The company created a technology that recognises spoken words ever closer to human parity. IBM reached a new AI milestone in speech recognition, achieving an industry record of 5.5% word error rate using the Switchboard linguistic corpus. The company broke the industry record by extending its deep learning technologies and incorporating an acoustic model that learns from positive examples while taking advantage of negative ones. The model gets smarter and performs better when similar speech patterns are repeated. IBM achieved another major AI milestone in conversational speech recognition last year with a computer system that reached a word error rate of 6.9%.

Forays by Google subsidiary DeepMind Health into the UK's medical institutions have been characterized by two major themes. First, amazing results powered by cutting-edge AI; and second, a lack of transparency over the handling of the UK's public-funded data. With the science going swimmingly, DeepMind Health is focusing more than ever on reassuring UK citizens that their medical records are in safe hands. Its latest plan is a public ledger that shows which bits of data it's using; when; and for what purposes. The initiative is called the "Verifiable Data Audit," and was announced this week in a blogpost written by DeepMind co-founder Mustafa Suleyman and the company's head of security and transparency, Ben Laurie.

I can highly recommend Deep Learning: A Practioner's Approach. From a preview of this, it's extremely well-written and easy to follow -- and includes lots of code examples. It is by the brilliant minds of the Deep Learning 4J people. Tutorials of Keras: This could be the Rosetta Stone, or at least the Apache Beam, of DL. Autopilot: TensorFlow: This is some great code to try and learn from.

Planning to Netflix and chill this weekend? The movie you choose to watch may be heavily influenced by Netflix's sophisticated algorithms. Similarly, decisions like where you choose to dine and what you choose to wear are increasingly facilitated by predictive technologies powered by deep learning. Here are five ways that popular consumer tech companies Netflix, Yelp, Yahoo, StitchFix, and Google improve your online experience with artificial intelligence. Historically, watching television is a unidirectional communication channel.

There is a gaping hole in the digital defences that companies use to keep out cyber thieves. The hole is the global shortage of skilled staff that keeps security hardware running, analyses threats and kicks out intruders. Currently, the global security industry is lacking about one million trained workers, suggests research by ISC2 - the industry body for security professionals. The deficit looks set to grow to 1.8 million within five years, it believes. The shortfall is widely recognised and gives rise to other problems, says Ian Glover, head of Crest - the UK body that certifies the skills of ethical hackers.

Google's DeepMind, a London-based artificial intelligence company the search-and-cloud giant acquired in 2014, has been closely associated with Google's quest to build general AI. Earlier this year, with little publicity, DeepMind unveiled what looks like a useful step in that direction. The DeepMind team released a paper describing a neural network approach that would allow automatic "transfer learning," meaning a neural network could reuse what it already "knows" on new problems. If history is any hint, this is one innovation Google will keep close to its chest. Neural networks are a common type of machine learning that mimic, perhaps distantly, how neurons interrelate in the brain to pass information around.

This is because deep-learning neural networks are a "black box": they consist of millions of connections between nodes that are fine-tuned in opaque, subtle ways as data is fed in. When a deep-learning network produces an output (e.g., the decision to stop or not to stop at a yellow light), that output cannot be traced to a particular sequence in the AV's software; rather, it is an emergent outcome of the overall system. Experts call this problem "lack of interpretability." As well as deep learning networks may perform at driving 99.9% of the time, this lack of interpretability becomes a real concern on those rare occasions when an AV makes the wrong decision and causes an accident. In those situations, humans have no way to explain what went wrong and no way to troubleshoot the error.

"We are applying AI to a number of problems such as speech recognition, natural language understanding, question answering, dialog systems," Rastogi said.Rajeev Rastogi, who heads the Machine Learning team at Amazon, explains how the global ecommerce giant employs Artificial Intelligence to improve the online shopping experience.Edited excerpts: In which areas does Amazon use AI? We are applying AI to a number of problems such as speech recognition, natural language understanding, question answering, dialog systems, product recommendations, product search, forecasting future product demand, among others. We have used Deep Learning to do better speech recognition. We use neural networks to convert speech (spoken by users) to text with very high accuracy. The speech recognition and understanding technology in Alexa (Amazon's voice-controlled virtual assistant) is powered by Deep Learning.

Anima Anandkumar is giving a talk at Strata Hadoop World San Jose and a tutorial and talk at Strata Hadoop World London. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Anima Anandkumar, a leading machine learning researcher, and currently a principal research scientist at Amazon. I took the opportunity to get an update on the latest developments on the use of tensors in machine learning.

No longer the subject of science fiction, Artificial Intelligence (AI) is profoundly transforming our daily lives. While computers have been mimicking human intelligence already for some decades using logic and if-then kind of rules, massive increases in computational power are now facilitating the creation of'deep learning' machines i.e. algorithms that permit software to train itself to recognize patterns and perform tasks, like speech and image recognition, through exposure to vast amounts of data. These deep learning algorithms are everywhere, shaping our preferences and behaviour. Facebook uses a set of algorithms to tailor what news stories an individual user sees and in what order. Bot activity on Twitter last year suppressed a protest against Mexico's now-president by overloading the hashtag used to organize the event.