Good morning, and happy Friday … unless, that is, you’re a quantum computing company.

A range of quantum stocks took a beating Thursday after Nvidia said that it is opening its own quantum research lab, despite previous skepticism from Jensen Huang that usable quantum computers will be here anytime soon.

In totally related news, the Celtics just got sold for $6.1 billion, a record.

Big numbers, but all I care about is whether the Knicks can actually beat them next time.

— Ian Krietzberg, Editor-in-Chief, The Deep View

In today’s newsletter:

AI for Good: Traumatic brain injuries

Traumatic brain injury (TBI) is often described as “the silent epidemic” for a simple confluence of reasons: a steady lack of awareness of and research into it, its widespread reach — impacting nearly 70 million people each year — and its damaging, sometimes lethal, impact to its victims. 

The causes of TBI are varied: car accidents, falls or, generally, blows to the head. And the resulting levels of TBI are varied, as well, ranging from mild concussions to severe injuries that can lead to permanent cognitive damage. 

The details: In the midst of this crisis, one that kills nearly 70,000 Americans every year, researchers are increasingly studying the diagnostic impact of specifically trained and tuned machine learning algorithms. And though complicated, the integration is promising. 

Why it matters: The implication of this is that a combination of different machine learning algorithms can work as a “good supporting tool” to reduce the workload of radiologists and clinicians, while boosting the odds of early detection. 

Still, it’s early days, here. The algorithms — both in machine learning and deep learning — have certain limitations, namely related to algorithmic bias resulting from data sets that don’t completely capture the full range of TBI. Much more research needs to be done for clinicians to see the introduction of a reliable, usable tool. 

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OpenAI secures major partnership with UK-based bank 

OpenAI’s list of enterprise partners expanded Thursday in the form of a partnership with NatWest, the first collaboration of its kind with a U.K.-headquartered bank, according to a statement. 

The details: The focus of the partnership is on the consumer end, with NatWest citing the development of digital assistants to enable “bank-wide simplification.” For NatWest, this represents a deeper push into chatbots; the bank has already deployed a customer-facing chatbot called Cora, and an internal chatbot, called AskArchie. 

NatWest’s code of conduct highlights commitments to transparency, explainability and data privacy — in this instance, dealing with highly sensitive financial data, it’s unclear how NatWest and OpenAI will ensure that it is protected. 

The landscape: OpenAI has been inking similar deals with firms across the U.S., recently securing a partnership with BNY, America’s oldest bank. OpenAI has also partnered with PWC. The terms of all of these deals — covering economics and data-sharing principles — remain unknown. 

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Anthropic chases OpenAI in competing with Google, Perplexity

The news: Anthropic on Thursday finally brought web search to Claude. 

The startup launched a preview version of this real-time search integration to paid users in the U.S., saying that it will soon roll out both globally and to users on Anthropic’s free tier. 

The landscape: Anthropic is a little late to the game on this one. The functionality of integrating a search function into a chatbot interface is one that has become increasingly popular, beginning with Perplexity, the AI search startup, and diffusing out to the heavy-hitters. 

OpenAI, for instance, incorporated search into ChatGPT months ago; Google said recently that it is doubling down on this format of conversational chatbots with integrated — and attributed — search results with an experimental version of something called AI Mode. 

Even as these alternative new search options steadily gain traction, Google’s dominance — AI mode or not — is hard to shake. The search engine has been operating with roughly 90% of the global search market for years, and that has yet to dramatically shift. 

One more thing: The combination of Chain of Thought inference, hallucinations and web search seems likely to dramatically increase Claude’s energy intensity and associated carbon emissions, especially considering the fact that many of these queries can be quickly, cheaply and reliably answered on a normal search engine, with its 10 blue links.

A crisis of hallucination: ChatGPT tells a father that he killed two of his sons

When Arve Hjalmar Holmen searched his name on ChatGPT, he was just curious. 

But the chatbot’s output — a blend of reality and fiction that presented Arve as a man who had been sentenced to 21 years in prison for the murder of two of his three sons — had him horrified. 

The chatbot got some things right, including his hometown in Norway and the number and gender of his children. But the major component of that output — that he is a convicted murderer — was completely false. 

What happened: The non-profit organization noyb has since filed a case against OpenAI with Norway’s data protection authority, seeking to rectify the issue. This case marks noyb’s second complaint against OpenAI for the same issue: hallucination. 

But the fact that the output is no longer showing up isn’t a cause for relief, for Holmen. 

“The incorrect data may still remain part of the LLM’s dataset,” according to noyb. “By default, ChatGPT feeds user data back into the system for training purposes. This means there is no way for the individual to be absolutely sure that this output can be completely erased.”

OpenAI did not return a request for comment. 

“The GDPR is clear. Personal data has to be accurate. And if it's not, users have the right to have it changed to reflect the truth,” Joakim Söderberg, a data protection lawyer at noyb, said. “Showing ChatGPT users a tiny disclaimer that the chatbot can make mistakes clearly isn’t enough. You can’t just spread false information and in the end add a small disclaimer saying that everything you said may just not be true.” 

Though this one comes in a remarkably specific form, the ramifications of hallucination — a term I like less and less each day — remain a paramount inhibitor to adoption. 

We’ve all heard about the lawyers who have been sanctioned over the fake case law their generative AI systems outputted. And we’ve all heard about the continual mistakes in Google’s AI Overviews.

Earlier this year, Apple had to roll back its AI-powered news summary tool in the U.K. after it was found relaying false information that was presented as fact and attributed to news organizations. Many of the lawsuits against the major developers additionally go beyond copyright infringement to address issues of reputational risk around the false, misleading and partial reproductions of articles, or fictitious inventions of information connected to a prominent organization’s name.

Hallucination is the trillion-dollar question in AI. 

If someone can come up with a system that doesn’t make these kinds of mistakes, or makes them in a predictable way, that person would be the envy of the entire industry. 

Perhaps that is what engineers actually mean when they talk about the pursuit of general intelligence. But there is no evidence at this stage that language models alone can get to a place where this is no longer a problem. 

Arve Hjalmar Holmen is not the first — and he won’t be the last — recipient of false output that is presented and packaged as truth. 

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