StradVision has raised $16.6M in total. We talked with Junhwan Kim, its CEO. How would you describe StradVision in a single tweet? StradVision is a pioneer in deep learning-based vision processing technology, providing the software that will allow Advanced Driver-Assistance Aystems (ADAS) in autonomous vehicles to reach the next level of safety, and usher in the era of the fully autonomous vehicle. How did it all start and why?

Natural language processing (NLP) is a branch of artificial intelligence. It helps computers understand, interpret and manipulate human text language. Today there are an enormous amount of emails, social media text, video stream, customer reviews, customer support requests, etc. All of these textual data become the perfect place to apply NLP. We need NLP tools and techniques to process, analyze, and understand unstructured "big data" in order to release the power in analytics.

Get the Full Experience Read the rest of this article, and view all articles in full from just £10 for 3 months.

In a media briefing at Dell's AI Experience Zone in Singapore yesterday, Dell Technologies announced that AI Singapore has chosen Dell Technologies to deliver High-Performance Computing (HPC) infrastructure that's optimised for AI workloads. AI Singapore, first announced in 2017, is a national program office launched by the National Research Foundation (NRF) to drive the adoption of artificial intelligence, develop the country's AI talent and help seed high-quality research efforts to develop fundamental AI novel techniques, algorithms and adjacent technologies. In the collaboration, Dell Technologies will provide three key computational building blocks for the new supercomputer at AI Singapore to help drive performance and flexibility for its researchers and to scale up its flagship 100 Experiments (100E) program. According to Laurence Liew, Director, AI Industry Innovation, for the 100E program, AI Singapore would partner with companies or industries that need AI solutions, but there are no commercially available solutions available for them in the market, or when they're committed to building their own products to compete globally. "The way we support them is by bringing our professors, researchers and engineering teams to work together with the companies to build their AI products and solutions," he explained.

Facebook AI Research today introduced ReAgent, a reinforcement learning toolkit for building decision-making AI that can receive feedback. ReAgent can assign scores to user actions and treat user input such as clicking on recommended content as training data. ReAgent is a small C library available for download on GitHub designed to be embedded in any application. The toolkit comes with a set of decision-making AI models to get started, an offline module for model performance assessment, and a platform to deploy AI into production using the TorchScript library in PyTorch. Horizon, a reinforcement learning platform for deployment of large-scale models in production open-sourced by Facebook in November 2018, is now part of ReAgent.

Meta-Learning describes the abstraction to designing higher level components associated with training Deep Neural Networks. The term "Meta-Learning" is thrown around in Deep Learning literature frequently referencing "AutoML", "Few-Shot Learning", or "Neural Architecture Search" when in reference to the automated design of neural network architectures. Emerging from comically titled papers such as "Learning to learn by gradient descent by gradient descent", the success of OpenAI's rubik's cube robotic hand demonstrates the maturity of the idea. Meta-Learning is the most promising paradigm to advance the state-of-the-art of Deep Learning and Artificial Intelligence. OpenAI set the AI world on fire by demonstrating ground-breaking capabilities of a robotic hand trained with Reinforcement Learning.

DeepCubeA, a deep reinforcement learning algorithm programmed by UCI computer scientists and mathematicians, can find the solution in a fraction of a second, without any specific domain knowledge or in-game coaching from humans. This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state -- each of six sides displaying a solid color -- which apparently can't be found through random moves. For a study published today in Nature Machine Intelligence, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 percent of the time. The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban. "Artificial intelligence can defeat the world's best human chess and Go players, but some of the more difficult puzzles, such as the Rubik's Cube, had not been solved by computers, so we thought they were open for AI approaches," said senior author Pierre Baldi, UCI Distinguished Professor of computer science.

Today, more and more retail companies understand that to stay ahead of the curve, they need to reshape their businesses, implementing tech-forward approaches and solutions. Data mining and analysis are key to making a shift. According to McKinsey, data-savvy retailers have the potential to increase operating margin by more than 60%. Companies can combine data from marketing, sales, transactions, customers, etc. to identify the existing patterns and make more accurate decisions. Data analysis brings diverse opportunities to businesses, allowing to reach customers and offer a smarter experience to them.

This paper by Joseph Suarez, Yilun Du, Phillip Isola, and Igor Mordatch shows their use of a gaming system to suggest and conduct research on how complex life on earth might have started. In this case, the team turned a massively multiplayer online role-playing game (MMORPG) into an artificially intelligent research environment, with all of the right characteristics to encourage this microcosm. Their results show how groups break up in different ways to avoid competition: "population size magnifies and incentivizes the development of skillful behaviors and results in agents that outcompete agents trained in smaller populations," and "the policies of agents with unshared weights naturally diverge to fill different niches in order to avoid competition."

Take the crash course in the'whys' and'whens' of using Deep Learning in Time Series Analysis. Time series is a sequence of data points, ordered using time stamps. And time series analysis is.. you guessed it.. analysis of the time series data:P From the daily price of your favorite fruit to the readings of the voltage output provided by a circuit, the scope of time series is huge and so is the field of time series analysis. Analyzing a time series data is usually focused on forecasting, but can also include classification, clustering, anomaly detection etc. For example, by studying the pattern of price variation in the past, you can try forecasting the price of that watch that you have been eyeing for so long, to judge what would be the best time to buy it!!