The co-founder of website builder Wix is embracing generative AI, and he’s not too worried that it might destroy the business models of the web.
Wix CEO Avishai Abrahami is embracing generative AI and thinks the business models of the web can survive it.
AI agents are what they ingest. Rather than scraping the internet, it would be better to confine their diets to books and encyclopedias, says Sorin Adam Matei.
Elon Musk is hyping the imminent release of his ChatGPT competitor Grok, yet another example of how his entire personality is just itself a biological LLM made by ingesting all of Reddit and 4chan. Grok already seems patterned in many ways off of the worst of Elon’s indulgences, with the sense of humor of a desperately unfunny and regressive internet troll, and biases informed by a man whose horrible, dangerous biases are fully invisible to himself.
There are all kinds of reasons to be wary of Grok, including the standard reasons to be wary of any current LLM-based AI technology, like hallucinations and inaccuracies. Layer on Elon Musk’s recent track record for disastrous social sensitivity and generally harmful approach to world-shaping issues, and we’re already looking at even more reason for concern. But the real risk probably isn’t yet so easy to grok, just because we have little understanding yet of the extent of the impact that widespread use of LLMs across our daily and online lives will have.
One key area where they’re already having and are bound to have much more of an impact is user-generated content. We’ve seen companies already deploying first-party integrations that start to embrace some of these uses, like Artifact with its AI-generated thumbnails for shared posts, and Meta adding chatbots to basically everything. Musk is debuting Grok on X as a feature reserved for Premium+ subscribers initially, with a rollout supposedly beginning this week.
Though SpaceX has yet to confirm, navigational warnings indicate a 4½-hour launch window will open from 11 p.m. Monday to 3:31 a.m. EST Tuesday.
Why it matters: While AI algorithms are seemingly everywhere, processing on the most popular platforms require powerful server GPUs to provide customers with their generative services. Arm is introducing a new dedicated chip design, set to provide AI acceleration even in the most affordable IoT devices starting next year.
The Arm Cortex-M52 is the smallest and most cost-efficient processor designed for AI acceleration applications, according to the company. This latest design from the UK-based fabless firm promises to deliver “enhanced” AI capabilities to Internet of Things (IoT) devices, as Arm states, without the need for a separate computing unit.
Paul Williamson, Arm’s SVP and general manager for the company’s IoT business, emphasized the need to bring machine learning optimized processing to “even the smallest and lowest-power” endpoint devices to fully realize the potential of AI in IoT. Despite AI’s ubiquity, Williamson noted, harnessing the “intelligence” from the vast amounts of data flowing through digital devices requires IoT appliances that are smarter and more capable.
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Biological computing machines, such as micro and nano-implants that can collect important information inside the human body, are transforming medicine. Yet, networking them for communication has proven challenging. Now, a global team, including EPFL researchers, has developed a protocol that enables a molecular network with multiple transmitters.
First, there was the Internet of Things (IoT) and now, at the interface of computer science and biology, the Internet of Bio-Nano Things (IoBNT) promises to revolutionize medicine and health care. The IoBNT refers to biosensors that collect and process data, nano-scale Labs-on-a-Chip that run medical tests inside the body, the use of bacteria to design biological nano-machines that can detect pathogens, and nano-robots that swim through the bloodstream to perform targeted drug delivery and treatment.
“Overall, this is a very, very exciting research field,” explained Assistant Professor Haitham Al Hassanieh, head of the Laboratory of Sensing and Networking Systems in EPFL’s School of Computer and Communication Sciences (IC). “With advances in bio-engineering, synthetic biology, and nanotechnology, the idea is that nano-biosensors will revolutionize medicine because they can reach places and do things that current devices or larger implants can’t,” he continued.
What role should text-generating large language models (LLMs) have in the scientific research process? According to a team of Oxford scientists, the answer — at least for now — is: pretty much none.
In a new essay, researchers from the Oxford Internet Institute argue that scientists should abstain from using LLM-powered tools like chatbots to assist in scientific research on the grounds that AI’s penchant for hallucinating and fabricating facts, combined with the human tendency to anthropomorphize the human-mimicking word engines, could lead to larger information breakdowns — a fate that could ultimately threaten the fabric of science itself.
“Our tendency to anthropomorphize machines and trust models as human-like truth-tellers, consuming and spreading the bad information that they produce in the process,” the researchers write in the essay, which was published this week in the journal Nature Human Behavior, “is uniquely worrying for the future of science.”
Studies find a densely connected network of neurons that communicate over long distances, rather than across synapses.
It’s an open secret that generative AI is terrible at coming up with original and creative writing.
In many ways, that’s to be expected, given its inherent nature — machine learning systems typically churn through the internet and remix what they’ve gobbled up, often in nonsensical or uninspiring ways.
To remedy the situation, some of Silicon Valley’s biggest AI companies are now resorting to hiring poets and writers with humanity degrees, Rest of World reports, which is an ironic twist, considering publishers have been laying off writers and editors while making big investments in generative AI.
