DeepMind has ambitions to solve some of the world's most complex problems using artificial intelligence. But first, it needs to get really good at StarCraft. After months of training, the Alphabet-owned AI firm's AlphaStar program is now capable of playing a full game of StarCraft II against a professional human player – and winning. It might sound frivolous, but mastering a game as complex as StarCraft is a major technological leap for DeepMind's AI brains. The company showed off AlphaStar in a livestream where the five agents created by the program were initially pitted against professional player Dario "TLO" Wünsch in a pre-recorded five-game series.

Artificial intelligence has beaten humans at chess, Scrabble and Go – and now it has the better of us at real-time strategy video game StarCraft II. In two best-of-five series, DeepMind's AI defeated two top-ranked professionals 5-0. "This is of course an exciting moment for us," said David Silver at DeepMind in a live stream watched by more than 55,000 people. "For the first time we saw an AI that was able to defeat a professional player." In StarCraft II, players have to control armies across a terrain.

After laying waste to the best Go players in the world, DeepMind has moved on to computer games. The Google-owned artificial intelligence company has been fine-tuning its AI to take on StarCraft II and today showed off its first head-to-head matches against professional gamers. The AI agent, named AlphaStar, managed to pick up 10 wins against StarCraft II pros TLO and MaNa in two separate five-game series that originally took place back in December. After racking up 10 straight losses, the pros finally scored a win against the AI when MaNa took on AlphaStar in a live match streamed by Blizzard and DeepMind. The pros and AlphaStar played their games on the map Catalyst using a slightly outdated version of StarCraft II that was designed to enable AI research.

I just tuned in, but if there were no restrictions in the gameplay, it is quite insane and I expected it to take several more years, although I'm not a machine learning expert but certainly was a Sc2 expert.

When we last checked in with DeepMind, Oriol Vinyals stepped on to the BlizzCon 2018 stage to share the exciting progress their AI had made in StarCraft II. The AI, or agent, was able to perform basic macro focused strategies as well as defend against cheesy tactics like cannon rushes. It's only been a few months since BlizzCon but DeepMind is ready to share more information on their research. The StarCraft games have emerged as a "grand challenge" for the AI community as they're the perfect environment for benchmarking progress against problems such as planning, dealing with uncertainty and spatial reasoning. On January 24, at 10:00 AM Pacific Time, head over to StarCraft's Twitch channel or DeepMind's YouTube channel to learn what developments have been made.

Nalantis' solutions are based on proprietary semantic technology - a set of techniques to find meaning in unstructured text, and to use meaning to find other texts. Finding meaning is done by analyzing text linguistically, mapping words and expressions onto a ConceptNet (a semantic network built from nodes representing words or short phrases of natural language, and labeled relationships between them) and using powerful semantic pattern matching to combine these concepts into meaningful entities. Machine/Deep Learning and Human Linguists are used to build these ConceptNets semi-automatically and API's are in place to scale the companies' Natural Language Understanding products rapidly. The basic semantic analysis and matching engine is language and domain independent. This means that whenever the engine has to handle a new domain, a new ConceptNet has to be built.

I started out writing a single blog on the coming year's expected AI chips, and how NVIDIA might respond to the challenges, but I quickly realized it was going to be much longer than expected. Since there is so much ground to cover, I've decided to structure this as three hopefully more consumable articles. I've included links to previous missives for those wanting to dig a little deeper. In the last five years, NVIDIA grew its data center business into a multi-billion-dollar juggernaut without once facing a single credible competitor. This is an amazing fact, and one that is unparalleled in today's technology world, to my recollection.

This post discusses semi-supervised learning algorithms that learn from proxy labels assigned to unlabelled data. Note: Parts of this post are based on my ACL 2018 paper Strong Baselines for Neural Semi-supervised Learning under Domain Shift with Barbara Plank. Unsupervised learning constitutes one of the main challenges for current machine learning models and one of the key elements that is missing for general artificial intelligence. While unsupervised learning on its own is still elusive, researchers have a made a lot of progress in combining unsupervised learning with supervised learning. This branch of machine learning research is called semi-supervised learning. Semi-supervised learning has a long history. For a (slightly outdated) overview, refer to Zhu (2005) [1] and Chapelle et al. (2006) [2].

As artificial intelligence is beginning to permeate our daily lives, we take a look at how it impacts CCTV security systems. We begin by examining how artificial intelligence (AI) is been developed at Netwatch and then we'll explore the direction other industries are taking with this technology. Let's first take a step back and look at the technological leaps in CCTV technology that have allowed us to get to this point: An Internet Protocol (IP) camera is a type of digital camera that can send and receive data via a computer network and the internet. Unlike their traditional CCTV predecessor (analogue closed-circuit television cameras), IP cameras can transmit high resolution footage which can be viewed remotely. Video Camera Analytics is the capability of automatically analysing video to detect and determine temporal and spatial events.

A modeling averaging ensemble combines the prediction from each model equally and often results in better performance on average than a given single model. Sometimes there are very good models that we wish to contribute more to an ensemble prediction, and perhaps less skillful models that may be useful but should contribute less to an ensemble prediction. A weighted average ensemble is an approach that allows multiple models to contribute to a prediction in proportion to their trust or estimated performance. In this tutorial, you will discover how to develop a weighted average ensemble of deep learning neural network models in Python with Keras. How to Develop a Weighted Average Ensemble for Deep Learning Neural Networks Photo by Simon Matzinger, some rights reserved. Model averaging is an approach to ensemble learning where each ensemble member contributes an equal amount to the final prediction. In the case of regression, the ensemble prediction is calculated as the average of the member predictions.