Elon Musk has long used his mighty Twitter megaphone to amplify the idea that Tesla's automated driving software isn't just safe -- it's safer than anything a human driver can achieve. That campaign kicked into overdrive last fall when the electric-car maker expanded its Full Self-Driving "beta" program from a few thousand people to a fleet that now numbers more than 100,000. The $12,000 feature purportedly lets a Tesla drive itself on highways and neighborhood streets, changing lanes, making turns and obeying traffic signs and signals. As critics scolded Musk for testing experimental technology on public roads without trained safety drivers as backups, Santa Monica investment manager and vocal Tesla booster Ross Gerber was among the allies who sprang to his defense. "There has not been one accident or injury since FSD beta launch," he tweeted in January.

A robot can create a model of itself to plan how to move and reach a goal – something its developers say makes it self-aware, though others disagree. Every robot is trained in some way to do a task, often in a simulation. By seeing what to do, robots can then mimic the task. But they do so unthinkingly, perhaps relying on sensors to try to reduce collision risks, rather than having any understanding of why they are performing the task or a true awareness of where they are within physical space. It means they will often make mistakes – bashing their arm into an obstacle, for instance – that humans wouldn't because they would compensate for changes.

It's unclear just how much the efficiency gains of robotics will affect the overall demand for warehouse space. Symbotic, for instance, claims it can deliver the same amount as a traditional warehouse operation in half the space. A human and robot tend to take up a similar amount of space on the warehouse floor, but only one needs a break room. A bigger challenge is the industry's aging spaces: A third of warehouses are more than 50 years old, with 70 percent constructed before the 21st century, according to a report from the real estate services firm Newmark. Landlords aren't typically making these investments themselves; tenants and big retailers tend to finance the robotics and automation improvements.

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.