Unlike other, more famous large language models such as OpenAI's GPT-3 and Google's LaMDA, BLOOM (which stands for BigScience Large Open-science Open-access Multilingual Language Model) is designed to be as transparent as possible, with researchers sharing details about the data it was trained on, the challenges in its development, and the way they evaluated its performance. OpenAI and Google have not shared their code or made their models available to the public, and external researchers have very little understanding of how these models are trained. BLOOM was created over the last year by over 1,000 volunteer researchers in a project called BigScience, which was coordinated by AI startup Hugging Face using funding from the French government. It officially launched on July 12. The researchers hope developing an open-access LLM that performs as well as other leading models will lead to long-lasting changes in the culture of AI development and help democratize access to cutting-edge AI technology for researchers around the world.

Teaching artificial intelligence to understand simple physics concepts, such as that one solid object can't occupy the same space as another, could lead to more capable software that takes less computational resources to train, say researchers at DeepMind. The UK-based company has previously created AI that can beat expert players at chess and Go, write computer software and solve the protein-folding problem. But these models are highly specialised and lack a general understanding of the world. As DeepMind's researchers say in their latest paper, "something fundamental is still missing". Now, Luis Piloto at DeepMind and his colleagues have created an AI called Physics Learning through Auto-encoding and Tracking Objects (PLATO) that is designed to understand that the physical world is composed of objects that follow basic physical laws.

A new robotic system called FuseBot developed at MIT combines visual information and radio-frequency signals to find hidden items buried under a pile of objects. To find a lost item, robots must use complex reasoning about the pile and objects in it.

In addition to his role at Meta, LeCun is Silver Professor at New York University (NYU) affiliated with the Courant Institute and the Center for Data Science, where he's a founding director. Considered one of the godfathers of deep learning, LeCun has worked since the mid-1980s to advance deep learning methods, particularly the convolutional neural network model, which is the basis of many products and services deployed by numerous companies including Meta, Google, Microsoft, and many others for image and video understanding and speech recognition. The character recognition technology he developed at Bell Labs is used by several banks around the world to read checks while his image compression technology, called DjVu, is used by hundreds of websites and publishers and millions of people to access scanned documents online. In 2018, LeCun was awarded the ACM A.M. Turing Award along with Geffrey Hinton and Yoshua Bengio for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing." More recently, his contributions to the advancement of AI were recognized with the Princess of Asturias Award for Technical & Scientific Research (bestowed by the King of Spain).

It's about the size of a golf cart, runs by remote control, and is 100% electric. Be-Bot is its name, and it's a machine built to clean the sands of your neighborhood beach. "The robot is designed to sift a very thin layer of sand and remove small pieces of debris," explained Pat DePlasco, executive director of Keep Pinellas Beautiful." That debris includes the tiny litter often overlooked and left behind; cigarette butts, bottle caps, food wrappers and straws. Be-Bot has been cleaning up the shoreline across Florida, and Monday, made Madeira Beach its latest cleanup shop.

Building a robot is hard. Building one that can sense its environment and learn how to get around on its own is even harder. But UCLA engineers took on an even bigger challenge. Not only did they create autonomous robots, they 3-D printed them in a single step. Each robot is about the size of a fingertip.

Bengaluru: Researchers at Indian Institute of Technology (IIT)-Madras have developed a machine learning (ML) algorithm to identify personalised genes that have the potential to form and drive cancer in individuals. The model uses a'multiomic' approach, the combined study of intersectional studies that end with the suffix '-omics'. Details of the algorithm were published in a peer-reviewed paper in the journal Frontier in Genetics last month. The findings are expected to help in devising more personalised cancer therapies, contributing to the growing field of targeted therapy and immunotherapy trials. Called'Personalized Identification of driVer OGs and TSGs', or PIVOT, the model identifies personalised drivers of cancer genes and classifies them as either tumour suppressor genes (TSG) or oncogenes (OG) -- the two types of genes involved in cancer.

Since Amazon began bringing robots to its warehouses in 2014, company executives have repeatedly claimed that they improve worker safety. But company records obtained by Reveal showed that between 2016 and 2019 serious injuries occurred more often in Amazon warehouses with robots than those without them, suggesting that robots made employees less safe by causing managers to raise performance quotas. Analysis of filings with the US Occupational Safety and Health Administration (OSHA) by The Washington Post found that in 2020, serious injuries were roughly twice as likely to occur in Amazon warehouses than those run by other companies. A separate analysis of OSHA data by labor union coalition the Strategic Organizing Center found the same pattern for 2021. Amazon didn't mention that track record late last month when it announced a machine called Proteus, which company officials call their first fully mobile and collaborative robot.

RoboCup 2022 will take place in Bangkok, Thailand, from 13-17 July. The event will see around 3000 participants, from 45 different countries take part in competitions and a symposium. You can see the schedule for the week here. The RoboCup symposium will take place on 17 July. The programme can be found here.

A group of autonomous vehicle developers are pressuring California lawmakers to introduce a regulatory process that would eventually allow autonomous trucks on public roads. Thirty-five autonomous vehicle leaders including Waymo, Uber, Volvo, and Aurora signed an open letter last week addressed to California Governor Gavin Newsom, arguing that if California does not soon permit testing of autonomous trucks on public roads, it could lose its competitive edge. While California has allowed for the testing of smaller, autonomous vehicles on public roads since 2019, semi-trucks and delivery vehicles weighing more than 10,001 pounds remain prohibited. California figures as an important testing ground for autonomous vehicles for two reasons: Not only is the state an innovation hub for self-driving technology, but it's also the home of several highways that connect multiple key cross-country freight routes. California's plans for allowing autonomous trucking still remain unclear.