Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More<\/em><\/p>\n\n\n\n Graphics processing units (GPUs), the expensive computer chips made by companies like\u00a0Nvidia,\u00a0AMD,<\/span> and Sima.ai, are no longer the only way to train and deploy artificial intelligence.<\/p>\n\n\n\n Biological Black Box (BBB), a Baltimore-founded startup developing a new class of AI hardware, has emerged from stealth with its Bionode platform\u2014a computing system that integrates living, lab-grown neurons with traditional processors. <\/p>\n\n\n\n The company, which has been operating quietly while filing patents and refining its technology, believes its biological computing approach \u2014 growing new neurons specifically to act as computer chips using donor human stem cells and rat-derived cells \u2014 could offer a low-power, adaptive alternative to conventional GPUs.<\/p>\n\n\n\n \u201cOver the last 20 years, three independent fields\u2014biology, hardware, and computational tools\u2014have advanced to the point where biological computing is now possible,\u201d said Alex Ksendzovsky, BBB\u2019s co-founder and CEO, in a video call interview with VentureBeat.<\/p>\n\n\n\n A member of Nvidia\u2019s Inception incubator, BBB is positioning itself as an advancement and augmentation to the dominant silicon-based AI chips that Nvidia and others produce. <\/p>\n\n\n\n By leveraging neurons\u2019 ability to physically rewire themselves, the company aims to reduce energy costs, improve processing efficiency, and accelerate AI model training\u2014challenges that have become increasingly urgent as AI adoption expands.<\/p>\n\n\n\n This isn\u2019t sci-fi, despite the incredible premise: BBB\u2019s neural chips are already powering computer vision and LLMs for customers. The company has entered talks with two partners to license its tech for computer vision apps\u2014though the company declined to name its customers and partners specifically, citing confidentiality agreements. It is also accepting inquiries from prospective partners and clients on its website.<\/p>\n\n\n\n At the core of BBB\u2019s approach is the Bionode platform, which uses lab-grown neurons wired into computing systems.<\/p>\n\n\n\n \u201cWe have multiple models that we use,\u201d Ksendzovsky told me. \u201cOne of those models is from rat cells. One of those models is from actually human stem cells that are converted into neurons.\u201d<\/p>\n\n\n\n The co-founder said that \u201chundreds of thousands of them\u201d are integrated into a dish containing 4,096 electrodes, which forms the basis of one Bionode chip. He also said they live for over a year before needing to be replaced. <\/p>\n\n\n\n The idea is to harness neurons\u2019 natural adaptability for AI processing, creating a hybrid computing system that differs fundamentally from today\u2019s rigid, transistor-based chips.<\/p>\n\n\n\n Ksendzovsky, who has been working with neurons on electrodes since 2005, originally considered using them to predict the stock market. His mentor, Steve Potter, dismissed the idea at the time.<\/p>\n\n\n\n \u201cWhy aren\u2019t we using neurons to predict the stock market so we can all be rich?\u201d Ksendzovsky recalled asking Potter, who laughed it off as impractical. \u201cAt the time, he was right,\u201d Ksendzovsky admitted.<\/p>\n\n\n\n Since then, improvements in electrode technology, computational tools, and neuron longevity have made biological computing viable. \u201cThe biological network has evolved over hundreds of millions of years into the most efficient computing system ever created,\u201d Ksendzovsky explained.<\/p>\n\n\n\n This setup offers two immediate advantages:<\/p>\n\n\n\n \u2022 More Efficient Computer Vision:<\/strong> Bionode has been tested as a pre-processing layer for AI classification tasks, reducing both inference times and GPU power consumption.<\/p>\n\n\n\n \u2022 Accelerated Large Language Model (LLM) Training:<\/strong> Unlike GPUs, which require frequent retraining cycles, neurons adapt on the fly. This could significantly reduce the time and energy needed to update large language models (LLMs), addressing a key bottleneck in AI scaling.<\/p>\n\n\n\n \u201cOne of our biggest breakthroughs is using biological networks to train LLM\u2019s more efficiently, reducing the massive energy consumption required today,\u201d Ksendzovsky said.<\/p>\n\n\n\n Nvidia\u2019s GPUs have been instrumental in AI\u2019s rapid advancement, but their high energy consumption and increasing cost have raised concerns about scalability. <\/p>\n\n\n\n BBB sees an opportunity to introduce a more power-efficient alternative while still operating within Nvidia\u2019s ecosystem.<\/p>\n\n\n\n \u201cWe don\u2019t see ourselves as direct competitors to Nvidia, at least in the near future,\u201d Ksendzovsky noted. \u201cBiological computing and silicon computing will coexist. We still need GPUs and CPUs to process the data coming from neurons.\u201d<\/p>\n\n\n\n In fact, according to the co-founder, \u201cwe can use our biological networks to augment and improve silicon-based AI models, making them more accurate and more energy-efficient.\u201d<\/p>\n\n\n\n He argued that the long-term vision for AI hardware will be a modular ecosystem in which biological computing, silicon chips, and even quantum computing each play a role.<\/p>\n\n\n\n \u201cThe future of computing will be a modular ecosystem where traditional silicon, biological computing, and quantum computing each play a role based on their strengths,\u201d he said.<\/p>\n\n\n\n Although BBB has yet to disclose a commercial launch date, the company is relocating from Baltimore, Maryland, to the Bay Area as it prepares to scale its technology.<\/p>\n\n\n\n While silicon-based GPUs remain the industry standard, BBB\u2019s brain-on-a-chip concept presents a glimpse into a future where AI hardware is no longer limited to transistors and circuits. <\/p>\n\n\n\n The ability of neurons to reconfigure themselves dynamically could enable AI systems that are more energy-efficient, adaptive, and capable of continuous learning.<\/p>\n\n\n\n \u201cWe\u2019re already applying biological computing to computer vision. We can encode images into a biological network, let neurons process them, and then decode the neural response to improve classification accuracy,\u201d Ksendzovsky said.<\/p>\n\n\n\n Beyond efficiency gains, BBB also believes that its biological approach can provide deeper insight into how AI models process data.<\/p>\n\n\n\n \u201cWe\u2019ve built a closed-loop system that allows neurons to rewire themselves, increasing efficiency and accuracy for AI tasks,\u201d he explained.<\/p>\n\n\n\n Despite the potential, Ksendzovsky acknowledges that ethical considerations will be an ongoing discussion. BBB is already working with ethicists and regulatory experts to ensure its technology is developed responsibly. <\/p>\n\n\n\n \u201cWe don\u2019t need millions of neurons to process the entire environment like a brain does. We use only what\u2019s necessary for specific tasks, keeping ethical considerations in mind,\u201d he emphasized.<\/p>\n\n\n\n BBB is betting that living tissue, not just silicon, could be the key to AI\u2019s next leap forward.<\/p>\n Daily insights on business use cases with VB Daily<\/strong><\/p>\n If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.<\/p>\n \n\t\t\t\t\tThanks for subscribing. Check out more VB newsletters here.\n\t\t\t\t<\/p>\n An error occured.<\/p>\n<\/p><\/div>\n
\n<\/div>Blending biology and hardware<\/h2>\n\n\n\n
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Building a viable, living GPU with Nvidia\u2019s help<\/h2>\n\n\n\n
The future of hybrid AI processing<\/h2>\n\n\n\n
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