Recently, there is an increasing interest in using artificial intelligence (AI) to automate aspects of the research process, or even autonomously conduct the full research cycle from idea generation, over data analysis, to composing and evaluation of scientific manuscripts. Examples of working AI scientist systems have been demonstrated for computer science tasks and running molecular biology labs. While some approaches aim for full autonomy of the scientific AI, others rather aim for leveraging human-AI teaming. Here, we address how to adapt such approaches for boosting Brain-Computer Interface (BCI) development, as well as brain research resp. neuroscience at large. We argue that at this time, a strong emphasis on human-AI teaming, in contrast to fully autonomous AI BCI researcher will be the most promising way forward. We introduce the collaborative workspaces concept for human-AI teaming based on a set of Janusian design principles, looking both ways, to the human as well as to the AI side. Based on these principles, we present ChatBCI, a Python-based toolbox for enabling human-AI collaboration based on interaction with Large Language Models (LLMs), designed for BCI research and development projects. We show how ChatBCI was successfully used in a concrete BCI project on advancing motor imagery decoding from EEG signals. Our approach can be straightforwardly extended to broad neurotechnological and neuroscientific topics, and may by design facilitate human expert knowledge transfer to scientific AI systems in general.

Our current era is now in the so-called third artificial intelligence (AI) boom. Professor Hirokazu Takahashi has been engaged in brain research using the techniques of reverse engineering, an approach that strives to shed light on the underlying structure of products by taking them apart. According to Takahashi, there are two types of intellectual cleverness, and fundamental differences distinguish our brains from artificial intelligence. In rat experiments, "futility" or "uselessness" is a key word that frequently comes into perspective. If we understand the features of the brain, is it not "futile" to be "uselessly" fearful of AI?

In some ways, learning to program a computer is similar to learning a new language. It requires learning new symbols and terms, which must be organized correctly to instruct the computer what to do. The computer code must also be clear enough that other programmers can read and understand it. In spite of those similarities, MIT neuroscientists have found that reading computer code does not activate the regions of the brain that are involved in language processing. Instead, it activates a distributed network called the multiple demand network, which is also recruited for complex cognitive tasks such as solving math problems or crossword puzzles.

Deep learning systems are revolutionizing technology around us, from voice recognition that pairs you with your phone to autonomous vehicles that are increasingly able to see and recognize obstacles ahead. But much of this success involves trial and error when it comes to the deep learning networks themselves. A group of MIT researchers recently reviewed their contributions to a better theoretical understanding of deep learning networks, providing direction for the field moving forward. "Deep learning was in some ways an accidental discovery," explains Tommy Poggio, investigator at the McGovern Institute for Brain Research, director of the Center for Brains, Minds, and Machines (CBMM), and the Eugene McDermott Professor in Brain and Cognitive Sciences. "We still do not understand why it works. A theoretical framework is taking form, and I believe that we are now close to a satisfactory theory. It is time to stand back and review recent insights."

Aiming for treatment they have spent more than 30 years describing, understanding and diagnosing the most common hereditary form of stroke, CADASIL. For this, the four French neuroscientists are now receiving the world's most valuable prize for brain research – the Lundbeck Foundation Brain Prize, worth 1 million euros. Each year 17 million people worldwide suffer a stroke. Around 30 percent of these are mini strokes caused by changes in the small vessels of the brain. To begin with, these strokes cause temporary symptoms such as weakness, numbness and impaired coordination.

Two new studies from MIT suggest that mindfulness -- the practice of focusing one's awareness on the present moment -- can enhance academic performance and mental health in middle schoolers. The researchers found that more mindfulness correlates with better academic performance, fewer suspensions from school, and less stress. "By definition, mindfulness is the ability to focus attention on the present moment, as opposed to being distracted by external things or internal thoughts. If you're focused on the teacher in front of you, or the homework in front of you, that should be good for learning," says John Gabrieli, the Grover M. Hermann Professor in Health Sciences and Technology, a professor of brain and cognitive sciences, and a member of MIT's McGovern Institute for Brain Research. The researchers also showed, for the first time, that mindfulness training can alter brain activity in students.

Voicesense makes an intriguing promise to its clients: give us someone's voice, and we'll tell you what they will do. The Israeli company uses real-time voice analysis during calls to evaluate whether someone is likely to default on a bank loan, buy a more expensive product, or be the best candidate for a job. It's one of a crop of companies looking for the personal insights contained in our speech. In recent years, researchers and startups have taken note of the rich trove of information that can be mined from voice, especially as the popularity of home assistants like Amazon's Alexa make consumers increasingly comfortable talking to their devices. The voice technology market is growing and is expected to reach $15.5 billion by 2029, according to a report by business analytics firm IdTechEx.

Voicesense makes an intriguing promise to its clients: give us someone's voice, and we'll tell you what they will do. The Israeli company uses real-time voice analysis during calls to evaluate whether someone is likely to default on a bank loan, buy a more expensive product, or be the best candidate for a job. It's one of a crop of companies looking for the personal insights contained in our speech. In recent years, researchers and startups have taken note of the rich trove of information that can be mined from voice, especially as the popularity of home assistants like Amazon's Alexa make consumers increasingly comfortable talking to their devices. The voice technology market is growing and is expected to reach $15.5 billion by 2029, according to a report by business analytics firm IdTechEx.

Best listening experience is on Chrome, Firefox or Safari. "Ai" used to be a crossword puzzle clue for three-toed sloth, but today it more likely stands for artificial intelligence. In information technology circles, AI is tossed around as a panacea for everything from disease to employee boredom. If you define AI as the application of algorithms that can learn and evolve as they're exposed to new data, the use cases do indeed seem endless. The ultimate goal of AI is machinery that acts autonomously but within bounds -- such as federal policy on this or that.

Eventually we were spending just about all our free time messing around with any machine we could get our hands on. At an age when other high school kids were sneaking out of the house to go partying, Paul and I would sneak out at night to go use the computers in a lab at the University of Washington. It sounds geeky, and it was, but it was also a formative experience, and I'm not sure I would have had the courage to do it without Paul. I know it would have been a lot less fun. Later, when I was a student at Harvard, I got in trouble for letting Paul use the campus computer lab without permission.)