Generative artificial intelligence (AI) systems based on large-scale pretrained foundation models (PFMs) such as vision-language models, large language models (LLMs), diffusion models and vision-language-action (VLA) models have demonstrated the ability to solve complex and truly non-trivial AI problems in a wide variety of domains and contexts. Multimodal large language models (MLLMs), in particular, learn from vast and diverse data sources, allowing rich and nuanced representations of the world and, thereby, providing extensive capabilities, including the ability to reason, engage in meaningful dialog; collaborate with humans and other agents to jointly solve complex problems; and understand social and emotional aspects of humans. Despite this impressive feat, the cognitive abilities of state-of-the-art LLMs trained on large-scale datasets are still superficial and brittle. Consequently, generic LLMs are severely limited in their generalist capabilities. A number of foundational problems —embodiment, symbol grounding, causality and memory — are required to be addressed for LLMs to attain human-level general intelligence. These concepts are more aligned with human cognition and provide LLMs with inherent human-like cognitive properties that support the realization of physically-plausible, semantically meaningful, flexible and more generalizable knowledge and intelligence. In this work, we discuss the aforementioned foundational issues and survey state-of-the art approaches for implementing these concepts in LLMs. Specifically, we discuss how the principles of embodiment, symbol grounding, causality and memory can be leveraged toward the attainment of artificial general intelligence (AGI) in an organic manner. — Read More

#human

Neurotech startup Paradromics on Monday announced it has implanted its brain-computer interface in a human for the first time.

The procedure took place May 14 at the University of Michigan with a patient who was already undergoing neurosurgery to treat epilepsy. The company’s technology was implanted and removed from the patient’s brain in about 20 minutes during that surgery.

Paradromics said the procedure demonstrated that its system can be safely implanted and record neural activity. — Read More

In a development straight out of science fiction, Australian startup Cortical Labs has released what it calls the world’s first code-deployable biological computer. The CL1, which debuted in March, fuses human brain cells on a silicon chip to process information via sub-millisecond electrical feedback loops.

Designed as a tool for neuroscience and biotech research, the CL1 offers a new way to study how brain cells process and react to stimuli. Unlike conventional silicon-based systems, the hybrid platform uses live human neurons capable of adapting, learning, and responding to external inputs in real time. — Read More

Few would argue that AI progress over the past few years has not been rapid. 

Large Language Models (LLMs) have provided an unexpected path to increasingly general capabilities. In 2019, OpenAI’s GPT-2 struggled to write a coherent paragraph. In 2025, LLMs write fluent essays, outcompete human experts at graduate-level science questions, and excel at competition mathematics and coding. The most advanced multi-modal AI models now produce images and video that are hard to distinguish from reality. 

These models are impressive (and useful!) but they still fall short of the north star that frontier AI companies are working towards. Artificial General Intelligence (AGI), which OpenAI describes as “a highly autonomous system that outperforms humans at most economically valuable work” has been the ultimate ambition of AI researchers for many decades. 

Most experts agree that AGI is possible. They also agree that it will have transformative consequences. There is less consensus about what these consequences will be. Some believe AGI will usher in an age of radical abundance. Others believe it will likely lead to human extinction. One thing we can be sure of is that a post-AGI world would look very different to the one we live in today. 

So, is AGI just around the corner? Or are there still hard problems in front of us that will take decades to crack, despite the speed of recent progress? This is a subject of live debate. Ask various groups when they think AGI will arrive and you’ll get very different answers, ranging from just a couple of years to more than two decades. 

Why is this? We’ve tried to pin down some core disagreements.  — Read More

We directly compare the persuasion capabilities of a frontier large language model (LLM; Claude Sonnet 3.5) against incentivized human persuaders in an interactive, real-time conversational quiz setting. In this preregistered, large-scale incentivized experiment, participants (quiz takers) completed an online quiz where persuaders (either humans or LLMs) attempted to persuade quiz takers toward correct or incorrect answers. We find that LLM persuaders achieved significantly higher compliance with their directional persuasion attempts than incentivized human persuaders, demonstrating superior persuasive capabilities in both truthful (toward correct answers) and deceptive (toward incorrect answers) contexts. We also find that LLM persuaders significantly increased quiz takers’ accuracy, leading to higher earnings, when steering quiz takers toward correct answers, and significantly decreased their accuracy, leading to lower earnings, when steering them toward incorrect answers. Overall, our findings suggest that AI’s persuasion capabilities already exceed those of humans that have real-money bonuses tied to performance. Our findings of increasingly capable AI persuaders thus underscore the urgency of emerging alignment and governance frameworks. — Read More

#human

“Don’t worry, I’ve got this. I am you.”

That’s what it says, with your voice, your smile, and even your nervous laugh.

Except you’re still here. Still breathing. Still watching this machine act like it’s you — because technically, it is.

Same memories. Same passions.
Same fears. — Read More

Large Language Models (LLMs) optimized for predicting subsequent utterances and adapting to tasks using contextual embeddings can process natural language at a level close to human proficiency. This study shows that neural activity in the human brain aligns linearly with the internal contextual embeddings of speech and language within large language models (LLMs) as they process everyday conversations.

How does the human brain process natural language during everyday conversations? Theoretically, large language models (LLMs) and symbolic psycholinguistic models of human language provide a fundamentally different computational framework for coding natural language. Large language models do not depend on symbolic parts of speech or syntactic rules. Instead, they utilize simple self-supervised objectives, such as next-word prediction and generation enhanced by reinforcement learning. This allows them to produce context-specific linguistic outputs drawn from real-world text corpora, effectively encoding the statistical structure of natural speech (sounds) and language (words) into a multidimensional embedding space.

Inspired by the success of LLMs, our team at Google Research, in collaboration with Princeton University, NYU, and HUJI, sought to explore the similarities and differences in how the human brain and deep language models process natural language to achieve their remarkable capabilities. Through a series of studies over the past five years, we explored the similarity between the internal representations (embeddings) of specific deep learning models and human brain neural activity during natural free-flowing conversations, demonstrating the power of deep language model’s embeddings to act as a framework for understanding how the human brain processes language. We demonstrate that the word-level internal embeddings generated by deep language models align with the neural activity patterns in established brain regions associated with speech comprehension and production in the human brain. — Read More

Modern neuroprostheses can now restore communication in patients who have lost the ability to speak or move. However, these invasive devices entail risks inherent to neurosurgery. Here, we introduce a non-invasive method to decode the production of sentences from brain activity and demonstrate its efficacy in a cohort of 35 healthy volunteers. For this, we present Brain2Qwerty, a new deep learning architecture trained to decode sentences from either electro- (EEG) or magneto-encephalography (MEG), while participants typed briefly memorized sentences on a QWERTY keyboard. With MEG, Brain2Qwerty reaches, on average, a character-error-rate (CER) of 32% and substantially outperforms EEG (CER: 67%). For the best participants, the model achieves a CER of 19%, and can perfectly decode a variety of sentences outside of the training set. While error analyses suggest that decoding depends on motor processes, the analysis of typographical errors suggests that it also involves higher- level cognitive factors. Overall, these results narrow the gap between invasive and non-invasive methods and thus open the path for developing safe brain-computer interfaces for non-communicating patients. — Read More

#human

Mark Zuckerberg’s Meta says it’s created a device that lets you produce text simply by thinking what you want to say.

As detailed in a pair of studies released by Meta last week, researchers used a state-of-the-art brain scanner and a deep learning AI model to interpret the neural signals of people while they typed, guessing what keys they were hitting with an accuracy high enough to allow them to reconstruct entire sentences.  — Read More

For the last few years or so, the story in the artificial intelligence that was accepted without question was that all of the big names in the field needed more compute, more resources, more energy, and more money to build better models. But simply throwing money and GPUs at these companies without question led to them getting complacent, and ripe to be upset by an underdog with fractions of the computing resources and funding. Perhaps that should have been more obvious from the start, since people have been building various machine learning algorithms on extremely limited computing platforms like this one built on the Atari 800 XL.

Unlike other models that use memory-intensive applications like gradient descent to train their neural networks, [Jean Michel Sellier] is using a genetic algorithm to work within the confines of the platform. Genetic algorithms evaluate potential solutions by evolving them over many generations and keeping the ones which work best each time. The changes made to the surviving generations before they are put through the next evolution can be made in many ways, but for a limited system like this a quick approach is to make small random changes. — Read More