DeepMind, an AI research lab acquired by Google for 400 million in 2014, has provided an update on how its DeepMind Health unit is doing. The London-based company told Business Insider on Tuesday that it has doubled the size of its team from 20 to 40 since launching in February this year, hiring several big names in the AI world along the way. New hires include security and privacy expert Ben Laurie, who is the founding director of the Apache Software Foundation, a director at the Open Rights Group, and a veteran Google software engineer, and former CIO Tony Corkett, who helped the NHS to digitise X-rays. Former Google Maps team leader Andrew Eland has been brought in to head up DeepMind Health's engineering efforts, while Will Cavendish, a former civil servant that worked on NHS online booking and prescription services, has joined as strategy lead. Elsewhere, ex-GE Healthcare executive Cathy Harris has been appointed as DeepMind Health's product lead.

Project[Project] Easily Create High Quality Object Detectors with Deep Learning (blog.dlib.net) Each image provides tons of samples for the negative class and a few samples for the positive class. There is a lot of information in just one image if you take advantage of it.

A few years ago I added an implementation of the max-margin object-detection algorithm (MMOD) to dlib. This tool has since become quite popular as it frees the user from tedious tasks like hard negative mining. You simply label things in images and it learns to detect them. It also produces high quality detectors from relatively small amounts of training data. For instance, one of dlib's example programs shows MMOD learning a serviceable face detector from only 4 images.

China's biggest search engine -- introduced an artificial intelligence-powered chatbot on Tuesday to connect with patients, field medical questions and suggest diagnoses to doctors. The company is calling the bot -- a new feature of the Baidu Doctor app it launched last year -- Melody the medical assistant. Baidu has developed advanced deep learning and natural language processing technologies to power Melody's artificially intelligent "brain." The bot is designed to be the first port of call for a person feeling sick at home. A patient poses a health query to Melody, which responds in real time with further questions, and compares responses with Baidu's database of medical information.

Artificial intelligence continues to have its moment in the spotlight, with a surge of interest in startups and efforts from huge tech companies to push the boundaries of how we might best use machine learning, computer vision and other areas of AI in the future. The latest development on that front comes from China's Huawei, which today announced that it would form a research partnership with UC Berkeley focused on AI, and fund it to the initial tune of 1 million. The alliance, between Huawei's Noah's Ark Laboratory and Berkeley Artificial Intelligence Research (BAIR), is being billed as a "strategic partnership into basic research", and it will cover areas like deep learning, reinforcement learning, machine learning, natural language processing and computer vision. "The two parties believe that this strategic partnership will fuel the advancement of AI technology and create completely new experiences for people, thus contributing greatly to society at large," Huawei notes. Some of these areas of AI you will have heard a lot about already.

But the really exciting part about this trend is the increasing ability to marry robotics with artificial intelligence. When this happens, robotics and machinery don't need to be programmed to perform certain tasks…they learn by trial and error. But first, there are three things that must come together to make advanced AI possible: Big Data, the computer-programming technique of deep learning and new concepts in both computer chips and how to use them. Big Data is the virtually limitless wilderness of facts, videos, infographics, statistics, public records and everything else stored on and collected from the internet. Software tools allow researchers to mine data to find useful nuggets -- they're digital needles in this infinite haystack of facts and information.

It may sound like a lost track from The Beatles, but the catchy pop song, 'Daddy's Car', was composed by artificial intelligence (AI). The tune was created by Flow Machines, a system Sony taught to make music by feeding it 13,000 samples from different genres. Although the software is capable of creating the lead sheet, a human composer instructed it to produce a record in the style of The Beatles and wrote the lyrics. It may sound like a lost track from The Beatles, but the catchy pop song, 'Daddy's Car', was composed by artificial intelligence (AI). Sony has taught its AI, Flow Machines, how to compose music.

AI is moving at a stellar speed and is probably one of most complex and present sciences. The complexity here is not meant as a level of difficulty in understanding and innovating (although of course, this is quite high), but as the degree of interrelation with other fields apparently disconnected. There are basically two schools of thought on how an AI should be properly built: the Connectionists start from the assumption that we should draw inspiration from the neural networks of the human brain, while the Symbolists prefer to move from banks of knowledge and fixed rules on how the world works. Given these two pillars, they think it is possible to build a system capable of reasoning and interpreting. In addition, a strong dichotomy is naturally taking shape in terms of problem-solving strategy: you can solve a problem through a simpler algorithm, which though it increases its accuracy in time (iteration approach), or you can divide the problem into smaller and smaller blocks (parallel sequential decomposition approach).