UNITED NATIONS The United Nations is ready to deliver aid into Syria's Aleppo, but needs commitments from all parties in the war - not just Russia - to abide by a 48-hour humanitarian truce, the U.N. aid chief, angered by lack of assistance to civilians, said on Monday.

Marco, what can we do about AI? Marco, are we doing enough on AI?" The questions all come from senior executives, desperate to harness the potential that AI promises. Yet Bressan is bemused by how the technology is talked about at board level and in the media. "Currently it denotes a vision of the future; an aspect of the sci-fi imagination; something that you still can't do. But the truth is senior financial executives have been doing AI-related work, research and deployment of products for years." At the most rudimentary level, AI involves teaching machines to learn and to interact in order to undertake cognitive tasks that were usually performed by humans. The type of AI featured in sci-fi films in which machines possess a human-like intelligence, sometimes referred to as general artificial intelligence, remains a distant and elusive prospect. The most optimistic experts, such as Google's director of engineering, Ray Kurzweil, predict that AI will be able to outsmart humans by 2029. Conservative predictions expect this to take at least 100 years, if at all. Of more immediate relevance to those working in financial services is the deployment of narrow artificial intelligence. These applications undertake specific tasks using problem solving, deduction, reasoning and natural language processing. Such programmes are being applied across financial services, from the development of customer service programmes that use natural language processing to manage and field customer queries, through to programmes that can conduct financial research and make sophisticated models of financial markets to identify trading opportunities. The potential for narrow applications has led to a boom in AI investment. Technology companies are undoubtedly leading the way. In 2015 the giants of AI – Microsoft, Google and Facebook – spent 8.5 billion on AI research, acquisitions and talent. In comparison, financial institutions have made a cautious foray into the field. A handful are making investments by hiring high-level data scientists or acquiring AI companies. The hedge fund Bridgewater Associates hired the former chief engineer behind IBM's Watson supercomputer. BlackRock has also been busy hiring some high-profile names and has announced a joint venture with Google to explore how to use AI to improve investment decision-making. Goldman Sachs has invested in a number of promising AI start-ups, including the financial research platform Kensho. Yet most financial institutions have been slow to adopt AI, even though it is likely to usher in a new type of bank, with data and technology as its heart. Failure to adapt may lead to extinction for some. As Neil Dwane, global strategist at Allianz Global Investors, explains: "Technological competence is absolutely essential for at least staying in the game.

A NEW technique using artificial intelligence (AI), to read satellite images could aid efforts to eradicate global poverty by indicating where help is needed most, a team of US researchers says. The method would assist governments and charities that are trying to fight poverty but that lack precise and reliable information on where poor people are living and what they need, the researchers based at Stanford University in California say. Eradicating extreme poverty, measured as people living on less than 1.25 a day, by 2030 is among the sustainable development goals adopted by UN member states in 2015. A team of computer scientists and satellite experts created a self-updating world map to locate poverty, says Marshall Burke, assistant professor in Stanford's department of earth system science. It uses a computer algorithm that recognises signs of poverty through a process called machine learning, a type of artificial intelligence, he says.

NVIDIA debuted its Drive PX2 in-car supercomputer at CES in January, and now the company is showing off the Parker system on a chip powering it. The 256-core processor boasts up to 1.5 teraflops of juice for "deep learning-based self-driving AI cockpit systems," according to a post on NVIDIA's blog. That's in addition to 24 trillion deep learning operations per second it can churn out, too. For a perhaps more familiar touchpoint, NVIDIA says that Parker can also decode and encode 4K video streams running at 60FPS -- no easy feat on its own. However, Parker is significantly less beefy than NVIDIA's other deep learning initiative, the DGX-1 for Elon Musk's OpenAI, which can hit 170 teraflops of performance.

In the first post of our non-technical ML intro series we discussed some general characteristics of ML tasks. In this post we take a first baby step towards understanding how learning algorithms work. We'll continue the dialog between an ML expert and an ML-curious person. Ok I see that an ML program can improve its performance at some task after being trained on a sufficiently large amount of data, without explicit instructions given by a human. Let's start with an extremely simple example.

From broad population health management to highly individualized clinical decision support, advanced approaches to data analytics are changing the way providers interact with data and patients--even what it means to be a doctor. Cognitive computing technologies mimic the way the human brain draws connections between seemingly unrelated data. Powered with these technologies, intelligent machines can understand information in context and even have the ability to reason and learn. Cognitive computing encompasses various forms of artificial intelligence (AI), including machine learning, reasoning, natural language processing, speech and vision, human-computer interaction, dialog and narrative generation, and more. With cognitive computing, providers can uncover patterns in health data that previously were hidden, enabling them to do more than was possible before.

Could a computer be better at identifying depression than a primary care physician? That's the suggestion of a new study that focused on using machine learning to analyze Instagram photos. The study, conducted by a researcher from the department of psychology at Harvard University and another from the University of Vermont, analyzed nearly 44,000 photographs posted to Instagram, exploring factors like what filter was used and how makes "likes" a photo received. The study included photographs from 166 people, some of whom were depressed, and some of whom were not. Instagram offers a variety of filters to change how a photo appears, and the researchers discovered that healthy participants were more likely to use a filter than depressed people.

The people who are selling these machines want them to augment human intelligence, not replace it #ROSS #LegalTech https://t.co/TfcQ4Fm9mf With news that ROSS, the world's first AI lawyer, built upon IBM's Watson was'hired' by law firm Baker & Hostetler, there was much weeping and gnashing of teeth by organic, flesh-and-bone lawyers. They were then summarily rounded up by their new machine overlords and sealed in pods where their bioelectric energy was harnessed and used to power the very computers that now subjugated them. But for all the alarm-raising over the rise of AI and what it means for tomorrow's legal professionals, does machine intelligence pose a legitimate threat to the practice of law by human beings? Will clients in the near future be represented in court by Lawbot 3000?

Since it uses C 11 features, it requires a compiler with good C 11 support. Compilation is carried out using a Makefile, so you will need to have a working make. This will produce object files for all the classes as well as the main binary fasttext. If you do not plan on using the default system-wide compiler, update the two macros defined at the beginning of the Makefile (CC and INCLUDES). If you inted to build with Docker, a Docker file is available here fastText-Docker.

Geordie Rose, Founder of D-Wave (recent clients are Google and NASA) believes that the power of quantum computing is that we can exploit parallel universes' to solve problems that we have no other means of confirming. Simply put, quantum computers can think exponentially faster and simultaneously such that as they mature they will out pace us. Listen to his talk now!