Deep learning and neural networks are increasingly important concepts in computer science with great strides being made by large companies like Google and startups like DeepMind. A zero that's difficult to distinguish from a six algorithmically

My (expanded) talking points from a presentation I gave at the Re.Work Investing in Deep Learning dinner in London on 1st December 2015. Keep up to date with AI news through my newsletter on tech, research and venture. It's my belief that artificial intelligence is one of the most exciting and transformative opportunities of our time. Consumers worldwide carry 2 billion smartphones, they're increasingly addicted to these devices and 40% of the world is online (KPCB). This means we're creating new data assets that never existed before (user behavior, preferences, interests, knowledge, connections).

We may have reached an inflection point. The problem space of chess is so astonishingly large that incremental increases in hardware speed and algorithms are unlikely to result in meaningful gains from here on out. Turns out I was kinda … totally completely wrong. The number of possible moves, or "problem space", of Chess is indeed astonishingly large, estimated to be 1050: Deep Blue was interesting because it forecast a particular kind of future, a future where specialized hardware enabled brute force attack of the enormous chess problem space, as its purpose built chess hardware outperformed general purpose CPUs of the day by many orders of magnitude. In the heady days of 1997, Deep Blue could evaluate 200 million chess positions per second.

Google's artificial intelligence (AI) ethics board, established when Google acquired London AI startup DeepMind in 2014, remains one of the biggest mysteries in tech, with both Google and DeepMind refusing to reveal who sits on it. Google set up the board at DeepMind's request after the cofounders of the 400 million research-intensive AI lab said they would only agree to the acquisition if Google promised to look into the ethics of the technology it was buying into. Business Insider asked Google once again who is on its AI ethics board and what they do but it declined to comment. A number of AI experts told Business Insider that it's important to have an open debate about the ethics of AI given the potential impact it's going to have on all of our lives. Artificial intelligence is the field of building computer systems that understand and learn from observations without the need to be explicitly programmed, as defined by Nathan Benaich, an AI investor at venture capital firm Playfair Capital.

First went checkers, then fell chess. Now, a computer program has defeated the world's top player in the ancient east Asian board game of Go -- a major milestone for artificial intelligence that brings to a close the era of board games as benchmarks in computing. At the Four Seasons Hotel in Seoul, Google DeepMind's AlphaGo capped a 3-0 week on Saturday against Lee Sedol, a giant of the game. Lee and AlphaGo were to play again Sunday and Tuesday, but with AlphaGo having already clinched victory in the five-game match, the results are in and history has been made. It was a feat that experts had thought was still years away.

Yahoo! Inc., is getting into the artificial intelligence (AI) game with the release of new internally-built software under an open-source license. Called CaffeOnSpark, the software is able to perform'deep learning' on the vast ocean of data kept in Yahoo's Hadoop file system. Now, the company has made it available on GitHub for everyone to use. Deep learning is a machine learning method that's particularly useful in helping computers come to sort through and recognize user-generated data, and one of its most exciting use cases is where images are concerned. As such, Yahoo built CaffeOnSpark to help identify the billions of images posted onto its Flickr photo sharing website.

Mobile capture and identity verification firm Mitek said on Wednesday (March 23) that it has appointed Stephen Ritter as the firm's chief technology officer. In that role, the company said in a release, Ritter will spearhead and lead the development of Mitek's core offerings, along with overseeing Mitek Labs, which is the company's scientific and computer vision operations. The firm said that, prior to coming on board at Mitek, Ritter served as senior vice president of engineering and research for Emotient, which was acquired by Apple. That firm used what are known as deep learning techniques in order to address computer vision issues. At Emotient, said Mitek in the release, Ritter helped fashion an emotion detection offering based on subjects' facial expressions.

In many ways, our current inability to replicate our own brains is not a particularly large concern. For driverless cars, for example, the aim is to create a system that combines pure logical processes with ethical judgements and a protective mentality. Google's self-driving car project proved this in February, when one of its vehicles was responsible for causing a low-speed crash. The error was a typically human one -- assuming that a bus would slow and let it pull out past some sand bags -- and Google's response is to teach the system through deep learning processes that certain vehicles will be less likely to slow down for it.

Sometimes, the field of artificial intelligence looks pretty ridiculous, like when Microsoft's teen chatbot was tricked by online trolls to say racist and sexist things. Consider how it's affecting our working world. Jen-Hsun Huang, the CEO of computer graphics hardware company Nvidia, can see this clearly. For a long time, gamers have loved Nvidia for the company's graphics processors (GPUs) that have the ability to gorgeously render insanely violent games. Here's the twist: Nvidia's GPUs also happen to be excellent for deep learning, an AI technique that allows algorithms to recognize patterns and become more adept at finding them over time.

Humans have the tendency to imbue machine learning models with more intelligence than they deserve, especially if it involves the magic phrases of artificial intelligence, deep learning, or neural networks. TayAndYou is a perfect example of this. TayAndYou was toxic before it ever made contact with the Internet. The media prefer the story that the Internet turned TayAndYou toxic. Mark V. Shaney was an elegant play on this concept all the way back in 1984.