Learn more about what comes next. Hailo, a startup developing AI accelerator chips for edge devices, today announced that it raised $136 million in a series C funding round led by Poalim Equity and entrepreneur Gil Agmon, with participation from ABB Technology Ventures, Latitude Ventures, OurCrowd, Carasso Motors, Comasco, Shlomo Group, Talcar Corporation, and Automotive Equipment. The capital brings Hailo's total raised to $224 million, and CEO Orr Danon says the company will put the funds toward mass manufacturing of its current chips and the development of next-generation hardware in new and existing markets. AI edge chips are coming into vogue as enterprises begin to realize the benefits of localized computing. Deloitte estimates that in 2020, over 750 million edge AI chips -- chips or parts of chips that perform or accelerate machine learning tasks on-device -- were sold globally.

Sometimes major shifts happen virtually unnoticed. CodeNet is a follow-up to ImageNet, a large-scale dataset of images and their descriptions; the images are free for non-commercial uses. ImageNet is now central to the progress of deep learning computer vision. CodeNet is an attempt to do for Artificial Intelligence (AI) coding what ImageNet did for computer vision: it is a dataset of over 14 million code samples, covering 50 programming languages, intended to solve 4,000 coding problems. The dataset also contains numerous additional data, such as the amount of memory required for software to run and log outputs of running code.

A robot avatar that mimics the motions of a human controller could take the place of workers in several dangerous jobs, such as tree trimming and construction, by the end of 2022. The challenge: If a tree branch gets too close to a power line, it can cause electrical outages or, even worse, dangerous fires (as Californians know all too well). To avoid this, utility companies have to regularly trim trees near their lines. But it's dangerous work, as workers are dozens of feet above the ground, using sharp power tools to trim trees while power lives are still active -- this puts them at risk of falls, cuts, and electrocution, all at once. By some estimates, tree trimming is one of the most dangerous jobs in the country.

If you are a software engineer, or count any of them among your circle of acquaintances, then you're probably already aware at some level of Copilot. Copilot is GitHub's new deep learning code completion tool. Autocomplete tools for programmers are nothing new, and Copilot is not even the first to make use of deep learning nor even the first to use a GPT transformer. After all, TabNine sprung out of a summer project by OpenAI alum Jacob Jackson and makes use of the GPT-2 general purpose transformer. Microsoft (which owns GitHub) has packaged their own IntelliSense code completion tool with programming products since at least 1996, and autocomplete and text correction has been an active area of research since the 1950s.

An army of more than 4,000 marching doglike robots is a vaguely menacing sight, even in a simulation. But it may point the way for machines to learn new tricks. The virtual robot army was developed by researchers from ETH Zurich in Switzerland and chipmaker Nvidia. They used the wandering bots to train an algorithm that was then used to control the legs of a real-world robot. In the simulation, the machines--called ANYmals--confront challenges like slopes, steps, and steep drops in a virtual landscape.

WASHINGTON, DC, October 4, 2021 (ENS) – A new international project is creating advanced artificial intelligence, AI, programs that will enable machines to learn progressively over a lifetime and share those experiences with each other. Uses of this new technology could include co-operating self-learning autonomous vehicles such as self-driving cars, robotic rescue and exploration systems, distributed monitoring systems to detect emergencies, or cybersecurity systems of agents that monitor large networks. Researchers hope the technology will allow machines to reuse information, adapt quickly to new conditions and collaborate by sharing information. The project is part of the initiative Shared-Experience Lifelong Learning, or ShELL, a program funded by the Defense Advanced Research Projects Agency, DARPA. This U.S. government military agency is credited with some of the biggest technological advances in recent history such as the Internet, the miniaturization of GPS, Siri, and the computer mouse.

Summary: Study reveals why human brains are so good at learning. The findings could help with the development of more efficient AI models. The new study found that by tweaking the electrical properties of individual cells in simulations of brain networks, the networks learned faster than simulations with identical cells. They also found that the networks needed fewer of the tweaked cells to get the same results, and that the method is less energy intensive than models with identical cells. The authors say that their findings could teach us about why our brains are so good at learning, and might also help us to build better artificially intelligent systems, such as digital assistants that can recognise voices and faces, or self-driving car technology.

What do Facebook co-founder Mark Zuckerberg and Tesla CEO Elon Musk have in common? Both are grappling with big problems that stem, at least in part, from putting faith in artificial intelligence systems that have underdelivered. Zuckerberg is dealing with algorithms that are failing to stop the spread of harmful content; Musk with software that has yet to drive a car in the ways he has frequently promised. There is one lesson to be gleaned from their experiences: AI is not yet ready for prime time. Furthermore, it is hard to know when it will be.

Singapore has trialled patrol robots that blast warnings at people engaging in "undesirable social behaviour", adding to an arsenal of surveillance technology in the tightly controlled city-state that is fuelling privacy concerns. From vast numbers of CCTV cameras to trials of lampposts kitted out with facial recognition tech, Singapore is seeing an explosion of tools to track its inhabitants. That includes a three-week trial in September, in which two robots were deployed to patrol a housing estate and a shopping centre. Officials have long pushed a vision of a hyper-efficient, tech-driven "smart nation", but activists say privacy is being sacrificed and people have little control over what happens to their data. Singapore is frequently criticised for curbing civil liberties and people are accustomed to tight controls, but there is still growing unease at intrusive tech.

An Uber driver who lost his job when automated face-scanning software failed to recognise him is accusing the firm of indirect race discrimination in a legal test case. The black driver, who worked on the Uber platform from 2016 until April 2021, has filed an employment tribunal claim alleging his account was illegally deactivated when facial-verification software used to log drivers on to the ride-hailing app decided he wasn't who he said he was. The Independent Workers of Great Britain trade union, which is backing the action, claimed at least 35 other drivers had had their registration with Uber terminated as a result of alleged mistakes with the software since the start of the pandemic. It is calling for Uber to scrap the "racist algorithm" and reinstate terminated drivers. An Uber spokeswoman said the firm "strongly refutes the completely unfounded claims" and said it is "committed to fighting racism and being a champion for equality--both inside and outside our company."