The AAN’s AI chatbot toolkit provides practical guidance on utilizing AI-powered chatbots to enhance patient access to neurological care, including implementation strategies, safety considerations, and real-world applications.
Category: robotics/AI
Practical changes could reduce AI energy demand by up to 90%
Artificial intelligence (AI) can be made more sustainable by making practical changes, such as reducing the number of decimal places used in AI models, shortening responses, and using smaller AI models, according to research from UCL published in a new UNESCO report.
In recent years, the use of generative AI has expanded rapidly, with large language models (LLMs) developed by companies such as OpenAI, Meta and Google becoming household names. For example, OpenAI’s ChatGPT service, powered by the GPT-4 LLM, receives about 1 billion queries each day.
Each generation of LLMs has become more sophisticated than the last, better able to perform tasks like text generation or knowledge retrieval. This has led to a vast and increasing demand on resources such as electricity and water, which are needed to run the data centers where these AI models are trained and deployed.
AI Does Something Subtly Bizarre If You Make Typos While Talking to It
New research suggests that medical AI chatbots are woefully unreliable at understanding how people actually communicate their health problems.
As detailed in yet-to-be-peer-reviewed study presented last month by MIT researchers, an AI chatbot is more likely to advise a patient not to seek medical care if their messages contained typos. The errors AI is susceptible to can be as seemingly inconsequential as an extra space between words, or if the patient used slang or colorful language. And strikingly, women are disproportionately affected by this, being wrongly told not to see a doctor at a higher rate than men.
People Are Rizzing on Tinder Using ChatGPT, Then Showing Up to Dates Completely Tongue-Tied
And with the advent of generative AI, that bleak landscape of modern dating is continuing to evolve in dystopian — and perhaps predictable — ways.
As the Washington Post reports, a 31-year-old named Richard Wilson was startled when his date “had none of the conversational pizzazz she had shown over text.”
Her messages had included “long, multi-paragraph messages” and acknowledgments of “each of his points.” But in person she lacked those conversational chops, and when she mentioned that she used ChatGPT “all the time” for work, the pieces started to fall into place for Wilson.
Real-time trial shows AI could speed cancer care
A new study by researchers at the Icahn School of Medicine at Mount Sinai, Memorial Sloan Kettering Cancer Center, and collaborators, suggests that artificial intelligence (AI) could significantly improve how doctors determine the best treatment for cancer patients—by enhancing how tumor samples are analyzed in the lab.
The findings, published in Nature Medicine, showed that AI can accurately predict genetic mutations from routine pathology slides—potentially reducing the need for rapid genetic testing in certain cases.
The paper is titled “Enhancing Clinical Genomics in Lung Adenocarcinoma with Real-World Deployment of a Fine-Tuned Computational Pathology Foundation Model.”
What It’s Like Using a Brain Implant With ChatGPT
The potential of chat gpt and neural link is limitless. Really chat gpt with agi would automate even an entire world and even do all work by itself basically taking the forever mental labor of work forever scenario away from humans so we can sit and drink tea or other leisure activities. Then if we miniaturize even chat gpt, neural link, and agi all in one whether it is in the neural link or even on a smartphone it could allow for near infinite money 💵 with little to no effort which takes away mental labor forever because we could solve anything or do all jobs with no need for even training it would be like an everything calculator for an eternity of work so no humans need suffer the dole of forever mental labor which can evolve earths civilization into complete abundance.
We spoke to two people pioneering ChatGPT’s integration with Synchron’s brain-computer-interface to learn what it’s like to use and where this technology is headed.
Read more on CNET: How This Brain Implant Is Using ChatGPT https://bit.ly/3y5lFkD
0:00 Intro.
0:25 Meet Trial Participant Mark.
0:48 What Synchron’s BCI is for.
1:25 What it’s like to use.
1:51 Why work with ChatGPT?
3:05 How Synchron’s BCI works.
3:46 Synchron’s next steps.
4:27 Final Thoughts.
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The rise of ‘wetware’: the strange world of fungi-computers
Explore the potential of “mushroom computers,” a fascinating technology that uses fungi to solve complex problems in AI, optimization, and more.
Spin as an input parameter: Machine learning predicts magnetic properties of materials
Magnetic materials are in high demand. They’re essential to the energy storage innovations on which electrification depends and to the robotics systems powering automation. They’re also inside more familiar products, from consumer electronics to magnetic resonance imaging (MRI) machines.
Current sources and supply chains won’t be able to keep up as demand continues to grow. We need to design new magnetic materials, and quickly.
A collaboration between Carnegie Mellon University, Lawrence Berkeley National Laboratory, and the Fritz-Haber-Institut der Max-Planck-Gesellschaft is broadening capabilities to screen potential new materials with machine learning models.
Hybrid model reveals people act less rationally in complex games, more predictably in simple ones
Throughout their everyday lives, humans are typically required to make a wide range of decisions, which can impact their well-being, health, social connections, and finances. Understanding the human decision-making processes is a key objective of many behavioral science studies, as this could in turn help to devise interventions aimed at encouraging people to make better choices.
Researchers at Princeton University, Boston University and other institutes used machine learning to predict the strategic decisions of humans in various games. Their paper, published in Nature Human Behavior, shows that a deep neural network trained on human decisions could predict the strategic choices of players with high levels of accuracy.
“Our main motivation is to use modern computational tools to uncover the cognitive mechanisms that drive how people behave in strategic situations,” Jian-Qiao Zhu, first author of the paper, told Phys.org.