Lifeboat Foundation

Artificial intelligence excels at sorting through information and detecting patterns or trends. But these machine learning algorithms need to be trained with large amounts of data first.

As researchers explore potential applications for AI, they have found scenarios where AI could be really useful—such as analyzing X-ray image data to look for evidence of rare conditions or detecting a rare fish species caught on a commercial fishing boat—but there’s not enough data to accurately train the algorithms.

Jenq-Neng Hwang, University of Washington professor of electrical and computer and engineering, specializes in these issues. For example, Hwang and his team developed a method that teaches AI to monitor how many distinct poses a baby can achieve throughout the day. There are limited training datasets of babies, which meant the researchers had to create a unique pipeline to make their algorithm accurate and useful.

In this episode, recorded during the 2024 Abundance360 Summit, Ray, Geoffrey, and Peter debate whether AI will become sentient, what consciousness constitutes, and if AI should have rights.

Ray Kurzweil, an American inventor and futurist, is a pioneer in artificial intelligence. He has contributed significantly to OCR, text-to-speech, and speech recognition technologies. He is the author of numerous books on AI and the future of technology and has received the National Medal of Technology and Innovation, among other honors. At Google, Kurzweil focuses on machine learning and language processing, driving advancements in technology and human potential.

Continue reading “Ray Kurzweil & Geoff Hinton Debate the Future of AI | EP #95” »

Strategy could stop an overdose or produce an antidote to a poison.

Scientists have created a method to produce 3D full-color holographic images using smartphone screens instead of lasers. This innovative technique, with additional advancements, holds the potential for augmented or virtual reality displays.

Whether augmented and virtual reality displays are being used for gaming, education, or other applications, incorporating 3D displays can create a more realistic and interactive user experience.

“Although holography techniques can create a very real-looking 3D representation of objects, traditional approaches aren’t practical because they rely on laser sources,” said research team leader Ryoichi Horisaki, from The University of Tokyo in Japan. “Lasers emit coherent light that is easy to control, but they make the system complex, expensive, and potentially harmful to the eyes.”

Carnegie Mellon University (CMU) researchers have developed H₂O – Human2HumanOid – a reinforcement learning-based framework that allows a full-sized humanoid robot to be teleoperated by a human in real-time using only an RGB camera. Which begs the question: will manual labor soon be performed remotely?

A teleoperated humanoid robot allows for the performance of complex tasks that are – at least at this stage – too complex for a robot to perform independently. But achieving whole-body control of human-sized humanoids to replicate our movements in real-time is a challenging task. That’s where reinforcement learning (RL) comes in.

Continue reading “How blue-collar workers will train the humanoids that take their jobs” »

Language models often need more exposure to fruitful mistakes during training, hindering their ability to anticipate consequences beyond the next token. LMs must improve their capacity for complex decision-making, planning, and reasoning. Transformer-based models struggle with planning due to error snowballing and difficulty in lookahead tasks. While some efforts have integrated symbolic search algorithms to address these issues, they merely supplement language models during inference. Yet, enabling language models to search for training could facilitate self-improvement, fostering more adaptable strategies to tackle challenges like error compounding and look-ahead tasks.

Researchers from Stanford University, MIT, and Harvey Mudd have devised a method to teach language models how to search and backtrack by representing the search process as a serialized string, Stream of Search (SoS). They proposed a unified language for search, demonstrated through the game of Countdown. Pretraining a transformer-based language model on streams of search increased accuracy by 25%, while further finetuning with policy improvement methods led to solving 36% of previously unsolved problems. This showcases that language models can learn to solve problems via search, self-improve, and discover new strategies autonomously.

Recent studies integrate language models into search and planning systems, employing them to generate and assess potential actions or states. These methods utilize symbolic search algorithms like BFS or DFS for exploration strategy. However, LMs primarily serve for inference, needing improved reasoning ability. Conversely, in-context demonstrations illustrate search procedures using language, enabling the LM to conduct tree searches accordingly. Yet, these methods are limited by the demonstrated procedures. Process supervision involves training an external verifier model to provide detailed feedback for LM training, outperforming outcome supervision but requiring extensive labeled data.

A newly developed AI method can calculate a fundamental problem in quantum chemistry: Schrödinger’s Equation. The technique could calculate the ground state of the Schrödinger equation in quantum chemistry.

Predicting molecules’ chemical and physical properties by relying on their atoms’ arrangement in space is the main goal of quantum chemistry. This can be achieved by solving the Schrödinger equation, but in practice, this is extremely difficult.

In our current age of artificial intelligence, computers can generate their own “art” by way of diffusion models, iteratively adding structure to a noisy initial state until a clear image or video emerges.

Diffusion models have suddenly grabbed a seat at everyone’s table: Enter a few words and experience instantaneous, dopamine-spiking dreamscapes at the intersection of reality and fantasy. Behind the scenes, it involves a complex, time-intensive process requiring numerous iterations for the algorithm to perfect the image.

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have introduced a new framework that simplifies the multi-step process of traditional diffusion models into a single step, addressing previous limitations. This is done through a type of teacher-student model: teaching a new computer model to mimic the behavior of more complicated, original models that generate images.

In the shadows of the digital age, a quiet revolution unfolds, reshaping the landscape of work with every passing moment. Artificial intelligence (AI), once the fodder of science fiction and speculative thought, now infiltrates every facet of our professional lives, often in ways so subtle that its impact goes unnoticed until it’s too late. This silent shift sees AI not just complementing human efforts but outright replacing them, leaving a trail of obsolescence in its wake. Thus, let’s delve into the stark realities of AI’s encroachment on human jobs, exploring the future landscape of employment and the duality of its impact, through a lens that does not shy away from the grim nuances of this transition.

Across industries, AI’s efficiency, relentless work ethic, and precision have made it an irresistible choice for employers. From manufacturing lines where robotic arms assemble products with inhuman speed and accuracy, to sophisticated algorithms that manage stock portfolios, outperforming their human counterparts, the signs are clear. AI doesn’t just work alongside humans; it often works instead of them. The adoption of AI in tasks ranging from customer service bots handling inquiries with unsettling empathy, to AI-driven analytics predicting market trends with eerie accuracy, showcases a reality where human involvement becomes increasingly redundant.

As AI continues to evolve, the future of human employment navigates a precarious path. On one hand, new realms of jobs and careers will emerge, focusing on managing, enhancing, and leveraging AI technologies. On the other, the specter of widespread job displacement looms large, a testament to the inexorable march of progress that waits for no one.