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What Would Hayek Think of AI?

What Would Hayek Think of AI?

It keeps happening—some shiny new idea or technology promises to solve all our problems. Give power to experts to arrange affairs 'scientifically,' and poverty, oppression, disease, war and all human ills will disappear. Today, we are asked to trust artificial intelligence.
The International Monetary Fund promises that 'AI can enhance democratic institutions by ensuring citizens' voices are truly heard.' Power wielded by a few experts can enhance democracy? Isn't that what the early 20th-century Progressive movement promised? For that matter, isn't that the thinking behind Soviet 'scientific socialism'?
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New memristor-based system by Chinese scientists boosts AI data sorting efficiency
New memristor-based system by Chinese scientists boosts AI data sorting efficiency

Yahoo

time33 minutes ago

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New memristor-based system by Chinese scientists boosts AI data sorting efficiency

In a bid to overcome shortcomings in scientific computing, Chinese scientists have unveiled a new approach to sorting data that promises both higher speed and lower energy consumption. The system combines memristors—electronic components that mimic the memory function of the human brain—with an advanced sorting algorithm to process large amounts of information more efficiently. Researchers say this method could help overcome performance bottlenecks in not just computing but also artificial intelligence (AI), and hardware design, where rapidly organizing and analyzing vast datasets is essential. Beyond AI, potential applications for this technology include smart traffic systems that analyze images in real time and financial services that require quick risk assessments. To demonstrate the potential of their technology, scientists from Peking University and the Chinese Institute for Brain Research created a hardware sorting prototype based on memristors. The system successfully handled tasks like route finding and neural network inference, delivering faster performance and lower energy consumption compared to traditional sorting methods, the South China Morning Post reported. Overall, the system achieved a 7.7-fold increase in throughput and improved energy efficiency by more than 160 times compared to conventional sorting methods. It also boosted area efficiency by over 32 times, marking a significant step towards integrating storage and computing for broader, general-purpose applications. In a paper published in Nature Electronics last month, the team explained that sorting remains a major performance limitation across applications ranging from artificial intelligence and databases to web search and scientific computing. Traditional computing systems rely on the Von Neumann architecture, which separates data storage and processing functions, typically using a central processing unit (CPU) to handle calculations. According to the researchers of the latest study, the conventional system has led to the Von Neumann bottleneck, which limits the speed of data transfer between memory and processing units. They explained that while sort-in-memory approaches using memristors could help overcome these limitations, current systems still depend on comparison operations, keeping sorting performance constrained. Unlike ordinary resistors, which simply reduce the flow of electricity in a circuit, memristors have the unique ability to remember how much electrical charge has passed through them. This memory function allows memristors to adjust their resistance based on previous activity, enabling them to act as both storage and processing components. By combining these functions, memristors could eliminate the need to transfer data between separate memory and processing units, potentially leading to faster and more energy-efficient computing systems. The scientific team aimed to simplify sorting by removing the need for comparison units. Traditional hardware sorting relies on CPUs, GPUs, or specialised chips that compare numbers step by step using sorting algorithms. Instead, the new method uses memristors to perform iterative search-based sorting, finding minimum or maximum values without directly comparing each pair, which in turn saves both time and energy.

I'm a university lecturer concerned that students are using AI to cheat. It's made my workload skyrocket, and I've had to make drastic changes.
I'm a university lecturer concerned that students are using AI to cheat. It's made my workload skyrocket, and I've had to make drastic changes.

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I'm a university lecturer concerned that students are using AI to cheat. It's made my workload skyrocket, and I've had to make drastic changes.

Risa Morimoto has been a lecturer for 18 years. In that time, she's always seen students cheat. But Morimoto said AI tools have made it harder to detect cheating, increasing her workload. Next year, Morimoto plans to introduce new assessment methods to address her AI concerns. This as-told-to essay is based on a transcribed conversation with Risa Morimoto, a senior lecturer in economics at SOAS University of London, in England. The following has been edited for length and clarity. Students always cheat. I've been a lecturer for 18 years, and I've dealt with cheating throughout that time, but with AI tools becoming widely available in recent years, I've experienced a significant change. There are definitely positive aspects to AI. It's much easier to get access to information and students can use these tools to improve their writing, spelling, and grammar, so there are fewer badly written essays. 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While students can use examples from internet sources in their work, I'm concerned that some students have just used AI to generate the essay content without reading or engaging with the original source. I started using AI detection tools to assess work, but I'm aware this technology has limitations. AI tools are easy to access for students who feel pressured by the amount of work they have to do. University fees are increasing, and a lot of students work part-time jobs, so it makes sense to me that they want to use these tools to complete work more quickly. During the first lecture of my module, I'll tell students they can use AI to check grammar or summarize the literature to better understand it, but they can't use it to generate responses to their assignments. SOAS has guidance for AI use among students, which sets similar principles about not using AI to generate essays. 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They said the university encouraged students to pursue work that is harder for AI to replicate and have "robust mechanisms" in place for investigating AI misuse. "The use of AI is constantly evolving, and we are regularly reviewing and updating our policies to respond to these changes," the spokesperson added. Do you have a story to share about AI in education? Contact this reporter at ccheong@ Read the original article on Business Insider

5 Ways AI Makes Google Search Work Harder For Your Brand
5 Ways AI Makes Google Search Work Harder For Your Brand

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time40 minutes ago

  • Forbes

5 Ways AI Makes Google Search Work Harder For Your Brand

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