The configurable, adaptable nature of FPGAs (Field Programmable Gate Arrays), as well system-on-chip architectures that employ them, make the technology invaluable in a multitude of applications, from AI-powered data centers to smart edge devices and the IoT. As part of its evolving strategy, Silicon Valley bellwether Xilinx has been integrating this adaptive technology into platform accelerator solutions for machine learning, as well as domain specific architecture solutions that incorporate various compute resources like ARM cores, high speed IO and even RF functions. One of the issues with modern heterogeneous compute architectures like this today, however, is that they're difficult for the average software developer to work with. You have to have a lot of hardware expertise to understand how to best utilize the various compute resources in modern systems, from CPUs to GPUs and FPGAs. However, today, Xilinx has announced a new, free, open source tool it calls Vitis.

Research in Deep Learning started as early as the 1960s, though the term itself was coined in 1986. With accurate predictions becoming the need of the hour, the amount of computing available and the massive data being collected, Deep Learning became the preferred algorithms at least over the last 5 years or so. As the complexity of problems arose, Deep Learning became the answer for problems that involved heavy datasets. A few millions of rows of supervised learning could effectively be crunched by ensemble tree based algorithms itself. However, for problems like computer vision or speech-to- text, deep learning was the answer.

According IoT Analytics, there are over 17 Billion connected devices in the world as of 2018, with over 7 Billion of these "internet of things" (IoT) devices. The Internet of Things is the collection of those various sensors, devices, and other technologies that aren't meant to directly interact with consumers, like phones or computers. Rather, IoT devices help provide information, control, and analytics to connect a world of hardware devices to each other and the greater internet. With the advent of cheap sensors and low cost connectivity, IoT devices are proliferating. It is no wonder that companies are inundated with data that comes from these devices and are looking to AI to help manage the devices as well as gain more insight and intelligence from the data exuded by these masses of chatty systems.

During the aggregative stage, the individual colony members conglomerate to form a single motile organism with rudimentary intelligence called a slug. This is common to all slime moulds. And two or more colonies can fuse to become a bigger one. This slug then migrates towards light - i.e. it moves towards the surface of the skin. Link: A robot controlled by a slime mould.

The recent announcement about Neuralink, Elon Musk's latest startup, added to the buzz already in the air about where tech is taking us in the not-so-distant future. Judging by its ambitious plans, which involve pairing computers wirelessly with the human brain, Neuralink demonstrates that the future is now. And a big part of that future is open source artificial intelligence (AI). Rapid advancements have opened up a whole new world of possibilities that startups can take advantage of right now. Traditionally, tech innovations were held close to the vest and developed in secret; no one wanted to give a leg up to the competition.

Artificial intelligence (AI) and facial expression technology is being used for the first time in job interviews in the UK to identify the best candidates. Unilever, the consumer goods giant, is among companies using AI technology to analyse the language, tone and facial expressions of candidates when they are asked a set of identical job questions which they film on their mobile phone or laptop. The algorithms select the best applicants by assessing their performances in the videos against about 25,000 pieces of facial and linguistic information compiled from previous interviews of those who have gone on to prove to be good at the job. Hirevue, the US company which has developed the interview technology, claims it enables hiring firms to interview more candidates in the initial stage rather than simply relying on CVs and that it provides a more reliable and objective indicator of future performance free of human bias. However, academics and campaigners warned that any AI or facial recognition technology would inevitably have in-built biases in its databases that could discriminate against some candidates and exclude talented applicants who might not conform to the norm. "It is going to favour people who are good at doing interviews on video and any data set will have biases in it which will rule out people who actually would have been great at the job," said Anna Cox, professor of human-computer interaction at UCL. Hirevue, which last month received a major investment injection from the multi-billion pound Carlyle Group, says it has already used its technology for 100,000 interviews in the UK.

Artificial intelligence is being increasingly used to classify employees, and there's a growing fear it might be used to classify people in other respects. These are only a small handful of the most well-known uses of artificial intelligence, yet there is one that, despite being on the margins for much of AI's recent history, is now threatening to grow significantly in prominence. This is AI's ability to classify and rank people, to separate them according to whether they're'good' or'bad' in relation to certain purposes. At the moment, Western civilization hasn't reached the point where AI-based systems are used en masse to categorize us according to whether we're likely to be'good' employees, 'good' customers, 'good' dates and'good' citizens. Nonetheless, all available indicators suggest that we're moving in this direction, and that this is regardless of whether Western nations consciously decide to construct the kinds of social credit system currently being developed by China.

The Aker group and the World Economic Forum (WEF) will develop what they call the Centre for the Fourth Industrial Revolution Norway (C4IR Norway), as part of efforts to preserve the ocean through advanced technology. Through the partnerships, the Centre aims to develop governance frameworks and solutions for a sustainable and profitable ocean economy, using digital technology ranging from Artificial Intelligence (AI) to Blockchain. The Centre will provide a platform for partnerships on governance policies, research and business solutions that can stimulate the application of science, data and technology in the public interest. The C4IR Norway will join the WEF's global C4IR Network and collaborate with the Government of Norway and the High Level Panel for a Sustainable Ocean Economy. The Centre will be an independent non-profit foundation, financed initially by founding partner the Aker group. According to a statement, Aker has gained valuable experience through engaging in cross-sector partnering between its own commercial and non-profit entities, such as REV Ocean, Ocean Data Foundation and VI Foundation.

A UK tax policy intended to boost the domestic video games industry has been used by some of the world's largest entertainment companies to avoid paying tens of millions of pounds in corporation tax, a Guardian investigation has found. WarnerMedia, which owns the British game development companies that make the Lego and Batman: Arkham series, has claimed up to £60m in corporation tax relief, according to company filings. Sony, the owner of PlayStation, claimed almost £30m. Japanese multinational Sega claimed up to £20m, according to audits of its UK subsidiaries that make the strategy-based Total War games and the hit sports series Football Manager. Video Games Tax Relief (VGTR), which enables game developers to claim back up to 20% of certain production costs, was introduced in 2014 after years of lobbying by the industry.

The Advantages and Challenges of Industry 4.0: Industry 4.0 will revolutionise the way manufacturing processes work in the near future. However, it is critical to weigh the benefits and challenges that a company might potentially face. Industrial revolution 4.0 promises a lot when it comes to investment, revenue, and technological advancements. However, employment remains one of the most mysterious aspects of the industry 4.0 revolution. Therefore, it is even harder to estimate or quantify the potential rate of employment.