If you write marketing or advertising text for a living, you may want to get a second job skill. That's because software that writes text is here, and it is tackling a growing list of assignments. Several companies offer software that regularly churns out thousands of stories and reports based on structured data, like financial results. Ads that literally write themselves emerged last week, as IBM announced a new service based on its Watson supercomputer. A program called Quakebot has generated earthquake stories for the LA Times.

Intel's strategy for tackling the AI CPU market, where it is facing competition from leading GPU makers and potentially also big customers that make their own specialized processors for this purpose, such as Google, rests to a great extent on designing systems that scale out rather than up. The latter, according to the chipmaker, is the conventional but inefficient approach to architecting these systems. Software code in today's machine learning systems (machine learning is one of the most active subfields in the development of artificial intelligence) is tough to scale and usually lives in a single box, Charles Wuischpard, VP of the Intel Data Center Group and general manager of the giant's HPC Platform Group, said. Companies generally buy high-power scale-up systems filled with GPUs. "In a way, there's an efficiency loss here," he said on a call with reporters last week.

Netflix does not usually jump to the top of the list when one thinks of leaders in artificial intelligence, but Netflix's VP of Product Innovation, Carlos Uribe-Gomez, and Chief Product Officer Neil Hunt published a paper informing investors that some of its algorithms help them save 1 billion each year. In the paper, the two executives detailed how the company's recommendation engine impacts its churn rate. The video streaming giant does not report its churn rate, but the paper mentions that the Netflix's retention rates "are already high enough that it takes a very meaningful improvement to make a retention difference of even 0.1%." This year, the streaming giant plans to spend 6 billion on content. With such a big investment, it could get all sorts of TV series and movies, but if it just presents the most popular selections to everyone, many titles would remain unseen.

The implications of this AI revolution are enormous. Isaac Newton thought that everything could be computed, but reality does not work like that all the time. As German physicist Heisenberg posited with his Uncertainty Principle, there is a lot of indeterminacy in the world. There are many things we don't fully understand yet. We don't really understand intelligence on a deep level.

Google says it's offering all of these details strictly for informational purposes and that you should always consult a real doctor for proper medical advice. However, the company did consult with a team of doctors to review symptom info and experts at Harvard Medical School and Mayo Clinic evaluated the conditions to help improve the lists. That's in addition to collected data from medical searches and doctors in Google's own Knowledge Graph. The company also wants to know if the information it gives you in response to those queries is helpful, and will ask for you to offer feedback on the feature. The new symptoms search is rolling out on mobile over the next few days in the US, but only in English. Google says that eventually it plans to expand the tool to other countries and languages while also including answers about more symptoms.

Trust greases the wheels of the sharing economy, paving the way for transactions to take place between total strangers. But figuring out who is trustworthy and who is not remains a sticky bottleneck for digital businesses wanting to scale faster. Meanwhile the consequences for customers when startups screw up these risk calculations can be very unpleasant indeed. The traditional route to assessing risk is to run a full background check on an individual -- a process that can be time-consuming and expensive, given it can involve sending an actual person to an actual courthouses to parse actual paper records. Which is why, in recent years as sharing economy businesses have been gunning to scale up, other entrepreneurs have spotted an opportunity to step in to offer online services for verifying identity and screening for unsavory behavior, to try to steal a march on more established but slower paced background checkers.

Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment. The experiment, developed by physicists from The Australian National University (ANU) and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from the ANU Research School of Physics and Engineering. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.

Editor's Note: The author of this post is a legal technology and management consultant. The legal media has lately had a mania for tech headlines. Many commentators claim that tech, especially artificial intelligence (AI), will do something to Big Law. Tech more likely will do something in it: incremental change. I start with the case against disruption, then look at four headline-grabbing technologies: AI, Bots, Big Data, and Blockchain.

Current deep learning technology is not advanced enough for computers to understand language, a major figure in the field said today. The ability to learn the way people learn -- through observation and experience -- is what Facebook will use to teach chatbots and computers to carry on a conversation like a human, said Yann LeCun, the head of Facebook's artificial intelligence (A.I.) research lab. LeCun spoke about A.I. and steps being taken to make virtual assistant M less reliant on human training at the 2016 Wired Business Conference, as Wired reported. Humans have played a role in the decision-making process for Facebook's M since the bot debuted last year, before the launch of the company's bot platform. Facebook has been researching ways to make machines understand language more independently.

Our method uses machine learning techniques together with a large-scale training dataset of manually created GIFs. The dataset consists of about 100k GIFs that people created from videos. Using this data we train a Deep Neural Network algorithm that learns to understand what makes GIFs awesome. Finally, we use this model to automatically rank video segments and generate GIFs from the best and most interesting ones.