Lately, machine learning, deep learning and other concepts which relate to data being used in order to adapt itself automatically have been very popular. Although this is pretty cool to be able to make an algorithm adapt itself automatically and this can be practical in some situations when humans do not have the capability to created the statistical models to use in order to analyse the available data, this is a mistake to think that this will replace every other data analysis techniques anytime soon because of some critical points. First, what are Artificial Intelligence, Machine Learning and Deep Learning? I guess that I have already scared people less familiar with data science techniques. Artificial intelligence (AI) is a set of rules that can be applied to a large set of data, it can be very advanced such as an autonomous car, which could decide to break or accelerate based on real time feedback from thousand of variables.

Chinese search giant Baidu Inc. is publicly releasing Chinese language APIs for its primary speech technologies as it and rivals continue to open up their portfolios of AI-based deep learning technologies. The Beijing-based company (NASDAQ: BIDU) said the APIs cover two variations of speech recognition, a speech synthesis tool and a fourth called "wake word" used to activate voice-controlled digital assistants such as the Amazon Echo. The speech APIs represent the latest in a series of public releases by the Chinese company that includes natural language processing along with facial and optical character recognition technologies. It also underscores how AI technology pioneers are attempting to attract application developers as competition heats up in the personal digital assistant and other emerging markets. In September, Baudu released a deep learning framework called "PaddlePaddle" that it pitches as a simple-to-use platform for developing new products and services.

Machine learning may once have been a topic of discussion only for computer scientists and researchers. Now, however, it is a technology businesses are eager to use. The need for machine learning and Artificial Intelligence (AI) is being driven by the massive amount of data being generated today. Statisticians can get insight from this data. But the volume is so large and growing at such a rate, the best way to tackle it is using the very same machines that are in part responsible for creating the data.

TechCrunch Disrupt in London is on December 5-6. As well as speakers and panels, we'll be featuring some demos by some amazing tech companies. The first will be by Boston Dynamics. Yes, folks, delegates to Disrupt London will get to see one of those amazing BD robots up close and personal, almost literally in the flesh (if they had any flesh, that is). Marc Raibert, CTO and Founder of Boston Dynamics will be demonstrating one of the amazing robots his team has created, but you'll have to come to find out which one… Raibert was Professor of Electrical Engineering and Computer Science at MIT and a member of the Artificial Intelligence Laboratory from 1986 through 1995.

Google's AI is not just better at grasping languages like Mandarin, but can now translate between two languages it hasn't even trained on. In a research paper, Google reveals how it uses its own "interlingua" to internally represent phrases, regardless of the language. The resulting "zero-shot" deep learning lets it translate a language pair with "reasonable" accuracy, as long as it has translated them both into another common language. The company recently switched its Translate feature to the deep-learning Google Neural Machine Translation (GNMT) system. That's an "end-to-end learning framework that learns from millions of examples," the company says, and has drastically improved translation quality.

Most car company executives appear at the annual Los Angeles Auto Show to tout their brand's new vehicles or crow about its balance sheet. Ford Chief Executive Mark Fields came to town this year to give the kick-off keynote speech at AutoMobility L.A., the industry-only portion of the massive car show, and didn't discuss those things at all. Instead, he talked about the future of mobility, the "city of tomorrow" and how Ford is trying to find its place in a changing transportation landscape that -- confronting autonomous driving, connected cars and ride-sharing -- may soon have no place for traditional car companies. The following is an edited version of the conversation. Ford produces the F-150 truck, the most popular vehicle sold in America, but is also offering a hydrogen fuel cell Clarity and a line of plug-in hybrids.

Google's AI is not just better at grasping languages like Mandarin, but can now translate between two languages it hasn't even trained on. In a research paper, Google reveals how it uses its own "interlingua" to internally represent phrases, regardless of the language. The resulting "zero-shot" deep learning lets it translate a language pair with "reasonable" accuracy, as long as it has translated them both into another common language. The company recently switched its Translate feature to the deep-learning Google Neural Machine Translation (GNMT) system. That's an "end-to-end learning framework that learns from millions of examples," the company says, and has drastically improved translation quality.

Two eye-catching Machine-Learning libraries, PHP-ML and Skale-ML, written in PHP and Node.js respectively, are found in this week. Is this a sign of the up-coming wide-spread of ML everywhere? Deep Learning Papers by Nam Vu Papers about deep learning ordered by task, date. Current state-of-the-art papers are labelled. If you have subscribed this blog, please make sure to change the feed address.

Shopping mall travel agency shops are so 1999 and online booking needs more interaction, right? Wouldn't you prefer to book after chatting to a virtual'bot' that has more cognitive power and destination intelligence than a human being? IBM is opening a new raft of UK cloud datacenters, but positive progress asside this is not necessarily news. Every weighty cloud player worth its salt is continuing to press ahead with datacenter expansion plans across the European mainland (Rackspace has just announced plans for a new German facility this week) and similar expansion is being seen in the Americas and the rest of the world. All of this (largely, it appears) irrespective of Brexit, the new US presidency and the current state of play on Dancing With The Stars and/or Strictly Come Dancing (delete as'transatlantically' appropriate).

Organisations including the Food Standards Agency and the Department for Work and Pensions will be able to see UK citizen's entire internet browsing history in weeks. The Investigatory Powers Bill, which was all but passed into law this week, forces internet providers to keep a full list of Internet Connection Records (ICRs) for a year, and make them available to the government if it asks. Those ICRs effectively serve as a full list of every website that people have visited, not collecting which specific pages are visited or what's done on them but serving as a full list of every site that someone has visited and when. And those same ICRs will be made available to a wide range of government bodies. Those include expected law enforcement organisations like the police, the military and the secret service – but also contain bodies like the Food Standards Agency, the Gambling Commission, council bodies and the Welsh Ambulance Services National Health Service Trust.