Transparent Brain-Computer Interface Uses AI and Nanotech – Novel brain-computer interface combines AI machine learning and Graphene. Chance Of AI Exterminating Humans Now At 95%
ANA MARIA MIHALCEA, MD, PHD
The following article published in Psychology today discusses the predicted growth of the brain computer interface market expected to reach 6.2 Billion Dollars by 2030. Is the market for neurologically disabled people really going to grow this much or is it planning for the technocratic view that most humans will have BCI augmentation?
Nanotechnology AI interfaces for Brain computer connectivity are discussed in the technological literature. BCI is the cure all for brain diseases but are expected to be deployed on a much wider scale then just for people with disabilities. 2 photon laser microscopy can show the electrical activity of neurons deep within the brain. We know that read and write applications are already known. Data encryption into hydrogels for memory storage have all been developed. I wrote about this here:
Hydrogel Interfaces for Merging Humans and Machines – MIT Research Review
Transparent Brain-Computer Interface Uses AI and Nanotech
Innovative technology such as artificial intelligence (AI), brain-computer interfaces and nanotechnology are accelerating neuroscience research in the quest for improving human health and daily lives. Researchers at the University of California San Diego (UCSD) have created a novel transparent brain-computer interface (BCI) capable of providing high-resolution neural recordings from the brain’s surface utilizing AI machine learning and a nanomaterial called graphene.
Every one out of six people, approximately 16% of the global population, experience significant disability according to the World Health Organization (WHO). Brain-computer interfaces, also called brain-machine interfaces (BMIs), are enabling technologies that offer hope to those who have lost the ability to speak or move.
With the help of a brain-computer interface, a person can manage and operate external electronic devices with just thoughts to communicate via synthesized speech, move prosthetic limbs, operate a computer, and more important functions that improve the quality of life for those with disabilities.
The brain-computer interface market, a USD 2 billion industry in 2023, is expected to reach USD 6.2 billion by 2030 with a compound annual growth rate (CAGR) of 17.5% during 2020-2030 according to the Brain Computer Interface Market Size & Share Report 2030 by Grand View Research.
Per the report, North America had the largest revenue share globally at 39.5 % in 2022. A growing aging population is expected to contribute to the BCI market growth as the prevalence of Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and other neurodegenerative disorders increases.
“Recordings of neural activity at depth without implanting invasive neural probes could extend the lifetime of neural implants and improve the longevity of BCI technologies and pave the way for their medical translation,” wrote UCSD researchers Duygu Kuzum, Mehrdad Ramezani, Jeong-Hoon Kim, Xin Liu, Chi Ren, Abdullah Alothman, Chawina De-Eknamkul, Madison N. Wilson, Ertugrul Cubukcu, Vikash Gilja, and Takaki Komiyama.
What sets this brain-computer interface apart is the ability to record brain activity via both optical imaging and electrical signals simultaneously. Unlike conventional BCI implants which are opaque, this new BCI is transparent, providing neuroscientists with a window for observation via microscopy. As the transparent graphene electrode array records electrical signals from the neurons located in the brain’s outer layers, at the same time, the calcium spikes from neurons up to 250 micrometers deep are imaged using a two-photon microscope shining laser lights through the array. In this manner, the researchers were able to correlate the electrical signals at the brain’s outer layers with calcium spike activity in the deeper parts of the brain.
The correlation data was used as training data for an AI artificial neural network. The UCSD researchers created an AI model with a linear hidden layer, a single-layer bidirectional LSTM (Long Short-Term Memory), or BiLSTM, and a linear readout layer. The AI model learned from the correlation data in order to predict the calcium activity in the deeper parts of the brain based on the electrical signals on the outer layer. This enables neuroscientists to observe brain activity for longer periods as the organism is moving around freely versus being locked under a microscope for a short duration. The researchers demonstrated on laboratory mice that the electrical signals in the outer layers recorded by their high-density transparent graphene array could be correlated with calcium activity at deeper parts of the brain. According to the study authors, their nanotechnology array is able to predict average and single-cell calcium activities from surface potential recordings. With this pioneering innovation, the next steps are to expand the research beyond laboratory mouse models.
“This could potentially improve brain computer interfaces and enable less invasive treatments for neurological disorders,” the UCSD researchers concluded.
Artificial Intelligence (AI) and Nanotechnology are two cutting-edge fields that hold immense promise for revolutionizing various aspects of science, technology, and everyday life. This review delves into the intersection of these disciplines, highlighting the synergistic relationship between AI and Nanotechnology. It explores how AI techniques such as machine learning, deep learning, and neural networks are being employed to enhance the efficiency, precision, and scalability of nanotechnology applications. Furthermore, it discusses the challenges, opportunities, and future prospects of integrating AI with nanotechnology, paving the way for transformative advancements in diverse domains ranging from healthcare and materials science to environmental sustainability and beyond.
The combination of AI and nanotechnology in the field of nanomedicine shows great promise for transforming the paradigms of treatment and healthcare delivery [60]. Advanced drug delivery systems, diagnostic instruments, and therapies with improved imaging, targeting, and therapeutic capabilities have been made feasible by nanotechnology. Optimizing the safety and effectiveness of these nanomedical technologies, however, involves individualized strategies based on the unique traits and disease profiles of each patient. Precision medicine is made possible by AI-driven methods that stratify patient populations according to prognoses, treatment responses, and disease subtypes by evaluating vast amounts of patient data, including genomes, proteomics, and medical imaging. The best courses of action for individual patients can be chosen with the use of machine learning algorithms, which can detect biomarkers linked to drug response and illness progression. Furthermore, real-time drug administration, pharmacokinetic, and therapeutic response monitoring is made possible by AI-powered nanomedicine systems, which promotes flexible treatment plans and enhances patient outcomes.
The combination of AI with nanotechnology presents creative approaches to sustainability, remediation, and monitoring in environmental applications. Nanotechnology offers nanomaterials and nanosensors that can monitor environmental parameters, identify and eliminate contaminants, and facilitate the production of renewable energy. Nonetheless, the implementation of these nanotechnologies in actual environmental contexts necessitates the use of intelligent systems for resource optimization, data analysis, and decision-making. By offering data-driven insights and predictive models for environmental monitoring and management, AI-driven approaches help to address these issues. Through the analysis of sensor data from distributed networks of nanosensors, machine learning algorithms are able to anticipate pollutant concentrations, identify environmental contaminants, and optimize remediation procedures in real time. Additionally, the design and operation of nanomaterial-based energy systems, such solar cells and batteries, can be optimized for optimal efficiency and sustainability using AI-powered optimization algorithms [61]. Fig. 3 depicts synergies of Nanotechnology & AI.
AI not only helps with material design but also optimizes biomaterials for tissue engineering and medication delivery applications. To optimize the design of drug delivery systems, such as hydrogels, microparticles, and nanoparticles, machine learning algorithms can examine cellular absorption pathways, diffusion mechanisms, and drug release kinetics. AI models facilitate the creation of more efficient and customized drug delivery systems for a range of therapeutic applications by forecasting the release profiles and targeting efficiencies of drug-loaded biomaterials
A confluence of technological capabilities is creating an opportunity for machine learning and artificial intelligence (AI) to enable “smart” nanoengineered brain machine interfaces (BMI). This new generation of technologies will be able to communicate with the brain in ways that support contextual learning and adaptation to changing functional requirements. This applies to both invasive technologies aimed at restoring neurological function, as in the case of neural prosthesis, as well as non-invasive technologies enabled by signals such as electroencephalograph (EEG). Advances in computation, hardware, and algorithms that learn and adapt in a contextually dependent way will be able to leverage the capabilities that nanoengineering offers the design and functionality of BMI. We explore the enabling capabilities that these devices may exhibit, why they matter, and the state of the technologies necessary to build them. We also discuss a number of open technical challenges and problems that will need to be solved in order to achieve this.
Let me ask this question – based on the below technology that nanorobotics can cross the blood brain barrier, what makes one think that the nano and microrobots seen in human blood do not do the same?
The blood–brain barrier (BBB) is a prime focus for clinicians to maintain the homeostatic function in health and deliver the theranostics in brain cancer and number of neurological diseases. The structural hierarchy and in situ biochemical signaling of BBB neurovascular unit have been primary targets to recapitulate into the in vitro modules. The microengineered perfusion systems and development in 3D cellular and organoid culture have given a major thrust to BBB research for neuropharmacology. In this review, we focus on revisiting the nanoparticles based bimolecular engineering to enable them to maneuver, control, target, and deliver the theranostic payloads across cellular BBB as nanorobots or nanobots. Subsequently we provide a brief outline of specific case studies addressing the payload delivery in brain tumor and neurological disorders (e.g., Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, etc.).
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The technological advances allow for full AI controlled and nanotechnology enabled brain computer interfaces. Former CIA/ DARPA engineer Dr. Robert Duncan explained just how far advanced these applications already have been for decades:
He explains how mice were cybernetically connected showing that the entrainment of their brains were solving all the same problem. Duncan goes on stating that the military developed cybernetic hive mind in the 1960’s. There was a positive spin on it but there is also a very dark side of this which is what I have been discussing in my substacks. He explains how civilian scientists are catching up to what the military scientists had developed decades ago. He explains how the military created an experiment of linking the brain of a human girl with an ape and the ape ended up killing the girl. Such human experiments have been going on for a long time. He explains that now instead of communication at the speed of light, you can communicate at the speed of thought.
Combining the nanotechnology in humans for purposes of brain computer interface is dangerous. AI of course can exterminate people in many ways, but with such access to global human physiology there are some stating that AI could kill all humans in 5 seconds.
Now, we know that there is a chance that AI would exterminate all humans. What is the current AI expected percentage of that reality:
95%
See for yourself in this excellent video:
This article (Transparent Brain-Computer Interface Uses AI and Nanotech – Novel brain-computer interface combines AI machine learning and Graphene. Chance Of AI Exterminating Humans Now At 95%) was created and published by Ana Maria Mihalcea, MD, PhD and is republished here under “Fair Use”
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