We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. Through this interface, humans communicate their intended objects of interest and actions to the robots using electroencephalography (EEG). Our novel system demonstrates success in an expansive array of 20 challenging, everyday household activities, including cooking, cleaning, personal care, and entertainment. The effectiveness of the system is improved by its synergistic integration of robot learning algorithms, allowing for NOIR to adapt to individual users and predict their intentions. Our work enhances the way humans interact with robots, replacing traditional channels of interaction with direct, neural communication. Project website: this https URL. — Read More
Recent Updates Page 148
Generative AI Passes the Legal Ethics Exam in Study by LegalOn Technologies
In a groundbreaking development, researchers at LegalOn Technologies have demonstrated that both OpenAI’s GPT-4 and Anthropic’s Claude 2 can pass the legal ethics exam, a test nearly all US lawyers are required to pass, alongside the bar exam. This milestone underscores the potential for AI to assist lawyers in legal work and demonstrates the increasingly advanced capabilities of large language models applied to law.
Earlier this year, research found that the generative AI model GPT-4 could surpass law students in passing the Uniform Bar Examination. LegalOn’s study extends this discovery, revealing that these models can also navigate complex rules and fact patterns around professional responsibility — Read More
Exclusive poll: AI is already great at faking video and audio, experts say
Nearly every respondent (95%) in a new Axios-Generation Lab-Syracuse University AI Experts Survey described AI’s audio and video deepfake capabilities as “advanced.”
Driving the news: 68% said the capabilities are moderately advanced; 27% said they are highly advanced. — Read More
Domain Adaptation of A Large Language Model
Large language models (LLMs) like BERT are usually pre-trained on general domain corpora like Wikipedia and BookCorpus. If we apply them to more specialized domains like medical, there is often a drop in performance compared to models adapted for those domains.
In this article, we will explore how to adapt a pre-trained LLM like Deberta base to medical domain using the HuggingFace Transformers library. Specifically, we will cover an effective technique called intermediate pre-training where we do further pre-training of the LLM on data from our target domain. This adapts the model to the new domain, and improves its performance.
This is a simple yet effective technique to tune LLMs to your domain and gain significant improvements in downstream task performance. — Read More
Exploring GPTs: ChatGPT in a trench coat?
The biggest announcement from last week’s OpenAI DevDay (and there were a LOT of announcements) was GPTs. Users of ChatGPT Plus can now create their own, custom GPT chat bots that other Plus subscribers can then talk to.
My initial impression of GPTs was that they’re not much more than ChatGPT in a trench coat—a fancy wrapper for standard GPT-4 with some pre-baked prompts.
Now that I’ve spent more time with them I’m beginning to see glimpses of something more than that. The combination of features they provide can add up to some very interesting results. — Read More
500 chatbots read the news and discussed it on social media. Guess how that went.
On a simulated day in July of a 2020 that didn’t happen, 500 chatbots read the news — real news, our news, from the real July 1, 2020. ABC News reported that Alabama students were throwing “COVID parties.” On CNN, President Donald Trump called Black Lives Matter a “symbol of hate.” The New York Times had a story about the baseball season being canceled because of the pandemic.
Then the 500 robots logged into something very much (but not totally) like Twitter, and discussed what they had read. Meanwhile, in our world, the not-simulated world, a bunch of scientists were watching. — Read More
Google DeepMind wants to define what counts as artificial general intelligence
AGI, or artificial general intelligence, is one of the hottest topics in tech today. It’s also one of the most controversial. A big part of the problem is that few people agree on what the term even means. Now a team of Google DeepMind researchers has put out a paper that cuts through the cross talk with not just one new definition for AGI but a whole taxonomy of them.
In broad terms, AGI typically means artificial intelligence that matches (or outmatches) humans on a range of tasks. But specifics about what counts as human-like, what tasks, and how many all tend to get waved away: AGI is AI, but better.
To come up with the new definition, the Google DeepMind team started with prominent existing definitions of AGI and drew out what they believe to be their essential common features.
The team also outlines five ascending levels of AGI: emerging (which in their view includes cutting-edge chatbots like ChatGPT and Bard), competent, expert, virtuoso, and superhuman (performing a wide range of tasks better than all humans, including tasks humans cannot do at all, such as decoding other people’s thoughts, predicting future events, and talking to animals). They note that no level beyond emerging AGI has been achieved. — Read More
Read the Paper
A Coder Considers the Waning Days of the Craft
Ihave always taken it for granted that, just as my parents made sure that I could read and write, I would make sure that my kids could program computers. It is among the newer arts but also among the most essential, and ever more so by the day, encompassing everything from filmmaking to physics. Fluency with code would round out my children’s literacy—and keep them employable. But as I write this my wife is pregnant with our first child, due in about three weeks. I code professionally, but, by the time that child can type, coding as a valuable skill might have faded from the world.
I first began to believe this on a Friday morning this past summer, while working on a small hobby project. A few months back, my friend Ben and I had resolved to create a Times-style crossword puzzle entirely by computer. In 2018, we’d made a Saturday puzzle with the help of software and were surprised by how little we contributed—just applying our taste here and there. Now we would attempt to build a crossword-making program that didn’t require a human touch.
… Something strange started happening. Ben and I would talk about a bit of software we wanted for the project. Then, a shockingly short time later, Ben would deliver it himself. At one point, we wanted a command that would print a hundred random lines from a dictionary file. I thought about the problem for a few minutes, and, when thinking failed, tried Googling. I made some false starts using what I could gather, and while I did my thing—programming—Ben told GPT-4 what he wanted and got code that ran perfectly. — Read More
Ten Ways AI Will Change Democracy
Artificial intelligence will change so many aspects of society, largely in ways that we cannot conceive of yet. Democracy, and the systems of governance that surround it, will be no exception. In this short essay, I want to move beyond the “AI-generated disinformation” trope and speculate on some of the ways AI will change how democracy functions—in both large and small ways.
When I survey how artificial intelligence might upend different aspects of modern society, democracy included, I look at four different dimensions of change: speed, scale, scope, and sophistication. Look for places where changes in degree result in changes of kind. Those are where the societal upheavals will happen.
Some items on my list are still speculative, but none require science-fictional levels of technological advance. And we can see the first stages of many of them today. When reading about the successes and failures of AI systems, it’s important to differentiate between the fundamental limitations of AI as a technology, and the practical limitations of AI systems in the fall of 2023. Advances are happening quickly, and the impossible is becoming the routine. We don’t know how long this will continue, but my bet is on continued major technological advances in the coming years. Which means it’s going to be a wild ride. — Read More
Generative AI will level up cyber attacks, according to new Google report
As technology gets smarter with developments such as generative AI, so do cybersecurity attacks. Google’s new cybersecurity forecast reveals the rise of AI brings new threats you should be aware of.
On Wednesday, Google launched its Google Cloud Cybersecurity Forecast 2024, a report put together through a collaboration with numerous Google Cloud security teams that deep dives into the cyber landscape for the upcoming year. — Read More
Rick's Cafe AI 