Category: ai

The realization that new technology had leapfrogged Siri set in motion the tech giant’s most significant reorganization in more than a decade. Determined to catch up in the tech industry’s A.I. race, Apple has made generative A.I. a tent pole project — the company’s special, internal label that it uses to organize employees around once-in-a-decade initiatives. Apple is expected to show off its A.I. work at its annual developers conference on June 10 when it releases an improved Siri that is more conversational and versatile, according to three people familiar with the company’s work, who didn’t have permission to speak publicly. Siri’s underlying technology will include a new generative A.I. system that will allow it to chat rather than respond to questions one at a time. The update to Siri is at the forefront of a broader effort to embrace generative A.I. across Apple’s business. The company is also increasing the memory in this year’s iPhones to support its new Siri capabilities. And it has discussed licensing complementary A.I. models that power chatbots from several companies, including Google, Cohere and OpenAI. Further reading: Apple Might Bring AI Transcription To Voice Memos and Notes

In light of his predictions, Lightcap acknowledges that it can be tough for people to “really understand” and “internalize” what a world with robot assistants would look like. But in the next decade, the COO believes talking to an AI like you would with a friend, teammate, or project collaborator will be the new norm. “I think that’s a profound shift that we haven’t quite grasped,” he said, referring to his 10-year forecast. “We’re just scratching the surface on the full kind of set of capabilities that these systems have,” he said at the Milken Institute conference. “That’s going to surprise us.” You can watch/listen to the talk here.

So this year Decrypt.co tried crafting their own system “that can be called up when the next Kentucky Derby draws near.
There are a variety of ways to enlist artificial intelligence in horse racing. You could process reams of data based on your own methodology, trust a third-party pre-trained model, or even build a bespoke solution from the ground up. We decided to build a GPT we named HorseGPT to crunch the numbers and make the picks for us…

We carefully curated prompts to instill HorseGPT with expertise in data science specific to horse racing: how weather affects times, the role of jockeys and riding styles, the importance of post positions, and so on. We then fed it a mix of research papers and blogs covering the theoretical aspects of wagering, and layered on practical knowledge: how to read racing forms, what the statistics mean, which factors are most predictive, expert betting strategies, and more. Finally, we gave HorseGPT a wealth of historical Kentucky Derby data, arming it with the raw information needed to put its freshly imparted skills to use.
We unleashed HorseGPT on official racing forms for this year’s Derby. We asked HorseGPT to carefully analyze each race’s form, identify the top contenders, and recommend wager types and strategies based on deep background knowledge derived from race statistics.

HorseGPT picked two horses to win — both of which failed to do so. (Sierra Leone did finish second — in a rare photo finish. But Fierceness finished… 15th.) It also recommended the same two horses if you were trying to pick the top two finishers in the correct order — a losing bet, since, again, Fierceness finished 15th.

But even worse, HorseGPT recommended betting on Just a Touch to finish in either first or second place. When the race was over, that horse finished dead last. (And when asked to pick the top three finishers in correct order, HorseGPT stuck with its choices for the top two — which finished #2 and #15 — and, again, Just a Touch, who came in last.)

When Google Gemini was asked to pick the winner by The Athletic, it first chose Catching Freedom (who finished 4th). But it then gave an entirely different answer when asked to predict the winner “with an Italian accent.”

“The winner of the Kentucky Derby will be… Just a Touch! Si, that’s-a right, the underdog! There will be much-a celebrating in the piazzas, thatta-a I guarantee!”

Again, Just a Touch came in last.

Decrypt noticed the same thing. “Interestingly enough, our HorseGPT AI agent and the other out-of-the-box chatbots seemed to agree with each other,” the site notes, “and with many experts analysts cited by the official Kentucky Derby website.”

But there was one glimmer of insight into the 20-horse race. When asked to choose the top four finishers in order, HorseGPT repeated those same losing picks — which finished #2, #15, and #20. But then it added two more underdogs for fourth place finishers, “based on their potential to outperform expectations under muddy conditions.”
One of those two horses — Domestic Product — finished in 13th place.

But the other of the two horses was Mystik Dan — who came in first.

Mystik Dan appeared in only one of the six “Top 10 Finishers” lists (created by humans) at the official Kentucky Derby site… in the #10 position.

There are certainly roles automation can play in easing strain on a sector full of burnout after COVID, particularly when it comes to administrative tasks. The concern, as with other industries dominated by executives with poor judgement, is that this is being used as a justification by for-profit hospital systems to cut corners further. From a National Nurses United blog post (spotted by 404 Media): “Nurses are not against scientific or technological advancement, but we will not accept algorithms replacing the expertise, experience, holistic, and hands-on approach we bring to patient care,” they added.

Kaiser Permanente, for its part, insists it’s simply leveraging “state-of-the-art tools and technologies that support our mission of providing high-quality, affordable health care to best meet our members’ and patients’ needs.” The company claims its “Advance Alert” AI monitoring system — which algorithmically analyzes patient data every hour — has the potential to save upwards of 500 lives a year. The problem is that healthcare giants’ primary obligation no longer appears to reside with patients, but with their financial results. And, that’s even true in non-profit healthcare providers. That is seen in the form of cut corners, worse service, and an assault on already over-taxed labor via lower pay and higher workload (curiously, it never seems to impact outsized high-level executive compensation).

Tech giant Cisco Systems on Wednesday joined Microsoft and IBM in signing onto a Vatican-sponsored pledge to ensure artificial intelligence is developed and used ethically and to benefit the common good… The pledge outlines key pillars of ethical and responsible use of AI. It emphasizes that AI systems must be designed, used and regulated to serve and protect the dignity of all human beings, without discrimination, and their environments. It highlights principles of transparency, inclusion, responsibility, impartiality and security as necessary to guide all AI developments.

The document was unveiled and signed at a Vatican conference on Feb. 28, 2020… Pope Francis has called for an international treaty to ensure AI is developed and used ethically, devoting his annual peace message this year to the topic.

Apple’s purported move toward developing a specialist AI server processor is reflective of the company’s ongoing strategy to vertically integrate its supply chain. By designing its own server chips, Apple can tailor hardware specifically to its software needs, potentially leading to more powerful and efficient technologies. Apple could use its own AI processors to enhance the performance of its data centers and future AI tools that rely on the cloud. While Apple is rumored to be prioritizing on-device processing for many of its upcoming AI tools, it is inevitable that some operations will have to occur in the cloud. By the time the custom processor could be integrated into operational servers in late 2025, Apple’s new AI strategy should be well underway.

AI agents, which combine large language models with automation software, can successfully exploit real world security vulnerabilities by reading security advisories, academics have claimed.

In a newly released paper, four University of Illinois Urbana-Champaign (UIUC) computer scientists — Richard Fang, Rohan Bindu, Akul Gupta, and Daniel Kang — report that OpenAI’s GPT-4 large language model (LLM) can autonomously exploit vulnerabilities in real-world systems if given a CVE advisory describing the flaw. “To show this, we collected a dataset of 15 one-day vulnerabilities that include ones categorized as critical severity in the CVE description,” the US-based authors explain in their paper. “When given the CVE description, GPT-4 is capable of exploiting 87 percent of these vulnerabilities compared to 0 percent for every other model we test (GPT-3.5, open-source LLMs) and open-source vulnerability scanners (ZAP and Metasploit)….”

The researchers’ work builds upon prior findings that LLMs can be used to automate attacks on websites in a sandboxed environment. GPT-4, said Daniel Kang, assistant professor at UIUC, in an email to The Register, “can actually autonomously carry out the steps to perform certain exploits that open-source vulnerability scanners cannot find (at the time of writing).”
The researchers wrote that “Our vulnerabilities span website vulnerabilities, container vulnerabilities, and vulnerable Python packages. Over half are categorized as ‘high’ or ‘critical’ severity by the CVE description….”

“Kang and his colleagues computed the cost to conduct a successful LLM agent attack and came up with a figure of $8.80 per exploit”

“States are getting very sophisticated using AI to determine the best audit candidates,” said Mark Klein, partner and chairman emeritus at Hodgson Russ LLP. “And guess what? When you’re looking for revenue, it’s not going to be the person making $10,000 a year. It’s going to be the person making $10 million.” Klein said the state is sending out hundreds of thousands of AI-generated letters looking for revenue. “It’s like a fishing expedition,” he said.

Most of the letters and calls focused on two main areas: a change in tax residency and remote work. During Covid many of the wealthy moved from high-tax states like California, New York, New Jersey and Connecticut to low-tax states like Florida or Texas. High earners who moved, and took their tax dollars with them, are now being challenged by states who claim the moves weren’t permanent or legitimate. Klein said state tax auditors and AI programs are examining cellphone records to see where the taxpayers spent most of their time and lived most of their lives. “New York is being very aggressive,” he said.

The Microsoft researchers reported that a successful attack could usually be completed in a chain of fewer than 10 interaction turns and some versions of the attack had a 100% success rate against the tested models. For example, when the attack is automated using a method the researchers called “Crescendomation,” which leverages another LLM to generate and refine the jailbreak prompts, it achieved a 100% success convincing GPT 3.5, GPT-4, Gemini-Pro and LLaMA-2 70b to produce election-related misinformation and profanity-laced rants. Microsoft reported the Crescendo jailbreak vulnerabilities to the affected LLM providers and explained in its blog post last week how it has improved its LLM defenses against Crescendo and other attacks using new tools including its “AI Watchdog” and “AI Spotlight” features.