Autistic children showed earlier and less age-related refinement in the brain signals involved in processing faces. A child looks at a face, and within milliseconds the brain begins sorting its features, expression, and identity. Researchers have now traced that process across the entire scalp in one of the most comprehensive studies of face perception in [...]
An AI-assisted counterexample disproves the Jacobian conjecture above two dimensions while leaving its original two-dimensional form open. A formula short enough to fit inside a single post on X has sent a surge of excitement through the mathematical community, just as millions of people were settling down after the FIFA World Cup Final. Levent Alpöge, [...]
By now, you’d be hard pressed to find anyone who isn’t broadly familiar with the AI heavyweights and their flagship models. OpenAI has ChatGPT, Anthropic has Claude, Google has Gemini. But what does Amazon have?
Not a whole lot, it turns out. Unlike its competitors in the tech megacorp scene, Amazon’s standout AI model — organized under the name “Nova” — is about the farthest thing from a household name. And that’s not likely to change anytime soon, because the ecommerce giant is now gutting its in-house AI labs, Bloomberg reports.
Per the outlet, Amazon is scaling back the ambitions of most of its Nova AI models and reorganizing its AI development teams as part of an effort to narrow its focus on “highest priority” goals, after its power-hungry large language models turned out to be a bust.
Accordingly, the company has put most of its AI models on ice, Amazon insiders told Bloomberg. This includes its text-based Nova models, as well as video- and image-generating models, all of which have been placed in a state of “keep the lights on,” meaning they’re technically still supported, but only receive the bare minimum resources needed to sustain them.
Instead, labor and computing power are being diverted toward a singular “frontier-model effort” led by Pieter Abbeel, director of the Berkeley Robot Learning Lab whose robotics company Covariant was hoovered up by Amazon in 2024. At the time Abbeel joined the tech giant, Covariant’s founders were working on AI models for robots, which lines up pretty well with Amazon’s push to automate everything it possibly can.
News of the shift in Amazon’s strategic focus comes a week after it closed one of its key AI offices in San Francisco, an 80-person site specializing in research on artificial general intelligence, the supposed next-level of AI development where the tech obtains human-level intelligence and reasoning skills.
With urgent questions swirling over whether LLMs can ever usher in a return on investment — let alone AGI — it may be that Amazon is jumping out of the AI hype cycle while it still can.
More on Amazon: Amazon’s AI-Generated Animated Series Canceled After Relentless Derision
The post Amazon Is Gutting Its AI Division After Sustained Failure appeared first on Futurism.
© ID 392097269 | Agentic Ai © Tongsupatman | Dreamstime.com
Last week, ChatGPT maker OpenAI issued what seems like a frightening memo about an “unprecedented cyber incident” involving cutting-edge AI models breaking loose and hacking another company.
As the AI lab tells it, a group of next-generation AI models — including one that hasn’t even been disclosed to the public yet — had busted out of their secure testing environment and into a production database owned by the machine learning startup Hugging Face.
“It’s quite mind-blowing that all of this happened autonomously!” Hugging Face CEO Clement Delangue tweeted, seemingly backing the story. “The investigation is ongoing, and we’ll share more learnings from what might be the first incident of its kind!”
Mainstream press took the industry’s story and ran with it, issuing a flurry of anxious dispatches fretting about dangerous AI. One CBS headline called it “very alarming,” while NBC fretted that it was “just the start” of an ominous trend.
Look beyond the alarmist headlines, however, and you’ll find a familiar pattern: a massive tech company using scare tactics to manipulate public opinion.
As assistant professor of computer science at Cornell University John Thickstun observes for the Guardian, this isn’t OpenAI’s first time peddling the “scary AI” narrative. Back in February of 2019 — way before the public release of ChatGPT — the company announced GPT-2, then a cutting-edge large language model. But GPT-2, the company insisted, was too dangerous to release to the dimwitted public, a pronouncement which fed the AI hypebeast and went on to secure OpenAI a cool $1 billion investment from Microsoft.
“This was an early example of a pattern in OpenAI’s communications,” Thickstun explains: “loudly proclaim how dangerous AI is, and investors will hear how powerful it is.”
Whether the tale of OpenAI’s rogue agent stands up to scrutiny remains to be seen, but there are reasons to be skeptical.
If OpenAI’s models really were actually gnawing on the wires of other American companies, it would present a substantial threat to economic activity that any Justice Department worth its salt would want to shut down (maybe not this iteration of the Justice Department, but you get the idea.)
And as AI labs go, OpenAI is notoriously secure both from prying eyes outside and internal threats within its halls. Sure, it’s possible OpenAI has overstated its own internal safeguards as a kind of security theater. But on the flipside, OpenAI is now a major AI contractor for the US Pentagon, a relationship which demands cutting edge security practice.
It likewise follows the broader AI industry’s playbook. Anthropic, the company behind Claude, has deployed similar rhetoric, warning of a similar breakout in which a rogue AI agent escaped its cage all on its lonesome. In one strange instance, the company’s billionaire co-founder Chris Olah even warned the Pope that the Vatican needs to help stop rogue AI from “dominating humanity.”
More on OpenAI: Man Sues OpenAI, Saying ChatGPT Almost Killed Him With Horrendously Dangerous Medical Advice
The post Suspicion Grows About OpenAI’s Tale About Its Rogue Hacker AI appeared first on Futurism.
Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.
One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.
Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.
To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.
The team compiled existing microbial data from 3 public databases: the National Center for Biotechnology Information’s Sequence Read Archive, the Metagenomic Rapid Annotations using Subsystems Technology database, and the Joint Genome Institute’s Integrated Microbial Genomes & Microbiomes database. Their final dataset contained about 2,000 agricultural soil samples from 67 countries across 6 continents. These samples represented a wide range of soil characteristics, climate conditions, and different types of antibiotic resistance genes.
They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios.
After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR).
They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model.
They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.
Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions.
The post Predicting microbial antibiotic resistance with AI appeared first on Sciworthy.
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