Our latest and most advanced technologies — from AI to Industrial IoT, advanced robotics, and self-driving cars — share serious problems: massive energy consumption, limited on-edge capabilities, system hallucinations, and serious accuracy gaps.  One possible solution is emerging in the Netherlands. The country is developing a promising ecosystem for neuromorphic computing, which draws on neuroscience to boost IT efficiencies and performance. Billions of euros are being invested in this new form of computing worldwide. The Netherlands aims to become a leader in the market by bringing together startups, established companies, government organisations, and academics in a neuromorphic computing ecosystem. A Dutch mission to the UK In March, a Dutch delegation landed in the UK to host an “Innovation Mission” with local tech and government representatives. Top Sector ICT, a Dutch government–supported organisation, led the mission, which sought to strengthen and discuss the future of neuromorphic computing in Europe and the Netherlands.  We contacted Top Sector ICT, who connected us with one of their collaborators: Dr Johan H. Mentink, an expert in computational physics at Radboud University in the Netherlands. Dr Mentink spoke about how neuromorphic computing can solve the energy, accuracy, and efficiency challenges of our current computing architectures.  Grab that deal “Current digital computers use power-hungry processes to handle data,” Dr Mentink said.  “The result is that some modern data centres use so much energy that they even need their own power plant.”  Computing today stores data in one place (memory) and processes it in another place (processors). This means that a lot of energy is spent on transporting data, Dr Mentink explained.  In contrast, neuromorphic computing architectures are different at the hardware and software levels. For example, instead of using processors and memories, neuromorphic systems leverage new hardware components such as memristors. These act as both memory and processors.  By processing and saving data on the same hardware component, neuromorphic computing removes the energy-intensive and error-prone task of transporting data. Additionally, because data is stored on these components, it can be processed more immediately, resulting in faster decision-making, reduced hallucinations, improved accuracy, and better performance. This concept is being applied to edge computing, Industrial IoT, and robotics to drive faster real-time decision-making.  “Just like our brains process and store information in the same place, we can make computers that would combine data storage and processing in one place,” Dr Mentink explained.   Early use cases for neuromorphic computing Neuromorphic computing is far from just experimental. A great number of new and established technology companies are heavily invested in developing new hardware, edge devices, software, and neuromorphic computing applications. Big tech brands such as IBM, NVIDIA, and Intel, with its Loihi chips, are all involved in neuromorphic computing, while companies in the Netherlands, aligned with a 2024 national white paper, are taking a leading regional role.  For example, the Dutch company Innatera — a leader in ultra-low power neuromorphic processors — recently secured €15 million in Series-A funding from Invest-NL Deep Tech Fund, the EIC Fund, MIG Capital, Matterwave Ventures, and Delft Enterprises.  Innatera is just the tip of the iceberg, as the Netherlands continues to support the new industry through funds, grants, and other incentives. Immediate use cases for neuromorphic computing include event-based sensing technologies integrated into smart sensors such as cameras or audio. These neuromorphic devices only process change, which can dramatically reduce power and data load, said Sylvester Kaczmarek, the CEO of OrbiSky Systems, a company providing AI integration for space technology.   Neuromorphic hardware and software have the potential to transfer AI running on the edge, especially for low-power devices such as mobile, wearables, or IoT.  Pattern recognition, keyword spotting, and simple diagnostics — such as real-time signal processing of complex sensor data streams for biomedical uses, robotics, or industrial monitoring — are some of the leading use cases, Dr Kaczmarek explained.  When applied to pattern recognition and classification or anomaly detection, neuromorphic computing can make decisions very quickly and efficiently,  Professor Dr Hans Hilgenkamp, Scientific Director of the MESA+ Institute at the University of Twente, agreed that pattern recognition is one of the fields where neuromorphic computing excels.  “One may also think about [for example] failure prediction in industrial or automotive applications,” he said.    The gaps creating neuromorphic opportunities Despite the recent progress, the road to establishing robust neuromorphic computing ecosystems in the Netherlands is challenging. Globalised tech supply chains and the standardisation of new technologies leave little room for hardware-level innovation.  For example, optical networks and optical chips have proven to outperform traditional systems in use today, but the tech has not been deployed globally. Deploying new hardware involves strategic coordination between the public and private sectors. The global rollout of 5G technology provides a good example of the challenges. It required telcos and governments around the world to deploy not only new antennas, but also smartphones, laptops, and a lot of hardware that could support the new standard.  On the software side, meanwhile, 5G systems had a pressing need for global standards to ensure integration, interoperability, and smooth deployment. Additionally, established telcos had to move from pure competition to strategic collaboration— an unfamiliar shift for an industry long built on siloed operations. Neuromorphic computing ecosystems face similar obstacles. The Netherlands recognises that the entire industry’s success depends on innovation in materials, devices, circuit designs, hardware architecture, algorithms, and applications.  These challenges and gaps are driving new opportunities for tech companies, startups, vendors, and partners.  Dr Kaczmarek told us that neuromorphic computing requires full-stack integration. This involves expertise that can connect novel materials and devices through circuit design and architectures to algorithms and applications. “Bringing these layers together is crucial but challenging,” he said.  On the algorithms and software side of things, developing new paradigms of programming, learning rules (beyond standard deep learning backpropagation), and software tools native to neuromorphic hardware are also priorities.  “It is crucial to make the hardware usable and efficient — co-designing hardware and algorithms because they are intimately coupled in neuromorphic systems,” said Dr Kaczmarek.  Other industries which have developed or are considering research on neuromorphic computing include healthcare (brain-computer interfaces and prosthetics), agri-food, and sustainable energy.  Neuromorphic computing modules or components can also be integrated with conventional CMOS, photonics, AI, and even quantum technologies.  Long-term opportunities in the Netherlands We asked Dr Hilgenkamp what expertise or innovations are most needed and offer the greatest opportunities for contribution and growth within this emerging ecosystem. “The long-term developments involve new materials and a lot of research, which is already taking place on an academic level,” Dr Hilgenkamp said.  He added that the idea of “materials that can learn” brings up completely new concepts in materials science that are exciting for researchers.  On the other hand, Dr Mentink pointed to the opportunity to transform our economies, which rely on processing massive amounts of data.  “Even replacing a small part of that with neuromorphic computing will lead to massive energy savings,” he said.  “Moreover, with neuromorphic computing, much more processing can be done close to where the data is produced. This is good news for situations in which data contains privacy-sensitive information.”  Concrete examples, according to Dr Mentink, also include fraud detection for credit card transactions, image analysis by robots and drones, anomaly detection of heartbeats, and processing of telecom data. “The most promising use cases are those involving huge data flows, strong demands for very fast response times, and small energy budgets,” said Dr Mentink.  As the use cases for neuromorphic computing increase, Dr Mentink expects the development of software toolchains that enable quick adoption of new neuromorphic platforms to see growth. This new sector would include services to streamline deployment. “Longer-term sustainable growth requires a concerted interdisciplinary effort across the whole computing stack to enable seamless integration of foundational discoveries to applications in new neuromorphic computing systems,” Dr Mentink said.  The bottom line The potential of neuromorphic computing has translated into billions of dollars in investment in the Netherlands and Europe, as well as in Asia and the rest of the world.  Businesses that can innovate, develop, and integrate hardware and software-level neuromorphic technologies stand to gain the most.   The potential of neuromorphic computing for greater energy efficiency and performance could ripple across industries. Energy, healthcare, robotics, AI, industrial IoT, and quantum tech all stand to benefit if they integrate the technology. And if the Dutch ecosystem takes off, the Netherlands will be in a position to lead the way. Supporting Dutch tech is a key mission of TNW Conference, which takes place on June 19-20 in Amsterdam. Tickets are now on sale — use the code TNWXMEDIA2025 at the checkout to get 30% off. Story by Ray Fernandez Ray Fernandez is a journalist with over a decade of experience reporting on technology, finance, science, and natural resources. His work ha (show all) Ray Fernandez is a journalist with over a decade of experience reporting on technology, finance, science, and natural resources. His work has been published in Bloomberg, TechRepublic, The Sunday Mail, eSecurityPlanet, and many others. He is a contributing writer for Espacio Media Incubator, which has reporters across the US, Europe, Asia, and Latin America. Get the TNW newsletter Get the most important tech news in your inbox each week. Also tagged with

The day after his inauguration in January, President Donald Trump announced Stargate, a $500 billion initiative to build out AI infrastructure, backed by some of the biggest companies in tech. Stargate aims to accelerate the construction of massive data centers and electricity networks across the US to ensure it keeps its edge over China. This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution. The whatever-it-takes approach to the race for worldwide AI dominance was the talk of Davos, says Raquel Urtasun, founder and CEO of the Canadian robotruck startup Waabi, referring to the World Economic Forum’s annual January meeting in Switzerland, which was held the same week as Trump’s announcement. “I’m pretty worried about where the industry is going,” Urtasun says.  She’s not alone. “Dollars are being invested, GPUs are being burned, water is being evaporated—it’s just absolutely the wrong direction,” says Ali Farhadi, CEO of the Seattle-based nonprofit Allen Institute for AI. But sift through the talk of rocketing costs—and climate impact—and you’ll find reasons to be hopeful. There are innovations underway that could improve the efficiency of the software behind AI models, the computer chips those models run on, and the data centers where those chips hum around the clock. Here’s what you need to know about how energy use, and therefore carbon emissions, could be cut across all three of those domains, plus an added argument for cautious optimism: There are reasons to believe that the underlying business realities will ultimately bend toward more energy-efficient AI. 1/ More efficient models The most obvious place to start is with the models themselves—the way they’re created and the way they’re run. AI models are built by training neural networks on lots and lots of data. Large language models are trained on vast amounts of text, self-driving models are trained on vast amounts of driving data, and so on. But the way such data is collected is often indiscriminate. Large language models are trained on data sets that include text scraped from most of the internet and huge libraries of scanned books. The practice has been to grab everything that’s not nailed down, throw it into the mix, and see what comes out. This approach has certainly worked, but training a model on a massive data set over and over so it can extract relevant patterns by itself is a waste of time and energy. There might be a more efficient way. Children aren’t expected to learn just by reading everything that’s ever been written; they are given a focused curriculum. Urtasun thinks we should do something similar with AI, training models with more curated data tailored to specific tasks. (Waabi trains its robotrucks inside a superrealistic simulation that allows fine-grained control of the virtual data its models are presented with.) It’s not just Waabi. Writer, an AI startup that builds large language models for enterprise customers, claims that its models are cheaper to train and run in part because it trains them using synthetic data. Feeding its models bespoke data sets rather than larger but less curated ones makes the training process quicker (and therefore less expensive). For example, instead of simply downloading Wikipedia, the team at Writer takes individual Wikipedia pages and rewrites their contents in different formats—as a Q&A instead of a block of text, and so on—so that its models can learn more from less. Training is just the start of a model’s life cycle. As models have become bigger, they have become more expensive to run. So-called reasoning models that work through a query step by step before producing a response are especially power-hungry because they compute a series of intermediate subresponses for each response. The price tag of these new capabilities is eye-watering: OpenAI’s o3 reasoning model has been estimated to cost up to $30,000 per task to run.   But this technology is only a few months old and still experimental. Farhadi expects that these costs will soon come down. For example, engineers will figure out how to stop reasoning models from going too far down a dead-end path before they determine it’s not viable. “The first time you do something it’s way more expensive, and then you figure out how to make it smaller and more efficient,” says Farhadi. “It’s a fairly consistent trend in technology.” One way to get performance gains without big jumps in energy consumption is to run inference steps (the computations a model makes to come up with its response) in parallel, he says. Parallel computing underpins much of today’s software, especially large language models (GPUs are parallel by design). Even so, the basic technique could be applied to a wider range of problems. By splitting up a task and running different parts of it at the same time, parallel computing can generate results more quickly. It can also save energy by making more efficient use of available hardware. But it requires clever new algorithms to coordinate the multiple subtasks and pull them together into a single result at the end.  The largest, most powerful models won’t be used all the time, either. There is a lot of talk about small models, versions of large language models that have been distilled into pocket-size packages. In many cases, these more efficient models perform as well as larger ones, especially for specific use cases. As businesses figure out how large language models fit their needs (or not), this trend toward more efficient bespoke models is taking off. You don’t need an all-purpose LLM to manage inventory or to respond to niche customer queries. “There’s going to be a really, really large number of specialized models, not one God-given model that solves everything,” says Farhadi. Christina Shim, chief sustainability officer at IBM, is seeing this trend play out in the way her clients adopt the technology. She works with businesses to make sure they choose the smallest and least power-hungry models possible. “It’s not just the biggest model that will give you a big bang for your buck,” she says. A smaller model that does exactly what you need is a better investment than a larger one that does the same thing: “Let’s not use a sledgehammer to hit a nail.” 2/ More efficient computer chips As the software becomes more streamlined, the hardware it runs on will become more efficient too. There’s a tension at play here: In the short term, chipmakers like Nvidia are racing to develop increasingly powerful chips to meet demand from companies wanting to run increasingly powerful models. But in the long term, this race isn’t sustainable. “The models have gotten so big, even running the inference step now starts to become a big challenge,” says Naveen Verma, cofounder and CEO of the upstart microchip maker EnCharge AI. Companies like Microsoft and OpenAI are losing money running their models inside data centers to meet the demand from millions of people. Smaller models will help. Another option is to move the computing out of the data centers and into people’s own machines. That’s something that Microsoft tried with its Copilot+ PC initiative, in which it marketed a supercharged PC that would let you run an AI model (and cover the energy bills) yourself. It hasn’t taken off, but Verma thinks the push will continue because companies will want to offload as much of the costs of running a model as they can. But getting AI models (even small ones) to run reliably on people’s personal devices will require a step change in the chips that typically power those devices. These chips need to be made even more energy efficient because they need to be able to work with just a battery, says Verma. That’s where EnCharge comes in. Its solution is a new kind of chip that ditches digital computation in favor of something called analog in-memory computing. Instead of representing information with binary 0s and 1s, like the electronics inside conventional, digital computer chips, the electronics inside analog chips can represent information along a range of values in between 0 and 1. In theory, this lets you do more with the same amount of power.  SHIWEN SVEN WANG EnCharge was spun out from Verma’s research lab at Princeton in 2022. “We’ve known for decades that analog compute can be much more efficient—orders of magnitude more efficient—than digital,” says Verma. But analog computers never worked well in practice because they made lots of errors. Verma and his colleagues have discovered a way to do analog computing that’s precise. EnCharge is focusing just on the core computation required by AI today. With support from semiconductor giants like TSMC, the startup is developing hardware that performs high-dimensional matrix multiplication (the basic math behind all deep-learning models) in an analog chip and then passes the result back out to the surrounding digital computer. EnCharge’s hardware is just one of a number of experimental new chip designs on the horizon. IBM and others have been exploring something called neuromorphic computing for years. The idea is to design computers that mimic the brain’s super-efficient processing powers. Another path involves optical chips, which swap out the electrons in a traditional chip for light, again cutting the energy required for computation. None of these designs yet come close to competing with the electronic digital chips made by the likes of Nvidia. But as the demand for efficiency grows, such alternatives will be waiting in the wings.  It is also not just chips that can be made more efficient. A lot of the energy inside computers is spent passing data back and forth. IBM says that it has developed a new kind of optical switch, a device that controls digital traffic, that is 80% more efficient than previous switches.    3/ More efficient cooling in data centers Another huge source of energy demand is the need to manage the waste heat produced by the high-end hardware on which AI models run. Tom Earp, engineering director at the design firm Page, has been building data centers since 2006, including a six-year stint doing so for Meta. Earp looks for efficiencies in everything from the structure of the building to the electrical supply, the cooling systems, and the way data is transferred in and out. For a decade or more, as Moore’s Law tailed off, data-center designs were pretty stable, says Earp. And then everything changed. With the shift to processors like GPUs, and with even newer chip designs on the horizon, it is hard to predict what kind of hardware a new data center will need to house—and thus what energy demands it will have to support—in a few years’ time. But in the short term the safe bet is that chips will continue getting faster and hotter: “What I see is that the people who have to make these choices are planning for a lot of upside in how much power we’re going to need,” says Earp. One thing is clear: The chips that run AI models, such as GPUs, require more power per unit of space than previous types of computer chips. And that has big knock-on implications for the cooling infrastructure inside a data center. “When power goes up, heat goes up,” says Earp. With so many high-powered chips squashed together, air cooling (big fans, in other words) is no longer sufficient. Water has become the go-to coolant because it is better than air at whisking heat away. That’s not great news for local water sources around data centers. But there are ways to make water cooling more efficient. One option is to use water to send the waste heat from a data center to places where it can be used. In Denmark water from data centers has been used to heat homes. In Paris, during the Olympics, it was used to heat swimming pools.   Water can also serve as a type of battery. Energy generated from renewable sources, such as wind turbines or solar panels, can be used to chill water that is stored until it is needed to cool computers later, which reduces the power usage at peak times. But as data centers get hotter, water cooling alone doesn’t cut it, says Tony Atti, CEO of Phononic, a startup that supplies specialist cooling chips. Chipmakers are creating chips that move data around faster and faster. He points to Nvidia, which is about to release a chip that processes 1.6 terabytes a second: “At that data rate, all hell breaks loose and the demand for cooling goes up exponentially,” he says. According to Atti, the chips inside servers suck up around 45% of the power in a data center. But cooling those chips now takes almost as much power, around 40%. “For the first time, thermal management is becoming the gate to the expansion of this AI infrastructure,” he says. Phononic’s cooling chips are small thermoelectric devices that can be placed on or near the hardware that needs cooling. Power an LED chip and it emits photons; power a thermoelectric chip and it emits phonons (which are to vibrational energy—a.k.a. temperature—as photons are to light). In short, phononic chips push heat from one surface to another. Squeezed into tight spaces inside and around servers, such chips can detect minute increases in heat and switch on and off to maintain a stable temperature. When they’re on, they push excess heat into a water pipe to be whisked away. Atti says they can also be used to increase the efficiency of existing cooling systems. The faster you can cool water in a data center, the less of it you need. 4/ Cutting costs goes hand in hand with cutting energy use Despite the explosion in AI’s energy use, there’s reason to be optimistic. Sustainability is often an afterthought or a nice-to-have. But with AI, the best way to reduce overall costs is to cut your energy bill. That’s good news, as it should incentivize companies to increase efficiency. “I think we’ve got an alignment between climate sustainability and cost sustainability,” says Verma. ”I think ultimately that will become the big driver that will push the industry to be more energy efficient.” Shim agrees: “It’s just good business, you know?” Companies will be forced to think hard about how and when they use AI, choosing smaller, bespoke options whenever they can, she says: “Just look at the world right now. Spending on technology, like everything else, is going to be even more critical going forward.” Shim thinks the concerns around AI’s energy use are valid. But she points to the rise of the internet and the personal computer boom 25 years ago. As the technology behind those revolutions improved, the energy costs stayed more or less stable even though the number of users skyrocketed, she says. It’s a general rule Shim thinks will apply this time around as well: When tech matures, it gets more efficient. “I think that’s where we are right now with AI,” she says. AI is fast becoming a commodity, which means that market competition will drive prices down. To stay in the game, companies will be looking to cut energy use for the sake of their bottom line if nothing else.  In the end, capitalism may save us after all. 

Author(s): Talha Nazar Originally published on Towards AI. Imagine a world where your thoughts can control machines. You think, and a robotic arm moves. You feel, and a digital avatar mimics your expression. Sounds like science fiction, right? But this is no longer just an idea scribbled in a cyberpunk novel — it’s happening right now, at the intersection of neuroscience and artificial intelligence. As someone who’s been closely following AI for years, I find this confluence between biology and code deeply fascinating. It’s as if we’re uncovering a hidden mirror: AI reflects how we think, while neuroscience peels back the layers of what thinking even is. In this story, we’ll journey from brainwaves to neural networks, exploring how scientists and engineers are blending biology with silicon to create machines that learn, adapt, and maybe one day, even feel. The Brain as a Blueprint for Machines Let’s start with a simple question: How did AI get so smart? The answer lies partly in how closely it’s modeled after us. When researchers first began building artificial intelligence, they didn’t pull the idea from thin air. Instead, they looked inward — to the brain. Our brains contain roughly 86 billion neurons, each connected to thousands of others, forming a massive web of electrical and chemical signals. Early AI pioneers like Warren McCulloch and Walter Pitts were inspired by this structure. In 1943, they introduced a computational model of a neuron, laying the groundwork for what would later become artificial neural networks. Fast forward to today, and these neural networks form the backbone of AI systems like GPT, Siri, and autonomous cars. While far simpler than a real brain, they mimic how we process information: through layers of pattern recognition, memory, and adjustment based on feedback. “The brain is not a computer, but it teaches us how to build better ones.” The parallels are stunning. Just like we learn from experience, AI models use algorithms like backpropagation to tweak their internal weights — essentially fine-tuning their ‘memory’ to make better decisions over time. Weirdly, it’s like machines are learning to think the way we do. From Mirror Neurons to Machine Empathy Here’s where things get even more sci-fi. In 1992, neuroscientists in Italy discovered mirror neurons — special brain cells that activate both when we perform an action and when we observe someone else doing it. It’s like your brain says, “Hey, I know what that feels like.” These neurons are believed to be central to empathy, learning by imitation, and even language acquisition. Now, imagine giving machines a similar ability. That’s precisely what researchers are trying to do. AI systems like OpenAI’s CLIP or Google DeepMind’s Gato are trained across multiple modalities — text, images, audio, and more — to better understand human context and emotion. Of course, machines don’t feel. However, they can approximate emotional responses using vast datasets of human expression. Think of AI-generated art that captures loneliness, or chatbots that recognize your tone and respond with sympathy. Are they truly empathetic? Probably not. But can they simulate empathy well enough to be helpful? Increasingly, yes. And that opens up enormous potential — especially in fields like mental health, where AI tools could one day assist therapists by detecting early signs of distress in patients’ speech or facial expressions. Brain-Computer Interfaces (BCIs): Reading Minds, Literally Let’s go a step further. What if machines didn’t just respond to your words or actions — what if they could read your thoughts? That’s the promise of brain-computer interfaces (BCIs), a fast-growing area at the crossroads of neuroscience, AI, and hardware engineering. Companies like Neuralink (yes, Elon Musk’s venture) are developing implantable devices that allow the brain to communicate directly with computers. These chips record electrical signals from neurons and translate them into digital commands. That means someone paralyzed could one day send emails or move a robotic arm — just by thinking. Sounds incredible, right? But it’s not just Neuralink. UC San Francisco researchers recently used AI to decode brain activity into speech in real time. Meanwhile, non-invasive devices — like EEG headsets — are getting better at detecting focus, fatigue, and even emotional states. This isn’t just about convenience — it could redefine accessibility, communication, and even what it means to be human. Still, there are ethical challenges. Who owns your neural data? Can it be hacked? And what happens if the interface misfires? These questions aren’t just theoretical. As BCI tech scales, we’ll need policies to ensure it enhances autonomy rather than undermines it. Where They Merge: Shared Architectures and Inspirations As the convergence of AI and neuroscience deepens, we begin to see a fascinating blend of ideas and structures. AI models inspired by the brain are not just theoretical anymore; they are real-world tools pushing the boundaries of what we thought possible. Let’s break down some of the key areas where the two fields come together. 1. Neural Networks & Deep Learning When you look at deep learning models, you might notice something oddly familiar: the way they’re structured. Although artificial neurons are simpler, they resemble biological neurons in some ways. Deep learning models are designed with layers — just like the visual cortex in the human brain. Early layers of neural networks detect basic features like edges, and as the network gets deeper, it begins to recognize complex patterns and objects. This mimics the brain’s hierarchical system of processing information, starting from simple features and building up to complex recognition. It’s this analogy that has led to breakthroughs like image recognition and language translation. Illustration by Author — Napkin.ai 2. Reinforcement Learning and Dopamine Reinforcement learning (RL) is a type of machine learning where agents learn by interacting with an environment, making decisions, and receiving rewards. This idea of learning through rewards and punishments draws directly from neuroscience. In the brain, dopaminergic neurons play a huge role in reward-based learning. The basal ganglia, a part of the brain involved in motor control and decision-making, is activated when we receive a reward. Similarly, in reinforcement learning, an agent’s actions are reinforced based on a reward signal, guiding the system toward better choices over time. Illustration by Author — Napkin.ai 3. Memory and Attention Mechanisms Have you ever wondered how we remember important details in a conversation or a lecture, despite distractions around us? That’s the power of attention mechanisms in the brain. These mechanisms allow us to focus on the most relevant pieces of information and filter out the noise. In AI, this is mimicked by models like Transformers, which have taken the machine-learning world by storm, particularly in natural language processing (NLP). By paying attention to key parts of input data, Transformers can process entire sentences, paragraphs, or even entire documents to extract meaning more effectively. It’s what powers tools like ChatGPT, Gemmni, Grok, Deepseek, and many others. Illustration by Author — Napkin.ai 4. Neuromorphic Computing The field of neuromorphic computing is a fascinating intersection where hardware and brain science collide. Neuromorphic chips are designed to replicate the brain’s efficiency and power in processing. These chips aren’t just inspired by the brain’s architecture but also mimic the way the brain communicates via spiking neural networks, which process information in discrete pulses — similar to how neurons fire in the brain. Companies like IBM with TrueNorth and Intel with Loihi are leading the way in neuromorphic chips, creating highly energy-efficient processors that can learn from their environments, much like a biological brain. Illustration by Author — Napkin.ai Top Impactful Applications of the AI-Neuroscience Merge The possibilities that arise from the blending of AI and neuroscience are not just theoretical. They’re already shaping the future, from the way we interface with machines to how we treat mental health. Let’s explore some of the most groundbreaking applications. 1. Brain-Computer Interfaces (BCIs) If you’ve ever dreamed of controlling a machine with just your thoughts, then you’re in luck. Brain-computer interfaces (BCIs) are making this possible. Companies like Neuralink are developing technologies that allow individuals to control devices using only their brain signals. For example, BCIs could allow someone paralyzed from the neck down to move a robotic arm or type with their mind. The big breakthrough came in 2023 when Neuralink received FDA approval for human trials. While this is a huge step forward, it’s only the beginning. These technologies could revolutionize the way we interact with technology and provide life-changing solutions for people with disabilities. 2. Mental Health Diagnosis and Treatment We all know how complex mental health is. But AI has started to play a pivotal role in helping us understand and treat mental illnesses. Imagine an AI system that analyzes speech, text, and behavior to detect early signs of depression, anxiety, or even schizophrenia. Neuroscience has validated these AI models by comparing them with brain imaging techniques like fMRI. Recent studies have shown that combining fMRI scans with deep learning can predict suicidal ideation in individuals at risk, a breakthrough that could save countless lives. 3. Brain-Inspired AI Models AI is increasingly drawing inspiration from how the brain works. For example, DeepMind’s AlphaFold revolutionized protein folding predictions, but its inspiration didn’t come solely from computers. By studying how the brain processes information, DeepMind developed models that learn and adapt in ways similar to human cognition. This approach has given birth to models like Gato, a single neural architecture capable of handling hundreds of tasks — just as the human brain can handle a wide array of functions with efficiency and ease. 4. Neuroprosthetics One of the most inspiring applications of AI in neuroscience is in neuroprosthetics. These prosthetics enable people to control artificial limbs directly with their brain signals, bypassing the need for physical motion. The DEKA Arm is an example of a prosthetic that allows people with paralysis to control their arms through neural input, helping them regain lost independence. 5. Cognitive Simulation & Brain Mapping Understanding the human brain in its entirety — from the smallest neuron to the largest cognitive functions — is one of the greatest challenges of modern science. Projects like the Human Brain Project and Blue Brain Project aim to simulate entire regions of the brain using AI models trained on massive datasets. These initiatives could unlock the mysteries of consciousness and cognition, making the human brain one of the most powerful tools in science. The Future: Beyond the Intersection of AI and Neuroscience The future of AI and neuroscience is incredibly exciting, and we’re only just scratching the surface. As AI models become more advanced and neuroscience continues to uncover the brain’s mysteries, we’ll see more refined and powerful applications that can change our lives in unimaginable ways. 1. Personalized Healthcare Imagine a world where AI doesn’t just treat illnesses based on generalized data but tailors treatments to your unique brain structure. With advances in neuroimaging and AI algorithms, personalized medicine could become a reality. AI could analyze your brain’s unique structure and function to predict diseases like Alzheimer’s, Parkinson’s, or even mental health disorders, offering treatments designed specifically for you. 2. AI-Augmented Cognition In the distant future, we may see a world where AI enhances human cognition. Augmenting our natural intelligence with AI-driven enhancements could help us solve complex problems faster and more accurately. Whether it’s through direct brain interfaces or enhanced learning techniques, this fusion of AI and neuroscience could reshape human potential in ways we can’t even begin to fathom. 3. Artificial Consciousness At the intersection of AI and neuroscience, some are exploring the possibility of artificial consciousness — the idea that AI could one day become self-aware. Though this concept is still very much in the realm of science fiction, the continued merging of AI and neuroscience might eventually lead to machines that can think, feel, and understand the world just as we do. The ethical implications of such a development would be profound, but the pursuit of consciousness in AI is something many researchers are already investigating. Conclusion The merging of AI and neuroscience is not just a passing trend; it’s an ongoing revolution that promises to change the way we interact with machines, understand the brain, and even treat neurological conditions. While AI has already made incredible strides, the integration of neuroscientific insights will accelerate these advancements, bringing us closer to a future where human and machine intelligence work together seamlessly. With the potential to reshape everything from healthcare to personal cognition, the collaboration between AI and neuroscience is poised to transform both fields. The journey ahead is long, but the possibilities are endless. The brain — our most sophisticated and enigmatic organ — may soon be the blueprint for a new era of intelligence, both human and artificial. References Thank you for reading! If you enjoyed this story, please consider giving it a clap, leaving a comment to share your thoughts, and passing it along to friends or colleagues who might benefit. Your support and feedback help me create more valuable content for everyone. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI