What do these terms mean? And what is the difference?

Data Science and Artificial Intelligence are creating a lot of buzzes these days. But what do these terms mean? And what is the difference between them?

While the terms Data Science and Artificial Intelligence (AI) comes under the same domain and are inter-connected to each other, they have their specific applications and meaning.

There’s no slowing down the spread of AI and data science. Many big tech giants are extensively investing in these technologies. As per the recent survey, it is estimated that artificial intelligence could add $15.7 trillion to the global economy by 2030.

Through this piece of writing, I will be explaining about the AI and data science concepts and their differences in detail. So, without wasting any more time, let’s get started!

This is the time when everyone is aware of the magic that AI is doing all over the world. Yes, it is true and no one can deny the fact that AI is ruling all over the world almost in all the fields and is making our lives more exciting and autonomous. Like other fields, it is playing an important role in the field of education.

According to a survey, Artificial Intelligence (AI) recently started to penetrate into other industries in China. It was forecasted that by 2022 the Chinese market size of online education aided by artificial intelligence would amount to around 70.3 billion yuan.

Blockchain possesses incredible potential, that's why blockchain systems are considered as a paradise for data. In all these years, the discovery of Blockchain has been enjoyed for the introduction of distributed systems to secure data by cryptography.

From the creation of cryptocurrency to distributed ledger systems and mobile applications, this technology is being welcomed by every business vertical, and its adoption has become complimentary for companies. Moreover, its adoption can majorly be cherished by the healthcare industry.

A number of IT institutions are engaged in finding the most promising usage of blockchain technology in healthcare. Let's take a brief look at Blockchain adoption in 2020.

The Global Blockchain Adoption

In 2020, the worldwide spending on blockchain systems is USD 4.3 billion. As per Statista, the market of Blockchain will be worth 20 billion USD by the year 2025. Its most critical adoption can be seen in the healthcare industry, where this technology is being considered as the biggest game-changer.



The truth is that this technology has shown a path to distributed systems coupled with unmatched security measures That secure data in a chain of blocks infused with cryptographic locks. Top-notch level security and quality of not being tempered by any external entity boost its adoption in several instances.

Machine learning has firmly entrenched in a variety of human fields, from speech recognition to medical diagnosing. The popularity of this approach is so great that people try to use it wherever they can. Some attempts to replace classical approaches with neural networks turn up unsuccessful. This time we'll consider machine learning in terms of creating effective static code analyzers for finding bugs and potential vulnerabilities.

Every other person is establishing a startup these days. Is it easy to make it work? Do you know what is the success rate of startups? You would be shocked to know that out of the total startups launched in the year 2014, only 56% of them could make it to the fifth year, i.e. 2018.

Every entrepreneur starts a business with a vision to take it to exceptional heights. The bitter truth is, no matter in which industry domain you are working, you cannot survive for long if you do not perform extraordinarily.

At present, we see artificial intelligence is implemented across the corridors of business operations and also the way we shop and trade online. To hit a home run in the retail game, genius AI applications, PIM solutions, and e-commerce development tools are now offering smart solutions: predictive analysis, recommendation engines, inventory management, and warehouse automation to create a more profitable shopping experience for consumers.

Now more than ever, e-commerce is an AI innovation game

Artificial Intelligence often sometimes seems complicated to newbies but in reality, it is simple in use and gives you the ability to predict customer needs. This paves the way for e-commerce companies to become a “big brand” or “big business” with revolutionary AI tools.

Now that AI algorithms making way for consumer acceptance of AI like never before, how can you use it to create more profitable outcomes in e-commerce?

Interesting E-commerce Stats:

With an estimated global population of 7.7 billion, 25 percent of people shopping through e-commerce stores. According to Statista, 52% of e-commerce stores will have omnichannel capabilities by 2020 which means they can communicate and sell with their consumers via multiple channels. For example, they can use their e-commerce website, Facebook e-shop, email account, and Instagram account.

Examples of AI tools and PIM software for e-commerce businesses that can help them have a high bar on customer service and marketing:

The beginning

It all started when I found an app. on Apple market, that supposedly was able to determine the ripeness of a water mellon. A program was… strange. Just think about it: instead of knocking using your knuckles, you were supposed to hit the water mellon with your iPhone! Nevertheless, I have decided to repeate that functionality on an Andtoid platform.

Technologies like AI, VR/AR, Cryptocurrency, IoT will take center stage in 2020 and also form as the backbone of tomorrow’s ecommerce tech innovation. Beyond the sales and revenue focused world of ecommerce, 2020 ecommerce tech trends will provide the capability to create new software solutions, mobile apps, and services that will lead to creating a richer and more satisfying shopping experience.

Author: Sergey Lukyanchikov, Sales Engineer at InterSystems

Fixing the terminology

A robot is not expected to be either huge or humanoid, or even material (in disagreement with Wikipedia, although the latter softens the initial definition in one paragraph and admits virtual form of a robot). A robot is an automate, from an algorithmic viewpoint, an automate for autonomous (algorithmic) execution of concrete tasks. A light detector that triggers street lights at night is a robot. An email software separating e-mails into “external” and “internal” is also a robot.

Artificial intelligence (in an applied and narrow sense, Wikipedia interpreting it differently again) is algorithms for extracting dependencies from data. It will not execute any tasks on its own, for that one would need to implement it as concrete analytic processes (input data, plus models, plus output data, plus process control). The analytic process acting as an “artificial intelligence carrier” can be launched by a human or by a robot. It can be stopped by either of the two as well. And managed by any of them too.

Artificial Intelligence refers to the theory of computer systems or human-made robots programmed with performing tasks as humans, such as learning, generalization, and reasoning. With this ability, AI has become a significant part of human lives. Similarly, AI and the education & tutoring web solutions are inseparable from being observed by the astounding inventions enabling machines to mimic human roles.

The AI Cup community and Mail.ru Group in collaboration with Codeforces.com invite you to the real battle! Get ready for the sleepless nights and calloused hands — take part in Russian AI Cup, which is one of the most challenging and vivid artificial intelligence programming competitions in the world. Believe us, managers of this madness did their best to create the game you'd want to play.

To become part of the competition, you need Internet access, computer, creativity, and enthusiasm for being a part of this extraordinary Cup. By the way, you might need some coffee. Welcome!

Visual Studio IntelliCode uses machine learning to offer useful, contextually-rich code completion suggestions as you type, allowing you to learn APIs more quickly and code faster. Although IntelliCode’s base model was trained on over 3000 top open source C# GitHub repositories, it does not include all the custom types in your code base. To produce useful, high-fidelity, contextually-rich suggestions, the model needs to be tailored to unique types or domain-specific APIs that aren’t used in open source code. To make IntelliSense recommendations based on the wisdom of your team’s codebase, the model needs to train with your team’s code.

Earlier this year, we extended our ML model training capabilities beyond our initial Github trained base model to enable you to personalize your IntelliCode completion suggestions by creating team models trained on your own code.

The artificial intelligence global market is expected to reach $190 billion by 2025. The bright future of this technology allures every entrepreneur. In fact, when we think about the technologies that are going to rule in the future, the one name that comes to our minds is ~ Artificial intelligence.

AI along with its subsets like machine learning and deep learning is making such things possible which were unimaginable by humankind a few years back. It is affecting the realities and sometimes changing reality completely.



The power of AI is well acknowledged by businesses as 84% of respondents in a study voted that they believe artificial intelligence will allow them to enjoy a competitive edge over competitors.

Although entrepreneurs have an idea about AI but what most of them lack is proper implementation. The use of optimum programming tools for a complex technology like AI can create wonders for the world of business.

Every custom web developer knows that a python is an apt tool for building AI-enabled -applications. The language has been used to create 126,424 websites so far. Since its launch in the late 1980s, python has seen remarkable growth not only in users but in applications too.

Python is the favorite language for software developers to create applications that have artificial intelligence, machine learning, etc features embedded in them. But there are reasons behind everything.

This blog is written with the intent to unveil these reasons. Let’s explore why python is extensively used in AI-enabled software development services.

“Instead of using technology to automate processes, think about using technology to enhance human interaction.” ~ Tony Zambito, Lead authority in Buyer Personas.

Do you know ~ according to research, 93% of customers make purchase decisions based on visual appearance. Visual elements of your brand are the key deciding factors for a majority of potential customers.

Your logo, website colors, chatbot texts, etc all have an impact on the psychology of people who come across them. Some colors or features attract them and some make them leave your website instantly.

In this era, interactive features with the help of technologies like Artificial intelligence are enhancing such effects. AI has the power to add interactive elements to your presentation. This creates a connection between your company and its customers.

ML.NET is a cross-platform, machine learning framework for .NET developers, and Model Builder is the UI tooling in Visual Studio that uses Automated Machine Learning (AutoML) to easily allow you to train and consume custom ML.NET models. With ML.NET and Model Builder, you can create custom machine learning models for scenarios like sentiment analysis, price prediction, and more without any machine learning experience!

ML.NET Model Builder

This release of Model Builder comes with bug fixes and two exciting new features:

• Image classification scenario – locally train image classification models with your own images
• Try your model – make predictions on sample input data right in the UI

A palette makes it easy for painters to arrange and mix paints of different colors as they create art on the canvas before them. Having a similar tool that could allow AI to jointly learn from diverse data sources such as those for conversations, narratives, images, and knowledge could open doors for researchers and scientists to develop AI systems capable of more general intelligence.


A palette allows a painter to arrange and mix paints of different colors. SpaceFusion seeks to help AI scientists do similar things for different models trained on different datasets.

You’re utterly focused. You’ve lost track of time. Nothing else in the world exists. You’re living in the moment.

While this might sound like meditation, it’s a description that can also be applied to the state of flow – the feeling of being so engaged by your work, that you lose yourself to it completely, while massively increasing your productivity in the process.

It’s the holy grail that we all strive for, whether it’s a hobby we’re passionate about, or a project at work. Achieving our best and utilising our maximum potential at all times, can however, be a struggle.
We had the pleasure of talking with Dr. Jack Lewis, a neuroscientist with a passion for exploring how our minds work, to see what motivates us to do our best work, and the important roles that workplace environments, culture, and technology can play.

Microsoft researchers have developed an artificial intelligence (AI) system that has taught itself the intricacies of Mahjong and can now match the skills of some of the world’s top players.

The complex board game of chance, bluff, and strategy was invented in China thousands of years ago and remains a passionate pastime for millions of Asians today, with many dedicated competitors playing online.

Computers have learned to play Chess and another ancient Chinese game, Go, amid much fanfare in the past. But scientists at Microsoft Research (MSR) Asia see their achievement as far more than just a case of technology mastering yet another game.

The researchers – who named their system Super Phoenix, or Suphx for short – developed a series of AI algorithmic breakthroughs to navigate the uncertain nature of Mahjong. With more work, these could potentially be applied in real situations to solve problems thrown up by unknown factors and random events.

We are excited to announce updates to Model Builder and improvements in ML.NET. You can learn more in the «What’s new in ML.NET?.» session at .NET Conf.

ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers.

ML.NET offers Model Builder (a simple UI tool) and CLI to make it super easy to build custom ML Models using AutoML.

Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more!..

With the advent of mobile phones with high-quality cameras, we started making more and more pictures and videos of bright and memorable moments in our lives. Many of us have photo archives that extend back over decades and comprise thousands of pictures which makes them increasingly difficult to navigate through. Just remember how long it took to find a picture of interest just a few years ago.

One of Mail.ru Cloud’s objectives is to provide the handiest means for accessing and searching your own photo and video archives. For this purpose, we at Mail.ru Computer Vision Team have created and implemented systems for smart image processing: search by object, by scene, by face, etc. Another spectacular technology is landmark recognition. Today, I am going to tell you how we made this a reality using Deep Learning.