indirect headhunting: This method involves recruiters getting in touch with their prospective candidates through indirect modes of communication such as email and phone calls. Indirect headhunting is less intrusive and allows candidates to respond at their convenience.Third-party recruitment: Companies approach external recruitment agencies or executive headhunters to recruit highly skilled professionals for top positions. This method often leverages the company’s extensive contact network and expertise in niche industries.

How does headhunting work?

Finding highly skilled professionals to fill critical positions can be tricky if there is no system for it. Expert executive headhunters employ recruitment software to conduct headhunting efficiently as it facilitates a seamless recruitment process for executive headhunters. Most software is AI-powered and expedites processes like candidate sourcing, interactions with prospective professionals and upkeep of communication history. This makes the process of executive search in recruitment a little bit easier. Apart from using software to recruit executives, here are the various stages of finding high-calibre executives through headhunting.

Identifying the role

Once there is a vacancy for a top job, one of the top executives like a CEO, director or the head of the company, reach out to the concerned personnel with their requirements. Depending on how large a company is, they may choose to headhunt with the help of an external recruiting agency or conduct it in-house. Generally, the task is assigned to external recruitment agencies specializing in headhunting. Executive headhunters possess a database of highly qualified professionals who work in crucial positions in some of the best companies. This makes them the top choice of conglomerates looking to hire some of the best talents in the industry.

Defining the job

Once an executive headhunter or a recruiting agency is finalized, companies conduct meetings to discuss the nature of the role, how the company works, the management hierarchy among other important aspects of the job. Headhunters are expected to understand these points thoroughly and establish a clear understanding of their expectations and goals.

Candidate identification and sourcing

Headhunters analyse and understand the requirements of their clients and begin creating a pool of suitable candidates from their database. The professionals are shortlisted after conducting extensive research of job profiles, number of years of industry experience, professional networks and online platforms.

Approaching candidates

Once the potential candidates have been identified and shortlisted, headhunters move on to get in touch with them discreetly through various communication channels. As such candidates are already working at top level positions at other companies, executive headhunters have to be low-key while doing so.

Assessment and Evaluation

In this next step, extensive screening and evaluation of candidates is conducted to determine their suitability for the advertised position.

Interviews and negotiations

Compensation is a major topic of discussion among recruiters and prospective candidates. A lot of deliberation and negotiation goes on between the hiring organization and the selected executives which is facilitated by the headhunters.

Finalizing the hire

Things come to a close once the suitable candidates accept the job offer. On accepting the offer letter, headhunters help finalize the hiring process to ensure a smooth transition.

The steps listed above form the blueprint for a typical headhunting process. Headhunting has been crucial in helping companies hire the right people for crucial positions that come with great responsibility. However, all systems have a set of challenges no matter how perfect their working algorithm is. Here are a few challenges that talent acquisition agencies face while headhunting.

Common challenges in headhunting

Despite its advantages, headhunting also presents certain challenges:

Cost Implications: Engaging headhunters can be more expensive than traditional recruitment methods due to their specialized skills and services.

Time-Consuming Process: While headhunting can be efficient, finding the right candidate for senior positions may still take time due to thorough evaluation processes.

Market Competition: The competition for top talent is fierce; organizations must present compelling offers to attract passive candidates away from their current roles.

Although the above mentioned factors can pose challenges in the headhunting process, there are more upsides than there are downsides to it. Here is how headhunting has helped revolutionize the recruitment of high-profile candidates.

Advantages of Headhunting

Headhunting offers several advantages over traditional recruitment methods:

Access to Passive Candidates: By targeting individuals who are not actively seeking new employment, organisations can access a broader pool of highly skilled professionals.

Confidentiality: The discreet nature of headhunting protects both candidates’ current employment situations and the hiring organisation’s strategic interests.

Customized Search: Headhunters tailor their search based on the specific needs of the organization, ensuring a better fit between candidates and company culture.

Industry Expertise: Many headhunters specialise in particular sectors, providing valuable insights into market dynamics and candidate qualifications.

Conclusion

Although headhunting can be costly and time-consuming, it is one of the most effective ways of finding good candidates for top jobs. Executive headhunters face several challenges maintaining the g discreetness while getting in touch with prospective clients. As organizations navigate increasingly competitive markets, understanding the nuances of headhunting becomes vital for effective recruitment strategies. To keep up with the technological advancements, it is better to optimise your hiring process by employing online recruitment software like HackerEarth, which enables companies to conduct multiple interviews and evaluation tests online, thus improving candidate experience. By collaborating with skilled headhunters who possess industry expertise and insights into market trends, companies can enhance their chances of securing high-caliber professionals who drive success in their respective fields.

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