Latest news with #MarvinMinsky
Forbes
13-07-2025
- Health
- Forbes
What Does Super-Powerful AI Look Like?
Brain activity,Human brain damage,Neural network,Artificial intelligence and idea concept Imagine you're going to visit the 'big AI boss,' the one that controls all kinds of systems: the post office, utilities, government systems, business operations, etc. You walk into the command room and there it is, one big beautiful brain, as it were, pulsating away in vibrant color while a set of wires connects its body to the network. Okay, I'll pull the curtain on this scintillating vision, because according to quite a few experts, this is not what you're going to see with general artificial intelligence. Instead, as we near the singularity, some of our best minds argue that you're likely to see something that looks more like what you would have if you visualized the Internet: something's happening over here, and something else is happening over here, and there are efficient conduits connecting everything together in real time, and it's all working smoothly, like a Swiss watch. Minsky's Mind If you read this blog with any regularity, you're probably tired of me referencing one of our great MIT people, Marvin Minsky, and his book, Society of the Mind, in which he pioneers this very idea. Minsky said, after much contemplation and research, that the human brain, despite being a single biological organ, is not one computer, but a series of many different 'machines' hooked up to one another. You might argue that this is a semantic idea: because we know, for example, how the cortex works, how the two halves of the brain coordinate, and the role of sub-organs like the amygdala. But that's not all that Minsky gave us in his treatise: he helped to introduce the idea of 'k-lines' or knowledge lines, the trajectories by which we remember things. Think of the next-hop journeys of packets along the Internet – there are similarities there. Minsky also referenced something some call the 'immanence of meaning' – the idea that meaning does not come to us inherently in the data, but arises from our processing itself. This is, in my view, very zen – in fact, if you look up the meaning of the word 'immanence,' you get the idea that it's sort of the opposite of transcendence: instead of rising above something, you go deep inside it. That makes a lot of sense, and I would argue, it gives us another useful lens with which to look at AI. When people argue about whether AI is 'real' or 'sentient' – I would say that in some ways, it's the ripples from the rock that are more real than the rock itself (to use a physical metaphor) – that the 'reality' of AI is in how we process its products. To be fair, as agents evolve, they're going to get pretty real in other ways, too. They'll be doing things and manipulating systems 24/7, getting into whatever they can get their digital hands on. That's where I wanted to cover a presentation given by Abhishek Singh at IIA in April. Here, Singh talks about our likely reaction to new digital 'species' of intelligence in a pretty compelling way. The Three-Fold Cord Singh talks about a 'trilemma' in intelligence: the intersection of three ideas or, in some cases, goals. One is scalability. Another is cooperation or coordination. The third is heterogeneity: how different are the tasks each agent completes? How fungible is one from another? Singh gives the example of a swarm of birds, and a tribe of wolves. The birds are highly homogenous, operating somewhat in unison, in a very large and scalable group. Wolves, he says, don't scale like that. 'Individuals are taking different roles, and different responsibilities,' he says. 'But at the same time, they are not (operating on) a large scale. … And what distinguishes our species, Homo sapiens, in this case, is the capability to do both high heterogeneity as well as scalability.' He mentions something used in distributed systems theory (think databases) called the CAP theorem, which says that out of three criteria, consistency, availability and partition tolerance, databases can only solve for two at once. 'You get a trilemma between these three,' he says, 'and it turns out we have a similar trilemma. It does not map exactly to distributed systems, but (there's a) similar notion in this ecosystem, of different intelligent species trying to work with each other.' Enter CHAOS Singh then cites chaos theory and its contribution to this study. 'What I'm going to introduce to you is chaos theory 2.0, which is in the context of these coordinating agents,' he explains. 'What we get to see in a centralized system is, as soon as you try to go for two of (the criteria), you are losing out on the other, and one way to get over this trilemma, not entirely, but at least (to)bridge the boundaries, is by operating in a decentralized manner.' Decentralization itself, he suggests, is not enough. 'You need to come up with algorithms (and) protocols that actually allow you to achieve these three goals in a decentralized fashion,' he says. 'And the way we are approaching this problem of bridging this trilemma is through two ideas: local protocols (and) emergent behavior.' Here's where Singh illustrates the idea that I brought up, in my own way, at the beginning of this post, perhaps in his case in a more articulate way: 'One way to think about how these two mental models fit together is the way we are solving intelligence right now,' he says. 'It's this idea of one big, large brain sitting at one large, big tech company and being capable of doing all the tasks at same time. And the other perspective, which is more coming from the decentralized angle, is these many small brains interacting with each other. None of the single small brains is powerful enough. But then together, using those protocols that I was mentioning before, there's an emergent phenomenon.' And then, interestingly, he touches on that same idea I mentioned above, that heterogeneity of tasks might be sort of a semantic idea, in that, within that one big brain, lots of different things are happening adjacent to each other. In other words, because of brain anatomy, the brain cells are not entirely fungible. 'This one big brain approach also has this notion of the trilemma, but in a fractal way,' he notes, 'where inside that one large neural network, you have lots of parameters - they're coordinating with each other, and they're solving different sub tasks, and that's why you have heterogeneity.' Watch the part of the video where Singh covers things like financial markets, social mores, and knowledge transfers, and you'll see more practical application of these ideas to real life. He also brings up the similarities between agent systems and the early Internet, where humans had to game out networking and connection with items like HTTP, SSL, etc. Singh mentions model context protocol, MCP, and sure enough, he drops the acronym NANDA, which represents MIT's own project to build an AI agent Internet protocol. Do we need more CHAOS in AI? Watch the video, and let me know.
Forbes
29-06-2025
- Health
- Forbes
Targeting The Heart With AI
Cardiologist doctor examine patient heart functions and blood vessel on virtual interface. Medical ... More technology and healthcare treatment to diagnose heart disorder and disease of cardiovascular system. It's one thing to talk about what AI will do in healthcare – they use cases and applications that will change the face of that field. It's something else to describe how this will happen – how the body's systems interact with the technology in ways that can, frankly, be pretty amazing. Our bodies are immensely complex – very sophisticated machines with literally dozens of functional systems put together in a unified whole. That's not to mention the immense structure of the human brain, which Marvin Minsky famously characterized as hundreds of machines working together in his Society of the Mind book, as well as his legacy of work at MIT. Complex Systems in Human Biology Just take the heart – the body's largest muscle, and responsible for keeping us alive by pumping blood through the body in particular ways. With its multiple chambers, its complex system of veins and arteries, its electrical impulses and more, the heart is in some ways enigmatic and difficult for clinicians to work on. The gold standard for cardiac evaluation is the EKG; at least, it has been for decades. But what if AI and other technologies could find new ways of getting cardiac information, and new ways of diagnosing and processing it for patient care? The Equipment of Cardiology Recently, my colleague, Daniela Rus, director of the MIT CSAIL lab, interviewed SandboxAQ CEO Jack Hidary at Imagination in Action this spring. They talked about specifically that: how quantum technology and artificial intelligence could be used to innovate heart care. Prior to that, though, Hidary talked about other medical use cases, pointing out, for example, that 85% of clinical trials fail, and that specific strategies with AI can save enormous amounts of time and money in looking at how proteins bind to receptors, or other outcomes. A Quick Glossary Prior to going into the specifics of new AI heart treatment Hidary referenced CUDA (Compute Unified Device Architecture) which is a parallel computing platform created by NVIDIA that allows developers to use some of the company's hardware for general-purpose and scientific computing. That's going to be relevant here. He also talks about tensors, in aid of explaining how teams can 'put quantum on GPUs' - he also mentioned quantum sensors, which are new ways to gather information by using quantum science for precision in data handling. That's where this theory on cardiology care comes in. Replacing the EKG The EKG assesses the electric field of the heart. A new quantum and AI method, Hidary suggested, would instead focus on the magnetic field of the heart. This could come through the body in a very direct and full way, in order to provide better and more detailed data. Think of it as a type of lossless signal compression that will deliver better data to cardiac assessment. 'This is something that is melding AI and quantum together,' he said. 'You can't do one without the other.' Here's how he described the process: 'Your skin conductance is very indirectly related to your heart,' Hidary said. 'Those wires (in the new system) are not on your heart itself. They're on your skin, but the magnetic field comes through the cavity of the body, undisturbed, unperturbed, intact in 360 degrees, (in data) around us that is a beautiful, pristine, high-density information view of the heart, unlike the EKG, which is very indirect and often has many false positives and many, many false negatives.' In listening to Hidary talk, you get the idea that we may be on the verge of revolutionary new kinds of heart treatments that rely on the intersection of quantum and AI to see what's really happening inside of a person's body. More on Heart Care This resource from Campanile Cardiology talks about changing care from reactive to proactive, and using pattern recognition and predictive power for early detection. The author also covers efforts to figure out the heart's 'real age' or biological age based on conditions like plaque buildup. Or you can take this set of predictions from JACC, notwithstanding the medical-ese in which they're written: · AI-enabled technologies are increasingly integrated into cardiovascular practice and investigation. · Over the next decade, we envision an AI-propelled future in which the cardiovascular diagnostic and therapeutic landscape will effectively leverage multimodal data at the point of care. · Innovations in biomedical discovery and cardiovascular research are also set to make the future of cardiovascular care more personalized, precise, and effective. · The path to this future requires equitable and regulated adoption that prioritizes fairness, equity, safety, and partnerships with innovators as well as our communities and society. In any case, it looks like we are close to unlocking new types of healthcare with the technologies at our disposal. And these are brand new. Five years ago, ten years ago, nobody was writing about these things, because they didn't functionally exist. What we've discovered is a new expanse of uncharted waters. That's going to keep us busy for quite a while.
