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Chandigarh: The Annual Sports Day Felicitation cum Alumni Meet of Government Model Senior Secondary School (GMSSS), Sector 16, Chandigarh, was celebrated on Nov 26. The event combined the joy of athletic achievements with a reunion of alumni. The occasion was graced by Sanjay Tandon, President of the UT Cricket Association, as the chief guest. The programme commenced with a warm welcome by the school’s principal, Bhavneet Kaur, who expressed her gratitude to the dignitaries. A crowd-favourite event, the Horse Riding Show, saw students showcase their equestrian skills. Adding to the lively atmosphere, nursery students showcased their song and dance performances. TNN We also published the following articles recently La Martiniere alumni debate tense present La Martiniere alumni in Kolkata voiced concerns about the schools' management, alleging deviations from the founder's will and a legal battle over its control. They argue the schools should operate as public institutions, not Christian ones. With key governing body positions vacant, the alumni pledged to protect the schools' legacy and heritage, even challenging recent alterations to a historic structure. LU to honour distinguished alumni on 104th Foundation Day Lucknow University (LU) will honor six distinguished alumni during its 104th Foundation Day celebration on November 25th. IIT Bombay alumni secure $500K funding at Y Combinator for innovative voice AI testing solution Three IIT Bombay graduates, including one from Panchkula, have secured a coveted spot in Y Combinator's accelerator program. Their startup, Vocera, received $500,000 in funding to further develop its innovative voice AI testing platform. This automated system streamlines the evaluation of voice AI agents, reducing manual testing time and improving accuracy.A new artificial intelligence (AI) model has just achieved human-level results on a test designed to measure “general intelligence”. On December 20, OpenAI's o3 system scored 85% on the ARC-AGI benchmark , well above the previous AI best score of 55% and on par with the average human score. It also scored well on a very difficult mathematics test. Creating artificial general intelligence, or AGI, is the stated goal of all the major AI research labs. At first glance, OpenAI appears to have at least made a significant step towards this goal. While scepticism remains, many AI researchers and developers feel something just changed. For many, the prospect of AGI now seems more real, urgent and closer than anticipated. Are they right? Generalisation and intelligence To understand what the o3 result means, you need to understand what the ARC-AGI test is all about. In technical terms, it's a test of an AI system's “sample efficiency” in adapting to something new – how many examples of a novel situation the system needs to see to figure out how it works. An AI system like ChatGPT (GPT-4) is not very sample efficient. It was “trained” on millions of examples of human text, constructing probabilistic “rules” about which combinations of words are most likely. The result is pretty good at common tasks. It is bad at uncommon tasks, because it has less data (fewer samples) about those tasks. Until AI systems can learn from small numbers of examples and adapt with more sample efficiency, they will only be used for very repetitive jobs and ones where the occasional failure is tolerable. The ability to accurately solve previously unknown or novel problems from limited samples of data is known as the capacity to generalise. It is widely considered a necessary, even fundamental, element of intelligence. Grids and patterns The ARC-AGI benchmark tests for sample efficient adaptation using little grid square problems like the one below. The AI needs to figure out the pattern that turns the grid on the left into the grid on the right. Each question gives three examples to learn from. The AI system then needs to figure out the rules that “generalise” from the three examples to the fourth. These are a lot like the IQ tests sometimes you might remember from school. Weak rules and adaptation We don't know exactly how OpenAI has done it, but the results suggest the o3 model is highly adaptable. From just a few examples, it finds rules that can be generalised. To figure out a pattern, we shouldn't make any unnecessary assumptions, or be more specific than we really have to be. In theory , if you can identify the “weakest” rules that do what you want, then you have maximised your ability to adapt to new situations. What do we mean by the weakest rules? The technical definition is complicated, but weaker rules are usually ones that can be described in simpler statements . In the example above, a plain English expression of the rule might be something like: “Any shape with a protruding line will move to the end of that line and ‘cover up' any other shapes it overlaps with.” Searching chains of thought? While we don't know how OpenAI achieved this result just yet, it seems unlikely they deliberately optimised the o3 system to find weak rules. However, to succeed at the ARC-AGI tasks it must be finding them. We do know that OpenAI started with a general-purpose version of the o3 model (which differs from most other models, because it can spend more time “thinking” about difficult questions) and then trained it specifically for the ARC-AGI test. French AI researcher Francois Chollet, who designed the benchmark, believes o3 searches through different “chains of thought” describing steps to solve the task. It would then choose the “best” according to some loosely defined rule, or “heuristic”. This would be “not dissimilar” to how Google's AlphaGo system searched through different possible sequences of moves to beat the world Go champion. You can think of these chains of thought like programs that fit the examples. Of course, if it is like the Go-playing AI, then it needs a heuristic, or loose rule, to decide which program is best. There could be thousands of different seemingly equally valid programs generated. That heuristic could be “choose the weakest” or “choose the simplest”. However, if it is like AlphaGo then they simply had an AI create a heuristic. This was the process for AlphaGo. Google trained a model to rate different sequences of moves as better or worse than others. What we still don't know The question then is, is this really closer to AGI? If that is how o3 works, then the underlying model might not be much better than previous models. The concepts the model learns from language might not be any more suitable for generalisation than before. Instead, we may just be seeing a more generalisable “chain of thought” found through the extra steps of training a heuristic specialised to this test. The proof, as always, will be in the pudding. Almost everything about o3 remains unknown. OpenAI has limited disclosure to a few media presentations and early testing to a handful of researchers, laboratories and AI safety institutions. Truly understanding the potential of o3 will require extensive work, including evaluations, an understanding of the distribution of its capacities, how often it fails and how often it succeeds. When o3 is finally released, we'll have a much better idea of whether it is approximately as adaptable as an average human. If so, it could have a huge, revolutionary, economic impact, ushering in a new era of self-improving accelerated intelligence. We will require new benchmarks for AGI itself and serious consideration of how it ought to be governed. If not, then this will still be an impressive result. However, everyday life will remain much the same. ( Authors: Michael Timothy Bennett , PhD Student, School of Computing, Australian National University and Elija Perrier , Research Fellow, Stanford Center for Responsible Quantum Technology, Stanford University ) ( Disclosure Statement: Michael Timothy Bennett receives funding from the Australian government. Elija Perrier receives funding from the Australian government) This article is republished from The Conversation under a Creative Commons license. Read the original article . (Except for the headline, this story has not been edited by NDTV staff and is published from a syndicated feed.)

WASHINGTON (AP) — American Airlines briefly grounded flights nationwide Tuesday because of a technical problem just as the Christmas travel season kicked into overdrive and winter weather threatened more potential problems for those planning to fly or drive. Government regulators cleared American flights to get airborne about an hour after the Federal Aviation Administration ordered a national ground stop for the airline. The order, which prevented planes from taking off, was issued at the airline's request. The airline said in an email that the problem was caused by trouble with vendor technology that maintains its flight operating system. Dennis Tajer, a spokesperson for the Allied Pilots Association, a union representing American Airlines pilots, said the airline told pilots at 7 a.m. Eastern that there was an outage affecting the system known as FOS. It handles different types of airline operations, including dispatch, flight planning, passenger boarding, as well as an airplane's weight and balance data, he said. Some components of FOS have gone down in the past, but a systemwide outage is rare, Tajer said. Hours after the ground stop was lifted, Tajer said the union had not heard about any “chaos out there beyond just the normal heavy travel day.” He said officials were watching for any cascading effects, such as staffing problems. Flights were delayed across American's major hubs, with only 37% leaving on time, according to Cirium, an aviation analytics company. Out of the 3,901 domestic and international American Airlines flights scheduled for Tuesday, 19 were canceled. Cirium noted that the vast majority of flights were departing within two hours of their scheduled departure time. A similar percentage — 36% — were arriving at their destinations as scheduled. Meanwhile, the flight-tracking site FlightAware reported that 3,712 flights entering or leaving the U.S., or serving domestic destinations, were delayed Tuesday, with 55 flights canceled. It did not show any flights from American Airlines. Cirium said Dallas-Fort Worth, New York’s Kennedy Airport and Charlotte, North Carolina, saw the greatest number of delays. Washington, Chicago and Miami experienced considerably fewer delays. Amid the travel problems, significant rain and snow were expected in the Pacific Northwest at least into Christmas Day. Showers and thunderstorms were developing in the South. Freezing rain was reported in the Mid-Atlantic region near Baltimore and Washington, and snow fell in New York. Because the holiday travel period lasts weeks, airports and airlines typically have smaller peak days than they do during the rush around Thanksgiving, but the grind of one hectic day followed by another takes a toll on flight crews. And any hiccups — a winter storm or a computer outage — can snowball into massive disruptions. That is how Southwest Airlines stranded 2 million travelers in December 2022, and Delta Air Lines suffered a smaller but significant meltdown after a worldwide technology outage in July caused by a faulty software update from cybersecurity company CrowdStrike. Many flights during the holidays are sold out, which makes cancellations even more disruptive than during slower periods. That is especially true for smaller budget airlines that have fewer flights and fewer options for rebooking passengers. Only the largest airlines, including American, Delta and United, have “interline agreements” that let them put stranded customers on another carrier’s flights. This will be the first holiday season since a Transportation Department rule took effect that requires airlines to give customers an automatic cash refund for a canceled or significantly delayed flight. Most air travelers were already eligible for refunds, but they often had to request them. Passengers still can ask to get rebooked, which is often a better option than a refund during peak travel periods. That’s because finding a last-minute flight on another airline tends to be expensive. An American spokesperson said Tuesday was not a peak travel day for the airline — with about 2,000 fewer flights than the busiest days — so the airline had somewhat of a buffer to manage the delays. The groundings happened as millions of travelers were expected to fly over the next 10 days. The Transportation Security Administration expects to screen 40 million passengers through Jan. 2. Airlines expect to have their busiest days on Thursday, Friday and Sunday. Many flights during the holidays are sold out, which makes cancellations more disruptive than during slower periods. Even with just a brief outage, the cancellations have a cascading effect that can take days to clear up. About 90% of Americans traveling far from home over the holidays will be in cars, according to AAA. “Airline travel is just really high right now, but most people do drive to their destinations, and that is true for every holiday,” AAA spokesperson Aixa Diaz said. Gasoline prices are similar to last year. The nationwide average Thursday was $3.04 a gallon, down from $3.13 a year ago, according to AAA. Charging an electric vehicle averages just under 35 cents per per kilowatt hour, but varies by state. Transportation-data firm INRIX says travel times on the nation’s highways could be up to 30% longer than normal over the holidays, with Sunday expected to see the heaviest traffic. Boston, New York City, Seattle and Washington are the metropolitan areas primed for the greatest delays, according to the company. —— Associated Press writers David Koenig, Mae Anderson and Mike Pesoli contributed to this report.

By Tom Balmforth (Reuters) - Ukrainian investigators are studying the debris of a new Russian intermediate-range ballistic missile that was fired at the city of Dnipro on Thursday, the first time such a powerful weapon has been used in the war. Reuters was among a small group of reporters given access to the wreckage of the missile on Sunday. Reporters were asked not disclose the exact location of the site for security reasons. The scorched and crumbled pieces of debris were laid out in a hanger at a facility which conducts weapons forensics. Ukrainian experts study such debris to gain insight into Russian military supply chains, production and how to develop counter-measures. Russia has dubbed the missile the Oreshnik (Hazel Tree) and said it is impossible to intercept it with air defences. Ukraine has said the weapon reached a top speed of more than 13,000 kph (8,000 mph) on its way towards Dnipro on Thursday. Intermediate-range ballistic missiles have a range of up to 5,500 kilometres. Two state experts provided cautious assessments, saying only that the weapon was ballistic, flew on a ballistic trajectory and that the strike resulted in civilian damage. They declined to take questions or give their surnames. "These are preliminary conclusions and to say something more concrete requires time and careful study of the remains of the missile," said Ivan, one of the experts. MORE: "This is the first time that such remnants of such a missile have been discovered on the territory of Ukraine," said Oleh, an investigator for the Security Service of Ukraine. Ukrainian President Volodymyr Zelenskiy has called the use of the weapon a severe escalation and urged his allies to respond. Ukraine originally said the weapon appeared to be an intercontinental ballistic missile. The Kremlin later said it fired a new intermediate-range missile at a Ukrainian military target in Dnipro in response to Kyiv striking Russia with U.S. and British made missiles for the first time after the U.S. granted its approval. The U.S. military has said the missile's design is based on the longer-range RS-26 Rubezh intercontinental ballistic missile (ICBM). The new missile was experimental and Russia likely possessed only a handful of them, they have said. Russian President Vladimir Putin said on Friday Moscow would keep testing the missile in combat and had a stock ready to use. Much remains unclear for now, including the extent of the damage caused by the missile. Ukraine seldom discloses damage to military targets, fearing such information would help Moscow. (Reporting by Tom Balmforth; editing by Elaine Hardcastle) Copyright 2024 Thomson Reuters .