#Brain-computer Interfaces

[jcmarchi]

The Way the Brain Learns is Different from the Way that Artificial Intelligence Systems Learn - Technology Org

New Post has been published on https://thedigitalinsider.com/the-way-the-brain-learns-is-different-from-the-way-that-artificial-intelligence-systems-learn-technology-org/

The Way the Brain Learns is Different from the Way that Artificial Intelligence Systems Learn - Technology Org

Researchers from the MRC Brain Network Dynamics Unit and Oxford University’s Department of Computer Science have set out a new principle to explain how the brain adjusts connections between neurons during learning.

This new insight may guide further research on learning in brain networks and may inspire faster and more robust learning algorithms in artificial intelligence.

Study shows that the way the brain learns is different from the way that artificial intelligence systems learn. Image credit: Pixabay

The essence of learning is to pinpoint which components in the information-processing pipeline are responsible for an error in output. In artificial intelligence, this is achieved by backpropagation: adjusting a model’s parameters to reduce the error in the output. Many researchers believe that the brain employs a similar learning principle.

However, the biological brain is superior to current machine learning systems. For example, we can learn new information by just seeing it once, while artificial systems need to be trained hundreds of times with the same pieces of information to learn them.

Furthermore, we can learn new information while maintaining the knowledge we already have, while learning new information in artificial neural networks often interferes with existing knowledge and degrades it rapidly.

These observations motivated the researchers to identify the fundamental principle employed by the brain during learning. They looked at some existing sets of mathematical equations describing changes in the behaviour of neurons and in the synaptic connections between them.

They analysed and simulated these information-processing models and found that they employ a fundamentally different learning principle from that used by artificial neural networks.

In artificial neural networks, an external algorithm tries to modify synaptic connections in order to reduce error, whereas the researchers propose that the human brain first settles the activity of neurons into an optimal balanced configuration before adjusting synaptic connections.

The researchers posit that this is in fact an efficient feature of the way that human brains learn. This is because it reduces interference by preserving existing knowledge, which in turn speeds up learning.

Writing in Nature Neuroscience, the researchers describe this new learning principle, which they have termed ‘prospective configuration’. They demonstrated in computer simulations that models employing this prospective configuration can learn faster and more effectively than artificial neural networks in tasks that are typically faced by animals and humans in nature.

The authors use the real-life example of a bear fishing for salmon. The bear can see the river and it has learnt that if it can also hear the river and smell the salmon it is likely to catch one. But one day, the bear arrives at the river with a damaged ear, so it can’t hear it.

In an artificial neural network information processing model, this lack of hearing would also result in a lack of smell (because while learning there is no sound, backpropagation would change multiple connections including those between neurons encoding the river and the salmon) and the bear would conclude that there is no salmon, and go hungry.

But in the animal brain, the lack of sound does not interfere with the knowledge that there is still the smell of the salmon, therefore the salmon is still likely to be there for catching.

The researchers developed a mathematical theory showing that letting neurons settle into a prospective configuration reduces interference between information during learning. They demonstrated that prospective configuration explains neural activity and behaviour in multiple learning experiments better than artificial neural networks.

Lead researcher Professor Rafal Bogacz of MRC Brain Network Dynamics Unit and Oxford’s Nuffield Department of Clinical Neurosciences says: ‘There is currently a big gap between abstract models performing prospective configuration, and our detailed knowledge of anatomy of brain networks. Future research by our group aims to bridge the gap between abstract models and real brains, and understand how the algorithm of prospective configuration is implemented in anatomically identified cortical networks.’

The first author of the study Dr Yuhang Song adds: ‘In the case of machine learning, the simulation of prospective configuration on existing computers is slow, because they operate in fundamentally different ways from the biological brain. A new type of computer or dedicated brain-inspired hardware needs to be developed, that will be able to implement prospective configuration rapidly and with little energy use.’

Source: University of Oxford

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[olivergisttv]

Neuralink for Normies: Brain-Interface Jobs You Can Land Without a Science Degree

Intro: The Brain-Tech Boom Isn’t Just for Scientists You don’t need to be Elon Musk or wear a lab coat to work in neurotechnology anymore. It’s 2025, and brain-computer interfaces (BCIs) have moved from sci-fi to Shopify. With Neuralink, Synchron, and other brain-tech startups launching consumer trials and commercial products, a new wave of non-technical jobs has emerged—roles anyone with soft…

[ailifehacks]

🧠 AI-Powered Brain Interfaces: Read and Write Brain Signals for Communication and Control

Explore how AI-powered brain interfaces are revolutionizing communication by decoding and encoding brain signals AI-powered brain interfaces are at the forefront of neuroscience and technology, enabling seamless communication between the human brain and external devices. These interfaces interpret neural signals, allowing for direct control of computers and prosthetics, and even facilitating…

[goodoldbandit]

Empowering Humans, Empowering Work: The New Frontier of Human Augmentation.

Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in A detailed look at wearable tech, exoskeletons, and brain-computer interfaces that enhance human performance and reshape workplace productivity. This post examines how wearable tech, exoskeletons, and brain-computer interfaces are reshaping workplace productivity and boosting human performance. We examine a mix of practical…

[in-sightjournal]

Ask A Genius 1080: Kurzweil's Millions are Sagan's Billions

Scott Douglas Jacobsen: Ray Kurzweil and The Guardian. The article’s title is “AI Scientist Ray Kurzweil: We Are Going to Expand Intelligence a Millionfold by 2045.”    Intelligence will be a million-fold more by 2045. Kurzweil is consistent with his predictions regarding what he makes and sticks to. What are your thoughts on this prediction of a million-fold expansion of intelligence?  Rick…

[techninja]

Unveiling the Potential: Wetware Computers Market Explodes with Innovation

In the realm of technological innovation, where the boundaries between science fiction and reality blur, wetware computers emerge as a fascinating frontier. Unlike traditional hardware, wetware computers are not built from silicon and metal but are instead composed of living biological material, such as neurons or DNA. This revolutionary approach to computing holds immense promise, igniting a surge of interest and investment in the Wetware Computers Market.

The concept of wetware computing draws inspiration from the most powerful computing system known to humanity: the human brain. Mimicking the brain's structure and functionality, wetware computers leverage biological components to perform complex computations with unparalleled efficiency and adaptability. This paradigm shift in computing heralds a new era of neuromorphic computing, where machines can learn, reason, and evolve in ways reminiscent of the human mind.

One of the most compelling applications of wetware computers lies in the realm of artificial intelligence (AI). Traditional AI systems often struggle with tasks that humans excel at, such as natural language processing and pattern recognition. Wetware computers, with their biological substrate, offer a more intuitive and seamless approach to AI, enabling machines to comprehend and interact with the world in a manner akin to human cognition.

Biocomputing, a subset of wetware computing, explores the integration of biological components, such as DNA molecules, into computational systems. DNA, with its remarkable data storage capacity and self-replicating nature, presents a tantalizing opportunity for developing ultra-compact and energy-efficient computing devices. Researchers envision DNA-based computers capable of solving complex problems in fields ranging from healthcare to environmental monitoring.

Another exciting avenue in the wetware computers market is the advancement of brain-computer interfaces (BCIs). BCIs establish direct communication pathways between the human brain and external devices, enabling individuals to control computers, prosthetics, or even smart appliances using their thoughts alone. With wetware-based BCIs, the potential for seamless integration and enhanced performance skyrockets, paving the way for transformative applications in healthcare, accessibility, and human augmentation.

The wetware computers market is not without its challenges and ethical considerations. As with any emerging technology, questions regarding safety, reliability, and privacy abound. Ensuring the ethical use of wetware technologies, safeguarding against potential misuse or unintended consequences, requires robust regulatory frameworks and interdisciplinary collaboration between scientists, ethicists, and policymakers.

Despite these challenges, the wetware computers market is poised for exponential growth and innovation. Companies and research institutions worldwide are investing heavily in R&D efforts to unlock the full potential of biological computing. From startups pushing the boundaries of biocomputing to established tech giants exploring neuromorphic architectures, the landscape is abuzz with creativity and ambition.

In addition to AI, biocomputing, and BCIs, wetware computers hold promise across diverse domains, including robotics, drug discovery, and environmental monitoring. Imagine robots endowed with biological brains, capable of learning and adapting to dynamic environments with human-like agility. Picture a future where personalized medicine is powered by DNA-based computing, revolutionizing healthcare delivery and treatment outcomes.

As the wetware computers market continues to evolve, collaborations between academia, industry, and government will be instrumental in driving innovation and addressing societal concerns. Interdisciplinary research initiatives, funding support for cutting-edge projects, and public engagement efforts are essential for navigating the complexities of this transformative technology landscape.

In conclusion, the rise of wetware computers represents a paradigm shift in computing, with profound implications for AI, biotechnology, and human-machine interaction. By harnessing the power of living biological material, we embark on a journey towards smarter, more adaptable, and ethically conscious computing systems. As we tread this uncharted territory, let us embrace the challenges and opportunities that lie ahead, shaping a future where wetware computers empower us to realize the full extent of our technological imagination.

[garyconkling]

Brain Waves and Cognitive Freedom

The awe of billions of stars in outer space may only be rivaled by the awesome billions of neurons in the human brain. For good reason, scientists are probing space and brainwaves. Both explorations hold great promise – and potential danger. Mankind has stared for eons at the vast sky and its chorus of stars. The staring became more serious and scientific after the telescope was invented.…

[stemgirlchic]

why neuroscience is cool

space & the brain are like the two final frontiers

we know just enough to know we know nothing

there are radically new theories all. the. time. and even just in my research assistant work i've been able to meet with, talk to, and work with the people making them

it's such a philosophical science

potential to do a lot of good in fighting neurological diseases

things like BCI (brain computer interface) and OI (organoid intelligence) are soooooo new and anyone's game - motivation to study hard and be successful so i can take back my field from elon musk

machine learning is going to rapidly increase neuroscience progress i promise you. we get so caught up in AI stealing jobs but yes please steal my job of manually analyzing fMRI scans please i would much prefer to work on the science PLUS computational simulations will soon >>> animal testing to make all drug testing safer and more ethical !! we love ethical AI <3

collab with...everyone under the sun - psychologists, philosophers, ethicists, physicists, molecular biologists, chemists, drug development, machine learning, traditional computing, business, history, education, literally try to name a field we don't work with

it's the brain eeeeee

[scipunk]

Love, Death & Robots - S1E1 - Sonnie's Edge (2019)

[jcmarchi]

From punch cards to mind control: Human-computer interactions - AI News

New Post has been published on https://thedigitalinsider.com/from-punch-cards-to-mind-control-human-computer-interactions-ai-news/

From punch cards to mind control: Human-computer interactions - AI News

The way we interact with our computers and smart devices is very different from previous years. Over the decades, human-computer interfaces have transformed, progressing from simple cardboard punch cards to keyboards and mice, and now extended reality-based AI agents that can converse with us in the same way as we do with friends.

With each advance in human-computer interfaces, we’re getting closer to achieving the goal of interactions with machines, making computers more accessible and integrated with our lives.

Where did it all begin?

Modern computers emerged in the first half of the 20th century and relied on punch cards to feed data into the system and enable binary computations. The cards had a series of punched holes, and light was shone at them. If the light passed through a hole and was detected by the machine, it represented a “one”. Otherwise, it was a “zero”. As you can imagine, it was extremely cumbersome, time-consuming, and error-prone.

That changed with the arrival of ENIAC, or Electronic Numerical Integrator and Computer, widely considered to be the first “Turing-complete” device that could solve a variety of numerical problems. Instead of punch cards, operating ENIAC involved manually setting a series of switches and plugging patch cords into a board to configure the computer for specific calculations, while data was inputted via a further series of switches and buttons. It was an improvement over punch cards, but not nearly as dramatic as the arrival of the modern QWERTY electronic keyboard in the early 1950s.

Keyboards, adapted from typewriters, were a game-changer, allowing users to input text-based commands more intuitively. But while they made programming faster, accessibility was still limited to those with knowledge of the highly-technical programming commands required to operate computers.

GUIs and touch

The most important development in terms of computer accessibility was the graphical user interface or GUI, which finally opened computing to the masses. The first GUIs appeared in the late 1960s and were later refined by companies like IBM, Apple, and Microsoft, replacing text-based commands with a visual display made up of icons, menus, and windows.

Alongside the GUI came the iconic “mouse“, which enabled users to “point-and-click” to interact with computers. Suddenly, these machines became easily navigable, allowing almost anyone to operate one. With the arrival of the internet a few years later, the GUI and the mouse helped pave the way for the computing revolution, with computers becoming commonplace in every home and office.

The next major milestone in human-computer interfaces was the touchscreen, which first appeared in the late 1990s and did away with the need for a mouse or a separate keyboard. Users could now interact with their computers by tapping icons on the screen directly, pinching to zoom, and swiping left and right. Touchscreens eventually paved the way for the smartphone revolution that started with the arrival of the Apple iPhone in 2007 and, later, Android devices.

With the rise of mobile computing, the variety of computing devices evolved further, and in the late 2000s and early 2010s, we witnessed the emergence of wearable devices like fitness trackers and smartwatches. Such devices are designed to integrate computers into our everyday lives, and it’s possible to interact with them in newer ways, like subtle gestures and biometric signals. Fitness trackers, for instance, use sensors to keep track of how many steps we take or how far we run, and can monitor a user’s pulse to measure heart rate.

Extended reality & AI avatars

In the last decade, we also saw the first artificial intelligence systems, with early examples being Apple’s Siri and Amazon’s Alexa. AI chatbots use voice recognition technology to enable users to communicate with their devices using their voice.

As AI has advanced, these systems have become increasingly sophisticated and better able to understand complex instructions or questions, and can respond based on the context of the situation. With more advanced chatbots like ChatGPT, it’s possible to engage in lifelike conversations with machines, eliminating the need for any kind of physical input device.

AI is now being combined with emerging augmented reality and virtual reality technologies to further refine human-computer interactions. With AR, we can insert digital information into our surroundings by overlaying it on top of our physical environment. This is enabled using VR devices like the Oculus Rift, HoloLens, and Apple Vision Pro, and further pushes the boundaries of what’s possible.

So-called extended reality, or XR, is the latest take on the technology, replacing traditional input methods with eye-tracking, and gestures, and can provide haptic feedback, enabling users to interact with digital objects in physical environments. Instead of being restricted to flat, two-dimensional screens, our entire world becomes a computer through a blend of virtual and physical reality.

The convergence of XR and AI opens the doors to more possibilities. Mawari Network is bringing AI agents and chatbots into the real world through the use of XR technology. It’s creating more meaningful, lifelike interactions by streaming AI avatars directly into our physical environments. The possibilities are endless – imagine an AI-powered virtual assistant standing in your home or a digital concierge that meets you in the hotel lobby, or even an AI passenger that sits next to you in your car, directing you on how to avoid the worst traffic jams. Through its decentralised DePin infrastructure, it’s enabling AI agents to drop into our lives in real-time.

The technology is nascent but it’s not fantasy. In Germany, tourists can call on an avatar called Emma to guide them to the best spots and eateries in dozens of German cities. Other examples include digital popstars like Naevis, which is pioneering the concept of virtual concerts that can be attended from anywhere.

In the coming years, we can expect to see this XR-based spatial computing combined with brain-computer interfaces, which promise to let users control computers with their thoughts. BCIs use electrodes placed on the scalp and pick up the electrical signals generated by our brains. Although it’s still in its infancy, this technology promises to deliver the most effective human-computer interactions possible.

The future will be seamless

The story of the human-computer interface is still under way, and as our technological capabilities advance, the distinction between digital and physical reality will more blurred.

Perhaps one day soon, we’ll be living in a world where computers are omnipresent, integrated into every aspect of our lives, similar to Star Trek’s famed holodeck. Our physical realities will be merged with the digital world, and we’ll be able to communicate, find information, and perform actions using only our thoughts. This vision would have been considered fanciful only a few years ago, but the rapid pace of innovation suggests it’s not nearly so far-fetched. Rather, it’s something that the majority of us will live to see.

(Image source: Unsplash)

[zoanzon]

Perhaps controversial, but: why the hell do people wanna download fics as EPUBs? I'd vastly rather they be PDFs

[goodoldbandit]

Transforming Interaction: A Bold Journey into HCI & UX Innovations.

Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in Explore the future of Human-Computer Interaction and User Experience. Uncover trends in intuitive interfaces, gesture and voice control, and emerging brain-computer interfaces that spark discussion. #HCI #UX #IntuitiveDesign In a world where technology constantly redefines our daily routines, Human-Computer Interaction (HCI)…

[gorrus]

[yesthattoo]

15 for the ultra-processed disability ask thing!

Ok for reference that's

What’s something your disability has stopped you from learning or doing?

Fun story!

For my Ph.D. I studied brain computer interfaces. Specifically P300 brain computer interfaces. Here's my dissertation, for reference.

As far as I can tell, most people studying brain computer interfaces will ever use them. It might not be for their own communication needs (I'm a a part-time AAC user but I do just fine using my hands to access my AAC; I don't need a brain computer interface.) But if you work in a lab with brain computer interfaces, you're probably going to get called on to test that, like, new settings do what they say they do / that it's still possible to make selections / that the data collection method actually collects the data by using the interface. Plus we get tapped to be "neurotypical controls" (what my papers say) and/or "healthy controls" (what a lot of other people's papers say; I've also seen people alternate between the two) on a semi-regular basis, if we're close enough on whatever other demographics they're matching on to be reasonable.

I'm obviously not a neurotypical control. But I also cannot use a P300 brain computer interface, because that's basically dependent on flashing lights a bunch of times per second. So I got a Ph.D. studying a kind of brain computer interface that I cannot use.

(There are other kinds, some of which I can use. One particularly desperate masters student working with motor imagery brain computer interfaces tried to change his thesis wording to "controls without Parkinson's" so he could use me as a control. Pretty sure his supervisors made him take me back out though; his final thesis says both neurotypical control and healthy control.)