An eerie four-legged robot is shown pacing through the woods with relative ease. But when brought inside and tested in other situations, such as slippery surfaces, it had balance issues and difficulty walking. In one example, when weighted bags were placed on its back, the robot fell over. With Facebook's AI software enabled, it wobbled but managed to stay upright and keep walking when the bags were tossed onto it. There are no cameras on the device. All of the robot's movements were guided by sensors in its feet and various joints, which allow it to experience the world through "touch."

Shortly before Prime Day in June, Amazon announced it was developing two robots for its infamously demanding distribution centers. Named "Bert" and "Ernie" after the Sesame Street Muppets, the robots, Amazon claimed, would help relieve the physical burden of its jobs by autonomously carting materials through distribution center floors and lifting heavy totes off shelves. They were not, the company stressed, intended to increase speed or replace workers, but to improve safety and free workers for tasks "that require…critical thinking skills." According to the company, the robots weren't some nefarious plot; instead, they embodied its empathy for workers and commitment to innovations that would help consumers and employees alike. The announcement's timing was convenient.

Tesla owners who want to tap into a self-driving feature when traveling local streets got a boost this weekend, when the electric car maker began releasing a much anticipated software update, reports say. The updates to the Full Self-Driving beta version 9 became available Saturday according to tech publication The Verge, and Electrek, a news site dedicated to reports about Tesla and other electric vehicles. The update expands assisted driving capabilities for a small pool of Tesla owners who get to try out features early, according to Electrek. Tesla's assisted driving programs have come under scrutiny in the wake of several accidents involving Teslas, including some that were in Autopilot mode. In the wake of those incidents, federal transportation officials have said Tesla has done an inadequate job of monitoring drivers to make sure they are engaged and also has permitted the Autopilot feature to be used on roads where it's not suitable.

But Peter Scott-Morgan has never been afraid of robots. As a scientist and roboticist by trade, he spent decades researching how artificial intelligence (AI) might transform our lives. Then, in 2017, Dr Scott-Morgan was diagnosed with motor neuron disease, the same paralysing condition that killed Stephen Hawking. Months after puzzling over his "wonky foot" falling asleep, he was told he had two years to live. To survive, he would turn to the technology he had spent his career researching.

To tackle climate change, scientists and advocates have called for a bevy of actions that include reducing fossil fuel use, electrifying transportation, reforming agriculture, and mopping up excess carbon dioxide from the atmosphere. But many of these challenges will be insurmountable without behind-the-scenes breakthroughs in materials science. Today's materials lack key properties needed for scalable climate-friendly technologies. Batteries, for example, require improved materials that can yield higher energy densities and longer discharge times. Without such improvements, commercial batteries won't be able to power mass-market electric vehicles and support a renewable-powered grid.

The frustration and anguish of trying and failing to piece together Ikea furniture may seem like an exercise in humiliation for you, but know this: The particleboard nightmare may one day lead to robots that aren't so stupid. In recent years, roboticists have been finding that building Ikea furniture is actually a great way to teach robots how to handle the chaos of the real world. One group of researchers coded a simulator in which virtual robot arms used trial and error to put chairs together. Others managed to get a different set of robot arms to construct Ikea chairs in the real world, though it took them 20 minutes. And now, a helpful robot can assist a human in assembling an Ikea bookcase by predicting what part they'll want next and handing it over.

Artificial intelligence is now helping to design computer chips--including the very ones needed to run the most powerful AI code. Sketching out a computer chip is both complex and intricate, requiring designers to arrange billions of components on a surface smaller than a fingernail. Decisions at each step can affect a chip's eventual performance and reliability, so the best chip designers rely on years of experience and hard-won know-how to lay out circuits that squeeze the best performance and power efficiency from nanoscopic devices. Previous efforts to automate chip design over several decades have come to little. But recent advances in AI have made it possible for algorithms to learn some of the dark arts involved in chip design.

Supervised machine learning, in which machine learning models learn from labeled training data, is only as good as the quality of that data. In a study published in the journal Quantitative Science Studies, researchers at consultancy Webster Pacific and the University of California, San Diego and Berkeley investigate to what extent best practices around data labeling are followed in AI research papers, focusing on human-labeled data. They found that the types of labeled data range widely from paper to paper and that a "plurality" of the studies they surveyed gave no information about who performed labeling -- or where the data came from. While labeled data is usually equated with ground truth, datasets can -- and do -- contain errors. The processes used to build them are inherently error-prone, which becomes problematic when these errors reach test sets, the subsets of datasets researchers use to compare progress. A recent MIT paper identified thousands to millions of mislabeled samples in datasets used to train commercial systems.

The tech giant, Google is involved in almost all forms of communication in the digital world and provides resources and features for a wide variety of problems faced by the users. However, with the increased share of the market, comes a greater responsibility to solve the problems that are faced by these markets. The determination and dedication of Google to keep on expanding and solving the issues faced by its users have provided it the status of the reputed organization. Even in recent years, where lockdown and increased time on the internet has paved way for more problems, Google has doubled up on the opportunity to provide more to its audience with platforms such as Google Meet, Search and Gmail. One of the biggest support that Google has in the development of its feature and the safety of its users is the expertise of machine learning that assists in removing most of the spam on its range of products.