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Why don’t we trust technology in sport?

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So why don’t we trust this kind of tech more?

One reason is a collectively very strong, in-built sense of “fairness”, argues Professor Gina Neff from Cambridge University.

“Right now, in many areas where AI is touching our lives, we feel like humans understand the context much better than the machine,” she said.

“The machine makes decisions based on the set of rules it’s been programmed to adjudicate. But people are really good at including multiple values and outside considerations as well – what’s the right call might not feel like the fair call.”

Prof Neff believes that to frame the debate as whether humans or machines are “better” isn’t fair either.

“It’s the intersection between people and systems that we have to get right,” she said.

“We have to use the best of both to get the best decisions.”

Human oversight is a foundation stone of what is known as “responsible” AI. In other words, deploying the tech as fairly and safely as possible.

It means someone, somewhere, monitoring what the machines are doing.

Not that this is working very smoothly in football, where VAR – the video assistant referee – has long caused controversy.

It was, for example, officially declared to be a “significant human error” that resulted in VAR failing to rectify an incorrect decision by the referee when Tottenham played Liverpool in 2024, ruling a vital goal to be offside when it wasn’t and unleashing a barrage of fury.

The Premier League said VAR was 96.4% accurate during “key match incidents” last season, although chief football officer Tony Scholes admitted “one single error can cost clubs”. Norway is said to be on the verge of discontinuing it.

Despite human failings, a perceived lack of human control plays its part in our reticence to rely on tech in general, says entrepreneur Azeem Azhar, who writes the tech newsletter The Exponential View.

“We don’t feel we have agency over its shape, nature and direction,” he said in an interview with the World Economic Forum.

“When technology starts to change very rapidly, it forces us to change our own beliefs quite quickly because systems that we had used before don’t work as well in the new world of this new technology.”

Our sense of tech unease doesn’t just apply to sport. The very first time I watched a demo of an early AI tool trained to spot early signs of cancer from scans, it was extremely good at it (this was a few years before today’s NHS trials) – considerably more accurate than the human radiologists.

The issue, its developers told me, was that people being told they had cancer did not want to hear that a machine had diagnosed it. They wanted the opinion of human doctors, preferably several of them, to concur before they would accept it.

Similarly, autonomous cars – with no human driver at the wheel – have done millions of miles on the roads in countries like the US and China, and data shows they have statistically fewer accidents than humans. Yet a survey carried out by YouGov last year suggested 37% of Brits would feel “very unsafe” inside one.

I’ve been in several and while I didn’t feel unsafe, I did – after the novelty had worn off – begin to feel a bit bored. And perhaps that is also at the heart of the debate about the use of tech in refereeing sport.

“What [sports organisers] are trying to achieve, and what they are achieving by using tech is perfection,” says sports journalist Bill Elliott – editor at large of Golf Monthly.

“You can make an argument that perfection is better than imperfection but if life was perfect we’d all be bored to death. So it’s a step forward and also a step sideways into a different kind of world – a perfect world – and then we are shocked when things go wrong.”



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Hungarian Researchers Reveal Why Surprising Experiences Are Key to Learning

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Hungarian Researchers Reveal Why Surprising Experiences Are Key to Learning – Hungarian Conservative
























Hungarian researchers have used AI-inspired mathematical models to explore how human memory works. Their study shows that surprising experiences play a uniquely important role in learning, challenging older theories about what the brain should remember.

Surprising experiences play a crucial role in learning, say researchers from Hungary’s HUN-REN Wigner Research Centre and Germany’s Max Planck Institute. Using mathematical models developed in artificial intelligence research, they found that unusual events help the brain update its understanding of the world more efficiently than routine experiences.

The findings, published in Nature Reviews Psychology, challenge the traditional view that rare or unexpected memories are less ‘worth storing’. Instead, the study argues that it is precisely these moments—those that deviate just enough from the norm—that serve as anchors for deeper learning.

‘Memory isn’t flawless. Sometimes, we remember things that never actually happened,’ the researchers wrote in a statement by the Hungarian Research Network (HUN-REN). But these recurring ‘mistakes’ can actually help uncover the principles that govern how memory works—and why certain details stick while others fade.

The team, led by Gergő Orbán of the HUN-REN Wigner Centre, and working with Dávid Gergely Nagy and Charley Wu in Tübingen, applied concepts from machine learning to better understand how different human memory systems interact. Instead of simply cataloguing memory errors, their goal was to uncover the logic behind them—specifically how they relate to learning and data compression strategies used by the brain.

‘Information theory helps us understand what’s worth remembering and what’s better forgotten,’ the researchers explained. Traditional information theory might suggest that very rare events aren’t useful to remember—but human memory doesn’t behave this way. On the contrary, people tend to retain surprising experiences more vividly.

The authors conclude that these standout moments play a crucial role in updating what we know. While routine memories help us predict future outcomes, surprising events act as catalysts that refresh our knowledge and adjust our expectations.

In practical terms, the findings also offer valuable insight into how we learn—or teach—most effectively. The researchers argue that machine learning models don’t just help us understand what we’ll remember or forget, but also guide us in optimizing when to repeat a concept and when it’s time to move on to something new.


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Hungarian researchers have used AI-inspired mathematical models to explore how human memory works. Their study shows that surprising experiences play a uniquely important role in learning, challenging older theories about what the brain should remember.








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The existential questions of artificial intelligence regulation in Congress

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NEWYou can now listen to Fox News articles!

Should we be alarmed by the acceleration of “artificial intelligence” (“AI”) and the “large language models” (LLMs) AI’s developers employ? 

Thanks to AI I can provide a short explanation of the LLM term: “Imagine AI as a large umbrella, with generative AI being a smaller umbrella underneath. LLMs are like a specific type of tool within the generative AI umbrella, designed for working with text.”

Clear? Of course not. The intricacies of AI and the tools it uses are the stuff of start-ups, engineers, computer scientists and the consumers feeding them data knowingly or unknowingly. 

TRUMP PRAISED BY FAITH LEADERS FOR AI LEADERSHIP AS THEY WARN OF TECHNOLOGY’S ‘POTENTIAL PERIL’

In the first Senate version of the “One Big Beautiful Bill,” Senator Ted Cruz sponsored and the drafting committees accepted a 10-year ban on state legislatures laying down rules of the road for AI. Senator Cruz advocated for a federal moratorium on states enforcing their unique AI laws. Senator Cruz argued that states’ regulations could create a confusing patchwork of rules that could hinder AI development and adoption.

After much discussion and debate, the proposal was stricken from the Senate bill, which then went on to pass the Senate and House and was signed into law on July 4, creating in six months an enormous set of legislative accomplishments for President Trump as every one of the priorities he campaigned on was delivered via the OBBB. 

What about the concerns about AI

Very, very few essays or columns or even books leave lasting marks. One that did so for me was penned by Dr. Charles Krauthammer in 2011 and included in the magnificent collection of his very best work, “Things That Matter.”

In that collection is the brief column titled “Are We Alone In The Universe?”

Krauthammer quickly recounts the reasons why we ought not to be alone as an intelligent species in the universe, as well as the explanation of why we haven’t “heard from” any other civilizations in even our own galaxy. 

The answer, Krauthammer states, “is to be found, tragically, in…the high probability that advanced civilizations destroy themselves.”

Krauthammer credits Carl Sagan and others with this gloomy proposition, but it is Krauthammer who sums it up nicely;

“[T]his silent universe is conveying not a flattering lesson about our uniqueness but a tragic story about our destiny,” Krauthammer continued. 

“It is telling us that intelligence may be the most cursed faculty in the entire universe —an endowment not just ultimately fatal but, on the scale of cosmic time, nearly instantly so.”

But no gloom and doom for Krauthammer, only clarity: “Intelligence is a capacity so godlike, so protean, that it must be contained and disciplined.”

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“This is the work of politics,” Krauthammer concludes, “understood as the ordering of society and the regulation of power to permit human flourishing while simultaneously restraining the most Hobbesian human instincts.”

Krauthammer is right and Senator Cruz was correct to tee up the debate which isn’t over, only begun. That’s the “politics” part which is never-ending until the civilization ends. AI is indeed “godlike” in the promises its boosters make but profoundly disruptive of all of human history that went before it. 

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Does it mean we are stepping off the edge of a cliff that destroyed all the other civilizations that went before us on distant planets from whom we will never hear a peep because they have run out their own string?

Impossible to say, but kudos to Senator Cruz for kicking off the debate. The conversation deserves much more attention than it has thus far received.  It’s too easy to simply go full “disaster is inevitable” mode, but some speed bumps —Cruz 2.0 in the next reconciliation?— would be welcome. 

Hugh Hewitt is host of “The Hugh Hewitt Show,” heard weekday mornings 6am to 9am ET on the Salem Radio Network, and simulcast on Salem News Channel. Hugh wakes up America on over 400 affiliates nationwide, and on all the streaming platforms where SNC can be seen. He is a frequent guest on the Fox News Channel’s news roundtable hosted by Bret Baier weekdays at 6pm ET. A son of Ohio and a graduate of Harvard College and the University of Michigan Law School, Hewitt has been a Professor of Law at Chapman University’s Fowler School of Law since 1996 where he teaches Constitutional Law. Hewitt launched his eponymous radio show from Los Angeles in 1990.  Hewitt has frequently appeared on every major national news television network, hosted television shows for PBS and MSNBC, written for every major American paper, has authored a dozen books and moderated a score of Republican candidate debates, most recently the November 2023 Republican presidential debate in Miami and four Republican presidential debates in the 2015-16 cycle. Hewitt focuses his radio show and his column on the Constitution, national security, American politics and the Cleveland Browns and Guardians. Hewitt has interviewed tens of thousands of guests from Democrats Hillary Clinton and John Kerry to Republican Presidents George W. Bush and Donald Trump over his 40 years in broadcast, and this column previews the lead story that will drive his radio/ TV show today.

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New AI model for researchers can make complex research 30% faster: GreyB launched Slate Prism Model

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Florida, July 08, 2025 (GLOBE NEWSWIRE) — Slate, a leading innovation discovery platform developed by GreyB, has announced the launch of “Prism”, a new structured research module to solve complex, constraint-based R&D challenges. Slate Prism breaks down your query, scans thousands of patents and papers, and delivers evidence-backed insights on what works, what doesn’t, and why. It goes beyond simple search results to deliver in-depth analysis of solutions.

In today’s research landscape overwhelmed by information overload, Slate Prism empowers users to cut through the noise and quickly grasp the core of any subject. It is designed to support researchers working on technical problems that involve multiple interdependent constraints, such as improving product performance without compromising regulatory compliance or reducing costs while preserving core functionality.

“In research and development, the most critical questions are often not binary. They involve trade-offs across efficacy, safety, stability, or compatibility,” said Anmol Saini, Head of the Product at GreyB. “Slate Prism is created to support deep, multi-constraint reasoning that researchers perform. It’s like having a team of expert researchers at your fingertips, capable of synthesizing complex topics into clear, actionable insights, saving users invaluable time and effort.”

Traditional methods force researchers to break down complex questions into isolated sub-queries, run multiple individual searches, and manually compare findings to find overlaps over extended periods. Even existing AI tools struggle with handling full, multi-layered questions, pushing researchers to simplify their approach. Slate Prism reduces the need for manual cross-referencing by allowing users to explore layered, constraint-heavy queries in one go.

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Key capabilities of Slate Prism include:

Targeted Search: It breaks down complex technical research questions into structured components based on constraints.Evidence Gathering: Retrieves and integrates insights from a multitude of sources, including patents, research papers, and technical documents.Contextual Analysis: Slate Prism analyzes the relationships between different constraints of the query, providing solutions from both supporting and contrasting studies, which helps in identifying trade-offs, edge cases, and knowledge gaps.Comprehensive Synthesis: It provides evidence-backed solutions, explaining their context and providing researchers a clearer understanding to support decision-making.Source Citation: All generated insights are meticulously linked back to their original studies for further exploration, ensuring transparency and reliability.Dynamic Exploration: Enables researchers to delve deeper into specific solutions and explore related technical literature in more detail with ease. In initial exclusive testing with the first few clients across industries such as cosmetics, F&B, packaging, pharmaceuticals, automotive, and healthcare, Slate Prism demonstrated significant time-saving and accuracy improvements. Early users reported a 30% reduction in the time required to make key decisions, while 45% of respondents noted an increase in decision-making accuracy when utilizing the platform.

As the demand for innovative, AI-powered solutions in R&D grows, Slate leads the way in tackling the emerging challenges faced by researchers. By focusing on multi-constraint reasoning, Slate Prism is aligning with the increasing reliance on AI to solve the toughest challenges in R&D.

With the addition of Prism, Slate continues its focus on supporting research workflows that require not only access to information but also tools that assist in interpretation and decision-making.

Slate is offering an exclusive 14-day free trial of Prism. To start your trial, visit https://slate.greyb.com/trial/

About Slate

Slate is an innovation discovery platform designed for R&D and innovation teams. It enables faster technology discovery, competitive intelligence, and decision support with advanced AI capabilities for complex problem-solving and in-depth analysis. From R&D labs to boardrooms, Slate empowers teams to find what’s out there and validate it quickly.

CONTACT: Name: Deepak Kumar

Email: [email protected]

Job Title: Marketing and Communications, GreyB



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