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US tech giant OpenAI on Monday unveiled a ChatGPT tool called “deep research” that can produce detailed reports, as China’s DeepSeek chatbot heats up competition in the artificial intelligence field.

The company made the announcement in Tokyo, where OpenAI chief Sam Altman also trumpeted a new joint venture with tech investor SoftBank Group to offer advanced artificial intelligence services to businesses.

AI newcomer DeepSeek has sent Silicon Valley into a frenzy, with some calling its high performance and supposed low cost a wake-up call for US developers.

Current societal challenges exceed the capacity of humans operating either alone or collectively. As AI evolves, its role within human collectives will vary from an assistive tool to a participatory member. Humans and AI possess complementary capabilities that, together, can surpass the collective intelligence of either humans or AI in isolation. However, the interactions in human-AI systems are inherently complex, involving intricate processes and interdependencies. This review incorporates perspectives from complex network science to conceptualize a multilayer representation of human-AI collective intelligence, comprising cognition, physical, and information layers.

Understanding how the complex connectivity structure of the brain shapes its information-processing capabilities is a long-standing question. By focusing on a paradigmatic architecture, we study how the neural activity of excitatory and inhibitory populations encodes information on external signals. We show that at long times information is maximized at the edge of stability, where inhibition balances excitation, both in linear and nonlinear regimes. In the presence of multiple external signals, this maximum corresponds to the entropy of the input dynamics. By analyzing the case of a prolonged stimulus, we find that stronger inhibition is instead needed to maximize the instantaneous sensitivity, revealing an intrinsic tradeoff between short-time responses and long-time accuracy.

On the persistent mischaracterization of Google and Facebook A/B tests: How to conduct and report online platform studies.


Users of social media platforms like Facebook, Instagram and TikTok might think they’re simply interacting with friends, family and followers, and seeing ads as they go. But according to research from the UBC Sauder School of Business, they’re part of constant marketing experiments that are often impossible, even for the companies behind them, to fully comprehend. The findings are published in the International Journal of Research in Marketing.

For the study, the researchers examined all known published, peer-reviewed studies of the use of A/B testing by Facebook and Google—that is, when different consumers are shown different ads to determine which are most effective—and uncovered significant flaws.

UBC Sauder Associate Professors and study co-authors Dr. Yann Cornil and Dr. David Hardisty say that at any given moment, billions of social media users are being tested to see what they click on, and most importantly for marketers, what they buy. From that, one would think advertisers could tell which messages are effective and which aren’t—but it turns out it isn’t nearly that simple.

AI models often rely on “spurious correlations,” making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem.

“This technique is novel in that it can be used even when you have no idea what spurious correlations the AI is relying on,” says Jung-Eun Kim, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University.

“If you already have a good idea of what the spurious features are, our technique is an efficient and effective way to address the problem. However, even if you are simply having performance issues, but don’t understand why, you could still use our technique to determine whether a spurious correlation exists and resolve that issue.”

Applying tissue maturation techniques to engineered cartilage grafts produces more functionally faithful grafts and leads to superior clinical outcomes in patients with knee cartilage injuries, shows a new multicenter clinical trial.

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Engineered hyaline-like cartilage tissues are superior to immature cell-based grafts for the therapy of cartilage defects in the human knee.

Scientists have now cracked this secret using computational simulations and lab experiments, paving the way for bioengineered silk with game-changing applications, from medical sutures to ultra-strong body armor.

Spiders Strengthen Their Silk with Stretching

When spiders spin their webs, they use their hind legs to pull silk from their spinnerets. This pulling action does more than just release the silk—it strengthens the fibers, making the web more durable.

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Hello and welcome! My name is Anton and in this video, we will talk about bitterness receptors on our skin.
Links:
https://faseb.onlinelibrary.wiley.com/doi/10.1096/fba.2024-00074
https://gut.bmj.com/content/63/1/179
https://www.frontiersin.org/journals/nutrition/articles/10.3…15889/full.
Other skin discoveries: https://youtu.be/8CRH-rYNleo.
#bitter #taste #biology.

0:00 Bitterness receptors on our skin! But why?
1:25 Taste receptors and 5 tastes.
2:30 Animal differences and why bitterness receptors vary so much.
4:00 Why humans are losing bitterness receptors.
5:10 Other effects of bitterness receptors.
6:15 Skin receptors?
6:45 New study and evidence for toxicity hypothesis.
8:20 Conclusions and what this means.

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