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This post is about some fairly recent improvements in the field of AI-based voice cloning. If we have hours and hours of footage of a particular voice at our disposal then that voice can be cloned using existing methods. But this recent breakthrough enables us to do the same using…

Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space — the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge.

Often, code reviews involve collaborations between the original code authors, their peers, and managers, with a view toward finding obvious errors before it gets to a more advanced phase. And the bigger a project is, the more lines of code there are to review, which is a time-consuming process. There are options out there for analyzing source code for errors, such as static analysis tool Lint, but these are often not holistic in terms of their scope — they’re focused on a smaller, targeted set of “annoying and repeatable stylistic issues, formatting and minor issues,” according to Paskalev.

DeepCode’s selling point is that it covers a broader range of problems, including vulnerabilities such as cross-site scripting and SQL injection, while it also promises to establish the intent behind the code, rather than spotting simple syntax mistakes. Underpinning all this is machine learning (ML) systems, which are trained using billions of lines of code from public open source projects, which constantly learn and update their knowledge base.

Though DeepCode can ingest code from any source code repositories, Paskalev told VentureBeat that the public knowledge base today contains mostly GitHub repositories.

Continue reading “DeepCode learns from GitHub project data to give developers AI-powered code reviews” »

Artificial intelligence (AI) is a broad field constituted of many disciplines like robotics or machine learning. The aim of AI is to create machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence. To get there, machines must be able to learn these opportunities automatically instead of having each of them to be explicitly programmed end-to-end.

Another task of AI is to write programs. Similar technology was developed by Microsoft in conjunction with Cambridge University. They developed a program which is able to create other programs, borrowing code. The invention is called DeepCoder. This software that can take into account the requirements of developers and find the code fragments in a large database. You can see the work of scientists here.

“The potential for the automation of writing software code is just incredible. This means a reduction of the huge amount of effort that is required to develop code. Such a system will be much more productive than any man. In addition, you can create a system that was previously impossible to build”,

Continue reading “DeepCoder from Microsoft can leave programmers without work” »

A new algorithm takes pixelated images of faces and creates realistic-looking versions with up to 64 times the resolution.

We’re releasing an API for accessing new AI models developed by OpenAI. Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose “text in, text out” interface, allowing users to try it on virtually any English language task. You can now request access in order to integrate the API into your product, develop an entirely new application, or help us explore the strengths and limits of this technology.

What does this have to do with AI self-driving cars?

AI Self-Driving Cars Will Need to Make Life-or-Death Judgements

At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars. One crucial aspect to the AI of self-driving cars is the need for the AI to make “judgments” about driving situations, ones that involve life-and-death matters.

AI, networked sensors, and heads up displays.