The research, which was published today in Nature Communications, is a joint effort by experts from the University of Sydney and the University of California at Los Angeles.
The artificial brain is made of nanowire networks, tiny wires a billion times smaller than a meter. The cables form random patterns that look like the game ‘Pick Up Sticks,’ but they also act like the neural networks in our brains. These networks can process information in different ways.
The biggest companies in AI aren’t interested in paying to use copyrighted material as training data, and here are their reasons why.
The US Copyright Office is taking public comment on potential new rules around generative AI’s use of copyrighted materials, and the biggest AI companies in the world had plenty to say. We’ve collected the arguments from Meta, Google, Microsoft, Adobe, Hugging Face, StabilityAI, and Anthropic below, as well as a response from Apple that focused on copyrighting AI-written code.
There are some differences in their approaches, but the overall message for most is the same: They don’t think they should have to pay to train AI models on copyrighted work.
The Copyright… More
Most argue training with copyrighted data is fair use.
‘Prompt injection’ attacks haven’t caused giant problems yet. But it’s a matter of time, researchers say.
Imagine a chatbot is applying for a job as your personal assistant. The pros: This chatbot is powered by a cutting-edge large language model. It can write your emails, search your files, summarize websites and converse with you.
The con: It will take orders from absolutely anyone.
AI chatbots are good at many things, but they struggle to tell the difference between legitimate commands from their users and manipulative commands from outsiders. It’s an AI Achilles’ heel, cybersecurity researchers say, and it’s a matter of time before attackers take advantage of it.
“Prompt injection” is a major risk to large language models and the chatbots they power. Here’s how the attack works, examples and potential fallout.
Scientists showcased the application of machine learning in the sodium-cooled fast reactor (SFR).
Machine learning technology has the potential to transform nuclear reactor operations, according to a team of experts from the US Department of Energy’s Argonne National Laboratory, who demonstrated how it may improve security and efficiency.
They showcased the application of machine learning in the sodium-cooled fast reactor (SFR), a specialized cutting-edge nuclear reactor.
Link :- Will AI be the source of new employment or the end of more traditional roles? A new study seeks to answer this one crucial question.
Demaerre/iStock.
AI-powered automation has the ability to take over routine, repetitive, and manual processes. Consequently, employment in sectors such as manufacturing, data entry and customer service may be vulnerable with workers in these industries losing their jobs.
Leaks show that OpenAI will be unveiling major updates to ChatGPT at its developer conference on 6 November. These include custom chatbots, a business subscription, and connections to Google and Microsoft.
OpenAI’s first-ever developer conference will take place on the 6th of November, where the company plans to unveil a number of updates. Leaks now show that these will include a new interface for ChatGPT as well as completely new features.
OpenAI introduces custom chatbots via Gizmo.
With “Grok”, Elon Musk introduces a chatbot built with “X” data for “X” premium users. In contrast to OpenAI with ChatGPT, Musk gives the chatbot more creative leeway in its responses.
Musk and his company describe Grok as a humorous, witty, and rebellious chatbot that can answer almost any question. Grok uses its model knowledge based on Internet and X data, as well as real-time information from X, to provide answers. According to xAI, the chatbot also answers “spicy questions” that would be rejected by most other AI systems.
Researchers present RoboGen, a generative robotic agent that automatically learns new skills in a generative simulation.
The work by researchers from CMU, Tsinghua IIIS, MIT CSAIL, UMass Amherst, and the MIT-IBM AI Lab aims to leverage recent advances in generative AI to generate infinite training data for automated robot learning.
According to the team, RoboGen is a generative robotic agent that learns various robotic tasks automatically and en masse through generative simulation. The team is using existing foundation models, such as OpenAI’s GPT-4, to “automatically generate diversified tasks, scenes, and training supervisions, thereby scaling up robotic skill learning with minimal human supervision.”
