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.
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.
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.
A few months ago I came across one interesting open source project on the Internet — Openpose the aim of which is to estimate a human pose in real-time on a video stream. Due to my professional activities, I was interested to run it on the latest iOS device from Apple to check the performance and figure out if it is possible at all. It was also interesting to see how the performance of the neural network framework for iOS has been changed in the last years.
«It's a dangerous business, Frodo, going out your door. You step onto the road, and if you don't keep your feet, there's no knowing where you might be swept off to.»
― J.R.R. Tolkien, The Lord of the Rings
Europe’s roads are the safest in the world. Current figures show that there are 50 fatalities per one million inhabitants, compared to the global figure of 174 deaths per million. Despite this, each loss remains a tragedy. In 2017, 25,300 people lost their lives on European roads.
The cause of these accidents can vary from human error and weather conditions, to damaged structures and surfaces. While some things are beyond the realms of control, road and bridge conditions are a variable which can be governed.
As soon as a road is paved, a combination of traffic and weather conditions begin to degrade and erode the surface. Undetected cracks, abrasions or defects can quickly lead to bigger problems, such as costly repairs, major traffic delays, and in the worst cases, unsafe condition. These problems are also shared by bridges, particularly when concrete is critical in maintaining the integrity of the structure. The earlier faults are detected, the sooner they can be addressed, saving time and money, while minimising disruption. Ultimately, this helps ensure that the roads themselves are safer for those travelling on them.
The detection of these faults, however, can be very difficult to carry out manually, especially as early-forming cracks are hard to spot with the naked eye. Predicting where faults are likely to occur ahead of time so that appropriate measures can be taken in advance also possess a massive challenge. Thankfully, technology is here to help.
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.
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?
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.
Inspiration sometimes arrives in strange ways. Here is the story of how a dirty disposable diaper led to the development of an artificial intelligence (AI) solution to help rid the world’s coasts of massive amounts of waste and garbage.
With the recent progress in Neural Networks in general and image Recognition particularly, it might seem that creating an NN-based application for image recognition is a simple routine operation. Well, to some extent it is true: if you can imagine an application of image recognition, then most likely someone have already did something similar. All you need to do is to Google it up and to repeat.
However, there are still countless little details that… they are not insolvable, no. They simply take too much of your time, especially if you are a beginner. What would be of help is a step-by-step project, done right in front of you, start to end. A project that does not contain «this part is obvious so let's skip it» statements. Well, almost :)
In this tutorial we are going to walk through a Dog Breed Identifier: we will create and teach a Neural Network, then we will port it to Java for Android and publish on Google Play.
For those of you who want to see a end result, here is the link to NeuroDog App on Google Play.