A new set of artificial intelligence models could make protein sequencing even more powerful for better understanding cell biology and diseases.
Category: robotics/AI – Page 66
The key to this development is an AI-powered streaming method. By decoding brain signals directly from the motor cortex – the brain’s speech control center – the AI synthesizes audible speech almost instantly.
“Our streaming approach brings the same rapid speech decoding capacity of devices like Alexa and Siri to neuroprostheses,” said Gopala Anumanchipalli, co-principal investigator of the study.
Anumanchipalli added, “Using a similar type of algorithm, we found that we could decode neural data and, for the first time, enable near-synchronous voice streaming. The result is more naturalistic, fluent speech synthesis.”
“The meaningful difference,” argues Silverstein, “comes down to our lifespan. For humans, our mortality defines so much of our experience. If a human commits murder and receives a life sentence, we understand what that means: a finite number of years. But if a UI with an indefinite lifespan commits murder, what do life sentences mean? Are we talking about a regular human lifespan? 300 years? A thousand? Then there’s love and relationships. Let’s say you find your soulmate and spend a thousand years together. At some point, you may decide you had a good run and move on with someone else. The idea of not growing old with someone feels alien and upsetting. But if we were to live hundreds or thousands of years, our perceptions of relationships and identity may change fundamentally.”
“One of the best” because — in addition to having a well-crafted, suspenseful, and heartfelt narrative about love and loss — thoughtfully engages with both the technical and philosophical questions raised by its cerebral premise: Is a perfect digital copy of a person’s mind still meaningfully human? Does uploaded intelligence, which combines the processing power of a supercomputer with the emotional intelligence of a sentient being, have a competitive edge over cold, unfeeling artificial intelligence? How would uploaded intelligence compromise ethics or geopolitical strategy?
“Underrated” because was produced by — and first aired on — AMC+, a streaming service that, owing to the dominance of Netflix, HBO Max, Disney+, and Amazon Prime, has but a fraction of its competitors’ subscribers and which, motivated by losses in ad revenue, ended up canceling the show’s highly anticipated (and fully completed) second season in exchange for tax write-offs. Although has since been salvaged by Netflix, […] its troubled distribution history resulted in the show becoming a bit of a hidden gem, rather than the global hit it could have been, had it premiered on a platform with more eyeballs.
Still, the fact that managed to endure and build a steadily growing cult following is a testament to the show’s quality and cultural relevance. Although the concept of uploaded intelligence is nothing new, and has been tackled by other prominent sci-fi properties like Black Mirror and Altered Carbon, is unique in that it not only explores how this hypothetical technology would affect us on a personal level, but also explores how it might play out on a societal level. Furthermore, take is a nuanced one, rejecting both techno-pessimism and techno-optimism in favor of what series creator Craig Silverstein calls “techno-realism.”
View a PDF of the paper titled Beyond Accuracy: What Matters in Designing Well-Behaved Models?, by Robin Hesse and 4 other authors
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Deep learning has become an essential part of computer vision, with deep neural networks (DNNs) excelling in predictive performance. However, they often fall short in other critical quality dimensions, such as robustness, calibration, or fairness. While existing studies have focused on a subset of these quality dimensions, none have explored a more general form of “well-behavedness” of DNNs. With this work, we address this gap by simultaneously studying nine different quality dimensions for image classification. Through a large-scale study, we provide a bird’s-eye view by analyzing 326 backbone models and how different training paradigms and model architectures affect the quality dimensions. We reveal various new insights such that (i) vision-language models exhibit high fairness on ImageNet-1k classification and strong robustness against domain changes; (ii) self-supervised learning is an effective training paradigm to improve almost all considered quality dimensions; and (iii) the training dataset size is a major driver for most of the quality dimensions. We conclude our study by introducing the QUBA score (Quality Understanding Beyond Accuracy), a novel metric that ranks models across multiple dimensions of quality, enabling tailored recommendations based on specific user needs.
Summary: ChatGPT4 has demonstrated superiority in various student exams, revealing its potential to support academic learning and improve educational outcomes, particularly in test preparation. With its accessibility and affordability compared to traditional tutoring services, AI tutoring can help address the increasing demand for academic support, especially as universities begin to reinstate standardized testing requirements.
In 2023, OpenAI shook the foundation of the education system by releasing ChatGPT4. The previous model of ChatGPT had already disrupted classrooms K–12 and beyond by offering a free academic tool capable of writing essays and answering exam questions. Teachers struggled with the idea that widely accessible artificial intelligence (AI) technology could meet the demands of most traditional classroom work and academic skills. GPT3.5 was far from perfect, though, and lacked creativity, nuance, and reliability. However, reports showed that GPT4 could score better than 90 percent of participants on the bar exam, LSAT, SAT reading and writing and math, and several Advanced Placement (AP) exams. This showed a significant improvement from GPT3.5, which struggled to score as well as 50 percent of participants.
This marked a major shift in the role of AI, from it being an easy way out of busy work to a tool that could improve your chances of getting into college. The US Department of Education published a report noting several areas where AI could support teacher instruction and student learning. Among the top examples was intelligent tutoring systems. Early models of these systems showed that an AI tutor could not only recognize when a student was right or wrong in a mathematical problem but also identify the steps a student took and guide them through an explanation of the process.
AI agents need two things to succeed in this space: infinite scalability and the ability to connect agents from different blockchains. Without the former, agents do not have infrastructure with sufficient capacity to transact. Without the latter, agents would be off on their own island blockchains, unable to truly connect with each other. As agent actions become more complex on chain, more of their data will also have to live on the ledger, making optimizing for both of these factors important right now.
Because of all of this, I believe the next frontier of AI agents on blockchains is in gaming, where their training in immersive worlds will inevitably lead to more agentic behavior crossing over to non-gaming consumer spaces.
If the future of autonomous consumer AI agents sounds scary, it is because we have not yet had a way to independently verify LLM training models or the actions of AI agents so far. Blockchain provides the necessary transparency and transaction security so that this inevitable phenomenon can operate on safer rails. I believe the final home for these AI agents will be Web3.
An extensive report on the future of AI research indicated that there’s skepticism about current approaches to AGI.
To compete in an increasingly complex market, companies will need to unleash distinctive capabilities, reduce low-value work, speed up decision-making, and harness AI and digital.
World’s First Universal Intelligent Human “Tong Tong” 2.0 Released, Expected to Have the Intelligence Level of 6-Year-Old Children Within the Year — ai, artificial intelligence,1ai.net
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March 30, 2012 — At yesterday’s 2025 Zhongguancun Forum At the annual meeting, the Beijing General Artificial Intelligence Research Institute launched theThe world’s first Universal Intelligent Man“complete” 2.0 officially released.
“Tom-Tom” is positioned as a virtual human with autonomous learning, cognitive and decision-making capabilities. Expected to have the intelligence of a 6 year old within this year..