Breakthroughs, discoveries, and DIY tips sent every weekday. It's something out of a dream or TV show: a married couple takes their dog for a walk and finds a buried treasure worth $10 million. But it actually happened, back in 2013. The treasure is the Saddle Ridge Hoard, the largest ever stash of gold coins found in the United States. The couple, who go by John and Mary in the press, have been careful to obscure their identity and the exact place where they live to prevent would-be treasure hunters from showing up on their property.

It sounds like a scenario straight out of a sci-fi movie. But researchers at Heriot-Watt University in Edinburgh have developed a new light detection system that can recognise human faces and objects from more than half a mile away – even at night or through smoke or fog. Scientists say the ground-breaking research could be a'step change' for security and defence and has the potential to'make identification significantly easier'. Using pulses of laser light to measure the distances to objects, the system can construct'high-resolution 3D images' of faces and other surfaces from as far away as ten football pitches in both daylight and darkness. Dr Aongus McCarthy, an optical scientist and research fellow at Heriot-Watt's Institute of Photonics and Quantum Sciences, said: 'If someone is standing behind camouflage netting, this system has the potential to determine whether they are on their mobile phone, holding something, or just standing there idle.'

From 325 metres away, your eyes can probably distinguish a person's head from their body – and not much else. But a new laser-based device can create a three-dimensional model of their face. Aongus McCarthy at Heriot-Watt University in Scotland and his colleagues built a device that can create detailed three-dimensional images, including ridges and indentations as small as 1 millimetre, from hundreds of metres away. It uses an imaging technique called lidar, emitting pulses of laser light that collide with objects then reflect back into the device. Based on how long each pulse takes to return, lidar can determine an object's shape.

For months, my colleague Will Douglas Heaven has been on a quest to go deeper to understand why everybody seems to disagree on exactly what AI is, why nobody even knows, and why you're right to care about it. He's been talking to some of the biggest thinkers in the field, asking them, simply: What is AI? It's a great piece that looks at the past and present of AI to see where it is going next. You can read it here. Here's a taste of what to expect: Artificial intelligence almost wasn't called "artificial intelligence" at all. The computer scientist John McCarthy is credited with coming up with the term in 1955 when writing a funding application for a summer research program at Dartmouth College in New Hampshire.

ELIZA, often considered the world's first chatbot, was written by Joseph Weizenbaum in the early 1960s. Weizenbaum did not intend to invent the chatbot, but rather to build a platform for research into human-machine conversation and the important cognitive processes of interpretation and misinterpretation. His purpose was obscured by ELIZA's fame, resulting in large part from the fortuitous timing of it's creation, and it's escape into the wild. In this paper I provide a rich historical context for ELIZA's creation, demonstrating that ELIZA arose from the intersection of some of the central threads in the technical history of AI. I also briefly discuss how ELIZA escaped into the world, and how its accidental escape, along with several coincidental turns of the programming language screws, led both to the misapprehension that ELIZA was intended as a chatbot, and to the loss of the original ELIZA to history for over 50 years.

Over the past decade, AI research has focused heavily on building ever-larger deep learning models. This approach has simultaneously unlocked incredible achievements in science and technology, and hindered AI from overcoming long-standing limitations with respect to explainability, ethical harms, and environmental efficiency. Drawing on qualitative interviews and computational analyses, our three-part history of AI research traces the creation of this "epistemic monoculture" back to a radical reconceptualization of scientific progress that began in the late 1980s. In the first era of AI research (1950s-late 1980s), researchers and patrons approached AI as a "basic" science that would advance through autonomous exploration and organic assessments of progress (e.g., peer-review, theoretical consensus). The failure of this approach led to a retrenchment of funding in the 1980s. Amid this "AI Winter," an intervention by the U.S. government reoriented the field towards measurable progress on tasks of military and commercial interest. A new evaluation system called "benchmarking" provided an objective way to quantify progress on tasks by focusing exclusively on increasing predictive accuracy on example datasets. Distilling science down to verifiable metrics clarified the roles of scientists, allowed the field to rapidly integrate talent, and provided clear signals of significance and progress. But history has also revealed a tradeoff to this streamlined approach to science: the consolidation around external interests and inherent conservatism of benchmarking has disincentivized exploration beyond scaling monoculture. In the discussion, we explain how AI's monoculture offers a compelling challenge to the belief that basic, exploration-driven research is needed for scientific progress. Implications for the spread of AI monoculture to other sciences in the era of generative AI are also discussed.

In the wake of large language models, there has been a resurgence of claims and questions about the Turing test and its value for AI, which are reminiscent of decades of practical "Turing" tests. If AI were quantum physics, by now several "Schr\"odinger's" cats could have been killed. Better late than never, it is time for a historical reconstruction of Turing's beautiful thought experiment. In this paper I present a wealth of evidence, including new archival sources, give original answers to several open questions about Turing's 1950 paper, and address the core question of the value of Turing's test.

From a Taylor Swift takeover to Capitol Hill chaos and everything in between, Fox News' Digital Originals takes a look back on the biggest headlines of 2023. As history books close the chapter on 2023, Fox News Digital takes a look at the biggest news headlines of the year. Another trip around the sun brought unprecedented political plays, a Hollywood holdout, war in the Middle East and an economic boom from a world-famous pop singer. California Rep. Kevin McCarthy, a Republican, was elected speaker of the House of Representatives Jan. 7, 2023, after 15 floor votes. The fight to elect the speaker was unprecedented.

COVID-19 has influenced virtually all aspects of our lives. Across the world, countries applied wildly varying mitigation strategies for the epidemic, ranging from minimal intrusion in the hope of obtaining "herd immunity", to imposing severe lockdowns on the other extreme. It seems clear at the first glance what all those measures are trying to achieve, and what the criteria of success are. But is it really that clear? Quoting an oft-repeated phrase, with COVID-19 we fight an unprecedented threat to health and economic stability [Soltani et al., 2020]. While fighting it, we must protect privacy, equality and fairness [Morley et al., 2020] and do a coordinated assessment of usefulness, effectiveness, technological readiness, cyber security risks and threats to fundamental freedoms and human rights [Stollmeyer et al., 2020]. Taken together, this is hardly a straightforward set of goals and requirements. Thus, paraphrasing [Stollmeyer et al., 2020], one may ask: What problem does a COVID mitigation strategy solve exactly? 1

Rep. Carlos Gimenez, R-Fla., joined'Fox & Friends Weekend' to discuss Israel's decision to declare war and the GOP's efforts to determine a new House Speaker. Multiple House Democrats had indicated to GOP lawmakers that they would help former speaker Kevin McCarthy, R-Calif., avoid being ousted on Tuesday, two sources told Fox News Digital. A GOP member of the Problem Solvers Caucus indicated that right up until the final days, Democrats signaled they may at least be open to voting "present" to lower the threshold needed for McCarthy's political survival. The lawmaker pointed to Rep. Matt Cartwright, D-Pa., who is not a member of the Problem Solvers Caucus, but suggested early on that he could be open to helping McCarthy. "Even people like that were saying they were going to vote present. And something changed over the weekend. So yes, the members of the Problem Solvers gave absolutely no indication that they were going to side with [Rep. Gaetz introduced the motion to vacate against McCarthy on Monday evening. The next day, seven other Republicans joined him and every House Democrat to oust McCarthy from leadership. Acknowledging that Gaetz would likely pull the move again if it failed the first time, the Republican who spoke with Fox News Digital said he and other GOP Problem Solvers appealed to Democrats to vote "present" on the initial procedural vote in order to buy time to pull together a bipartisan proposal on a House Rules overhaul, which would have likely made it harder for members to topple the speaker. "We wanted them to vote present for the first round on the motion, to make the motion to table, so that they could have time to rewrite the rules package.