Much of the internet appears to have gone down in Turkey. People in the country are having problems accessing much of the internet's biggest websites and services, including Facebook, WhatsApp, YouTube, Twitter and more. The website Down Detector confirmed problems in the country, particularly in the west. Some have reported that the sites are simply slow, but that it is still possible to access them. Others say they are down entirely.

Ben Dickson is a software engineer and the founder of TechTalks. While machine learning is introducing innovation and change to many sectors, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy. From rooting out your intimate and embarrassing secrets to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven't uttered and actions you haven't taken. Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands.

At the time of writing, AI ethics is a hot topic, given that several global players (namely Google, Amazon, Facebook, IBM and Microsoft) have founded the Partnership on Artificial Intelligence to Benefit People and Society. Their aims are to advance public awareness and define standards that researchers can use as guidelines. You have to wonder at the future benefits of this organization when the chosen name sounds like a company in Southeast Asia. Apple is conspicuously absent, perhaps unwilling to participate in any venture where the supply chain is not under its full control or a profit margin is not defined. However, there are other efforts underway.

Artificially-intelligent medical devices capable of continuously monitoring critically-ill patients and administering treatments when needed, are being investigated in a UK-wide research project. The research network, involving the Universities of Nottingham, Oxford and Warwick, will identify technologies that can provide more personalised and responsive care for cancer and intensive care patients, and those with chronic wounds. The three-year, EPSRC-funded project, which is being led by Prof Stephen Morgan at Nottingham University, will investigate technologies to monitor patients and administer medicines or adjust treatments as necessary, using information from built-in sensors. A particular focus of the research will be on devices that use the type of closed loop control system of feedback and intelligence found in power electronics used to control motors, said Morgan. "These continuously measure and feed their output back into the system, so they can constantly adjust," he said.

The dismal science is getting a data boost through a research partnership between a global bank and the U.K.'s national center for data science. The Alan Turing Institute and London-based HSBC Holdings (NYSE: HSBC) said they are launching the multimillion-pound data science research effort to better "understand changes in the U.K. economy." Launched last year, the Turing Institute brings together researchers from top British universities focusing on mathematics, computer science, artificial intelligence and machine learning. The goal of the economics data research, the partners said, is to "catalyze research into'big data' and algorithms." Also participating in the economic data initiative is the U.K. Engineering and Physical Sciences Research Council.

The Chinese are coming and they're hungry for games companies. They need new content to feed their 560 million avid gamers, who contribute to the biggest gaming market in the world - worth an estimated $24.4bn (£19.8bn) in 2016, according to Newzoo. Chinese firms have already spent more than $111bn on foreign acquisitions this year, according to Dealogic, with some of the biggest deals involving gaming companies. Internet giant Tencent - which owns the WeChat and QQ Games platforms - bought Finnish Clash of Clans mobile games maker Supercell for $8.6bn earlier this year. Tencent already owns League of Legends maker Riot Games, and has minority stakes in Epic Games and Activision Blizzard, the World of Warcraft maker.

AI startup Graphcore has emerged from stealth mode with the announcement of $30 million in initial Series A funding. The Bristol, UK-based company will use the cash infusion to complete development of its Intelligent Processing Unit (IPU), a custom-built chip aimed at machine learning workloads. The funding was led by Robert Bosch Venture Capital GmbH and Samsung Catalyst Fund; also joining were Amadeus Capital Partners, C4 Ventures, Draper Esprit plc, Foundation Capital and Pitango Venture Capital. The IPU has been under development at Graphcore for two years, with the first product slated to be released in the second half of 2017. It's designed to work across a range of machine learning application and is applicable to both training and inferencing neural networks.

Amazon, Google, Facebook, IBM, and Microsoft have announced they are forming a non-for-profit organisation to educate the public about artificial intelligence (AI) technologies, as well as alleviate anxieties around its application. The collective, which includes Google's AI subsidiary DeepMind, also plans to develop best practices on the challenges and opportunities within the field of AI. The organisation, called Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI), will address legal and ethical challenges that AI presents, encourage public discourse, and identify opportunities to use AI to bring improvements to society. The organisation does not intend to be a regulatory body, with a statement saying it does "not intend to lobby government or other policymaking bodies." Members of the Partnership on AI will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology.

Artificial intelligence has become the pet anxiety of luminaries like Elon Musk, Bill Gates, and Stephen Hawking. They have all expressed concerns about our Promethean quest to develop machine intelligence, and those concerns seem to be spreading every day. But there's another dimension of technological change that ought to worry us every bit as much as AI, if not more so. Bioengineering has already allowed human beings to take control of their own evolution. Whether it's emergent cloning technologies or advanced gene therapy, we're quickly approaching a world in which humans can -- and will -- change the way they live and die. Michael Bess is a historian of science at Vanderbilt University and the author of a fascinating new book, Our Grandchildren Redesigned: Life in a Bioengineered Society. Bess's book offers a sweeping look at our genetically modified future, a future as terrifying as it is promising. "We're going to give ourselves a power that we may not have the wisdom to control very well," he told me.

Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph. In some applications, this structure may be partly determined by design constraints or pre-determined sensing arrangements, like in road transportation networks for example. In general though, the data structure is not readily available and becomes pretty difficult to define. In particular, the global smoothness assumptions, that most of the existing works adopt, are often too general and unable to properly capture localized properties of data. In this paper, we go beyond this classical data model and rather propose to represent information as a sparse combination of localized functions that live on a data structure represented by a graph. Based on this model, we focus on the problem of inferring the connectivity that best explains the data samples at different vertices of a graph that is a priori unknown. We concentrate on the case where the observed data is actually the sum of heat diffusion processes, which is a quite common model for data on networks or other irregular structures. We cast a new graph learning problem and solve it with an efficient nonconvex optimization algorithm. Experiments on both synthetic and real world data finally illustrate the benefits of the proposed graph learning framework and confirm that the data structure can be efficiently learned from data observations only. We believe that our algorithm will help solving key questions in diverse application domains such as social and biological network analysis where it is crucial to unveil proper geometry for data understanding and inference.