Technology Engineering Thomas Edison's failed rechargeable battery may get a second life The famed inventor's nickel-iron idea isn't suited for EVs, but it could help solar farms and data centers. Breakthroughs, discoveries, and DIY tips sent six days a week. A rechargeable battery based on technology pioneered by Thomas Edison may finally get its due. But while the famous inventor envisioned nickel-iron batteries powering the automobile industry over a century ago, researchers now believe the underlying concepts are more suited for renewable energy centers. According to a study published in the journal, a team including engineers from the University of California, Los Angeles have developed a prototype battery that recharges in seconds and withstands over 12,000 cycles of use--an equivalent to over 30 years of daily activity.

In 1916, hybrid cars could've changed history. But Ford wouldn't allow it. Henry Ford's monopoly on the automobile industry meant that hybrids wouldn't see the light of day for decades. In 1916, Clinton Edgar Woods, a forgotten automobile inventor, designed the first commercial hybrid cars. But Ford's Model T had already cornered the market.

Could an AI scientist help researchers come up with breakthroughs by analysing data and searching the existing scientific literature? That's the claim of the inventors of Kosmos, but not everyone is convinced Artificial intelligence can process large amounts of data, but can it do science? An AI scientist can work independently for hours while doing research that would take humans months to complete, and has made several "novel contributions" to science, its creators claim - but others are more doubtful. The system, called Kosmos, is actually a collection of AI agents that are specialised in analysing data and searching through the existing scientific literature, in an effort to make new scientific breakthroughs. "We've been working on building an AI scientist for about two years now," says Sam Rodriques at Edison Scientific, the US-based firm behind Kosmos.

Armed with drones, long-distance camera lenses and metal detectors, a hillside in Eaton Canyon has become the focus of intense scrutiny over the last month by teams of private investigators now seeking clues on whether Southern California Edison equipment caused the massive fire that destroyed large swaths of Altadena. Some of the findings and theories of these privately hired teams of fire investigators and electrical engineers have emerged in more than 40 lawsuits that residents have filed against the utility. Much of the focus has been centered on a group of transmission towers where the first flames were seen just as the Eaton fire exploded. Earlier this week, a new lawsuit alleged that an idle transmission tower on the hillside -- one that has not been in use for more than 50 years -- might have sparked the devastating blaze. With more than 9,000 homes lost and 17 people killed, liability is going to be a costly question that could affect how Altadena is rebuilt.

As world leaders gather in New York City for the United Nations General Assembly, there's a lot to get done, with just six years left to achieve the bold ambitions laid out for the world's 2030 agenda. When world governments agreed to the 2030 plan back in 2015, a decade and a half seemed like plenty of time to achieve the 17 Sustainable Development Goals (SDGs) designed to create a more prosperous, safe and fair global society. While amazing progress has been made, we are in danger of falling short. I believe the U.N.'s goals can be attained through a collaborative commitment to make digital networks available to everybody in the world. Mobility, broadband and the cloud are the infrastructure of 21st century life and everybody should have that opportunity.

Pre-trained language models (PLMs) have shown unprecedented potential in various fields, especially as the backbones for question-answering (QA) systems. However, they tend to be easily deceived by tricky questions such as "How many eyes does the sun have?". Such frailties of PLMs often allude to the lack of knowledge within them. In this paper, we find that the PLMs already possess the knowledge required to rebut such questions, and the key is how to activate the knowledge. To systematize this observation, we investigate the PLMs' responses to one kind of tricky questions, i.e., the false premises questions (FPQs). We annotate a FalseQA dataset containing 2365 human-written FPQs, with the corresponding explanations for the false premises and the revised true premise questions. Using FalseQA, we discover that PLMs are capable of discriminating FPQs by fine-tuning on moderate numbers (e.g., 256) of examples. PLMs also generate reasonable explanations for the false premise, which serve as rebuttals. Further replaying a few general questions during training allows PLMs to excel on FPQs and general questions simultaneously. Our work suggests that once the rebuttal ability is stimulated, knowledge inside the PLMs can be effectively utilized to handle FPQs, which incentivizes the research on PLM-based QA systems.

Since 2017, ophthalmology has been the busiest of all the medical specialties in the UK's National Health Service in terms of clinical appointments. Nearly 10 percent of all NHS outpatient appointments are related to eye problems. That's nearly 10 million appointments per year, and that number has risen by more than a third in the past five years. Between the ages of 18 and 65, the main cause of blindness is diabetic eye disease. But the population is getting older, and we're also seeing an increasing prevalence of diseases like age-related macular degeneration (AMD).

Cenk Sidar is the cofounder and CEO of Enquire AI, combining AI, data science, and human intelligence to deliver real-time insights. In recent years, tech-celeration has changed the way humans interact in and beyond the workplace. While rapid tech adoption is considered good, it also fuels the emergence of new risks and "unknown unknowns" in an ever-changing macro landscape. As we enter 2023 on the brink of economic strife, something must balance the scales and help business leaders tackle their biggest problems. One answer lies in another tech breakthrough: Artificial intelligence is ready to perform at scale. Its full implementation cannot be predicted at this point, but it promises real-time actionable insights and offers newfound agility in an uncertain world.

Bengaluru, NFAPost: C3 AI (NYSE: AI), one of the leading Enterprise AI software provider, today announced the general availability of C3 AI Ex Machina, a next-generation predictive analytics application that empowers anyone to develop, scale, and produce AI-based insights without writing code. Analysts, operators, and subject matter experts across all industries and business functions are increasingly required to develop predictive and prescriptive insights compiled from vast and disparate datasets. While there are many no-code tools available that lower the barrier for users to build ML models and perform data analysis, none provide end-to-end capabilities that enable them to capture and process the volume and variety of data required, automatically generate interpretable AI models, and productise, deploy, and scale the results across their company. Current predictive analytics tools are typically complicated to use and limit the ability of their insights to drive real business outcomes. Con Edison's data analysts use C3 AI Ex Machina to identify malfunctioning meters in near-real-time, realizing significant business value.

But the kind of power generation that Edison pioneered on that September day in 1882 put us on a trajectory that has had unfortunate outcomes. He kicked into overdrive our reliance on fossil fuels for energy, allowing it to permeate all aspects of our lives--from the electricity we need to power our homes, offices and factories, to the petroleum we need to run our cars, ships and planes. This forced us down a path of high-energy consumption that has resulted in the rapid depletion of naturally occurring carbon-based fuel sources and inflicted near-irreversible damage on our planet. Edison's choice of coal as the fuel source for his power plant should not be taken as indicative of his support for fossil fuels as a source of energy. At least in the context of transportation, he believed that automobiles should run on electricity--not petrol--and even built a vehicle powered by alkaline batteries of his own invention.