Tharika Tellicherry

The artificial intelligence industry is expected to reach $59.8 billion by 2025. With use cases in almost every industry vertical, artificial intelligence is predicted to be the future of technology by thought leaders including Bill Gates. From sales forecasting to improving productivity, the application of artificial intelligence (AI) is immense for companies worldwide.

As companies race to scale up their AI capabilities, the demand for experts in the field is expected to rise. Geography-wise, the United States accounts for 66 % percent of the total global investment in AI. As companies bet big time on AI, recruiters are paying impressive salaries to hire AI talent. Glassdoor Research estimates the average annual base pay for AI-based jobs at $111,118 per year.

Here are the top companies that are hiring AI talent as per the Glassdoor research:

1) Amazon

The online retail giant applies AI and ML technologies to improve both their products and services. Amazon Echo is one of their most popular AI-based products that use Alexa, an intelligent personal assistant. After acquiring Kiva, a robotics company in 2012, Amazon implemented an ML algorithm to automate their picking and packaging process. This brought down their ‘Click to ship’ cycle to just 15 minutes, thereby reducing operating costs by 20% while improving inventory capacity by 50%. The company also uses ML technology to identify workflows and enhance their customer interactions. Amazon also has a cloud computing division, Amazon Web Services, which offers AI services. With many AI and ML projects in their bucket, Amazon is one of the top AI companies to work for.

2) NVIDIA

The IT company which featured among Fortune’s top 100 companies to work for in 2017 has big plans for AI. Nvidia’s products include computer chips and platforms with ARM/ GPU that can be used in a variety of devices from drones to automobiles. Their latest graphics processing unit (GPU), Titan V is one of the most powerful GPU of all time and can be used for research in AI and ML. The Glassdoor research ranks Nvidia at number 2 on their list of top companies hiring for AI talent.

3) Microsoft

As one of the leading software companies, Microsoft has been building its AI capabilities on different fronts to drive their business. With a variety of AI-based products and services like Cortona, CNTK, cognitive services, and industry-specific AI apps, Microsoft offers developers many interesting and challenging projects in AI.

4) IBM

Watson is IBM’s most well-known AI projects. IBM’s Watson division is focused on developing cloud-based artificial intelligence technologies for their own products and other organizations. The technology has been used in several spheres including cancer research and retail. IBM is investing heavily in developing their AI capabilities for a wide range of use cases from self-driving cars to hospitality.

5) Accenture

Accenture is investing heavily in combining different technologies with AI and IoT. With the objective of developing AI-based solutions for its clients, Accenture has set up a global network of innovation hubs for developing AI technologies in San Jose, California, and Arlington, Virginia, in the United States; Sophia Antipolis, France; Beijing, China; Bangalore, India; and Dublin, Ireland.

6) Facebook

With over 3 billion users, worldwide, Facebook is the leading social networking site in the world. The company recognized as one of the best places to work in 2018 by Glassdoor is also home to cutting-edge innovations in AI. Their internal group called Facebook AI Research (FAIR) is committed to solving challenges in AI. Apart from acquiring AI companies like Masquerade and Zurich Eye, the company has also invested strategically in their own artificial intelligence labs. The company’s AI research team led by deep learning pioneer, Yann LeCun has many major initiatives planned for 2018 to improve the efficiency of the social media platform.

7) Intel

Intel is investing big time in AI and ML technologies. Apart from developing new ML frameworks and AI chips, the company has invested in many AI startups and acquired AI-focused companies. Saffron Technology is one such company that was acquired by Intel. With a focus on building greater AI capabilities, Intel is among the top 10 companies hiring AI talent in the market.

8) Samsung

The smartphone manufacturer is developing AI technologies to improve camera features, security and user experience of mobile phones. Their AI-powered assistant, Bixby, is designed to deliver a better user experience for mobile phone users. The company is also investing in AI-based startups and have set up AI research centers worldwide.

9) Lenovo

To leverage on AI and ML technologies for manufacturing, the company will invest $1.2 billion in the next two to four years. Their range of AI concept devices includes SmartCast+, an intelligent, interactive speaker that delivers AR experience. Apart from working with renowned tech universities, Lenovo has also set up specialized research labs in the US, Germany, and China.

10)Adobe

Adobe has several new programs and projects focused on building better tools powered by AI. With their Sensei platform based on AI and ML, Adobe plans to offer better user experience to its clients. The company plans to incorporate more AI-based technology in its services and products.

By leading the AI revolution, these top AI companies are among the best places to work for AI experts. In their report titled, How AI Boosts Industry Profits and Innovation, Accenture Research, and Frontier Economics predict that artificial intelligence has the potential to enable 38% profit gains and result in an economic boost of $14 trillions by 2035. With the potential to increase corporate profitability, the AI buzz is here to stay and will pave the way for technological advancements in the future.

The meteoric rise of artificial intelligence in the last decade has spurred a huge demand for AI and ML skills in today’s job market. ML-based technology is now used in almost every industry vertical from finance to healthcare. In this blog, we have compiled a list of best frameworks and libraries that you can use to build machine learning models.

1) TensorFlow
Developed by Google, TensorFlow is an open-source software library built for deep learning or artificial neural networks. With TensorFlow, you can create neural networks and computation models using flowgraphs. It is one of the most well-maintained and popular open-source libraries available for deep learning. The TensorFlow framework is available in C++ and Python. Other similar deep learning frameworks that are based on Python include Theano, Torch, Lasagne, Blocks, MXNet, PyTorch, and Caffe. You can use TensorBoard for easy visualization and see the computation pipeline. Its flexible architecture allows you to deploy easily on different kinds of devices
On the negative side, TensorFlow does not have symbolic loops and does not support distributed learning. Further, it does not support Windows.

2)Theano
Theano is a Python library designed for deep learning. Using the tool, you can define and evaluate mathematical expressions including multi-dimensional arrays. Optimized for GPU, the tool comes with features including integration with NumPy, dynamic C code generation, and symbolic differentiation. However, to get a high level of abstraction, the tool will have to be used with other libraries such as Keras, Lasagne, and Blocks. The tool supports platforms such as Linux, Mac OS X, and Windows.

3) Torch
The Torch is an easy to use open-source computing framework for ML algorithms. The tool offers an efficient GPU support, N-dimensional array, numeric optimization routines, linear algebra routines, and routines for indexing, slicing, and transposing. Based on a scripting language called Lua, the tool comes with an ample number of pre-trained models. This flexible and efficient ML research tool supports major platforms such as Linux, Android, Mac OS X, iOS, and Windows.

4) Caffe
Caffe is a popular deep learning tool designed for building apps. Created by Yangqing Jia for a project during his Ph.D. at UC Berkeley, the tool has a good Matlab/C++/ Python interface. The tool allows you to quickly apply neural networks to the problem using text, without writing code. Caffe partially supports multi-GPU training. The tool supports operating systems such as Ubuntu, Mac OS X, and Windows.

5) Microsoft CNTK
Microsoft cognitive toolkit is one of the fastest deep learning frameworks with C#/C++/Python interface support. The open-source framework comes with powerful C++ API and is faster and more accurate than TensorFlow. The tool also supports distributed learning with built-in data readers. It supports algorithms such as Feed Forward, CNN, RNN, LSTM, and Sequence-to-Sequence. The tool supports Windows and Linux.

6) Keras
Written in Python, Keras is an open-source library designed to make the creation of new DL models easy. This high-level neural network API can be run on top of deep learning frameworks like TensorFlow, Microsoft CNTK, etc. Known for its user-friendliness and modularity, the tool is ideal for fast prototyping. The tool is optimized for both CPU and GPU.



7) SciKit-Learn
SciKit-Learn is an open-source Python library designed for machine learning. The tool based on libraries such as NumPy, SciPy, and matplotlib can be used for data mining and data analysis. SciKit-Learn is equipped with a variety of ML models including linear and logistic regressors, SVM classifiers, and random forests. The tool can be used for multiple ML tasks such as classification, regression, and clustering. The tool supports operating systems like Windows and Linux. On the downside, it is not very efficient with GPU.

8)Accord.NET
Written in C#, Accord.NET is an ML framework designed for building production-grade computer vision, computer audition, signal processing and statistics applications. It is a well-documented ML framework that makes audio and image processing easy. The tool can be used for numerical optimization, artificial neural networks, and visualization. It supports Windows.

9)Spark MLIib
Apache Spark’s MLIib is an ML library that can be used in Java, Scala, Python, and R. Designed for processing large-scale data, this powerful library comes with many algorithms and utilities such as classification, regression, and clustering. The tool interoperates with NumPy in Python and R libraries. It can be easily plugged into Hadoop workflows.

10) Azure ML Studio
Azure ML studio is a modern cloud platform for data scientists. It can be used to develop ML models in the cloud. With a wide range of modeling options and algorithms, Azure is ideal for building larger ML models. The service provides 10GB of storage space per account. It can be used with R and Python programs.

11) Amazon Machine Learning
Amazon Machine Learning (AML) is an ML service that provides tools and wizards for creating ML models. With visual aids and easy-to-use analytics, AML aims to make ML more accessible to developers. AML can be connected to data stored in Amazon S3, Redshift, or RDS.

Machine learning frameworks come with pre-built components that are easy to understand and code. A good ML framework thus reduces the complexity of defining ML models. With these open-source ML frameworks, you build your ML models easily and quickly.

Know an ML framework that should be on this list? Share them in comments below.

Data visualization is helping companies worldwide to identify patterns, predict outcomes, and improve business returns. Visualization is an important aspect of data analysis. Simply put, data visualization conveys outcomes of tabular or spatial data in a visual format. Images have the power to capture attention and convey ideas clearly. This aids decision making and drives action for improvements.

With the use of the right tools, you can sketch a convincing visual story from your raw data. Here are some free and open source tools for data visualization:

1) Candela

If you know Javascript, then you can use this open source tool to make rich data visualizations. Candela is an open-source suite of interoperable web visualization components.

2) Charted

Charted is a free data visualization tool that lets you create line graphs or bar charts from CSV files and Google spreadsheets. The toll comes with integrated components including LineUp component, UpSet component, OnSet component, Vega visualizations, and GeoJS geospatial visualizations. The tool does not store the data or manipulate it. Focused purely on visualization, it comes with basic features to create a line or stacked charts with labels and notes.

3) Datawrapper

Datawrapper is a mobile-friendly data visualization tool that lets you create charts and reports in seconds. The free version of the tool meant for a single user supports 10,000 monthly chart views. Using the tool, you can create different types of visualization such as bar chart, split chart, stacked chart, dot plot, arrow plot, area chart, scatter plot, symbol map, and choropleth map. You don’t need coding or designing skills to use the tool.

4) Google Data Studio

Google’s data visualization tool is free and easy to set up if you have a Gmail account. You can connect it easily with Google products such as Google AdWords, Google Analytics, YouTube Analytics, and Google Sheets.

5) Google Charts

Another simple and free data visualization tool by Google is the Google chart tool. The tool comes with interactive charts and data tools for visualization.

6) Leaflet

The leaflet is an open-source JavaScript library that allows you to make mobile-friendly interactive maps. The tool has a lot of plugins for adding features and works well on various desktop and mobile platforms.

7) MyHeatMap

MyHeatMap is a free tool to view your geographic data interactively. The free version of the tool offers only public maps and you can add only 20 data points for each of those free maps. The tool makes it easy to understand the data with color-coded heat maps. You can also switch between data sets within the same map.

8) Openheatmap

This free tool lets you turn your spreadsheet into a map. You can upload your CSV file or Google sheet to create an interactive online map in seconds. The tool can be used to explain data like customer demographics by zip codes.

9) Palladio

Palladio is a free tool designed to visualize complex historical data. It comes with features like map view, graph view, list view and gallery view. You can use the tool to visualize data in CSV, TAB, or TSV files. With the graph view, you can visualize the relationship between dimensions of your data. The data is displayed as nodes connected by lines. The list view, on the other hand, allows you to arrange data to make customized lists. The tool also has a gallery view to display data within a grid.

10) RawGraphs

RawGraphs is an open-source platform that helps you visualize TSV, CSV, DSV, or JSON data. The free tool is simple to use and helps in converting data to charts.

11) Tableau Public

Tableau Public is a free business intelligence tool that allows users to create and share interactive charts, graphs, maps, and app. The free version of the tool comes with 10 GB of storage. You can connect it to data sources like Google Sheets, Microsoft Excel, Text files, JSON files, Spatial files, Web Data Connectors, OData, and statistical files such as SAS (*.sas7bdat), SPSS (*.sav), and R (*.rdata, *.rda).

12) Timeline

A timeline is a free tool that allows you to create timelines for reports. You can connect your Google Drive account to create a timeline from Google Spreadsheet using the templates given in the tool. Using JSON, you can create custom installations.

13) Chartist.js

Chartist.js is a free data visualization that allows you to create responsive charts fast and easy.
The tool offers great flexibility and is customizable. You can even use CSS animations and transitions to your SVG elements.

14) ColorBrewer

ColorBrewer is a free tool that can be used to make your maps better in terms of color schemes. The tool makes it easy to differentiate colors on a complex map.

15) D3.JS

D3.JS is a free JavaScript library that helps you create images using data. The tool enables you to connect arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. With DOM programming API, programmers can access documents as objects.

16) Plotly

Plotly is an open-source tool that allows you to compose, edit and share interactive data visualizations. You can use the tool to create D3.js charts and maps by uploading CSV files or connecting to the SQL database. You can also create charts with R or Python.

17) Polymaps

Polymaps is a free javascript library for creating dynamic, interactive maps in browsers. You can use the tool to get a display of multi-zoom datasets over maps. The tool uses scalable vector graphics (SVG) to display images, thus enabling you to define the design using CSS.

18) Weave

The Weave is a free data visualization platform that is ADA-compliant. The tool comes with a full keyboard and assistive device navigation and complete screen reader support. The tool also automatically gives descriptions of the images in real time.

19) Dygraphs

Dygraphs is an open-source charting library based on JavaScript. This free tool can be used to analyze dense data sets. The tool is highly customizable and works well in all browsers. The tool offers strong support for error bars/ confidence intervals.

Dygraphs

20) GanttPro

Apart from these, there are many data visualization tools that offer a free trial for a limited time GanttPro, a project management tool, for instance, helps you create charts for projects for free during their 15-day trial period.

Data visualization is crucial for accurate data analysis. With the right tools in hand, you can easily summarize and explain complex data to your stakeholders. By leveraging actionable insights generated from data, companies can make big profits and savings. Just how big are we talking about? Netflix saved around $1 billion in 2017 with its ML algorithm that recommends personalized TV shows and movies to subscribers. When used right, data analysis and visualization have the power to change the way people live their lives.

Know a great open source tool for data visualization? Share it in comments below.

If you are reading this thinking it’s a merry read praising women and saying women are better than men, this is not for you. However, before you get mad at me for writing this, I request you to read till the end before you get all riled up.

Not all opinions are the same, but some people wish they were. Remember the Google engineer James Damore and his memo about ”why men are naturally better at computers than women”? It took the tech world by storm and every large company started coming out with their own statements about how they are a diverse organisation and how they support gender diversity.

I don’t necessarily disagree with him. The skills of a developer, however, don’t depend on the gender. So, why are women lagging behind and why did Damore feel that way?

This is because of generations of people who have passed on this mindset and generations of holding women back. Yes, on average, women are not better than men in STEM, but did you ever question if women get the same number of opportunities men do? Research shows that diversity in a team leads to better problem solving but women make up less than 25% of the STEM workforce in the United States.ESA calculations on gender share

Potential reasons why fewer women choose to pursue careers in STEM while considering the long term were discussed in a recent article on Code Like A Girl by Kriti Khare.

The nature of jobs does not permit them to continue them for a long time, as usually it’s the woman in the family who has to take care of the family. If there’s more studying involved, and a child is on the way, there would most likely be a break that would make it tough to manage a higher academic degree. — Kriti Khare

Diversity has nothing to do with how good a team can be in terms of skills but if you look at it from an organization perspective, organizations with women leaders and team members do better than those that don’t. The 2015 "Women on Boards" study by MSCI on gender diversity shows, "Companies in the MSCI World Index with strong female leadership generated a Return on Equity of 10.1% per year versus 7.4% for those without." This is because women make up a large segment of the customers. Diverse teams and leadership thus help in diverse thoughts and strategies, and deliver better performance.

Being a woman, I don’t want someone to see me as a unicorn or pity me and give me special treatment of some kind. I do not want to be a woman developer. I just want to be recognized as a developer and earn the respect my skills deserve. I don’t want to be caught between two words “woman” and “developer” and then be judged by the rest of the world.

The only way to change the world of women is by creating more and more opportunities to showcase their skills. This is not going to be easy, changing the thinking and fighting generations of stereotype that we have in our society. We have to accept that no one is going to come and speak for us. If we want things to change, we have to take every chance we get and punch stereotype in the face. Women can change the world for women.

As Dinah Davis mentions in her article Girls need Role models, “Let’s get started and deluge girls with STEM Role Models!” I believe that you can be the role model she speaks of. HackerEarth and Schlumberger brings to you International Women’s Hackathon 2018, which is a great platform to help you be that role model.

We should try and show everyone that we can change our own world. Maybe somewhere out there, there is a little girl who will see you doing your bit to make a difference in the world and realize that she too can grow up to be a brilliant, successful engineer or developer.

Cursing the society won’t make a change but calling out capable women to inspire the next generation of women will.

Superhero movies and super successful companies have one thing in common. They both have a great team that strives to win, no matter what the challenge is. It is always the hero’s team that saves the day.

A challenge like a hackathon is no different. At hackathons, teams of developers, designers, and product managers work together to build prototypes and solve real-world problems within a specific time frame. The team that works the most efficiently and builds the best solution takes home the prize. Getting the right people on board gives your team a competitive advantage over others. Here are the top tips to form a winning team for your next hackathon:

1) Start your hunt early

Start looking for teammates from the time you decide to participate in a hackathon. Search for potential teammates on online platforms and communities like Stack Exchange, Stack Overflow, and Quora. Start with your own network of friends and like-minded acquaintances. You can also find other hackers on meetups and developer groups. Further, hackathons conducted on platforms like HackerEarth allow participants to form teams on the platform itself.

2) Work on your pitch

To attract talented people to your team, you need to sell your idea and your profile well. Update your Github profile and create a good pitch to share with potential teammates. Showcase your past projects. Highlight your skills and domain knowledge. Explain your idea and the problem that you plan to solve. Be proactive in pitching your idea and listen to feedback. Even if you arrive at the hackathon alone, with a good pitch, you still have a good chance of forming a great hackathon team.

3) Seek diversity

Diversity is what makes Avengers, the team of superheroes great as a team. Each member brings a unique strength to the table. While forming your hackathon team, hunt for talent from diverse domains like designing, frontend development, business management, etc. Having a team of people with diverse skill sets will help you in building a complete product.

4) Be aware of different working styles

Different people work differently and have different approaches to solving the same problem. Assign tasks and roles to team members based on their core strengths. Give everyone a fair chance to work on specific tasks which they are good at. Encourage mutual learning and problem-solving. Getting the chemistry right is the key to a successful team. If you have the time to prepare, do a mock run with your team to plan tasks and organize yourselves better as a team.

‘Getting the right people and the right chemistry is more important than getting the right idea.’?—?Ed Catmull, previously CEO of Pixar, author of Creativity, Inc.

A good team adapts well to any situation. Your final prototype is a result of your team’s work. By forming a good team, you can build better products and improve your chances of winning a hackathon.

Got a tip for building stronger hackathon teams? Share it with us in the comments below.

“We didn’t do anything wrong, but somehow, we lost,” said Nokia’s CEO Stephen Elop in his speech soon after Nokia’s announcement of being acquired by Microsoft in September 2013. The mobile giant that was once valued at $222 billion at its peak was acquired for just $7.2 billion by Microsoft that year. Failure to adapt to new trends in smartphone technology drove the legendary mobile company from market domination to sell-off.

The biggest lesson for from Nokia’s collapse in the mobile industry is to adapt to new tech trends before your consumers abandon you. Fast innovation is the key to keeping up with new technologies. By discerning the latest trends, companies can leverage the right technology and adapt in time to succeed. Here are the top tech trends that will define the IT landscape in 2018:

1) The incredible AI

Artificial intelligence (AI) is going to get better and smarter in 2018. Thanks to artificial intelligence, your everyday appliances from the security system to entertainment console will become more automated and smarter. Using software algorithms and sensors, artificial intelligence based devices will be able to do more complex actions and will play a significant role in several domains including healthcare, smart cars, and personal security. From making the financial sector more accurate and secure to powering smart personal assistants like Siri, you will see more of artificial intelligence based technology in your everyday life.

2) Planet of the robots

With more research and investment in robotics, you will also see more of intelligent drones and robots in 2018. According to IDC’s FutureScape: Worldwide Robotics 2017 Predictions report, 45% of the 200 leading global e-commerce and omnichannel commerce companies will deploy robotics systems in their order fulfillment, warehousing, and delivery operations. 2018 is going to witness the emergence of robotics in fields including agriculture, manufacturing, and medicine. Lifelike robots like Sophia could even play the role of human companions and help take care of children, the elderly and people with special needs. Sophia who looks like the late actor Audrey Hepburn was even awarded full citizenship of Saudi Arabia. Those worried about a robotic invasion can put their fears to rest. Robotics of the future will be designed to help people achieve more.

3) Mixed reality: The rise of immersive experience

With the digital revolution, today we have access to more content than ever before. Earlier, augmented reality (AR) and virtual reality (VR) defined the way people interacted with the digital world. The coming days will see the emergence of mixed reality (MR) that combines both augmented reality and virtual reality. The mixed reality technology enables users to interact in an environment where both physical and digital objects co-exist. 2018 will witness more application of the mixed reality technology in fields ranging from simulation-based learning, military training, aviation, healthcare to interactive product content management. According to the research firm Reportbuyer.com, the global mixed reality market size is expected to touch $2.8 billion by 2023.

4) Blockchain: The force awakens

The blockchain is the fundamental technology behind cryptocurrencies like bitcoin. The exponential rise of bitcoins has rekindled everyone’s interest in blockchain technology. The technology provides a secure way of sharing encrypted data on anything, from money to medical records, between companies, people, and institutions. Blockchain has the potential to revolutionize the financial sector and the world economy. In 2018, companies are going to invest heavily in developing their blockchain and fintech capabilities. As we move toward a digital, technologically advanced financial world, blockchain will play a crucial role in making our financial systems faster, more secure and efficient.

5) The age of machine learning

Businesses will increasingly leverage on machine learning to gain a competitive advantage in 2018. Machine learning deals with the technology that enables computers to learn explicitly without being programmed. The technology can be used to analyze large volumes of data and predict patterns. In the coming days, machine learning will be widely used in fields including data security, personal security, financial trading, healthcare, personalized marketing and online search.

6) IoT: Unchained

IoT is here to stay and thrive. According to Business Insider, the business spends on IoT solutions will reach $6 trillion by 2021. Internet of Things (IoT) is a network of devices that is embedded with software and sensors that enable these devices to connect and exchange data. The technology is applied in verticals including wearables, connected cars, connected homes, connected cities and industrial internet. 2018 will see the rise of “digital twins,” the next step in IoT-based technology. Companies will rely on the technology to predict problems through data analysis and simulations. Another disruptive technology that will emerge will be based on a combination of IoT and Blockchain technologies. The technology will be applied in domains including warehousing, healthcare, and financial sectors. There will be connected devices everywhere.

As the waves of new and emerging technologies hit the world, it is a good idea to start learning more about these new and upcoming tech domains. In today’s high-tech era, leveraging the right technology can mean the difference between success and failure. Consumers are quick to punish those who don’t innovate fast.

Nokia did not do anything wrong except miss out on the latest trends in mobile technology. While the competitors quickly caught on the demand for smarter phones, Nokia was left far behind. In the end, it is the lack of innovation and the failure to adapt to emerging technologies that force companies out of business. The lesson for everyone is to adapt and innovate before it is too late.