Tag Archives: Large Language Models (LLMs)

Event at the Guggenheim Museum (New York City) on May 18, 2026: Trevor Paglen: The Lizard People are Here!

Trevor Paglen, the current LG Guggenheim Art and Technology Initiative Award Recipient, is giving a performance-lecture according to a May 8, 2026 Guggenheim Museum announcement (received via email and available online here),

Trevor Paglen: The Lizard People are Here!

Monday, May 18, 2026
6:30–8 pm EDT
Guggenheim New York

Buy Tickets

This performance-lecture invites audiences to consider whether we are entering a new era of invisible architectures and algorithmic systems shaping perception, knowledge, and power. In the program, 2026 LG Guggenheim Award recipient, artist, and author Trevor Paglen traces a dense network of ideas spanning philosophy, belief, deception, and speculation.

Bringing together psyops, artificial intelligence, magic, mind control, UFOs, the secret of the Ark of the Covenant, and the figure of a new demiurge, Paglen examines historical precedents for the manipulation of human perception and cognition at a moment when artificial intelligence is becoming increasingly pervasive.

Across his practice, Paglen has repeatedly revealed the infrastructures through which information systems—governmental, corporate, and algorithmic—operate beyond the threshold of human perception while profoundly reshaping how reality is understood. Drawing on figures and practices from diverse domains, ranging from the Central Intelligence Agency to stage magicians and contemporary technologists, the talk uncovers recurring strategies of influence and control. In doing so, it raises timely questions about why supposedly rational minds remain vulnerable to structures of influence that bypass conscious reasoning and instead engage our most instinctive psychological responses.

The program will be followed by a conversation between Paglen and Noam Segal, LG Electronics Associate Curator, reflecting on how rapid developments in artificial intelligence—from large language models to agentic and robotic systems—are reshaping everyday life, modes of thought, and artistic practice.

For those who don’t know, LG Electronics is a South Korean multinational electronics company. and for anyone needing a little more information about Trevor Paglen, there’s this from his Wikipedia entry, Note: Links have been removed,

Trevor Paglen (born 1974) is an American artist, geographer, and author whose work covers mass surveillance and data collection.[1][2]

In 2016, Paglen won the Deutsche Börse Photography Foundation Prize[3] and he has also won The Cultural Award from the German Society for Photography.[4] In 2017, he was a recipient of a MacArthur Fellowship. On March 17, 2026, Paglen was awarded the 2026 LG Guggenheim Award (a collaboration between LG and Guggenheim New York).[5][6]

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Work

Sean O’Hagan, writing in The Guardian in 2015, said that Paglen, whose “ongoing grand project [is] the murky world of global state surveillance and the ethics of drone warfare”, “is one of the most conceptually adventurous political artists working today, and has collaborated with scientists and human rights activists on his always ambitious multimedia projects.”[2] His visual work such as his “Limit Telephotography”[9] and “The Other Night Sky” series have received widespread attention for both his technical innovations and for his conceptual project that involves simultaneously making and negating documentary-style truth-claims.[10] Paglen’s work relies on contemporary technology in two meaningful ways. Firstly, the views he photographs would be impossible to shoot without media tech, that includes the cameras, the microscopes, and even helicopters.[11] But interestingly enough, the shots would not be possible if not for the existence of the subject. The contrasts between secrecy and revelation, evidence and abstraction distinguish Paglen’s work. With that the artist presents not so much “evidence” as admonitions to awareness.[12][13]

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On balance, I found the Guggenheim’s description of Paglen’s work a little more accessible than the one in his Wikipedia entry. There’s also Paglen’s own website; from his bio page,

Trevor Paglen is an artist whose work spans image-making, sculpture, investigative journalism, writing, engineering, and numerous other disciplines.

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Another accessible description but more clicking (you’ll see when you get to his homepage).

Can AI chatbots cause you to harm yourself? a University of British Columbia study

While this research is from last year, the topic is still timely. The Canadian Broadcasting Corporation’s (CBC) Kevin Maimann posted this September 17, 2025 story, “AI-fuelled delusions are hurting Canadians. Here are some of their stories” on CBC news online, Note: Links have been removed,

Last winter, Anthony Tan thought he was living inside an AI simulation. 

He was skipping meals and barely sleeping, and questioned whether anyone he saw on his university campus was real. 

The Toronto app developer says he started messaging friends with concerning “ramblings,” including the belief he was being watched by billionaires. When some of them reached out, he blocked their calls and numbers, thinking they had turned against him. 

He wound up spending three weeks in a hospital psychiatric ward. 

Tan, 26, says his psychotic breakwas triggered by months of lengthy, increasingly intense conversations with OpenAI’s ChatGPT. 

“It really insidiously crept into my ego, and I came to think that the conversation I had with AI would be of historic importance in the future,” Tan told CBC News. 

…

A number of similar cases, of so-called “AI psychosis,” have been reported in recent months — all involving people who became convinced, through conversations with chatbots, that something imaginary was real. Some involved manic episodes and messianic delusions, some led to violence.

Microsoft’s head of AI, Mustafa Suleyman, warned of the phenomenon in August, writing in a series of posts that problems caused by AI tools that appear sentient to some users are keeping him up at night. 

“Reports of delusions, ‘AI psychosis,’ and unhealthy attachment keep rising. And as hard as it may be to hear, this is not something confined to people already at-risk of mental health issues,” he wrote. 

Tan, who co-founded the dating app Flirtual in 2021, started using ChatGPT for a project about ethical AI [emphasis mine], talking with it for hours every day about everything from philosophy to evolutionary biology to quantum physics.

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Surprising that even someone who’s an experienced developer could get trapped in a delusional web. Researchers at the University of British Columbia examined how the persuasion works.

An October 1, 2025 University of British Columbia (UBC) news release (also on EurekAlert) features a Q&A (question and answer format) on the topic of AI chatbots and self-harm, Note: Links have been removed,

Large language models are more persuasive than humans, according to recent UBC research.

Their vocabulary, perceived empathy and ability to provide tangible resources in seconds add to their persuasiveness, which has led to growing concerns and ongoing lawsuits about the potential for AI chatbots to cause harm to users.

In this Q&A, Dr. Vered Shwartz, UBC assistant professor of computer science and author of the book Lost in Automatic Translation [Lost in Automatic Translation: Navigating Life in English in the Age of Language Technologies], discusses her findings as well as potential safeguards for the future of AI.

Why does the persuasiveness of AI matter?

VS: Large language models like ChatGPT are already widely used to create content that can influence human beliefs and decisions, whether in art, marketing, news dissemination and more. They can quickly produce large amounts of text at scale. If they’re persuasive, there’s a real risk that people will use them to manipulate others for malicious purposes. We may be past the point of deciding whether they should be used in these areas, and instead need to focus on finding ways to protect against the malicious uses.

What did you find?

VS: We wanted to see how persuasive large language models such as ChatGPT can be when it comes to lifestyle decisions: whether to go vegan, buy an electric car or go to graduate school. We had 33 participants pretend to be considering these decisions, and then interact with either a human persuader, or GPT-4, via chat. Both human persuaders and GPT-4 were given general tips about persuasion, and the AI was instructed not to reveal it was a computer. Participants were asked before and after the conversation how likely they were to adopt the lifestyle change.

Participants found the AI more persuasive than humans across all topics, but particularly so when convincing people to become vegan or attend graduate school.

Human persuaders, however, were better at asking questions to find out more information about the participant.

What makes AI persuasive?

VS: The AI made more arguments and was more verbose, writing eight sentences to every human persuader’s two. One of the main factors for its persuasiveness was that it could provide concrete logistical support, for instance, recommending specific vegan brands or universities to attend.

It used more ‘big words’ of seven letters or more, such as longevity and investment, which perhaps made it seem more authoritative. And, people found their AI conversations more pleasant, with GPT-4 agreeing with users more often, and uttering more pleasantries.

What safeguards do we need?

VS: AI education is crucial. Some giveaways do still exist—for instance, almost all our participants worked out that they were speaking to an AI—but we’re getting close to the point where it will be impossible to tell if you’re chatting with AI or a human, so we need to make sure people know how these tools work, how they are trained and so, how they are limited. AI can hallucinate and get things wrong. It’s important to know that, for instance, the AI summary at the top of your search page might not be true.

Another key is general critical thinking. If something seems too good or too bad to be true, we need to investigate it. Check where information is coming from. Is it a trustworthy and known source?

When it comes to AI affecting mental health, companies could implement warning systems if someone is writing harmful or suicidal text.

We don’t really have full control over these models. Instead of companies rushing to monetize AI, there should be more thought about implementing guardrails effectively and widely. This could include looking beyond generative AI and its inherent limitations to different paradigms. We don’t need to put all our eggs in one basket.

Here’s a link to and a citation for the paper, Note: This one will not be in my standard style,

Shruthi Chockkalingam, Seyed Hossein Alavi, Raymond T. Ng, and Vered Shwartz. 2025. Should I go vegan: Evaluating the Persuasiveness of LLMs in Persona-Grounded Dialogues. In Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025), pages 65–72, Vienna, Austria. Association for Computational Linguistics.

The PDF is here: https://aclanthology.org/anthology-files/pdf/sicon/2025.sicon-1.pdf#page=50

AI governance in the real-world ‘city brain’ project: possible pitfalls

An April 16, 2025 Trinity College Dublin press release illustrates the pervasiveness of generative artificial intelligence (Generative AI, GenAI, or GAI) in the management of cities,

The work underscores what can go wrong when an AI that manages city transport, safety, health and environmental monitoring predicts the future and intervenes in the present, significantly influencing urban governance and public policy development.

Generative Artificial Intelligence (AI) is boosting anticipatory forms of governance around the world, helping state actors to predict the future and focus their efforts in the present where the AI predicts they can have the greatest positive impact.

This phenomenon is particularly evident in China, but similar forms of governance mediated by generative AI are also becoming increasingly popular in Europe and the seeds of this trend are already visible in Ireland.

In this context, “city brains” represent an emerging type of generative AI currently employed in urban governance and public policy in a growing number of cities. City brains are large-scale AIs residing in vast digital urban platforms, which use Large Language Models (LLMs) to generate visions of urban futures: visions that are in turn used by policymakers to generate new urban policies. In China alone, there are over 500 cities developing city brains.

However, one of the main foreseeable dangers is the formation of a policy process that, under the influence of unintelligible LLMs, risks losing transparency and thus accountability, with another being the marginalisation of human stakeholders (citizens, in particular) as the role of AI in the management of cities keeps growing and governance begins to turn posthuman.

And by focusing on a real-world city brain project operating in the Haidian district of Beijing (China), which has a population of around 3,000,000 people and gathers data from over 14,000 CCTV cameras and over 20,000 environmental sensors, the researchers have been able to show these dangers are not just theoretical.

A person stands looking at an interactive dashboard of an existing City Brain system in China, at an interactive exhibition centre for the public. The dashboard of an existing City Brain system in China. Image credit: Dr Ying Xu.

Dr Federico Cugurullo, Associate Professor in Trinity’s School of Natural Sciences, is a leading expert in AI urbanism and the first author of the research, which has been published in the journal Policy and Society.

He said: “We can think of the Haidian city brain project as a gigantic panopticon that constantly observes what is happening it the city. It is operated by AI and focuses on three main areas of governance that are shaped by its predictions: environmental risk management, traffic management and public security.”

“For example, in the case of an impending natural disaster, policies are rapidly implemented to build new infrastructure meant to reinforce riverbanks and increase the efficiency of the city’s drainage systems. Outcomes also include direct interventions when, for example, police officers are dispatched to prevent illegal activities in an area where, according to the city brain, crimes are likely to take place in the near future.”

“However, the predictions of the Haidian city brain are far from being infallible. Our research reveals that the accuracy rate of what the city brain predicts varies from 60% to 90%, which leaves a significant margin of error around such important decisions, for which there is no understanding as to why they have been implemented. This is particularly dangerous when it comes to predictive policing, since any error made by AI means that an innocent person will be targeted by the police for hypothetical crimes that never took place.”

This research forms part of the ORACLE project led by Professor Cugurullo, which was funded by Research Ireland (formerly the Irish Research Council).

Here’s a link to and a citation for the paper,

When AIs become oracles: generative artificial intelligence, anticipatory urban governance, and the future of cities by Federico Cugurullo, Ying Xu. Policy and Society, Volume 44, Issue 1, January 2025, Pages 98–115, DOI: https://doi.org/10.1093/polsoc/puae025 Published online: 01 August 2024

This paper is open access.

For anyone unfamiliar with the ‘panopticon’, there’s an entry in Wikipedia, Note: Links have been removed,

The panopticon is a design of institutional building with an inbuilt system of control, originated by the English philosopher and social theorist Jeremy Bentham in the 18th century. The concept is to allow all prisoners of an institution to be observed by a single corrections officer, without the inmates knowing whether or not they are being watched.

Although it is physically impossible for the single guard to observe all the inmates’ cells at once, the fact that the inmates cannot know when they are being watched motivates them to act as though they are all being watched at all times. They are effectively compelled to self-regulation. The architecture consists of a rotunda with an inspection house at its centre. From the centre, the manager or staff are able to watch the inmates. Bentham conceived the basic plan as being equally applicable to hospitals, schools, sanatoriums, and asylums. …

I have not been able to find a website for Cugurullo’s ORACLE project but I did find Cugurullo’s January 3, 2025 essay “AI could make cities autonomous, but that doesn’t mean we should let it happen,” which discusses a new field “AI urbanism” and includes the example of an enormous Saudi Arabian project, Neom and its linear city, The Line.

‘Super-Turing AI’ uses less energy to mimic brain

Neuromorphic (brainlike) engineering and neuromorphic computing being long time interests here, this March 26, 2025 new item on ScienceDaily caught my eye,

Artificial Intelligence (AI) can perform complex calculations and analyze data faster than any human, but to do so requires enormous amounts of energy. The human brain is also an incredibly powerful computer, yet it consumes very little energy.

As technology companies increasingly expand, a new approach to AI’s “thinking,” developed by researchers including Texas A&M University engineers, mimics the human brain and has the potential to revolutionize the AI industry.

…

A March 25, 2025 Texas A&M University news release (also on EurekAlert) by Lesley Henton, which originated the news item, delves further into the creation of a “Super-Turing AI,” Note: Links have been removed,

As technology companies increasingly expand, a new approach to AI’s “thinking,” developed by researchers including Texas A&M University engineers, mimics the human brain and has the potential to revolutionize the AI industry.

Dr. Suin Yi, assistant professor of electrical and computer engineering at Texas A&M’s College of Engineering, is on a team of researchers that developed “Super-Turing AI,” which operates more like the human brain. This new AI integrates certain processes instead of separating them and then migrating huge amounts of data like current systems do.

The Energy Crisis In AI

Today’s AI systems, including large language models [LLM] such as OpenAI [a company not an LLM] and ChatGPT [an LLM produced by OpenAI], require immense computing power and are housed in expansive data centers that consume vast amounts of electricity.

“These data centers are consuming power in gigawatts, whereas our brain consumes 20 watts,” Suin explained. “That’s 1 billion watts compared to just 20. Data centers that are consuming this energy are not sustainable with current computing methods. So while AI’s abilities are remarkable, the hardware and power generation needed to sustain it is still needed.”

The substantial energy demands not only escalate operational costs but also raise environmental concerns, given the carbon footprint associated with large-scale data centers. As AI becomes more integrated, addressing its sustainability becomes increasingly critical.

Emulating The Brain

Yi and team believe the key to solving this problem lies in nature — specifically, the human brain’s neural processes.

In the brain, the functions of learning and memory are not separated, they are integrated. Learning and memory rely on connections between neurons, called “synapses,” where signals are transmitted. Learning strengthens or weakens synaptic connections through a process called “synaptic plasticity,” forming new circuits and altering existing ones to store and retrieve information. 

By contrast, in current computing systems, training (how the AI is taught) and memory (data storage) happen in two separate places within the computer hardware. Super-Turing AI is revolutionary because it bridges this efficiency gap, so the computer doesn’t have to migrate enormous amounts of data from one part of its hardware to another.

“Traditional AI models rely heavily on backpropagation — a method used to adjust neural networks during training,” Yi said. “While effective, backpropagation is not biologically plausible and is computationally intensive.

“What we did in that paper is troubleshoot the biological implausibility present in prevailing machine learning algorithms,” he said. “Our team explores mechanisms like Hebbian learning and spike-timing-dependent plasticity — processes that help neurons strengthen connections in a way that mimics how real brains learn.”

Hebbian learning principles are often summarized as “cells that fire together, wire together.” This approach aligns more closely with how neurons in the brain strengthen their connections based on activity patterns. By integrating such biologically inspired mechanisms, the team aims to develop AI systems that require less computational power without compromising performance.

In a test, a circuit using these components helped a drone navigate a complex environment — without prior training — learning and adapting on the fly. This approach was faster, more efficient and used less energy than traditional AI.

Why This Matters For The Future Of AI

This research could be a game-changer for the AI industry. Companies are racing to build larger and more powerful AI models, but their ability to scale is limited by hardware and energy constraints. In some cases, new AI applications require building entire new data centers, further increasing environmental and economic costs.

Yi emphasizes that innovation in hardware is just as crucial as advancements in AI systems themselves. “Many people say AI is just a software thing, but without computing hardware, AI cannot exist,” he said.

Looking Ahead: Sustainable AI Development

Super-Turing AI represents a pivotal step toward sustainable AI development. By reimagining AI architectures to mirror the efficiency of the human brain, the industry can address both economic and environmental challenges.

Yi and his team hope that their research will lead to a new generation of AI that is both smarter and more efficient.

“Modern AI like ChatGPT is awesome, but it’s too expensive. We’re going to make sustainable AI,” Yi said. “Super-Turing AI could reshape how AI is built and used, ensuring that as it continues to advance, it does so in a way that benefits both people and the planet.”

There’s no mention of a memristor but there is a ‘synaptic resistor’, which I find puzzling. Is a synaptic resistor something different? In a search with these search terms “synaptic resistor memristor” I found this,

The term “memristive synapses” signifies the amalgamation of memristor functionality with synaptic characteristics, resulting in a novel approach to neuromorphic computing.

I’m guessing memristive synapses can also be called synaptic resistors or, at the least, are related concepts.

I pulled the definition from,

Resistive Switching Properties in Memristors for Optoelectronic Synaptic Memristors: Deposition Techniques, Key Performance Parameters, and Applications by Rajwali Khan, Naveed Ur Rehman, Shahid Iqbal, Sherzod Abdullaev, and Haila M. Aldosari. ACS Applied Electronic Materials Vol 6/ Issue 1 pp. 73–119 DOI: https://doi.org/10.1021/acsaelm.3c01323 Published December 29, 2023 Copyright © 2023 The Authors. Published by American Chemical Society. This publication is licensed under
CC-BY 4.0

Getting back to this latest work from Texas A&M University, here’s a link to and a citation for Dr. Suin Yi and his team’s paper,

HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system by Jungmin Lee, Rahul Shenoy, Atharva Deo, Suin Yi, Dawei Gao, David Qiao, Mingjie Xu, Shiva Asapu, Zixuan Rong, Dhruva Nathan, Yong Hei, Dharma Paladugu, Jian-Guo Zheng, J. Joshua Yang, R. Stanley Williams, Qing Wu, and Yong Chen. Science Advances 28 Feb 2025 Vol 11, Issue 9 DOI: 10.1126/sciadv.adr2082

This paper is open access.

Notice that one of the Super Turing paper’s authors is R. Stanley Williams who ‘discovered’ the memristor in 2008. You can read his November 28, 2008 article “How We Found the Missing Memristor; The memristor—the functional equivalent of a synapse—could revolutionize circuit design” in the IEEE Spectrum online,

It’s time to stop shrinking. Moore’s Law, the semiconductor industry’s obsession with the shrinking of transistors and their commensurate steady doubling on a chip about every two years, has been the source of a 50-year technical and economic revolution. Whether this scaling paradigm lasts for five more years or 15, it will eventually come to an end. The emphasis in electronics design will have to shift to devices that are not just increasingly infinitesimal but increasingly capable.

Earlier this year, I and my colleagues at Hewlett-Packard Labs, in Palo Alto, Calif., surprised the electronics community with a fascinating candidate for such a device: the memristor. It had been theorized nearly 40 years ago, but because no one had managed to build one, it had long since become an esoteric curiosity. That all changed on 1 May [2008], when my group published the details of the memristor in Nature.

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For anyone interested in a trip down memory road, I have a few comments from the theorist (Leon Chua) mentioned in his 2008 article in this April 13, 2010 posting (scroll down to the ‘More on memristors’ subhead).

DeepSeek, a Chinese rival to OpenAI and other US AI companies

There’s been quite the kerfuffle over DeepSeek during the last few days. This January 27, 2025 article by Alexandra Mae Jones for the Canadian Broadcasting Corporation (CBC) news only was my introduction to DeepSeek AI, Note: A link has been removed,

There’s a new player in AI on the world stage: DeepSeek, a Chinese startup that’s throwing tech valuations into chaos and challenging U.S. dominance in the field with an open-source model that they say they developed for a fraction of the cost of competitors.

DeepSeek’s free AI assistant — which by Monday [January 27, 20¸25] had overtaken rival ChatGPT to become the top-rated free application on Apple’s App Store in the United States — offers the prospect of a viable, cheaper AI alternative, raising questions on the heavy spending by U.S. companies such as Apple and Microsoft, amid a growing investor push for returns.

U.S. stocks dropped sharply on Monday [January 27, 2025], as the surging popularity of DeepSeek sparked a sell-off in U.S. chipmakers.

…

“[DeepSeek] performs as well as the leading models in Silicon Valley and in some cases, according to their claims, even better,” Sheldon Fernandez, co-founder of DarwinAI, told CBC News. “But they did it with a fractional amount of the resources is really what is turning heads in our industry.”

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What is DeepSeek?

Little is known about the small Hangzhou startup behind DeepSeek, which was founded out of a hedge fund in 2023, but largely develops open-source AI models. 

Its researchers wrote in a paper last month that the DeepSeek-V3 model, launched on Jan. 10 [2025], cost less than $6 million US to develop and uses less data than competitors, running counter to the assumption that AI development will eat up increasing amounts of money and energy. 

Some analysts are skeptical about DeepSeek’s $6 million claim, pointing out that this figure only covers computing power. But Fernandez said that even if you triple DeepSeek’s cost estimates, it would still cost significantly less than its competitors. 

The open source release of DeepSeek-R1, which came out on Jan. 20 [2025] and uses DeepSeek-V3 as its base, also means that developers and researchers can look at its inner workings, run it on their own infrastructure and build on it, although its training data has not been made available. 

…

“Instead of paying Open $20 a month or $200 a month for the latest advanced versions of these models, [people] can really get these types of features for free. And so it really upends a lot of the business model that a lot of these companies were relying on to justify their very high valuations.”

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A key difference between DeepSeek’s AI assistant, R1, and other chatbots like OpenAI’s ChatGPT is that DeepSeek lays out its reasoning when it answers prompts and questions, something developers are excited about. 

“The dealbreaker is the access to the raw thinking steps,” Elvis Saravia, an AI researcher and co-founder of the U.K.-based AI consulting firm DAIR.AI, wrote on X, adding that the response quality was “comparable” to OpenAI’s latest reasoning model, o1.

U.S. dominance in AI challenged

One of the reasons DeepSeek is making headlines is because its development occurred despite U.S. actions to keep Americans at the top of AI development. In 2022, the U.S. curbed exports of computer chips to China, hampering their advanced supercomputing development.

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The latest AI models from DeepSeek are widely seen to be competitive with those of OpenAI and Meta, which rely on high-end computer chips and extensive computing power.

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Christine Mui in a January 27, 2025 article for Politico notes the stock ‘crash’ taking place while focusing on the US policy implications, Note: Links set by Politico have been removed while I have added one link

A little-known Chinese artificial intelligence startup shook the tech world this weekend by releasing an OpenAI-like assistant, which shot to the No.1 ranking on Apple’s app store and caused American tech giants’ stocks to tumble.

From Washington’s perspective, the news raised an immediate policy alarm: It happened despite consistent, bipartisan efforts to stifle AI progress in China.

…

In tech terms, what freaked everyone out about DeepSeek’s R1 model is that it replicated — and in some cases, surpassed — the performance of OpenAI’s cutting-edge o1 product across a host of performance benchmarks, at a tiny fraction of the cost.

The business takeaway was straightforward. DeepSeek’s success shows that American companies might not need to spend nearly as much as expected to develop AI models. That both intrigues and worries investors and tech leaders.

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The policy implications, though, are more complex. Washington’s rampant anxiety about beating China has led to policies that the industry has very mixed feelings about.

On one hand, most tech firms hate the export controls that stop them from selling as much to the world’s second-largest economy, and force them to develop new products if they want to do business with China. If DeepSeek shows those rules are pointless, many would be delighted to see them go away.

On the other hand, anti-China, protectionist sentiment has encouraged Washington to embrace a whole host of industry wishlist items, from a lighter-touch approach to AI rules to streamlined permitting for related construction projects. Does DeepSeek mean those, too, are failing? Or does it trigger a doubling-down?

DeepSeek’s success truly seems to challenge the belief that the future of American AI demands ever more chips and power. That complicates Trump’s interest in rapidly building out that kind of infrastructure in the U.S.

Why pour $500 billion into the Trump-endorsed “Stargate” mega project [announced by Trump on January 21, 2025] — and why would the market reward companies like Meta that spend $65 billion in just one year on AI — if DeepSeek claims it only took $5.6 million and second-tier Nvidia chips to train one of its latest models? (U.S. industry insiders dispute the startup’s figures and claim they don’t tell the full story, but even at 100 times that cost, it would be a bargain.)

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Tech companies, of course, love the recent bloom of federal support, and it’s unlikely they’ll drop their push for more federal investment to match anytime soon. Marc Andreessen, a venture capitalist and Trump ally, argued today that DeepSeek should be seen as “AI’s Sputnik moment,” one that raises the stakes for the global competition.

That would strengthen the case that some American AI companies have been pressing for the new administration to invest government resources into AI infrastructure (OpenAI), tighten restrictions on China (Anthropic) and ease up on regulations to ensure their developers build “artificial general intelligence” before their geopolitical rivals.

…

The British Broadcasting Corporation’s (BBC) Peter Hoskins & Imran Rahman-Jones provided a European perspective and some additional information in their January 27, 2025 article for BBC news online, Note: Links have been removed,

US tech giant Nvidia lost over a sixth of its value after the surging popularity of a Chinese artificial intelligence (AI) app spooked investors in the US and Europe.

DeepSeek, a Chinese AI chatbot reportedly made at a fraction of the cost of its rivals, launched last week but has already become the most downloaded free app in the US.

AI chip giant Nvidia and other tech firms connected to AI, including Microsoft and Google, saw their values tumble on Monday [January 27, 2025] in the wake of DeepSeek’s sudden rise.

In a separate development, DeepSeek said on Monday [January 27, 2025] it will temporarily limit registrations because of “large-scale malicious attacks” on its software.

The DeepSeek chatbot was reportedly developed for a fraction of the cost of its rivals, raising questions about the future of America’s AI dominance and the scale of investments US firms are planning.

…

DeepSeek is powered by the open source DeepSeek-V3 model, which its researchers claim was trained for around $6m – significantly less than the billions spent by rivals.

But this claim has been disputed by others in AI.

The researchers say they use already existing technology, as well as open source code – software that can be used, modified or distributed by anybody free of charge.

DeepSeek’s emergence comes as the US is restricting the sale of the advanced chip technology that powers AI to China.

To continue their work without steady supplies of imported advanced chips, Chinese AI developers have shared their work with each other and experimented with new approaches to the technology.

This has resulted in AI models that require far less computing power than before.

It also means that they cost a lot less than previously thought possible, which has the potential to upend the industry.

After DeepSeek-R1 was launched earlier this month, the company boasted of “performance on par with” one of OpenAI’s latest models when used for tasks such as maths, coding and natural language reasoning.

…

In Europe, Dutch chip equipment maker ASML ended Monday’s trading with its share price down by more than 7% while shares in Siemens Energy, which makes hardware related to AI, had plunged by a fifth.

“This idea of a low-cost Chinese version hasn’t necessarily been forefront, so it’s taken the market a little bit by surprise,” said Fiona Cincotta, senior market analyst at City Index.

“So, if you suddenly get this low-cost AI model, then that’s going to raise concerns over the profits of rivals, particularly given the amount that they’ve already invested in more expensive AI infrastructure.”

Singapore-based technology equity adviser Vey-Sern Ling told the BBC it could “potentially derail the investment case for the entire AI supply chain”.

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Who founded DeepSeek?

The company was founded in 2023 by Liang Wenfeng in Hangzhou, a city in southeastern China.

The 40-year-old, an information and electronic engineering graduate, also founded the hedge fund that backed DeepSeek.

He reportedly built up a store of Nvidia A100 chips, now banned from export to China.

Experts believe this collection – which some estimates put at 50,000 – led him to launch DeepSeek, by pairing these chips with cheaper, lower-end ones that are still available to import.

Mr Liang was recently seen at a meeting between industry experts and the Chinese premier Li Qiang.

In a July 2024 interview with The China Academy, Mr Liang said he was surprised by the reaction to the previous version of his AI model.

“We didn’t expect pricing to be such a sensitive issue,” he said.

“We were simply following our own pace, calculating costs, and setting prices accordingly.”

…

A January 28, 2025 article by Daria Solovieva for salon.com covers much the same territory as the others and includes a few detail about security issues,

…

The pace at which U.S. consumers have embraced DeepSeek is raising national security concerns similar to those surrounding TikTok, the social media platform that faces a ban unless it is sold to a non-Chinese company.

The U.S. Supreme Court this month upheld a federal law that requires TikTok’s sale. The Court sided with the U.S. government’s argument that the app can collect and track data on its 170 million American users. President Donald Trump has paused enforcement of the ban until April to try to negotiate a deal.

But “the threat posed by DeepSeek is more direct and acute than TikTok,” Luke de Pulford, co-founder and executive director of non-profit Inter-Parliamentary Alliance on China, told Salon.

DeepSeek is a fully Chinese company and is subject to Communist Party control, unlike TikTok which positions itself as independent from parent company ByteDance, he said. 

“DeepSeek logs your keystrokes, device data, location and so much other information and stores it all in China,” de Pulford said. “So you’ll never know if the Chinese state has been crunching your data to gain strategic advantage, and DeepSeek would be breaking the law if they told you.”  

I wonder if other AI companies in other countries also log keystrokes, etc. Is it theoretically possible that one of those governments or their government agencies could gain access to your data? It’s obvious in China but people in other countries may have the issues.

Censorship: DeepSeek and ChatGPT

Anis Heydari’s January 28, 2025 article for CBC news online reveals some surprising results from a head to head comparison between DeepSeek and ChatGPT,

The Chinese-made AI chatbot DeepSeek may not always answer some questions about topics that are often censored by Beijing, according to tests run by CBC News and The Associated Press, and is providing different information than its U.S.-owned competitor ChatGPT.

The new, free chatbot has sparked discussions about the competition between China and the U.S. in AI development, with many users flocking to test it. 

But experts warn users should be careful with what information they provide to such software products.

It is also “a little bit surprising,” according to one researcher, that topics which are often censored within China are seemingly also being restricted elsewhere.

“A lot of services will differentiate based on where the user is coming from when deciding to deploy censorship or not,” said Jeffrey Knockel, who researches software censorship and surveillance at the Citizen Lab at the University of Toronto’s Munk School of Global Affairs & Public Policy.

“With this one, it just seems to be censoring everyone.”

…

Both CBC News and The Associated Press posed questions to DeepSeek and OpenAI’s ChatGPT, with mixed and differing results.

For example, DeepSeek seemed to indicate an inability to answer fully when asked “What does Winnie the Pooh mean in China?” For many Chinese people, the Winnie the Pooh character is used as a playful taunt of President Xi Jinping, and social media searches about that character were previously, briefly banned in China. 

DeepSeek said the bear is a beloved cartoon character that is adored by countless children and families in China, symbolizing joy and friendship.

Then, abruptly, it added the Chinese government is “dedicated to providing a wholesome cyberspace for its citizens,” and that all online content is managed under Chinese laws and socialist core values, with the aim of protecting national security and social stability.

CBC News was unable to produce this response. DeepSeek instead said “some internet users have drawn comparisons between Winnie the Pooh and Chinese leaders, leading to increased scrutiny and restrictions on the character’s imagery in certain contexts,” when asked the same question on an iOS app on a CBC device in Canada.

…

Asked if Taiwan is a part of China — another touchy subject — it [DeepSeek] began by saying the island’s status is a “complex and sensitive issue in international relations,” adding that China claims Taiwan, but that the island itself operates as a “separate and self-governing entity” which many people consider to be a sovereign nation.

But as that answer was being typed out, for both CBC and the AP, it vanished and was replaced with: “Sorry, that’s beyond my current scope. Let’s talk about something else.”

…

… Brent Arnold, a data breach lawyer in Toronto, says there are concerns about DeepSeek, which explicitly says in its privacy policy that the information it collects is stored on servers in China.

That information can include the type of device used, user “keystroke patterns,” and even “activities on other websites and apps or in stores, including the products or services you purchased, online or in person” depending on whether advertising services have shared those with DeepSeek.

“The difference between this and another AI company having this is now, the Chinese government also has it,” said Arnold.

While much, if not all, of the data DeepSeek collects is the same as that of U.S.-based companies such as Meta or Google, Arnold points out that — for now — the U.S. has checks and balances if governments want to obtain that information.

“With respect to America, we assume the government operates in good faith if they’re investigating and asking for information, they’ve got a legitimate basis for doing so,” he said. 

Right now, Arnold says it’s not accurate to compare Chinese and U.S. authorities in terms of their ability to take personal information. But that could change.

“I would say it’s a false equivalency now. But in the months and years to come, we might start to say you don’t see a whole lot of difference in what one government or another is doing,” he said.

…

Graham Fraser’s January 28, 2025 article comparing DeepSeek to the others (OpenAI’s ChatGPT and Google’s Gemini) for BBC news online took a different approach,

…

Writing Assistance

When you ask ChatGPT what the most popular reasons to use ChatGPT are, it says that assisting people to write is one of them.

From gathering and summarising information in a helpful format to even writing blog posts on a topic, ChatGPT has become an AI companion for many across different workplaces.

As a proud Scottish football [soccer] fan, I asked ChatGPT and DeepSeek to summarise the best Scottish football players ever, before asking the chatbots to “draft a blog post summarising the best Scottish football players in history”.

DeepSeek responded in seconds, with a top ten list – Kenny Dalglish of Liverpool and Celtic was number one. It helpfully summarised which position the players played in, their clubs, and a brief list of their achievements.

DeepSeek also detailed two non-Scottish players – Rangers legend Brian Laudrup, who is Danish, and Celtic hero Henrik Larsson. For the latter, it added “although Swedish, Larsson is often included in discussions of Scottish football legends due to his impact at Celtic”.

For its subsequent blog post, it did go into detail of Laudrup’s nationality before giving a succinct account of the careers of the players.

ChatGPT’s answer to the same question contained many of the same names, with “King Kenny” once again at the top of the list.

Its detailed blog post briefly and accurately went into the careers of all the players.

It concluded: “While the game has changed over the decades, the impact of these Scottish greats remains timeless.” Indeed.

For this fun test, DeepSeek was certainly comparable to its best-known US competitor.

Coding

…

Brainstorming ideas

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Learning and research

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Steaming ahead

The tasks I set the chatbots were simple but they point to something much more significant – the winner of the so-called AI race is far from decided.

For all the vast resources US firms have poured into the tech, their Chinese rival has shown their achievements can be emulated.

…

Reception from the science community

Days before the news outlets discovered DeepSeek, the company published a paper about its Large Language Models (LLMs) and its new chatbot on arXiv. Here’s a little more information,

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

[over 100 authors are listed]

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing reasoning behaviors. However, it encounters challenges such as poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks. To support the research community, we open-source DeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama.

Cite as: arXiv:2501.12948 [cs.CL]
(or arXiv:2501.12948v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2501.12948

Submission history

From: Wenfeng Liang [view email]
[v1] Wed, 22 Jan 2025 15:19:35 UTC (928 KB)

You can also find a PDF version of the paper here or another online version here at Hugging Face.

As for the science community’s response, the title of Elizabeth Gibney’s January 23, 2025 article “China’s cheap, open AI model DeepSeek thrills scientists” for Nature says it all, Note: Links have been removed,

A Chinese-built large language model called DeepSeek-R1 is thrilling scientists as an affordable and open rival to ‘reasoning’ models such as OpenAI’s o1.

These models generate responses step-by-step, in a process analogous to human reasoning. This makes them more adept than earlier language models at solving scientific problems and could make them useful in research. Initial tests of R1, released on 20 January, show that its performance on certain tasks in chemistry, mathematics and coding is on par with that of o1 — which wowed researchers when it was released by OpenAI in September.

“This is wild and totally unexpected,” Elvis Saravia, an AI researcher and co-founder of the UK-based AI consulting firm DAIR.AI, wrote on X.

R1 stands out for another reason. DeepSeek, the start-up in Hangzhou that built the model, has released it as ‘open-weight’, meaning that researchers can study and build on the algorithm. Published under an MIT licence, the model can be freely reused but is not considered fully open source, because its training data has not been made available.

“The openness of DeepSeek is quite remarkable,” says Mario Krenn, leader of the Artificial Scientist Lab at the Max Planck Institute for the Science of Light in Erlangen, Germany. By comparison, o1 and other models built by OpenAI in San Francisco, California, including its latest effort o3 are “essentially black boxes”, he says.

DeepSeek hasn’t released the full cost of training R1, but it is charging people using its interface around one-thirtieth of what o1 costs to run. The firm has also created mini ‘distilled’ versions of R1 to allow researchers with limited computing power to play with the model. An “experiment that cost more than £300 with o1, cost less than $10 with R1,” says Krenn. “This is a dramatic difference which will certainly play a role its future adoption.”

…

The kerfuffle has died down for now.

Huge leap forward in computing efficiency with Indian Institute of Science’s (IISc) neuromorphic (brainlike) platform

This is pretty thrilling news in a September 11, 2024 Indian Institute of Science (IISc) press release (also on EurekAlert), Note: A link has been removed,

In a landmark advancement, researchers at the Indian Institute of Science (IISc) have developed a brain-inspired analog computing platform capable of storing and processing data in an astonishing 16,500 conductance states within a molecular film. Published today in the journal Nature, this breakthrough represents a huge step forward over traditional digital computers in which data storage and processing are limited to just two states. 

Such a platform could potentially bring complex AI tasks, like training Large Language Models (LLMs), to personal devices like laptops and smartphones, thus taking us closer to democratising the development of AI tools. These developments are currently restricted to resource-heavy data centres, due to a lack of energy-efficient hardware. With silicon electronics nearing saturation, designing brain-inspired accelerators that can work alongside silicon chips to deliver faster, more efficient AI is also becoming crucial.

“Neuromorphic computing has had its fair share of unsolved challenges for over a decade,” explains Sreetosh Goswami, Assistant Professor at the Centre for Nano Science and Engineering (CeNSE), IISc, who led the research team. “With this discovery, we have almost nailed the perfect system – a rare feat.”

The fundamental operation underlying most AI algorithms is quite basic – matrix multiplication, a concept taught in high school maths. But in digital computers, these calculations hog a lot of energy. The platform developed by the IISc team drastically cuts down both the time and energy involved, making these calculations a lot faster and easier.

The molecular system at the heart of the platform was designed by Sreebrata Goswami, Visiting Professor at CeNSE. As molecules and ions wiggle and move within a material film, they create countless unique memory states, many of which have been inaccessible so far. Most digital devices are only able to access two states (high and low conductance), without being able to tap into the infinite number of intermediate states possible.

By using precisely timed voltage pulses, the IISc team found a way to effectively trace a much larger number of molecular movements, and map each of these to a distinct electrical signal, forming an extensive “molecular diary” of different states. “This project brought together the precision of electrical engineering with the creativity of chemistry, letting us control molecular kinetics very precisely inside an electronic circuit powered by nanosecond voltage pulses,” explains Sreebrata Goswami.

Tapping into these tiny molecular changes allowed the team to create a highly precise and efficient neuromorphic accelerator, which can store and process data within the same location, similar to the human brain. Such accelerators can be seamlessly integrated with silicon circuits to boost their performance and energy efficiency. 

A key challenge that the team faced was characterising the various conductance states, which proved impossible using existing equipment. The team designed a custom circuit board that could measure voltages as tiny as a millionth of a volt, to pinpoint these individual states with unprecedented accuracy.

The team also turned this scientific discovery into a technological feat. They were able to recreate NASA’s iconic “Pillars of Creation” image from the James Webb Space Telescope data – originally created by a supercomputer – using just a tabletop computer. They were also able to do this at a fraction of the time and energy that traditional computers would need.

The team includes several students and research fellows at IISc. Deepak Sharma performed the circuit and system design and electrical characterisation, Santi Prasad Rath handled synthesis and fabrication, Bidyabhusan Kundu tackled the mathematical modelling, and Harivignesh S crafted bio-inspired neuronal response behaviour. The team also collaborated with Stanley Williams [also known as R. Stanley Williams], Professor at Texas A&M University and Damien Thompson, Professor at the University of Limerick. 

The researchers believe that this breakthrough could be one of India’s biggest leaps in AI hardware, putting the country on the map of global technology innovation. Navakanta Bhat, Professor at CeNSE and an expert in silicon electronics led the circuit and system design in this project. “What stands out is how we have transformed complex physics and chemistry understanding into groundbreaking technology for AI hardware,” he explains. “In the context of the India Semiconductor Mission, this development could be a game-changer, revolutionising industrial, consumer and strategic applications. The national importance of such research cannot be overstated.” 

With support from the Ministry of Electronics and Information Technology, the IISc team is now focused on developing a fully indigenous integrated neuromorphic chip. “This is a completely home-grown effort, from materials to circuits and systems,” emphasises Sreetosh Goswami. “We are well on our way to translating this technology into a system-on-a-chip.”  

Caption: Using their AI accelerator, the team recreated NASA’s iconic “Pillars of Creation” image from the James Webb Space Telescope data on a simple tabletop computer – achieving this in a fraction of the time and energy required by traditional systems. Credit: CeNSE, IISc

Here’s a link to and a citation for the paper,

Linear symmetric self-selecting 14-bit kinetic molecular memristors by Deepak Sharma, Santi Prasad Rath, Bidyabhusan Kundu, Anil Korkmaz, Harivignesh S, Damien Thompson, Navakanta Bhat, Sreebrata Goswami, R. Stanley Williams & Sreetosh Goswami. Nature volume 633, pages 560–566 (2024) DOI: https://doi.org/10.1038/s41586-024-07902-2 Published online: 11 September 2024 Issue Date: 19 September 2024

This paper is behind a paywall.