#brain computer interface applications

[neophony]

Discover the future with Neuphony& BCI technology. Explore brain computer interfaces, mind-controlled technology, EEG Headsets & more

[sprwork]

Brain Computer Interface Technology

The development of Brain-Computer Interface (BCI) technology is a game-changing step in the convergence of neuroscience and computing. BCIs enable direct communication between the human brain and outside hardware or software, opening up a wide range of application possibilities. BCIs enable people with disabilities to control wheelchairs, prosthetic limbs, or even to communicate through text or speech synthesis by converting neural signals into usable commands. BCIs also have the potential to revolutionise healthcare by monitoring and diagnosing neurological diseases, improve human cognition, and the gaming industry. Though yet in its infancy, BCI technology has the potential to fundamentally alter how we engage with technology and perceive the brain, ushering in a new era of human-machine connection.

[mehmetyildizmelbourne-blog]

Brainoware: The Hybrid Neuromorphic System for a Brighter Tomorrow

A glimpse into the double-edged nature of Brain Organoid Reservoir Computing, with the pros/cons of this biological computing approach From a young age, I was captivated by the mysteries of science and the promise of technology, wondering how they could shape our understanding of the world. I was fortunate to receive STEM education early on in a specialized school, where my creativity and…

[jcmarchi]

Meta AI’s Big Announcements

New Post has been published on https://thedigitalinsider.com/meta-ais-big-announcements/

Meta AI’s Big Announcements

New AR glasses, Llama 3.2 and more.

Created Using Ideogram

Next Week in The Sequence:

Edge 435: Our series about SSMs continues discussing Hungry Hungry Hippos (H3) which has become one of the most important layers in SSM models. We review the original H3 paper and discuss Character.ai’s PromptPoet framework.

Edge 436: We review Salesforce recent work in models specialized in agentic tasks.

You can subscribe to The Sequence below:

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📝 Editorial: Meta AI’s Big Announcements

Meta held its big conference, *Connect 2024*, last week, and AI was front and center. The two biggest headlines from the conference were the launch of the fully holographic Orion AI glasses, which represent one of the most important products in Meta’s ambitious and highly controversial AR strategy. In addition to the impressive first-generation Orion glasses, Meta announced that the company is developing a new brain-computer interface for the next version.

The other major release at the conference was Llama 3.2, which includes smaller language models of sizes 1B and 3B, as well as larger 11B and 90B vision models. This is Meta’s first major attempt to open source image models, signaling its strong commitment to open-source generative AI. Additionally, Meta AI announced the Llama Stack, which provides standard APIs in areas such as inference, memory, evaluation, post-training, and several other aspects required in Llama applications. With this release, Meta is transitioning Llama from isolated models to a complete stack for building generative AI apps.

There were plenty of other AI announcements at *Connect 2024*:

Meta introduced voice capabilities to its Meta AI chatbot, allowing users to have realistic conversations with the chatbot. This feature puts Meta AI on par with its competitors, like OpenAI and Google, which have already introduced voice modes to their products.

Meta announced an AI-powered, real-time language translation feature for its Ray-Ban smart glasses. This feature will allow users to translate text from Spanish, French, and Italian by the end of the year.

Meta is developing an AI feature for Instagram and Facebook Reels that will automatically dub and lip-sync videos into different languages. This feature is currently in testing in the US and Latin America.

Meta is adding AI image generation features to Facebook and Instagram. The new feature will be similar to existing AI image generators, such as Apple’s Image Playground, and will allow users to share AI-generated images with friends or create posts.

It was an impressive week for Meta AI, to say the least.

🔎 ML Research

AlphaProteo

Google DeepMind published a paper introducing AlphaProteo, a new family of model for protein design. The model is optimized for novel, high strength proteins that can improve our understanding of biological processes —> Read more.

Molmo and PixMo

Researchers from the Allen Institute for AI published a paper detailing Molmo and Pixmo, an open wegit and open data vision-language model(VLM). Molmo showcased how to train VLMs from scratch while Pixmo is the core set of datasets used during training —> Read more.

Instruction Following Without Instruction Tuning

Researchers from Stanford University published a paper detailing a technique called implicit instruction tuning that surfaces instruction following behaviors without explicity fine tuning the model. The paper also suggests some simple changes to a model distribution that can yield that implicity instruction tuning behavior —> Read more.

Robust Reward Model

Google DeepMind published a paper discussing some of the challenges of traditional reward models(RMs) to identify preferences in prompt indepdendent artifacts. The paper introduces the notion of robust reward model(RRM) that addresses this challenge and shows great improvements in models like Gemma —> Read more.

Real Time Notetaking

Researchers from Carnegie Mellon University published a paper outlining NoTeeline, a real time note generation method for video streams. NoTeeline generates micronotes that capture key points in a video while maintaining a consistent writing style —> Read more.

AI Watermarking

Researchers from Carnegie Mellon University published a paper evaluating different design choices in LLM watermarking. The paper also studies different attacks that result in the bypassing or removal of different watermarking techniques —> Read more.

🤖 AI Tech Releases

Llama 3.2

Meta open sourced Llama 3.2 small and medium size models —> Read more.

Llama Stack

As part of the Llama 3.2 release, Meta open sourced the Llama Stack, a series of standarized building blocks to develop Llama-powered applications —> Read more.

Gemini 1.5

Google released two updated Gemini models and new pricing and performance tiers —> Read more.

Cohere APIs

Cohere launched a new set of APIs that improve its experience for developers —> Read more.

🛠 Real World AI

Data Apps at Airbnb

Airbnb discusses Sandcastle, an internal framework that allow data scientists rapidly protype data driven apps —> Read more.

Feature Caching at Pinterest

The Pinterest engineering team discusses its internal architecture for feature caching in AI recommender systems —> Read more.

📡AI Radar

Meta introduced Orion, its very impressive augmented reality glasses.

James Cameron joined Stability AI’s Board of Directors.

The OpenAI soap opera continues with the resignation of their long time CTO and rumours of shifting its capped profit status.

OpenAI’s Chief Research Officer also resigned this week.

Letta, one of the most anticipated startups from UC Berkeley’s Sky Computing Lab, just came out of stealth mode with a $10 million round.

Image model platform Black Forest Labs is closing a new $100 million round.

Google announced a new $120 million fund dedicated to AI education.

Airtable unveiled a new suite of AI capabilities.

Enterprise AI startup Ensemble raised $3.3 million to improve the data quality problem for building models.

Microsoft unveiled its Trustworthy AI initiative.

Runway plans to allocate $5 million for producing AI generated films.

Data platform Airbyte can now create connectors directly from the API documentation.

Skills intelligence platform Workera unveiled a new agent that can assess, develop adn verify skills.

Convergence raised $12 million for building AI agents with long term memory.

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

[mindblowingscience]

Researchers who want to bridge the divide between biology and technology spend a lot of time thinking about translating between the two different "languages" of those realms. "Our digital technology operates through a series of electronic on-off switches that control the flow of current and voltage," said Rajiv Giridharagopal, a research scientist at the University of Washington. "But our bodies operate on chemistry. In our brains, neurons propagate signals electrochemically, by moving ions—charged atoms or molecules—not electrons." Implantable devices from pacemakers to glucose monitors rely on components that can speak both languages and bridge that gap. Among those components are OECTs—or organic electrochemical transistors—which allow current to flow in devices like implantable biosensors. But scientists long knew about a quirk of OECTs that no one could explain: When an OECT is switched on, there is a lag before current reaches the desired operational level. When switched off, there is no lag. Current drops almost immediately. A UW-led study has solved this lagging mystery, and in the process paved the way to custom-tailored OECTs for a growing list of applications in biosensing, brain-inspired computation and beyond.

Continue Reading.

[tanadrin]

The invention of the basic BCI was revolutionary, though it did not seem so at the time. Developing implantable electronics that could detect impulses from, and provide feedback to, the body's motor and sensory neurons was a natural outgrowth of assistive technologies in the 21st century. The Collapse slowed the development of this technology, but did not stall it completely; the first full BCI suite capable of routing around serious spinal cord damage, and even reducing the symptoms of some kinds of brain injury, was developed in the 2070s. By the middle of the 22nd century, this technology was widely available. By the end, it was commonplace.

But we must distinguish, as more careful technologists did even then, between simpler BCI--brain-computer interfaces--and the subtler MMI, the mind-machine interface. BCI technology, especially in the form of assistive devices, was a terrific accomplishment. But the human sensory and motor systems, at least as accessed by that technology, are comparatively straightforward. Despite the name, a 22nd century BCI barely intrudes into the brain at all, with most of its physical connections being in the spine or peripheral nervous system. It does communicate *with* the brain, and it does so much faster and more reliably than normal sensory input or neuronal output, but there nevertheless still existed in that period a kind of technological barrier between more central cognitive functions, like memory, language, and attention, and the peripheral functions that the BCI was capable of augmenting or replacing.

*That* breakthrough came in the first decades of the 23rd century, again primarily from the medical field: the subarachnoid lace or neural lace, which could be grown from a seed created from the patient's own stem cells, and which found its first use in helping stroke patients recover cognitive function and suppressing seizures. The lace is a delicate web of sensors and chemical-electrical signalling terminals that spreads out over, and carefully penetrats certain parts of the brain; in its modern form, its function and design can be altered even after it is implanted. Most humans raised in an area with access to modern medical facilities have at least a diagnostic lace in place; and, in most contexts, they are regarded as little more than a medical tool.

But of course some of the scientists who developed the lace were interested in pushing the applications of the device further, and in this, they were inspired by the long history of attempts to develop immersive virtual reality that had bedevilled futurists since the 20th century. Since we have had computers capable of manipuating symbolic metaphors for space, we have dreamed of creating a virtual space we can shape to our hearts' content: worlds to escape to, in which we are freed from the tyranny of physical limitations that we labor under in this one. The earliest fiction on this subject imagined a kind of alternate dimension, which we could forsake our mundane existence for entirely, but outside of large multiplayer games that acted rather like amusement parks, the 21st century could only offer a hollow ghost of the Web, bogged down by a cumbersome 3D metaphor users could only crudely manipulate.

The BCI did little to improve the latter--for better or worse, the public Web as we created it in the 20th century is in its essential format (if not its scale) the public Web we have today, a vast library of linked documents we traverse for the most part in two dimensions. It feeds into and draws from the larger Internet, including more specialized software and communications systems that span the whole Solar System (and which, at its margins, interfaces with the Internet of other stars via slow tightbeam and packet ships), but the metaphor of physical space was always going to be insufficient for so complex and sprawling a medium.

What BCI really revolutionized was the massively multiplayer online game. By overriding sensory input and capturing motor output before it can reach the limbs, a BCI allows a player to totally inhabit a virtual world, limited only by the fidelity of the experience the software can offer. Some setups nowadays even forgo overriding the motor output, having the player instead stand in a haptic feedback enclosure where their body can be scanned in real time, with only audio and visual information being channeled through the BCI--this is a popular way to combine physical exercise and entertainment, especially in environments like space stations without a great deal of extra space.

Ultra-immersive games led directly, I argue, to the rise of the Sodalities, which were, if you recall, originally MMO guilds with persistent legal identities. They also influenced the development of the Moon, not just by inspiring the Sodalities, but by providing a channel, through virtual worlds, for socialization and competition that kept the Moon's political fragmentation from devolving into relentless zero-sum competition or war. And for most people, even for the most ardent players of these games, the BCI of the late 22nd century was sufficient. There would always be improvements in sensory fidelity to be made, and new innovations in the games themselves eagerly anticipated every few years, but it seemed, even for those who spent virtually all their waking hours in these spaces, that there was little more that could be accomplished.

But some dreamers are never satisfied; and, occasionally, such dreamers carry us forward and show us new possibilities. The Mogadishu Group began experimenting with pushing the boundaries of MMI and the ways in which MMI could augment and alter virtual spaces in the 2370s. Mare Moscoviensis Industries (the name is not a coincidence) allied with them in the 2380s to release a new kind of VR interface that was meant to revolutionize science and industry by allowing for more intuitive traversal of higher-dimensional spaces, to overcome some of the limits of three-dimensional VR. Their device, the Manifold, was a commercial disaster, with users generally reporting horrible and heretofore unimagined kinds of motion-sickness. MMI went bankrupt in 2387, and was bought by a group of former Mogadishu developers, who added to their number a handful of neuroscientists and transhumanists. They relocated to Plato City, and languished in obscurity for about twenty years.

The next anybody ever heard of the Plato Group (as they were then called), they had bought an old interplanetary freighter and headed for the Outer Solar System. They converted their freighter into a cramped-but-servicable station around Jupiter, and despite occasionally submitting papers to various neuroscience journals and MMI working groups, little was heard from them. This prompted, in 2410, a reporter from the Lunar News Service to hire a private craft to visit the Jupiter outpost; she returned four years later to describe what she found, to general astonishment.

The Plato Group had taken their name more seriously, perhaps, than anyone expected: they had come to regard the mundane, real, three-dimensional world as a second-rate illusion, as shadows on cave walls. But rather than believing there already existed a true realm of forms which they might access by reason, they aspired to create one. MMI was to be the basis, allowing them to free themselves not only of the constraints of the real world (as generations of game-players had already done), but to free themselves of the constraints imposed on those worlds by the evolutionary legacy of the structures of their mind.

They decided early on, for instance, that the human visual cortex was of little use to them. It was constrained to apprehending three-dimensional space, and the reliance of the mind on sight as a primary sense made higher-dimensional spaces difficult or impossible to navigate. Thus, their interface used visual cues only for secondary information--as weak and nondirectional a sense as smell. They focused on using the neural lace to control the firing patterns of the parts of the brain concerned with spatial perception: the place cells, neurons which periodically fire to map spaces to fractal grides of familiar places, and the grid cells, which help construct a two-dimensional sense of location. Via external manipulation, they found they could quickly accommodate these systems to much more complex spaces--not just higher dimensions, but non-Euclidean geometries, and vast hierarchies of scale from the Planck length to many times the size of the observable universe.

The goal of the Plato Group was not simply to make a virtual space to inhabit, however transcendent; into that space they mapped as much information they could, from the Web, the publicly available internet, and any other database they could access, or library that would send them scans of its collection. They reveled in the possibilities of their invented environment, creating new kinds of incomprehensible spatial and sensory art. When asked what the purpose of all this was--were they evangelists for this new mode of being, were they a new kind of Sodality, were they secessionists protesting the limits of the rest of the Solar System's imagination?--they simply replied, "We are happy."

I do not think anyone, on the Moon or elsewhere, really knew what to make of that. Perhaps it is simply that the world they inhabit, however pleasant, is so incomprehensible to us that we cannot appreciate it. Perhaps we do not want to admit there are other modes of being as real and moving to those who inhabit them as our own. Perhaps we simply have a touch of chauvanism about the mundane. If you wish to try to understand yourself, you may--unlike many other utopian endeavors, the Plato Group is still there. Their station--sometimes called the Academy by outsiders, though they simply call it "home"--has expanded considerably over the years. It hangs in the flux tube between Jupiter and Io, drawing its power from Jupiter's magnetic field, and is, I am told, quite impressive if a bit cramped. You can glimpse a little of what they have built using an ordinary BCI-based VR interface; a little more if your neural lace is up to spec. But of course to really understand, to really see their world as they see it, you must be willing to move beyond those things, to forsake--if only temporarily--the world you have been bound to for your entire life, and the shape of the mind you have thus inherited. That is perhaps quite daunting to some. But if we desire to look upon new worlds, must we not always risk that we shall be transformed?

--Tjungdiawain’s Historical Reader, 3rd edition

[frank-olivier]

Theoretical Foundations to Nobel Glory: John Hopfield’s AI Impact

The story of John Hopfield’s contributions to artificial intelligence is a remarkable journey from theoretical insights to practical applications, culminating in the prestigious Nobel Prize in Physics. His work laid the groundwork for the modern AI revolution, and today’s advanced capabilities are a testament to the power of his foundational ideas.

In the early 1980s, Hopfield’s theoretical research introduced the concept of neural networks with associative memory, a paradigm-shifting idea. His 1982 paper presented the Hopfield network, a novel neural network architecture, which could store and recall patterns, mimicking the brain’s memory and pattern recognition abilities. This energy-based model was a significant departure from existing theories, providing a new direction for AI research.A year later, at the 1983 Meeting of the American Institute of Physics, Hopfield shared his vision. This talk played a pivotal role in disseminating his ideas, explaining how neural networks could revolutionize computing. He described the Hopfield network’s unique capabilities, igniting interest and inspiring future research.

Over the subsequent decades, Hopfield’s theoretical framework blossomed into a full-fledged AI revolution. Researchers built upon his concepts, leading to remarkable advancements. Deep learning architectures, such as Convolutional Neural Networks and Recurrent Neural Networks, emerged, enabling breakthroughs in image and speech recognition, natural language processing, and more.

The evolution of Hopfield’s ideas has resulted in today’s AI capabilities, which are nothing short of extraordinary. Computer vision systems can interpret complex visual data, natural language models generate human-like text, and AI-powered robots perform intricate tasks. Pattern recognition, a core concept from Hopfield’s work, is now applied in facial recognition, autonomous vehicles, and data analysis.

The Nobel Prize in Physics 2024 honored Hopfield’s pioneering contributions, recognizing the transformative impact of his ideas on society. This award celebrated the journey from theoretical neural networks to the practical applications that have revolutionized industries and daily life. It underscored the importance of foundational research in driving technological advancements.

Today, AI continues to evolve, with ongoing research pushing the boundaries of what’s possible. Explainable AI, quantum machine learning, and brain-computer interfaces are just a few areas of exploration. These advancements build upon the strong foundation laid by pioneers like Hopfield, leading to more sophisticated and beneficial AI technologies.

John J. Hopfield: Collective Properties of Neuronal Networks (Xerox Palo Alto Research Center, 1983)

Hopfield Networks (Artem Kirsanov, July 2024)

Boltzman machine (Artem Kirsanov, August 2024)

Dimitry Krotov: Modern Hopfield Networks for Novel Transformer Architectures (Harvard CSMA, New Technologies in Mathematics Seminar, May 2023)

Dr. Thomas Dietterich: The Future of Machine Learning, Deep Learning and Computer Vision (Craig Smith, Eye on A.I., October 2024)

Friday, October 11, 2024

[bidirectionalbci]

The science of a Bidirectional Brain Computer Interface with a function to work from a distance is mistakenly reinvented by laymen as the folklore of Remote Neural Monitoring and Controlling

Critical thinking

How good is your information when you call it RNM? It’s very bad. Is your information empirically validated when you call it RNM? No, it’s not empirically validated.

History of the RNM folklore

In 1992, a layman Mr. John St. Clair Akwei tried to explain a Bidirectional Brain Computer Interface (BCI) technology, which he didn't really understand. He called his theory Remote Neural Monitoring. Instead of using the scientific method, Akwei came up with his idea based on water. Lacking solid evidence, he presented his theory as if it were fact. Without any real studies to back him up, Akwei twisted facts, projected his views, and blamed the NSA. He lost his court case and was sadistically disabled by medical practitioners using disabling pills. They only call him something he is not. Since then, his theory has gained many followers. Akwei's explanation is incorrect and shallow, preventing proper problem-solving. As a result, people waste life-time searching for a true scientific explanation that can help solve this issue. When you call it RNM, the same will be done to you as to Mr. Akwei (calling you something you are not and sadistically disabling you with pills).

Critical thinking

Where does good research-based information come from? It comes from a university or from an R&D lab.

State of the art in Bidirectional BCI

Science-based explanation using Carnegie Mellon University Based on the definition of BCI (link to a scientific paper included), it’s a Bidirectional Brain Computer Interface for having a computer interact with the brain, and it’s extended only with 1 new function to work from a distance.

It’s the non-invasive BCI type, not an implanted BCI. The software running on the computer is a sense and respond system. It has a command/function that weaponizes the device for a clandestine sabotage against any person. It’s not from Tesla, it’s from an R&D lab of some secret service that needs it to do surveillance, sabotages and assassinations with a plausible deniability.

You need good quality information that is empirically validated, and such information comes from a university or from an R&D lab of some large organization. It won’t come from your own explanations because you are not empirically validating them which means you aren’t using the scientific method to discover new knowledge (it’s called basic research).

Goal: Detect a Bidirectional BCI extended to work from a distance (it’s called applied research, solving a problem using existing good quality information that is empirically validated)

Strategy: Continuous improvement of Knowledge Management (knowledge transfer/sharing/utilization from university courses to the community) to come up with hypotheses + experimentation with Muse2 to test your hypotheses and share when they are proved).

This strategy can use existing options as hypotheses which is then an applied research. Or, it can come up with new, original hypotheses and discover new knowledge by testing them (which is basic research). It can combine both as needed.

Carnegie Mellon University courses from Biomedical Engineering (BME)

Basics (recommended - make sure you read):

42665 | Brain-Computer Interface: Principles and Applications:

Intermediate stuff (optional - some labs to practice):

2. 42783 | Neural Engineering laboratory - Neural engineering involves the practice of using tools we use to measure and manipulate neural activity: https://www.coursicle.com/cmu/courses/BMD/42783/

Expert stuff (only if you want to know the underlying physics behind BCI):

3. 18612 | Neural Technology: Sensing and Stimulation (this is the physics of brain cells, explaining how they can be read from and written into) https://www.andrew.cmu.edu/user/skkelly/18819e/18819E_Syllabus_F12.pdf

You have to read those books to facilitate knowledge transfer from the university to you.

With the above good quality knowledge that is empirically validated, the Bidirectional BCI can be likely detected (meaning proved) and in the process, new knowledge about it can be discovered.

Purchase a cheap unidirectional BCI device for experiments at home

Utilize all newly gained knowledge from the above books in practice to make educated guesses based on the books and then empirically validate them with Muse2. After it is validated, share your good quality, empirically validated information about the undisclosed Bidirectional BCI with the community (incl. the steps to validate it).

Python Project

Someone who knows Python should try to train an AI model to detect when what you hear is not from your ear drums. Here is my initial code: https://github.com/michaloblastni/insultdetector You can try this and send me your findings and improvements.

How to do research

Basic research makes progress by doing a literature review regarding a phenomenon, then identifying main explanatory theories, making new hypotheses and conducting experiments to find what happens. When new hypotheses are proved the existing knowledge is extended. New findings can be contributed back to extend existing theories.

In practice, you will review existing scientific theories that explain i.e. the biophysics behind sensing and stimulating brain activity, and you will try to extend those theories by coming up with new hypotheses and experimentally validating them. And then, you will repeat the cycle to discover more new knowledge. When it's a lot of iterations, you need a team.

In applied research, you start with a problem that needs solving. You do a literature review and study previous solutions to the problem. Then, you should synthesize a new solution from the existing ones, and it should involve extending them in a meaningful way. Your new solution should solve the problem in some measurably better way. You have to demonstrate what your novel solution does better i.e. by measuring it, or by proving it with some other way.

In practice, you will do a literature review of past designs of Bidirectional BCI and make them your design options. Then, you will synthesize a new design option from all the design options you reviewed. The new design will get you closer toward making a Bidirectional BCI work from a distance. Then, you will repeat the cycle to improve upon your design further until you eventually reach the goal. When it's a lot of iterations, you need a team.

Using a Bidirectional BCI device to achieve synthetic telepathy

How to approach learning, researching and life

At the core, the brain is a biological neural network. You make your own connections in it stronger when you repeatedly think of something (i.e. while watching an expert researcher on youtube). And your connections weaken and disconnect/reconnect/etc. when you stop thinking of something (i.e. you stop watching an expert on how to research and you start watching negative news instead).

You train yourself by watching/listening/hanging out with people, and by reading about/writing about/listening about/doing certain tasks, and also by other means.

The brain has a very limited way of functioning because when you stop repeatedly thinking of something it soon starts disappearing. Some people call it knowledge evaporation. It’s the disconnecting and reconnecting of neurons in your biological neural network. Old knowledge is gone and new knowledge is formed. It’s called neuroplasticity. It’s the ability of neurons to disconnect, connect elsewhere, etc. based on what you are thinking/reading/writing/listening/doing.

Minimize complexity by starting from the big picture (i.e. a theory that explains a phenomenon). Then, proceed and do problem solving with a top-down decomposition into subproblems. Focus only on key information for the purpose of each subproblem and skip other details. Solve separate subproblems separately.

[neophony]

Real time EEG Data, Band Powers, Neurofeedback | Neuphony

Neuphony Desktop Application offers real-time EEG data, Band Powers, stress, mood, focus, fatigue and readiness tracking, neurofeedback & more

[sprwork]

Top Information Technology Companies

Sprwork Infosolutions is counted among the top information technology companies. If you also want to get the best for your business and looking for development and marketing solutions. Contact us today and get the top services.

[srkshaju]

Elon Musk's Neuralink Implants First Brain Chip in Human: A Look at the Tech and the Future

Elon Musk's brain-computer interface (BCI) company, Neuralink, has taken a significant step forward with the successful implantation of its first wireless brain chip in a human patient.

This marks a major milestone in the field of neurotechnology and has sparked both excitement and debate about the potential of this technology.

What is Neuralink's Brain Chip?

The chip, currently in its early stages of development, is designed to connect the human brain directly to computers.

It uses thin, flexible threads implanted in the brain to record and transmit neural activity wirelessly.

This technology has the potential to revolutionize how we interact with the world around us, potentially allowing for mind-controlled devices, enhanced communication for those with disabilities, and even treatment for neurological conditions.

Initial Results and Future Goals:

While the initial results only show promising detection of neural activity, it's a significant first step.

Musk has stated that the first product, called "Telepathy," aims to enable control of phones, computers, and other devices using only thoughts.

He envisions this technology initially benefiting those with paralysis, allowing them to communicate and interact with the world more easily.

Challenges and Concerns:

Despite the potential benefits, Neuralink's technology faces several challenges and ethical concerns.

Safety is paramount, and the company has faced criticism regarding its animal testing practices.

Additionally, the potential for misuse and the ethical implications of directly accessing and manipulating brain activity need careful consideration.

The Race for Brain-Computer Interfaces:

Neuralink is not alone in this field. Other companies, like Blackrock Neurotech and Precision Neuroscience, are also developing similar technologies.

This race for BCI dominance could lead to rapid advancements, but it's crucial to ensure responsible development and prioritize safety and ethical considerations.

The Future of Neurotechnology:

Neuralink's first human implant is just the beginning.

As BCI technology continues to evolve, we can expect to see even more groundbreaking applications in various fields, from healthcare and communication to entertainment and gaming.

However, it's important to approach this technology with caution and ensure it's developed and used ethically and responsibly.

This blog post provides an informative overview of Neuralink's recent achievement, its potential impact, and the challenges and considerations surrounding this emerging technology.

It encourages readers to stay informed and engage in discussions about the future of neurotechnology.

Checkout More for Tech Updates

[jcmarchi]

AlphaProteo: Google DeepMind’s Breakthrough in Protein Design

New Post has been published on https://thedigitalinsider.com/alphaproteo-google-deepminds-breakthrough-in-protein-design/

AlphaProteo: Google DeepMind’s Breakthrough in Protein Design

In the constantly evolving field of molecular biology, one of the most challenging tasks has been designing proteins that can effectively bind to specific targets, such as viral proteins, cancer markers, or immune system components. These protein binders are crucial tools in drug discovery, disease treatment, diagnostics, and biotechnology. Traditional methods of creating these protein binders are labor-intensive, time-consuming, and often require numerous rounds of optimization. However, recent advances in artificial intelligence (AI) are dramatically accelerating this process.

In September 2024, Neuralink successfully implanted its brain chip into the second human participant as part of its clinical trials, pushing the limits of what brain-computer interfaces can achieve. This implant allows individuals to control devices purely through thoughts.

At the same time, DeepMind’s AlphaProteo has emerged as a groundbreaking AI tool that designs novel proteins to tackle some of biology’s biggest challenges. Unlike previous models like AlphaFold, which predict protein structures, AlphaProteo takes on the more advanced task of creating new protein binders that can tightly latch onto specific molecular targets. This capability could dramatically accelerate drug discovery, diagnostic tools, and even the development of biosensors. For example, in early trials, AlphaProteo has successfully designed binders for the SARS-CoV-2 spike protein and proteins involved in cancer and inflammation, showing binding affinities that were 3 to 300 times stronger than existing methods.

What makes this intersection between biology and AI even more compelling is how these advancements in neural interfaces and protein design reflect a broader shift towards bio-digital integration.

In 2024, advancements in the integration of AI and biology have reached unprecedented levels, driving innovation across fields like drug discovery, personalized medicine, and synthetic biology. Here’s a detailed look at some of the key breakthroughs shaping the landscape this year:

1. AlphaFold3 and RoseTTAFold Diffusion: Next-Generation Protein Design

The 2024 release of AlphaFold3 by Google DeepMind has taken protein structure prediction to a new level by incorporating biomolecular complexes and expanding its predictions to include small molecules and ligands. AlphaFold3 uses a diffusion-based AI model to refine protein structures, much like how AI-generated images are created from rough sketches. This model is particularly accurate in predicting how proteins interact with ligands, with an impressive 76% accuracy rate in experimental tests—well ahead of its competitors.

In parallel, RoseTTAFold Diffusion has also introduced new capabilities, including the ability to design de novo proteins that do not exist in nature. While both systems are still improving in accuracy and application, their advancements are expected to play a crucial role in drug discovery and biopharmaceutical research, potentially cutting down the time needed to design new drugs​(

2. Synthetic Biology and Gene Editing

Another major area of progress in 2024 has been in synthetic biology, particularly in the field of gene editing. CRISPR-Cas9 and other genetic engineering tools have been refined for more precise DNA repair and gene editing. Companies like Graphite Bio are using these tools to fix genetic mutations at an unprecedented level of precision, opening doors for potentially curative treatments for genetic diseases. This method, known as homology-directed repair, taps into the body’s natural DNA repair mechanisms to correct faulty genes.

In addition, innovations in predictive off-target assessments, such as those developed by SeQure Dx, are improving the safety of gene editing by identifying unintended edits and mitigating risks. These advancements are particularly important for ensuring that gene therapies are safe and effective before they are applied to human patients​(

3. Single-Cell Sequencing and Metagenomics

Technologies like single-cell sequencing have reached new heights in 2024, offering unprecedented resolution at the cellular level. This allows researchers to study cellular heterogeneity, which is especially valuable in cancer research. By analyzing individual cells within a tumor, researchers can identify which cells are resistant to treatment, guiding more effective therapeutic strategies.

Meanwhile, metagenomics is providing deep insights into microbial communities, both in human health and environmental contexts. This technique helps analyze the microbiome to understand how microbial populations contribute to diseases, offering new avenues for treatments that target the microbiome directly​(

A Game-Changer in Protein Design

Proteins are fundamental to virtually every process in living organisms. These molecular machines perform a vast array of functions, from catalyzing metabolic reactions to replicating DNA. What makes proteins so versatile is their ability to fold into complex three-dimensional shapes, allowing them to interact with other molecules. Protein binders, which tightly attach to specific target molecules, are essential in modulating these interactions and are frequently used in drug development, immunotherapies, and diagnostic tools.

The conventional process for designing protein binders is slow and relies heavily on trial and error. Scientists often have to sift through large libraries of protein sequences, testing each candidate in the lab to see which ones work best. AlphaProteo changes this paradigm by harnessing the power of deep learning to predict which protein sequences will effectively bind to a target molecule, drastically reducing the time and cost associated with traditional methods.

How AlphaProteo Works

AlphaProteo is based on the same deep learning principles that made its predecessor, AlphaFold, a groundbreaking tool for protein structure prediction. However, while AlphaFold focuses on predicting the structure of existing proteins, AlphaProteo takes a step further by designing entirely new proteins.

How AlphaProteo Works: A Deep Dive into AI-Driven Protein Design

AlphaProteo represents a leap forward in AI-driven protein design, building on the deep learning techniques that powered its predecessor, AlphaFold.

While AlphaFold revolutionized the field by predicting protein structures with unprecedented accuracy, AlphaProteo goes further, creating entirely new proteins designed to solve specific biological challenges.

AlphaProteo’s underlying architecture is a sophisticated combination of a generative model trained on large datasets of protein structures, including those from the Protein Data Bank (PDB), and millions of predicted structures generated by AlphaFold. This enables AlphaProteo to not only predict how proteins fold but also to design new proteins that can interact with specific molecular targets at a detailed, molecular level.

This diagram showcases AlphaProteo’s workflow, where protein binders are designed, filtered, and experimentally validated

Generator: AlphaProteo’s machine learning-based model generates numerous potential protein binders, leveraging large datasets such as those from the Protein Data Bank (PDB) and AlphaFold predictions.

Filter: A critical component that scores these generated binders based on their likelihood of successful binding to the target protein, effectively reducing the number of designs that need to be tested in the lab.

Experiment: This step involves testing the filtered designs in a lab to confirm which binders effectively interact with the target protein.

AlphaProteo designs binders that specifically target key hotspot residues (in yellow) on the surface of a protein. The blue section represents the designed binder, which is modeled to interact precisely with the highlighted hotspots on the target protein.

For the C part of the image; it shows the 3D models of the target proteins used in AlphaProteo’s experiments. These include therapeutically significant proteins involved in various biological processes such as immune response, viral infections, and cancer progression.

Advanced Capabilities of AlphaProteo

High Binding Affinity: AlphaProteo excels in designing protein binders with high affinity for their targets, surpassing traditional methods that often require multiple rounds of lab-based optimization. It generates protein binders that attach tightly to their intended targets, significantly improving their efficacy in applications such as drug development and diagnostics. For example, its binders for VEGF-A, a protein associated with cancer, showed binding affinities up to 300 times stronger than existing methods​.

Targeting Diverse Proteins: AlphaProteo can design binders for a wide range of proteins involved in critical biological processes, including those linked to viral infections, cancer, inflammation, and autoimmune diseases. It has been particularly successful in designing binders for targets like the SARS-CoV-2 spike protein, essential for COVID-19 infection, and the cancer-related protein VEGF-A, which is crucial in therapies for diabetic retinopathy​.

Experimental Success Rates: One of AlphaProteo’s most impressive features is its high experimental success rate. In laboratory tests, the system’s designed binders demonstrated high success in binding to target proteins, reducing the number of experimental rounds typically required. In tests on the viral protein BHRF1, AlphaProteo’s designs had an 88% success rate, a significant improvement over previous methods​.

Optimization-Free Design: Unlike traditional approaches, which often require several rounds of optimization to improve binding affinity, AlphaProteo is able to generate binders with strong binding properties from the outset. For certain challenging targets, such as the cancer-associated protein TrkA, AlphaProteo produced binders that outperformed those developed through extensive experimental optimization​.

Experimental Success Rate (Left Graph) – Best Binding Affinity (Right Graph)

AlphaProteo outperformed traditional methods across most targets, notably achieving an 88% success rate with BHRF1, compared to just under 40% with previous methods.

AlphaProteo’s success with VEGF-A and IL-7RA targets were significantly higher, showcasing its capacity to tackle difficult targets in cancer therapy.

AlphaProteo also consistently generates binders with much higher binding affinities, particularly for challenging proteins like VEGF-A, making it a valuable tool in drug development and disease treatment.

How AlphaProteo Advances Applications in Biology and Healthcare

AlphaProteo’s novel approach to protein design opens up a wide range of applications, making it a powerful tool in several areas of biology and healthcare.

1. Drug Development

Modern drug discovery often relies on small molecules or biologics that bind to disease-related proteins. However, developing these molecules is often time-consuming and costly. AlphaProteo accelerates this process by generating high-affinity protein binders that can serve as the foundation for new drugs. For instance, AlphaProteo has been used to design binders for PD-L1, a protein involved in immune system regulation, which plays a key role in cancer immunotherapies​. By inhibiting PD-L1, AlphaProteo’s binders could help the immune system better identify and eliminate cancer cells.

2. Diagnostic Tools

In diagnostics, protein binders designed by AlphaProteo can be used to create highly sensitive biosensors capable of detecting disease-specific proteins. This can enable more accurate and rapid diagnoses for diseases such as viral infections, cancer, and autoimmune disorders. For example, AlphaProteo’s ability to design binders for SARS-CoV-2 could lead to faster and more precise COVID-19 diagnostic tools​.

3. Immunotherapy

AlphaProteo’s ability to design highly specific protein binders is particularly valuable in the field of immunotherapy. Immunotherapies leverage the body’s immune system to fight diseases, including cancer. One challenge in this field is developing proteins that can bind to and modulate immune responses effectively. With AlphaProteo’s precision in targeting specific proteins on immune cells, it could enhance the development of new, more effective immunotherapies​.

4. Biotechnology and Biosensors

AlphaProteo-designed protein binders are also valuable in biotechnology, particularly in the creation of biosensors—devices used to detect specific molecules in various environments. Biosensors have applications ranging from environmental monitoring to food safety. AlphaProteo’s binders could improve the sensitivity and specificity of these devices, making them more reliable in detecting harmful substances​.

Limitations and Future Directions

As with any new technology, AlphaProteo is not without its limitations. For instance, the system struggled to design effective binders for the protein TNF𝛼, a challenging target associated with autoimmune diseases like rheumatoid arthritis. This highlights that while AlphaProteo is highly effective for many targets, it still has room for improvement.

DeepMind is actively working to expand AlphaProteo’s capabilities, particularly in addressing challenging targets like TNF𝛼. The team is also exploring new applications for the technology, including using AlphaProteo to design proteins for crop improvement and environmental sustainability.

Conclusion

By drastically reducing the time and cost associated with traditional protein design methods, AlphaProteo accelerates innovation in biology and medicine. Its success in creating protein binders for challenging targets like the SARS-CoV-2 spike protein and VEGF-A demonstrates its potential to address some of the most pressing health challenges of our time.

As AlphaProteo continues to evolve, its impact on science and society will only grow, offering new tools for understanding life at the molecular level and unlocking new possibilities for treating diseases.

[brainanalyse]

The Intricacies of Cognitive Neuroscience

Introduction

Cognitive neuroscience is a multidisciplinary field that seeks to understand the complex interplay between the brain, cognition, and behaviour. It merges principles from psychology, neuroscience, and computer science to explore the neural mechanisms underlying various cognitive processes.

1. The Fundamentals of Cognitive Neuroscience

Cognitive neuroscience aims to unravel the mysteries of the mind by studying how neural activity gives rise to cognitive functions such as perception, memory, language, and decision-making. By examining brain structure and function using advanced imaging techniques like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), researchers can map cognitive processes onto specific brain regions.

2. Neural Basis of Perception and Sensation

Perception and sensation are fundamental processes through which organisms interpret and make sense of the world around them. Cognitive neuroscience investigates how sensory information is processed in the brain, from the initial encoding of sensory stimuli to higher-order perceptual processes that shape our conscious experience of the world.

3. Memory Encoding, Storage, and Retrieval

Memory is a cornerstone of cognition, allowing us to retain and retrieve information from past experiences. Cognitive neuroscience examines the neural mechanisms underlying memory encoding, storage, and retrieval, shedding light on how memories are formed, consolidated, and recalled. This research has implications for understanding memory disorders and developing strategies to enhance memory function.

4. Language Processing and Communication

Language is a uniquely human ability that plays a central role in communication and social interaction. Cognitive neuroscience investigates how language is processed in the brain, from the comprehension of spoken and written words to the production of speech and the interpretation of linguistic meaning. By studying language disorders like aphasia, researchers gain insights into the neural basis of language processing.

5. Decision-Making and Executive Function

Decision-making is a complex cognitive process that involves weighing multiple options, evaluating potential outcomes, and selecting the most appropriate course of action. Cognitive neuroscience explores the neural circuits involved in decision-making and executive function, including areas of the prefrontal cortex responsible for cognitive control, planning, and goal-directed behaviour.

6. Emotion Regulation and Affective Neuroscience

Emotions play a crucial role in shaping our thoughts, behaviours, and social interactions. Affective neuroscience investigates the neural basis of emotion processing, regulation, and expression, shedding light on how emotions are represented in the brain and influence decision-making, memory, and social behaviour. This research has implications for understanding mood disorders and developing interventions to promote emotional well-being.

7. Neuroplasticity and Brain Plasticity

Neuroplasticity refers to the brain’s remarkable ability to reorganize and adapt in response to experience, learning, and environmental changes. Cognitive neuroscience examines the mechanisms underlying neuroplasticity, from synaptic plasticity at the cellular level to large-scale changes in brain connectivity and function. Understanding neuroplasticity has implications for rehabilitation after brain injury and for enhancing cognitive function throughout the lifespan.

8. Applications of Cognitive Neuroscience

Cognitive neuroscience findings have far-reaching applications in fields such as education, healthcare, technology, and beyond. By elucidating the neural mechanisms underlying cognition and behaviour, cognitive neuroscience informs the development of interventions for cognitive enhancement, rehabilitation therapies for neurological disorders, and technological innovations like brain-computer interfaces.

9. Future Directions and Challenges

As technology advances and our understanding of the brain grows, cognitive neuroscience continues to evolve. Future research may focus on integrating data from multiple levels of analysis, from genes to behaviour, to gain a comprehensive understanding of brain function. Challenges in cognitive neuroscience include navigating ethical considerations, addressing methodological limitations, and fostering interdisciplinary collaboration to tackle complex questions about the mind and brain.

Conclusion

Cognitive neuroscience offers a fascinating window into the inner workings of the human mind, exploring the neural basis of cognition, perception, emotion, and behaviour. By combining insights from psychology, neuroscience, and computational modelling, cognitive neuroscience continues to unravel the mysteries of the brain, paving the way for advances in education, healthcare, and technology.

FAQs

1. What careers are available in cognitive neuroscience? Cognitive neuroscience opens doors to various career paths, including research, academia, clinical practice, and industry roles in technology and healthcare.

2. How does cognitive neuroscience differ from traditional neuroscience? While traditional neuroscience focuses on the structure and function of the brain, cognitive neuroscience specifically investigates how these processes give rise to cognitive functions like perception, memory, and language.

3. Can cognitive neuroscience help improve mental health treatments? Yes, cognitive neuroscience provides insights into the neural mechanisms underlying mental health disorders, leading to more effective treatments and interventions.

4. Is cognitive neuroscience only relevant to humans? No, cognitive neuroscience research extends to other species, providing valuable insights into the evolution of cognitive processes across different organisms.

5. How can I get involved in cognitive neuroscience research as a student? Many universities offer undergraduate and graduate programs in cognitive neuroscience, allowing students to pursue research opportunities and gain hands-on experience in the field.

[demetrio-student]

Course Outline

Foundations of Neuroscience | Month #1

Weeks 1—2 | Introduction to Neuroscience, Neurons, and Neural Signaling

terminologies | neuron, action potential, synapse, neurotransmitter

concepts | structure & function of neurons, membrane potential, neurotransmission

Questions & Objectives → What are the basic building blocks of the nervous system? → How do neurons communicate with each other? → What role do neurotransmitters play in neural signaling?

Weeks 3—4 | Brain Development, Neuroanatomy, & Neutral Circuits

terminologies | neurogenesis, synaptogenesis, cortex, hippocampus, basal ganglia

concepts | embryonic brain development, brain regions & their functions, neural circuits

Questions & Objectives → How does the brain develop from embryo to adulthood? → What are the major anatomical structures oft he brain, and what functions do they serve? → How are neural circuits formed and how do they contribute to behaviour?

Weeks 5—6 | Sensory Systems & Motor Control

terminologies | sensory receptors, somatosensory cortex, motor cortex, proprioception

concepts | sensory processing motor control, sensory-motor integration

Questions & Objectives - > How do sensory systems detect and process environmental stimuli? → What neural mechanisms underlie voluntary and involuntary movement? → How does the brain coordinate sensory inputs with motor outputs?

Weeks 7 | Mid-terms Review and Assessment

Objective | Review key concepts, terminology, & principles covered in the first month. Assess understanding through quizzes, assignments, or exams.

Advanced Topics & Applications | Month 2.

Weeks 1—2 | Learning & Memory, Emotions, & Motivation

terminologies | hippocampus, amygdala, long-term potentiation, reward pathway

concepts | neural basis of learning & memory, emotional processing, motivation

Questions & Objectives → How are memories formed and stored in the brain? → What brain regions are involved in emotional processing, and how do they interact? → How does the brain regulate motivation and reward-seeking behaviour?

Weeks 3—4 | Neurological Disorders, Neuroplasticity, & Repair

terminologies | Neurodegeneration, neuroplasticity, stroke, traumatic brain injury

concepts | causes and mechanisms of neurological disorders, neural repair & regeneration

Questions & Objectives → What are the underlying causes of neurodegenerative diseases such as Alzheimer’s and Parkinson’s? → How does the brain recover from injury or disease through neuroplasticity? → What are the current approaches to neural repair and regeneration?

Weeks 5—6 | Cognitive Neuroscience & Consciousness

terminologies | prefrontal cortex, executive function, consciousness, neural correlates

concepts | Higher cognitive functions, consciousness & awareness, neural correlates of consciousness

Questions & Objectives → How does the prefrontal cortex contribute to executive functions such as decision-making and problem-solving? → What is consciousness, and how can it be studied from a neuroscience perspective? → What neural correlates are associated with different states of consciousness?

Weeks 7—8 | Future Directions and Ethical Considerations

terminologies | optogenetics, connectome, neuroethics, brain-computer interface

concepts | emerging technologies in neuroscience, ethical considerations in neuroscientific research

Questions & Objectives - > What are the potential applications of optogenetics and brain-computer interfaces in neuroscience research and clinical practice? → How can ethical considerations be integrated into neuroscience research & technology development → What are the future directions and challenges in the field of neuroscience, & how can they be addressed?

Week 8 | Final Review and Assessment

Objectives | Review key concepts, terminologies, and emerging topics covered in the course. Assess understanding through a final exam or project.

Final.

[digitalprosolutions]

Neuralink project

Neuralink Project: A Deep Dive

Neuralink, founded by Elon Musk in 2016, is a neurotechnology company working on a revolutionary brain-computer interface (BCI) system. This system aims to bridge the gap between the human brain and computers, allowing for a new kind of interaction. Let's delve deeper into the details of this ambitious project.

Goals of Neuralink:

Medical Applications:

Restore lost abilities: The primary focus currently is on helping people with paralysis or neurological conditions like ALS regain control over their environment and communication. By deciphering brain signals, Neuralink hopes to allow users to control prosthetic limbs, wheelchairs, or computer interfaces directly with their thoughts.

Treat brain disorders: Neuralink's technology has the potential to treat various brain disorders by directly monitoring and potentially stimulating brain activity.

Human Augmentation: Beyond medical applications, Neuralink envisions a future where BCIs can enhance human capabilities. This could involve:

Direct memory access: Uploading and downloading memories or knowledge could become a reality.

Brain-to-brain communication: Imagine telepathic communication facilitated by BCIs.

Technical aspects of Neuralink:

Neuralink Device: The core of the project is a surgically implanted chip. This coin-sized device contains tiny electrodes that interface with the brain tissue.

Electrode threads: Neuralink uses ultra-thin threads containing multiple electrodes. These threads are inserted into specific brain regions to record neural activity.

Neurosurgical Robot: A specialized robot is used for precise and minimally invasive implantation of the threads.

Wireless communication: Neural signals are wirelessly transmitted to an external device for processing and decoding.

Challenges:

Biocompatibility: Ensuring the long-term safety and compatibility of the implant with brain tissue is a crucial challenge.

Signal processing: Decoding complex brain signals into understandable commands for external devices requires significant advancements in machine learning and artificial intelligence.

Ethical considerations: The potential for brain augmentation raises ethical concerns about privacy, memory manipulation, and human identity.

Current Status:

Animal Testing: Neuralink has conducted experiments on animals like monkeys, demonstrating the ability to record and interpret brain signals.

Human Trials: As of January 2024, Neuralink has begun human trials with the first implant in a patient with quadriplegia. These initial trials are focused on safety and basic functionality.

The Future of Neuralink:

The Neuralink project is still in its early stages, but it holds immense promise for revolutionizing healthcare and human-computer interaction. While there are significant technical and ethical hurdles to overcome, the potential benefits for people with disabilities and the broader implications for human potential make Neuralink a highly watched project.

[bodyalive]

I just saw this headline on an AP story: "Device taps brain waves to help paralyzed man communicate." It's about research going on at the University of California at San Francisco - and elsewhere, for sure. Some of the reporters - most? - who are covering this seem to think this is a totally new area of research and application of a brain/computer interface.

Well, if they actually looked into the background of what they are writing about, they might be more accurate and be able to put this development in context -- because, while there are exciting advances in brain/computer interfaces to help the paralyzed, to help those "locked in" communicate with the neural signals of their brains - this research has been going on since the l990s, at least. About twelve years or so ago, I witnessed an early success with a young "locked in" man "speaking" a few words in a lab via a brain implant and a computer.

But this technology is even more. It is part of the future of humankind which will not only help the paralyzed but will also be key in the creation of cyborgs, be used in space exploration and in other ways to augment the faculties and abilities of Earthlings...

I wrote about this - and interviewed many neuroscientists working in this field - well over a decade ago. If you'd like background, i found a link to the story. I'm posting a screen shot of the first page of the print version ( because the art is far better than the awful illustration on the Discover post of the story) . But here's a link for the whole thing,

if anyone is interested: https://www.discovermagazine.com/.../the-rise-of-the-cyborgs

[from 2021 :: Thanks Sherry Baker]