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.


Have you thought about the influence of the nearest metro to the price of your flat? 
What about several kindergartens around your apartment? Are you ready to plunge in the world of geo-spatial data?





This post is a small abstract of full-scaled research focused on keyword recognition. Technique of semantics extraction was initially applied in field of social media research of depressive patterns. Here I focus on NLP and math aspects without psychological interpretation. It is clear that analysis of single word frequencies is not enough. Multiple random mixing of collection does not affect the relative frequency but destroys information totally — bag of words effect. We need more accurate approach for the mining of semantics attractors.

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!..

A/B test is not enough

There is a common opinion that A/B test is a universal, half-automatic tool that always helps to increase conversion, loyalty and UX. However misinterpretation of results or wrong sampling leads to the loss of loyal audience and decrease of margin. Why? A/B is based on the basic assumption that this sample is homogeneous and representative, scalability of results. In reality, the audience is heterogeneous — recall the “20/80” distribution for income. Heterogeneity means that sensitivity to A/B varies significantly within the sample.

Have you ever looked for a flat? Would you like to add some machine learning and make a process more interesting?

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.

Deep Learning applications have changed a lot of things. Some which give hope for a brighter future, and some which raise suspicions. However, for developers, the growth of deep learning applications has made them more perplexed about choosing the best among so many deep learning frameworks out there.

TensorFlow is one of the deep learning frameworks that comes in mind. It is arguably the most popular deep learning framework out there. Nothing justifies the statement better than the fact that Tensorflow is used by the likes of Uber, Nvidia, Gmail among other big corporations for developing state-of-the-art deep learning applications.

But right now, I am on a quest to find whether it indeed is the best deep learning framework. Or perhaps find what makes it the best out of all other frameworks it competes against.

For the past two years, there’s been a lot of buzzing about the Internet of Things (IoT). This has to lead to the rapid selection of connected devices over industries and is determined to pass the 11 billion mark by the end of the year. Major Companies including IoT software development as their major services.

All these “things” are now creating their things, namely, lots and lots of data. This data will be at the core of commercial and industrial digital transformation (which is essentially the underlying force behind the fourth industrial revolution).

In other words, life as we know it is about to change forever! How is it going to change? Let’s take a look.

1. AI (Artifical Intelligence) can Effectively Manage Oceans of information

We can’t talk about IoT without AI as the latter has the power to make IoT a whole lot smarter and more efficient.

In fact, consultants believe that AI is the brains behind IoT systems that may facilitate build them run power tool.

For example, as more and more connected devices start communicating with each other, enterprises will need to leverage deep learning, image recognition, natural language processes, and neural-network driven decisions to help them understand each other (and us humans) better.

So far, we can say that IoT has felt like an isolated experience where it was just about simple data. Going forward, businesses will strive to achieve highly integrated experiences by using AI to better understand their employees, customers, and the general public living in smart cities.

Backlog prioritization requires simplification and weighting of tasks. Each one belongs to strategy like ads acquisition or CRO. We may consider turnover, operational costs, other metrics as input; profit margin, ROI — as output in case of retail. The perfect goal is to find 20/80 solution and focus resources on a single strategy at a time. Metrics tied to strategies gives the dimension of model. Sometimes unit economy relations are violated because of non-linearity. In practice it means low/insignificant correlation and poor regression. Example: it is impossible to separate acquisition and conversion — the quantity of acquisition affect its quality and vice versa. Decomposition of tasks/strategies assumes linear decomposition of nonlinear system. Besides nonlinear statistical evaluation of strategies is required when CJM can't be tracked or online/offline channels can't be separated.

Nowadays, talking to conversational agents is becoming a daily routine, and it is crucial for dialogue systems to generate responses as human-like as possible. As one of the main aspects, primary attention should be given to providing emotionally aware responses to users. In this article, we are going to describe the recurrent neural network architecture for emotion detection in textual conversations, that participated in SemEval-2019 Task 3 “EmoContext”, that is, an annual workshop on semantic evaluation. The task objective is to classify emotion (i.e. happy, sad, angry, and others) in a 3-turn conversational data set.

Hi everybody! I’m a research engineer at the Mail.ru Group computer vision team. In this article, I’m going to tell a story of how we’ve created AI-based photo restoration project for old military photos. What is «photo restoration»? It consists of three steps:

• we find all the image defects: fractures, scuffs, holes;
  
• we inpaint the discovered defects, based on the pixel values around them;
  
• we colorize the image.
  

Further, I’ll describe every step of photo restoration and tell you how we got our data, what nets we trained, what we accomplished, and what mistakes we made.

The most important single ingredient in the formula of success is knowing how to get along with people. Theodore Roosevelt

In the previous article I tried to cover the basics of pricing analytics. Now I'd like to talk about something more interesting.

Have you ever thought about why you choose certain products in stores, why you prefer them to other similar ones? Many shopping trips are spontaneous, so it's probably impossible to give a clear answer for all the times you go shopping. But the general idea is obvious: you go shopping for a specific reason (to get food, a gadget, for entertainment, to play blackjack). In this article I'm going to use available data from grocery retailers to talk about how a set of basic logical assumptions and community analysis can help us determine the way customers choose products.

Backlog prioritization leads to the choice between strategies. Each one has its metrics. There is a requirement to choose the most important one. ML scoring is a solution when non linearity exists and economy is nonlinear. See introduction here. Two groups are considered. First (I) corresponds to web conversion {bounce rate, micro conversion, time, depth}. Second (II) corresponds to attraction of new visitors from organic channel {visits, viewers, views}. The target function is a number of commercial offers per day. The task is to reduce the dimension to get the optimal simple strategy. In this case online/offline B2B channels can't be separated: market is thin and new customers may have some information about 'the brand' from both channels. Therefore statistical evaluation is closer to reality than direct CJM tracking in this case.

If you were asked to draw a picture of several people in ski gear, standing in the snow, chances are you’d start with an outline of three or four people reasonably positioned in the center of the canvas, then sketch in the skis under their feet. Though it was not specified, you might decide to add a backpack to each of the skiers to jibe with expectations of what skiers would be sporting. Finally, you’d carefully fill in the details, perhaps painting their clothes blue, scarves pink, all against a white background, rendering these people more realistic and ensuring that their surroundings match the description. Finally, to make the scene more vivid, you might even sketch in some brown stones protruding through the snow to suggest that these skiers are in the mountains.

Now there’s a bot that can do all that.

Today, even mobile application developing company has begun to consolidate ML related to other cutting edge technologies, for example, AI and predictive analysis. This is on the grounds that ML empowers mobile applications to learn, adjust, and improve after some time.

It’s an incredible accomplishment when you consider the way that changes requested an express order from designers for gadgets to execute a particular activity. At the point when this was the standard, software engineers needed to estimate and record for each conceivable situation (and this was a fantastic test).

Be that as it may, with ML in portable applications, we have removed the speculating game from the condition. It can likewise upgrade User Experience (UX) by understanding client conduct. So you can wager that ML in versatile won’t be restricted to voice associates and chatbots.

When I used to start a conversation about neural networks over a bottle of beer, people were casting glances at me of what seemed to be fear; they grew sad, sometimes with their eyelid twitching. In rare cases, they were even eager to take refuge under the table. Why? These networks are simple and instinctive, actually. Yes, believe me, they are! Just let me prove this is true!

Suppose there are two things I’m aware of about the girl: she looks pretty to my taste or not, and I have lots to talk about with her or I haven’t. True and false will be one and zero respectively. We’ll take similar principle for appearance. The question is: “What girl I’ll fall in love with, and why?”

We also can think it straight and uncompromisingly: “If she looks pretty and there’s plenty to talk about, then I will fall in love. If neither is true, then I quit”.

But what if I like the lady but there’s nothing to talk about with her? Or vice versa?

Hi guys,

Before you is an article guide to open data sets for machine learning. In it, I, for a start, will collect a selection of interesting and fresh (relatively) datasets. And as a bonus, at the end of the article, I will attach useful links on independent search of datasets.

Less words, more data.

A selection of datasets for machine learning:

• Data deaths and battles from the game of thrones — This data set combines three data sources, each based on information from a series of books.
• Global Terrorism Database — Over 180,000 terrorist attacks worldwide, 1970-2017.
• Bitcoin, historical data — Bitcoin data with an interval of 1 minute from selected exchanges, January 2012 — March 2019