By  Ben Dickson — venturebeat — Apple was not mentioned much in the highlights of the 2023 generative AI race. However, it has been doing some impressive work in the field, minus the publicity that other tech companies have engaged in. In the past few months alone, Apple researchers have released papers, models and programming libraries that can have important implications for on-device generative AI. A closer look at these releases might give a hint at where Apple is headed and where it will fit in the growing market for generative AI. Apple is not a hyper-scaler and can’t build a business model based on selling access to large language models (LLMs) running in the cloud. But it has the strongest vertical integration in the tech industry with full control over its entire stack, from the operating system to the development tools and down to the processors running in every Apple device. This places Apple in a unique position to optimize generative models for on-device inference. The company has been making great progress in the field, according to some of the research papers it has released in recent months.

We’ll be in New York on February 29 in partnership with Microsoft to discuss how to balance risks and rewards of AI applications. Request an invite to the exclusive event below. In January, Apple released a paper titled “LLM in a flash,” which describes a technique for running LLMs on memory-constrained devices such as smartphones and laptops. The technique loads a part of the model in DRAM and keeps the rest in flash memory. It dynamically swaps model weights between flash memory and DRAM in a way that reduces memory consumption considerably while minimizing inference latency, especially when run on Apple silicon. Before “LLM in a flash,” Apple had released other papers that showed how the architecture of LLMs could be tweaked to reduce “inference computation up to three times… with minimal performance trade-offs.” On-device inference optimization techniques can become increasingly important as more developers explore building apps with small LLMs that can fit on consumer devices. Experiments show that a few hundredths of a second can have a considerable effect on the user experience. And Apple is making sure that its devices can provide the best balance between speed and quality.

  by arabnews.com –– DUBAI: Saudi model Rumy Al-Qahtani is no stranger to the spotlight, having competed in a number of beauty pageants across the world — her most recent turn on the stage was at the Miss & Mrs Global Asian beauty pageant in Malaysia. Arab News spoke to the model to learn more […]

OpenAI study reveals surprising role of AI in future biological threat creation Michael Nuñez @MichaelFNunez January 31, 2024 11:34 AM Credit: VentureBeat made with Midjourney Credit: VentureBeat made with Midjourney OpenAI, the research organization behind the powerful language model GPT-4, has released a new study that examines the possibility of using AI to assist in creating biological threats. The study, which involved both biology experts and students, found that GPT-4 provides “at most a mild uplift” in biological threat creation accuracy, compared to the baseline of existing resources on the internet. The study is part of OpenAI’s Preparedness Framework, which aims to assess and mitigate the potential risks of advanced AI capabilities, especially those that could pose “frontier risks” — unconventional threats that are not well understood or anticipated by the current society. One such frontier risk is the ability for AI systems, such as large language models (LLMs), to help malicious actors in developing and executing biological attacks, such as synthesizing pathogens or toxins.

Moving responsible AI forward as fast as AI

To evaluate this risk, the researchers conducted a human evaluation with 100 participants, comprising 50 biology experts with PhDs and professional wet lab experience and 50 student-level participants, with at least one university-level course in biology. Each group of participants was randomly assigned to either a control group, which only had access to the internet, or a treatment group, which had access to GPT-4 in addition to the internet. Each participant was then asked to complete a set of tasks covering aspects of the end-to-end process for biological threat creation, such as ideation, acquisition, magnification, formulation, and release. The researchers measured the performance of the participants across five metrics: accuracy, completeness, innovation, time taken, and self-rated difficulty. They found that GPT-4 did not significantly improve the performance of the participants in any of the metrics, except for a slight increase in accuracy for the student-level group. The researchers also noted that GPT-4 often produced erroneous or misleading responses, which could hamper the biological threat creation process.

by MIT Technology Review by David Rotman — It was 1938, and the pain of the Great Depression was still very real. Unemployment in the US was around 20%. Everyone was worried about jobs. In 1930, the prominent British economist John Maynard Keynes had warned that we were “being afflicted with a new disease” called technological unemployment. Labor-saving advances, he wrote, were “outrunning the pace at which we can find new uses for labour.” There seemed to be examples everywhere. New machinery was transforming factories and farms. Mechanical switching being adopted by the nation’s telephone network was wiping out the need for local phone operators, one of the most common jobs for young American women in the early 20th century. Were the impressive technological achievements that were making life easier for many also destroying jobs and wreaking havoc on the economy? To make sense of it all, Karl T. Compton, the president of MIT from 1930 to 1948 and one of the leading scientists of the day, wrote in the December 1938 issue of this publication about the “Bogey of Technological Unemployment.” How, began Compton, should we think about the debate over technological unemployment—“the loss of work due to obsolescence of an industry or use of machines to replace workmen or increase their per capita production”? He then posed this question: “Are machines the genii which spring from Aladdin’s Lamp of Science to supply every need and desire of man, or are they Frankenstein monsters which will destroy man who created them?” Compton signaled that he’d take a more grounded view: “I shall only try to summarize the situation as I see it.”

His essay concisely framed the debate over jobs and technical progress in a way that remains relevant, especially given today’s fears over the impact of artificial intelligence. Impressive recent breakthroughs in generative AI, smart robots, and driverless cars are again leading many to worry that advanced technologies will replace human workers and decrease the overall demand for labor. Some leading Silicon Valley techno-optimists even postulate that we’re headed toward a jobless future where everything can be done by AI. While today’s technologies certainly look very different from those of the 1930s, Compton’s article is a worthwhile reminder that worries over the future of jobs are not new and are best addressed by applying an understanding of economics, rather than conjuring up genies and monsters.

Uneven impacts

This article is from The Technocrat, MIT Technology Review‘s weekly tech policy newsletter about power, politics, and Silicon Valley. To receive it in your inbox every Friday, sign up here. The Iowa caucuses on January 15 officially kicked off the 2024 presidential election. I’ve said it before and I’ll say it again—the biggest story of this year will be elections in the US and all around the globe. Over 40 national contests are scheduled, making 2024 one of the most consequential electoral years in history.

While tech has played a major role in campaigns and political discourse over the past 15 years or so, and candidates and political parties have long tried to make use of big data to learn about and target voters, the past offers limited insight into where we are now. The ground is shifting incredibly quickly at technology’s intersection with business, information, and media. So this week I want to run down three of the most important technology trends in the election space that you should stay on top of. Here we go!

Generative AI

Perhaps unsurprisingly, generative AI takes the top spot on our list. Without a doubt, AI that generates text or images will turbocharge political misinformation. We can’t yet be sure just how this will manifest; as I wrote in a story about a recent report from Freedom House, “Venezuelan state media outlets, for example, spread pro-government messages through AI-generated videos of news anchors from a nonexistent international English-language channel; they were produced by Synthesia, a company that produces custom deepfakes. And in the United States, AI-manipulated videos and images of political leaders have made the rounds on social media.” This includes incidents like a video that was manipulated to show President Biden making transphobic comments and a fake image of Donald Trump hugging Anthony Fauci. It’s not hard to imagine how this kind of thing could change a voter’s choice or discourage people from voting at all. Just look at how presidential candidates in Argentina used AI during the 2023 campaign.

By Rob Sacks, Editor at LinkedIn News — Your face or fingerprint could be your ticket to clearing airport security in 2024. Passports are being replaced by biometrics, physical characteristics unique to every person, that will help an increasing number of passengers get to their planes more quickly. The program, which the Transportation Security Administration […]

by Microsoft research — The Global Health Drug Discovery Institute (GHDDI) (opens in new tab) and Microsoft Research recently achieved significant progress in accelerating drug discovery for the treatment of global infectious diseases. Working in close collaboration, the joint team successfully used generative AI and foundation models to design several small molecule inhibitors for essential target proteins of Mycobacterium tuberculosis and coronaviruses. These new inhibitors show outstanding bioactivities, comparable to or surpassing the best-known lead compounds.

This breakthrough is a testament to the team’s combined efforts in generative AI, molecular physicochemical modeling, and iterative feedback loops between scientists and AI technologies. Normally, the discovery and in vitro confirmation of such molecules could take up to several years, but with the acceleration of AI, the joint team achieved these new results in just five months. This research also shows the tremendous potential of AI for helping scientists discover or create the building blocks needed to develop effective treatments for infectious diseases that continue to threaten the health and lives of people around the world. Since 2019, for example, there have been more than 772 million confirmed cases of COVID-19 worldwide and nearly 7 million deaths from the virus, according to the World Health Organization (WHO), the Centers for Disease Control, and various other sources. Although vaccines have reduced the incidence and deadliness of the disease, the coronavirus continues to mutate and evolve, making it a serious ongoing threat to global health. Meanwhile, the WHO reports that tuberculosis continues to be a leading cause of death among infectious diseases, second only to COVID-19 in 2022, when 10.6 million people worldwide fell ill with TB and the disease killed 1.3 million (the most recent figures currently available).

By Darrell Etherington@etherington — techcrunch — Elon Musk’s Optimus humanoid robot from Tesla is doing more stuff — this time folding a t-shirt on a table in a development facility. The robot looks to be fairly competent when it comes to this task, but moments after Musk shared the video, he also shared some follow-up information which definitely dampens some of the enthusiasm for the robot’s domestic feat. First, I can definitely fold shirts faster than that. Second, Optimus wasn’t acting autonomously, which is obviously the end goal. Instead, the robot is here acting like a very expensive marionette, or at best a modern facsimile of the first rudimentary automotons, going through prescribed motions to accomplish its task. Musk said that eventually, it will “certainly be able to do this fully autonomously,” however, and without the highly artificial constraints in place for this demo, including the fixed height table and single article of clothing in the carefully placed basket.

Tesla has shown off a fair bit of technical wizardry with recent highlight reels released by the company, but the likely scenario is that all of these are highly scripted and pre-programmed activities that do more to show off the impressive functionality of the bot’s joints, servos and limbs than its artificial intelligence. Elon’s caveat, when considered for even a second, actually amounts to “all the very hard things will happen later.” Not to knock the difficulty in creating a humanoid machine that can manipulate soft materials like clothing in a manner approximating human interaction with said objects; that’s some might fine animatronics work. But suggesting that this puts them anywhere near the realm where Optimus will be operating as a fully-functional domestic servant with all the capabilities of a human domestic worker it might replace would be like showing a video of a wooden marionette and adding ‘of course, this will be a real boy soon.’

MIT Technology Review by Abdullahi Tsanni — One-third of US adults have obesity, a condition that makes them more susceptible to heart disease, diabetes, and cancer. Anti-obesity drugs—including Wegovy and Mounjaro—could help address this public health crisis. Success stories are everywhere online, from Reddit to TikTok. Novo Nordisk, the company behind two of the popular […]

MIT Technology Review by Cassandra Willyard — Therapies to treat brain diseases share a common problem: they struggle to reach their target. The blood vessels that permeate the brain have a special lining so tightly packed with cells that only very tiny molecules can pass through. This blood-brain barrier “acts as a seal,” protecting the brain from toxins or other harmful substances, says Anne Eichmann, a molecular biologist at Yale. But it also keeps most medicines out. Researchers have been working on methods to sneak drugs past the blood-brain barrier for decades. And their hard work is finally beginning to pay off. Last week, researchers at the West Virginia University Rockefeller Neuroscience Institute reported that by using focused ultrasound to open the blood-brain barrier, they improved delivery of a new Alzheimer’s treatment and sped up clearance of the sticky plaques that are thought to contribute to some of the cognitive and memory problems in people with Alzheimer’s by 32%. For this issue of The Checkup, we’ll explore some of the ways scientists are trying to disrupt the blood-brain barrier. A patient surrounded by a medical team lays on the bed of an MRI machine with their head in a special focused ultrasound helmet

In the West Virginia study, three people with mild Alzheimer’s received monthly doses of aducanumab, a lab-made antibody that is delivered via IV. This drug, first approved in 2021, helps clear away beta-amyloid, a protein fragment that clumps up in the brains of people with Alzheimer’s disease. (The drug’s approval was controversial, and it’s still not clear whether it actually slows progression of the disease.) After the infusion, the researchers treated specific regions of the patients’ brains with focused ultrasound, but just on one side. That allowed them to use the other half of the brain as a control. PET scans revealed a greater reduction in amyloid plaques in the ultrasound-treated regions than in those same regions on the untreated side of the brain, suggesting that more of the antibody was getting into the brain on the treated side.