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

I was wondering about machine learning/data science project structure/workflow and was reading different opinions on the subject. And when people start to talk about workflow they want their workflows to be reproducible. There are a lot of posts out there that suggest to use make for keeping workflow reproducible. Although make is very stable and widely-used I personally like cross-platform solutions. It is 2019 after all, not 1977. One can argue that make itself is cross-platform, but in reality you will have troubles and will spend time on fixing your tool rather than on doing the actual work. So I decided to have a look around and to check out what other tools are available. Yes, I decided to spend some time on tools.


This post is more an invitation for a dialogue rather than a tutorial. Perhaps your solution is perfect. If it is then it will be interesting to hear about it.

In this post I will use a small Python project and will do the same automation tasks with different systems:

• CMake
• PyBuilder
• pynt
• Paver
• doit
• Luigi

There will be a comparison table in the end of the post.

We are excited to announce the release of ML.NET 1.0 today.  ML.NET is a free, cross-platform and open source machine learning framework designed to bring the power of machine learning (ML) into .NET applications.

https://github.com/dotnet/machinelearning 

Get Started: http://dot.net/ml

Some days ago we announced the preview of Windows Vision Skills, a set of NuGet packages that make it easy for application developers to solve complex computer vision problems using a simple set of APIs.

Figure 1- From left to right, you are seeing in action the Object Detector, Skeletal Detector, and Emotion Recognizer skills.

Everyone uniquely perceives texts, regardless of whether this person reads news on the Internet or world-known classic novels. This also applies to a variety of algorithms and machine learning techniques, which understand texts in a more mathematical way, namely, using high-dimensional vector space.

This article is devoted to visualizing high-dimensional Word2Vec word embeddings using t-SNE. The visualization can be useful to understand how Word2Vec works and how to interpret relations between vectors captured from your texts before using them in neural networks or other machine learning algorithms. As training data, we will use articles from Google News and classical literary works by Leo Tolstoy, the Russian writer who is regarded as one of the greatest authors of all time.

We go through the brief overview of t-SNE algorithm, then move to word embeddings calculation using Word2Vec, and finally, proceed to word vectors visualization with t-SNE in 2D and 3D space. We will write our scripts in Python using Jupyter Notebook.

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Quick links

— The Road to Version 12
— First, Some Math
— The Calculus of Uncertainty
— Classic Math, Elementary and Advanced
— More with Polygons
— Computing with Polyhedra
— Euclid-Style Geometry Made Computable
— Going Super-Symbolic with Axiomatic Theories
— The n-Body Problem
— Language Extensions & Conveniences
— More Machine Learning Superfunctions
— The Latest in Neural Networks
— Computing with Images
— Speech Recognition & More with Audio
— Natural Language Processing
— Computational Chemistry
— Geographic Computing Extended
— Lots of Little Visualization Enhancements
— Tightening Knowledgebase Integration
— Integrating Big Data from External Databases
— RDF, SPARQL and All That
— Numerical Optimization
— Nonlinear Finite Element Analysis
— New, Sophisticated Compiler
— Calling Python & Other Languages
— More for the Wolfram “Super Shell”
— Puppeting a Web Browser
— Standalone Microcontrollers
— Calling the Wolfram Language from Python & Other Places
— Linking to the Unity Universe
— Simulated Environments for Machine Learning
— Blockchain (and CryptoKitty) Computation
— And Ordinary Crypto as Well
— Connecting to Financial Data Feeds
— Software Engineering & Platform Updates
— And a Lot Else…

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

Today we’re announcing the ML.NET 1.0 RC (Release Candidate) (version 1.0.0-preview) which is the last preview release before releasing the final ML.NET 1.0 RTM in 2019 Q2 calendar year.

Soon we will be ending the first main milestone of a great journey in the open that started on May 2018 when releasing ML.NET 0.1 as open source. Since then we’ve been releasing monthly, 12 preview releases so far, as shown in the roadmap below:

In this release (ML.NET 1.0 RC) we have initially concluded our main API changes. For the next sprint we are focusing on improving documentation and samples and addressing major critical issues if needed.

The goal is to avoid any new breaking changes moving forward.

Create your first intelligent bot with Microsoft AI

Artificial intelligence (AI) is accelerating the digital transformation for every industry, with examples spanning manufacturing, retail, finance, healthcare, and many others. At this rate, every industry will be able to use AI to amplify human ingenuity. In this e-book, Anand Raman and Wee Hyong Tok from Microsoft provide a comprehensive roadmap for developers to build their first AI-infused application.

Using a Conference Buddy as an example, you’ll learn the key ingredients needed to develop an intelligent chatbot that helps conference participants interact with speakers. This e-book provides a gentle introduction to the tools, infrastructure, and services on the Microsoft AI Platform, and teaches you how to create powerful, intelligent applications.

You’ve probably heard about Yandex’s DeepHD technology they once used to improve the quality of old Soviet cartoons. Unfortunately, it’s not public yet, and we, regular programmers, don’t have the dedication to write our own solution. But I personally really wanted to watch Rick and Morty on my 2880x1880 Retina display. And I was deeply disappointed, as even 1080p video (the highest available for this series) looks really blurry on a Retina display! Don’t get me wrong, 1080p is often good enough, but Retina is designed in such a way that an animation with its pronounced outlines in 1080p looks awfully blurry, like 480p on a FullHD monitor.

I decided I want to see Rick and Morty in 4K, even though I can’t write neural networks. And, amazingly, I found a solution. You don’t even need to write any code: all you need is around 100GB of free space and a bit of patience. The result is a sharp 4K image that looks better than any interpolation.

Attack detection has been a part of information security for decades. The first known intrusion detection system (IDS) implementations date back to the early 1980s.

Nowadays, an entire attack detection industry exists. There are a number of kinds of products—such as IDS, IPS, WAF, and firewall solutions—most of which offer rule-based attack detection. The idea of using some kind of statistical anomaly detection to identify attacks in production doesn’t seem as realistic as it used to. But is that assumption justified?

Last year we announced ML.NET, cross-platform and open ML system for .NET developers. During this time, it has evolved greatly and has gone through many versions. Today we are sharing a guide on how to create your first ml.net application in 10 minutes.

The biggest issue with AI is not that it is stupid but a lack of definition for intelligence and hence a lack of formal measure for it [1a] [1b].

Turing test is not a good measure because gorilla Koko [2a] and bonobo Kanzi [2b] wouldn't pass though they could solve more problems than many disabled human beings.

It is quite possible that people in the future might wonder why people back in 2019 thought that an agent trained to play a fixed game in a simulated environment such as Go had any intelligence [3a] [3b] [3c] [3d] [3e] [3f] [3g] [3h].

Intelligence is more about applying/transferring old knowledge to new tasks (playing Quake Arena good enough without any training after mastering Doom) than compressing agent's experience into heuristics to predict a game score and determining agent's action in a given game state to maximize final score (playing Quake Arena good enough after million games after mastering Doom) [4].

Human intelligence is about ability to adapt to the physical/social world, and playing Go is a particular adaptation performed by human intelligence, and developing an algorithm to learn to play Go is a more performant one, and developing a mathematical theory of Go might be even more performant.

A few years ago I had a feeling that Oleg Chumakov (then working at the game studio Nival) was the most famous programmer in the game development industry. He was giving speeches, hosted Gamesjams and frequently showed up on the podcast How games are made.

When VR hit the market, Oleg was chosen to lead the company’s new department — NivalVR. But, as you probably know, VR didn’t quite take off as much as people expected.

I kind of moved to other to other things in life and stopped keeping up with game development for a while, but after getting into it again I noticed that things were looking up for Oleg’s team. Now it’s called Luden.io, and their machine learning expert simulator, while True: learn() became a huge hit in its admittedly small niche. Lots of cool stories are happening around the game and the team.

We decided to do an interview with Oleg, but I couldn’t stick to one topic — his life up to this moment has been, for the lack of a better word, “interesting”. He’s seen it all. And, to ensure that a programmer could talk about programming without fear of looking too “nerdy”, the interview was conducted by my friend, colleague and an experienced developer of its own fillpackart.