Lifeboat Foundation

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.

Stephen Hawking and Jacob Bekenstein calculated the entropy of a black hole in the 1970s, but it took physicists until now to figure out the quantum effects that make the formula work.

By Leah Crane

The performance of numerous cutting-edge technologies, from lithium-ion batteries to the next wave of superconductors, hinges on a physical characteristic called intercalation. Predicting which intercalated materials will be stable poses a significant challenge, leading to extensive trial-and-error experimentation in the development of new products.

Now, in a study recently published in ACS Physical Chemistry Au, researchers from the Institute of Industrial Science, The University of Tokyo, and collaborating partners have devised a straightforward equation that correctly predicts the stability of intercalated materials. The systematic design guidelines enabled by this work will speed up the development of upcoming high-performance electronics and energy-storage devices.

USA: Using an electronic health record (EHR)-based algorithm plus practice facilitators embedded in primary care clinics did not reduce hospitalization at one year, according to a pragmatic trial involving patients with the triad of chronic kidney disease, hypertension, and type 2 diabetes.

“The hospitalization rate of patients in the intervention group at one year was about the same as that with usual care (20.7% vs 21.1%),” the researchers reported in the ICD-Pieces study published in the New England Journal of Medicine.

Patients with chronic kidney disease (CKD), type 2 diabetes (T2D), and hypertension (the kidney-dysfunction triad) are at high risk for multiple complications, end-stage kidney disease, and premature death. Despite the availability of effective therapies for these patients, there is a lack of results of large-scale trials examining the implementation of guideline-directed therapy to reduce death and complications risk in this population.

A theory of consciousness should capture its phenomenology, characterize its ontological status and extent, explain its causal structure and genesis, and describe its function. Here, I advance the notion that consciousness is best understood as an operator, in the sense of a physically implemented transition function that is acting on a representational substrate and controls its temporal evolution, and as such has no identity as an object or thing, but (like software running on a digital computer) it can be characterized as a law. Starting from the observation that biological information processing in multicellular substrates is based on self organization, I explore the conjecture that the functionality of consciousness represents the simplest algorithm that is discoverable by such substrates, and can impose function approximation via increasing representational coherence. I describe some properties of this operator, both with the goal of recovering the phenomenology of consciousness, and to get closer to a specification that would allow recreating it in computational simulations.

In science fiction, holograms are used for anything from basic communications to advanced military weaponry. In the real world, 3D holographic displays have yet to break through to everyday products and devices. That’s because creating holograms that look real and have significant fidelity requires laser emitters or other advanced pieces of optical equipment. This situation has stymied commercial development, as these components are complex and expensive.

More recently, research scientists were able to create realistic 3D holographic images without lasers by using a white chip-on-board light-emitting diode. Unfortunately, that method required two spatial light modulators to control the wave fronts of the emitted light, adding a prohibitive amount of complexity and cost.

Now, those same scientists say they have created a simpler, more cost-effective way to create realistic-looking 3D holographic displays using only one spatial light modulator and new software algorithms. The result is a simpler and cheaper method for creating holograms that an everyday technology like a smartphone screen can emit.