Phil Schiller To Join OpenAI Board In ‘Observer’ Role Following Apple’s ChatGPT Deal

As reported by Bloomberg, Apple will get an “observer role” on OpenAI’s board of directors as part of its partnership to integrate ChatGPT into iOS 18. That role will reportedly be filled by Apple Fellow, Phil Schiller. 9to5Mac reports: Apple having an “observer role” on the OpenAI board matches the role of Microsoft. Schiller will be able to observe and attend board meetings, but will not have any voting power: “The board observer role will put Apple on par with Microsoft, OpenAI’s biggest backer and its main AI technology provider. The job allows someone to attend board meetings without being able to vote or exercise other director powers. Observers, however, do gain insights into how decisions are made at the company.” The arrangement will take effect later this year, according to Bloomberg. Schiller “hasn’t yet attended any meetings” of the OpenAI board and “details of the situation could still change.”

Schiller served as Apple’s long-time marketing chief before transitioning to an Apple Fellow role in 2020. In this role, Schiller continues to lead the App Store and Apple events and reports directly to Apple CEO Tim Cook. Schiller is also leading Apple’s efforts to defend the App Store against antitrust allegations around the world.

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Brazil Data Regulator Bans Meta From Mining Data To Train AI Models

Brazil’s national data protection authority ruled on Tuesday that Meta must stop using data originating in the country to train its artificial intelligence models. The Associated Press reports: Meta’s updated privacy policy enables the company to feed people’s public posts into its AI systems. That practice will not be permitted in Brazil, however. The decision stems from “the imminent risk of serious and irreparable or difficult-to-repair damage to the fundamental rights of the affected data subjects,” the agency said in the nation’s official gazette. […] Hye Jung Han, a Brazil-based researcher for the rights group, said in an email Tuesday that the regulator’s action “helps to protect children from worrying that their personal data, shared with friends and family on Meta’s platforms, might be used to inflict harm back on them in ways that are impossible to anticipate or guard against.”

But the decision regarding Meta will “very likely” encourage other companies to refrain from being transparent in the use of data in the future, said Ronaldo Lemos, of the Institute of Technology and Society of Rio de Janeiro, a think-tank. “Meta was severely punished for being the only one among the Big Tech companies to clearly and in advance notify in its privacy policy that it would use data from its platforms to train artificial intelligence,” he said. Compliance must be demonstrated by the company within five working days from the notification of the decision, and the agency established a daily fine of 50,000 reais ($8,820) for failure to do so. In a statement, Meta said the company is “disappointed” by the decision and insists its method “complies with privacy laws and regulations in Brazil.”

“This is a step backwards for innovation, competition in AI development and further delays bringing the benefits of AI to people in Brazil,” a spokesperson for the company added.

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Alzheimer’s Scientist Indicted For Allegedly Falsifying Data In $16 Million Scheme

“A federal grand jury has indicted an embattled Alzheimer’s researcher for allegedly falsifying data to fraudulently obtain $16 million in federal research funding from the National Institutes of Health for the development of a controversial Alzheimer’s drug and diagnostic test,” writes Beth Mole via Ars Technica. “Wang is charged with one count of major fraud against the United States, two counts of wire fraud, and one count of false statements. If convicted, he faces a maximum penalty of 10 years in prison for the major fraud charge, 20 years in prison for each count of wire fraud, and five years in prison for the count of false statements […].” From the report: Hoau-Yan Wang, 67, a medical professor at the City University of New York, was a paid collaborator with the Austin, Texas-based pharmaceutical company Cassava Sciences. Wang’s research and publications provided scientific underpinnings for Cassava’s Alzheimer’s treatment, Simufilam, which is now in Phase III trials. Simufilam is a small-molecule drug that Cassava claims can restore the structure and function of a scaffolding protein in the brain of people with Alzheimer’s, leading to slowed cognitive decline. But outside researchers have long expressed doubts and concerns about the research.

In 2023, Science magazine obtained a 50-page report from an internal investigation at CUNY that looked into 31 misconduct allegations made against Wang in 2021. According to the report, the investigating committee “found evidence highly suggestive of deliberate scientific misconduct by Wang for 14 of the 31 allegations,” the report states. The allegations largely centered around doctored and fabricated images from Western blotting, an analytical technique used to separate and detect proteins. However, the committee couldn’t conclusively prove the images were falsified “due to the failure of Dr. Wang to provide underlying, original data or research records and the low quality of the published images that had to be examined in their place.” In all, the investigation “revealed long-standing and egregious misconduct in data management and record keeping by Dr. Wang,” and concluded that “the integrity of Dr. Wang’s work remains highly questionable.” The committee also concluded that Cassava’s lead scientist on its Alzheimer’s disease program, Lindsay Burns, who was a frequent co-author with Wang, also likely bears some responsibility for the misconduct.

In March 2022, five of Wang’s articles published in the journal PLOS One were retracted over integrity concerns with images in the papers. Other papers by Wang have also been retracted or had statements of concern attached to them. Further, in September 2022, the Food and Drug Administration conducted an inspection of the analytical work and techniques used by Wang to analyze blood and cerebrospinal fluid from patients in a simufilam trial. The investigation found a slew of egregious problems, which were laid out in a “damning” report (PDF) obtained by Science. In the indictment last week (PDF), federal authorities were explicit about the allegations, claiming that Wang falsified the results of his scientific research to NIH “by, among other things, manipulating data and images of Western blots to artificially add bands [which represent proteins], subtract bands, and change their relative thickness and/or darkness, and then drawing conclusions” based on those false results.

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Anthropic Looks To Fund a New, More Comprehensive Generation of AI Benchmarks

AI firm Anthropic launched a funding program Monday to develop new benchmarks for evaluating AI models, including its chatbot Claude. The initiative will pay third-party organizations to create metrics for assessing advanced AI capabilities. Anthropic aims to “elevate the entire field of AI safety” with this investment, according to its blog. TechCrunch adds: As we’ve highlighted before, AI has a benchmarking problem. The most commonly cited benchmarks for AI today do a poor job of capturing how the average person actually uses the systems being tested. There are also questions as to whether some benchmarks, particularly those released before the dawn of modern generative AI, even measure what they purport to measure, given their age.

The very-high-level, harder-than-it-sounds solution Anthropic is proposing is creating challenging benchmarks with a focus on AI security and societal implications via new tools, infrastructure and methods.

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Microsoft Tells Yet More Customers Their Emails Have Been Stolen

Microsoft revealed that the Russian hackers who breached its systems earlier this year stole more emails than initially reported. “We are continuing notifications to customers who corresponded with Microsoft corporate email accounts that were exfiltrated by the Midnight Blizzard threat actor, and we are providing the customers the email correspondence that was accessed by this actor,” a Microsoft spokesperson told Bloomberg (paywalled). “This is increased detail for customers who have already been notified and also includes new notifications.” The Register reports: We’ve been aware for some time that the digital Russian break-in at the Windows maker saw Kremlin spies make off with source code, executive emails, and sensitive U.S. government data. Reports last week revealed that the issue was even larger than initially believed and additional customers’ data has been stolen. Along with Russia, Microsoft was also compromised by state actors from China not long ago, and that issue similarly led to the theft of emails and other data belonging to senior U.S. government officials.

Both incidents have led experts to call Microsoft a threat to U.S. national security, and president Brad Smith to issue a less-than-reassuring mea culpa to Congress. All the while, the U.S. government has actually invested more in its Microsoft kit. Bloomberg reported that emails being sent to affected Microsoft customers include a link to a secure environment where customers can visit a site to review messages Microsoft identified as having been compromised. But even that might not have been the most security-conscious way to notify folks: Several thought they were being phished.

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Caching Is Key, and SIEVE Is Better Than LRU

USENIX, the long-running OS/networking research group, also publishes a magazine called ;login:. Today the magazine’s editor — security consultant Rik Farrow — stopped by Slashdot to share some new research. rikfarrow writes:
Caching means using faster memory to store frequently requested data, and the most commonly used algorithm for determining which items to discard when the cache is full is Least Recently Used [or “LRU”]. These researchers have come up with a more efficient and scalable method that uses just a few lines of code to convert LRU to SIEVE.
Just like a sieve, it sifts through objects (using a pointer called a “hand”) to “filter out unpopular objects and retain the popular ones,” with popularity based on a single bit that tracks whether a cached object has been visited:

As the “hand” moves from the tail (the oldest object) to the head (the newest object), objects that have not been visited are evicted… During the subsequent rounds of sifting, if objects that survived previous rounds remain popular, they will stay in the cache. In such a case, since most old objects are not evicted, the eviction hand quickly moves past the old popular objects to the queue positions close to the head. This allows newly inserted objects to be quickly assessed and evicted, putting greater eviction pressure on unpopular items (such as “one-hit wonders”) than LRU-based eviction algorithms.
It’s an example of “lazy promotion and quick demotion”. Popular objects get retained with minimal effort, with quick demotion “critical because most objects are not reused before eviction.”

After 1559 traces (of 247,017 million requests to 14,852 million objects), they found SIEVE reduces the miss ratio (when needed data isn’t in the cache) by more than 42% on 10% of the traces with a mean of 21%, when compared to FIFO. (And it was also faster and more scalable than LRU.)

“SIEVE not only achieves better efficiency, higher throughput, and better scalability, but it is also very simple.”

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