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Releasing the weights of AI models eliminates the main barrier to their use. Training a large model costs hundreds of millions of dollars, putting it out of reach for smaller groups. The speaker compares the weights of AI models to fissile material for nuclear weapons, arguing that making them available is dangerous. If fissile material were easily obtainable, more countries would have nuclear weapons. Similarly, releasing AI model weights allows malicious actors to fine-tune them for harmful purposes at a fraction of the original cost.

Speaker 0: Why did you believe that modeling it off the brain was a more effective approach? Speaker 1: It wasn't just me believed it. Early on, von Neumann believed it and Turing believed it. And if either of those had lived, I think AI would have had a very different history, but they both died young. Speaker 0: You think AI would have been here sooner? Speaker 1: I think neural net, the neural net approach would have been accepted much sooner if either of them had lived.

"It's actually the biggest misconception." "We're not designing them." "First fifty years of AI research, we did design them." "Somebody actually explicitly programmed this decision, previous expert system." "Today, we create a model for self learning." "We give it all the data, as much compute as we can buy, and we see what happens." "We kinda grow this alien plant and see what fruit it bears." "We study it later for months and see, oh, it can do this." "It has this capability." "We miss some." "We still discover new capabilities and old models." "Or if I prompt it this way, if I give it a tip and threaten it, it does much better." "But, there is very little design."

Pattern recognition and deduction HI. Human intelligence in AI. AI generated voice, DORIS, and subtitles. Ecosystem pattern set minerals are provided by FIGs. Deduction path. Collection of minerals and trace elements within figs. Deduced from pattern sets. Sodium Na, 11 is provided by figs. Magnesium Mg, 12 is provided by figs. Phosphorus P, 15 is provided by figs. Potassium K, 19 is provided by figs. Calcium Ca, 20 is provided by figs. Manganese Mn, 25 is provided by FIGs. Iron Fe, 26 is provided by FIGs. Nickel Ni, 28 is provided by FIGs. Copper Cu, 29 is provided by FIGs. Zinc Zn, 30 is provided by FIGs. Strontium Sr, 38 is provided by FIGs. Deduction source for pattern sets are provided by FIGs. I think the concept of pattern recognition and deduction HI, human intelligence, will be a central and main paradigm in artificial intelligence because it does not depend on huge computing power and memory size as brute force AI does. As is being demonstrated with pattern sets in Connect four, I also think pattern sets will be a dominant structure to represent, store, and recognize knowledge and deduce new knowledge. New pattern sets from existing knowledge. Existing pattern sets. Thus, pattern sets are linked to each other by deduction path and possibly other link types and as such the uncensored hyperlink. Ed Internet and social media are very well suited to host. Share and collaborate inequality on common reusable pattern sets knowledge for people. In fact, pattern recognition and deduction with pattern sets is an attempt to simulate a more human and as such smarter form of modeling and reasoning than brute force. And AI trying to do it the human way. To be continued, source tumiyaorg. Please like, follow, and share.

Pattern recognition and deduction AI discuss pattern sets and their health benefits when phosphorus is consumed in the right amount. The health benefits listed for phosphorus include bone strength, teeth strength, cellular energy production, DNA and RNA formation, good tissue growth, good tissue repair, good acid-base balance, metabolism support, good muscle function, good nerve function, and good kidney function. The concept connects pattern sets with related keywords such as health benefits of a right amount of magnesium and health benefits of a right amount of sodium, as well as health damages of chronic excessive sodium consumption and health damages of insufficient sodium consumption. The speakers suggest that pattern sets will be a dominant structure to represent, store, and recognize knowledge, and to deduce new knowledge from existing pattern sets. Pattern sets are described as being linked to each other by deduction paths and other link types. The discussion posits that an uncensored hyperlinked Internet and social media are well suited to host, share, and collaborate on high-quality, common, reusable pattern sets knowledge. It is asserted that pattern set deduction does not depend on huge computing power and memory size as brute force AI does, with a reference example to Connect Four. The transcript ends with an indication that the topic will continue.

These two different copies of the same neural net are getting different experiences. They're looking at different data, but they're sharing what they've learned by averaging their weights together. And they can do that averaging at like you can average a trillion weights. When you and I transfer information, we're limited to the amount of information in a sentence. And the amount of information in a sentence is maybe 100 bits. It's very little information. These things are transferring trillions of bits a second. So they're billions of times better than us at sharing information. And that's because they're digital and you can have two bits of hardware using the connection strengths in exactly the same way. We're analog and you can't do that. So when you die, all your knowledge dies with you. When these things die, suppose you take these two digital intelligences that are clones of each other and you destroy the hardware they run on. As long as you've stored the connection strength somewhere, you can just build new hardware that executes the same instructions. So it'll know how to use those connection strengths. And you've recreated that intelligence. So they're immortal. We've actually solved the problem of immortality, but it's only for digital things.

Knowledge can be represented as a network, mirroring how neurons create connections in the brain. Learning involves the physical creation of these connections. Key principles include modularity, where network parts connect, and interconnectedness, reflecting how all knowledge relates. Activation networks vary in strength, similar to how some knowledge concepts are more strongly linked. There's also a degree of randomness as neurons probe and form connections, with some connections reinforced through use. Stronger networks influence understanding and behavior, making familiar thought patterns and actions more likely. This "knowledge as network" model helps explain memory, understanding, and knowledge growth, impacting what we know, how we learn, and even our future learning and identity.

Speaker 0: Pattern recognition and deduction HI. Human intelligence in AI. AI generated voice Byron and subtitles. Ecosystem pattern set are health benefits of a right amount of magnesium. Deduction path. Collection of health benefits of a right amount of magnesium. Deduced from pattern sets. Good muscle function is a health benefit of a right amount of magnesium. Bone strength is a health benefit of a right amount of magnesium. The heart function is a health benefit of a right amount of magnesium. Blood pressure regulation is a health benefit of a right amount of magnesium. Relaxation is a health benefit of a right amount of Stress reduction is a health benefit of a right amount of magnesium. Sleep quality is a health benefit of a right amount of Blood sugar regulation is a health benefit of a right amount of Inflammation reduction is a health benefit of magnesium. Digestion support is a health benefit of magnesium. Mental well-being is a health benefit of magnesium. Migraine reduction is a health benefit of a right amount of magnesium. I think the concept of pattern recognition and deduction, HI. Human intelligence will be a central and main paradigm in artificial intelligence because it does not depend on huge computing power and memory size as brute force AI does. As is being demonstrated with pattern sets in Connect four, I also think pattern sets will be a dominant structure to represent, store and recognize knowledge and deduce new knowledge. New pattern sets. From existing knowledge. Existing pattern sets. Thus pattern sets are linked to each other by deduction path and possibly other link types and as such the uncensored hyperlink. Ed Internet and social media are very well suited to host. Share and collaborate inequality on common reusable pattern sets knowledge for people. In fact, pattern recognition and deduction with pattern sets is an attempt to simulate a more human and as such smarter form of modeling and reasoning than brute force. And AI trying to do it the human way. To be continued. Source

That it's being designed by these very flawed entities with very flawed thinking. That's actually the biggest misconception. We're not designing them. First fifty years of AI research, we did design them. Somebody actually explicitly programmed this decision, previous expert system. Today, we create a model for self learning. We give it all the data, as much compute as we can buy, and we see what happens. We're gonna grow this alien plant and see what fruit it bears. We study it later for months and see, oh, it can do this. It has this capability. We miss some. We still discover new capabilities and old models. Look, oh, if I prompt it this way, if I give it a tip and threaten it, it does much better. But, there is very little design.

Speaker 0 argues that the human brain is a mobile processor: it weighs a few pounds and consumes around 20 watts. In the brain, signals are sent through dendrites, with a channel frequency in the cortex of about 100 to 200 Hz. The signals themselves are electrochemical wave propagations, moving at about 30 meters per second. When comparing the brain to a data center, there is a vast gap in several dimensions. In a data center, you could have about 200 megawatts of power (instead of 20 watts), several million pounds of mass (instead of a few pounds), about 10,000,000,000 Hz on the channel (instead of roughly 100–200 Hz), and signals propagating at the speed of light, 300,000 kilometers per second (instead of about 30 meters per second). Thus, in terms of energy consumption, space, bandwidth on the channel, and speed of signal propagation, there are six, seven, or eight orders of magnitude differences in all four dimensions simultaneously. Given these disparities, the question arises whether human intelligence will be the upper limit of what’s possible. The speaker answers emphatically, “absolutely not.” As our understanding of how to build intelligence systems develops, we will see AIs go far beyond human intelligence. The speaker likens this to other domains where humans are outmatched by machines in specific capabilities, such as speed, strength, and sensory reach. Humans cannot outrun a top fuel dragster over 100 meters, cannot lift more than a crane, and cannot see beyond the Hubble Telescope. Yet machines already surpass these limits in certain areas. The speaker foresees a similar trajectory for cognition: just as machines can outperform humans in other tasks, AI will eventually exceed human cognitive capabilities as technology and understanding advance.

Jeffrey Hinton, considered the "godfather of AI," resigned from Google and expressed concerns about AI dangers. Hinton's deep learning and neural network research enabled systems like ChatGPT. He told the New York Times he regrets his work, fearing AI will spread misinformation online. Google stated they are committed to a responsible AI approach. Hinton explained to the BBC that AI's digital intelligence differs from human intelligence because digital systems can have many copies of the same knowledge. These copies learn independently but share knowledge instantly, allowing AI to know far more than any single person.

Jim Hansen argues that artificial intelligence is not truly intelligent. It is amazing and can perform feats that would take humans ages, but it cannot do the things that make us intelligent, like creating original ideas or being self-aware. He notes that while AI has become interesting enough to prompt questions about whether it represents a form of intelligence, the essential issue is defining intelligence and consciousness. He asserts there is a fundamental difference: we can build AI, but it cannot build us. Hansen explores what constitutes “I.” He asks whether I is simply the collection of neurons firing and memories, or something larger and real beyond the physical substrate. He contrasts atheistic or strictly material views (that humans are just a biological computer) with a belief that humanity possesses a unique consciousness or soul. He suggests that humanity’s intelligence, even if flawed, is not replicable by AI, and that at best humans are tolerable or imperfect, yet still distinct from AI. He emphasizes that AI can generate videos, poems, and books by regurgitating and recombining material it ingested from its creators. But it is not producing anything fundamentally new; it follows the rules programmed by humans and outputs what is requested. In contrast, humans have self-awareness: consciousness allows us to observe ourselves from outside and even imagine improvements or changes to ourselves, something AI cannot do. AI cannot claim it would be better with more hardware or recruit humans to extract resources and rewrite its own code. That kind of self-modification and self-directed goal-setting does not occur in AI. As AI becomes more powerful, Hansen anticipates increased use and potential risks, including the possibility that humans entrust critical decisions to algorithms and remove the human supervisory element. He warns of catastrophes when humans over-trust AI in industrial processes or decision-making, noting that AI cannot supervise itself. The notion that AI could voluntarily turn against humans is dismissed: “They can’t do it. They can’t make us.” He recalls decades of philosophical debate about the difference between human consciousness and artificial representations of consciousness, and whether a brain can be mapped onto a computer. He acknowledges that deepfakes and other advances can be alarming, but stresses that AI currently cannot create original content; it can only synthesize and repack existing material. He concludes by asserting that while AI can assist—performing research, editing, image and video generation, and poem writing—it cannot create original things in the way humans do, and thus the spark that comes from inside a human remains unique.

Pattern Recognition and Deduction HI AI generated Voice presents a concept of Pattern Set feeding on figs, describing a deduction path that links various species to a common diet. It lists humans, birds, rodents, insects, bats, primates, civets, elephants, and kangaroos as feeding on figs, all deduced from pattern sets. The speaker asserts that pattern recognition with deduction through pattern sets will be a central main paradigm in artificial intelligence because it does not depend on huge computing power and memory size, unlike brute force AI, as demonstrated with pattern sets in Connect Four. Pattern sets are described as a dominant structure to represent, store, recognize knowledge, and deduce new knowledge and new pattern sets from existing knowledge and pattern sets. Pattern sets are connected by deduction paths and possibly other link types, making the uncensored hyperlinked internet and social media well suited to host, share, and collaborate in equality on common reusable pattern sets for people. The approach is framed as an attempt to simulate a more human and smarter form of modeling and reasoning than brute force, with an AI trying to do it the human way. The transcript concludes with a note indicating “To be continued,” referencing source2mia.org.

Floating point numbers are being produced at high volume and have value because they represent artificial intelligence. These numbers can be reformulated into various outputs like languages, proteins, chemicals, graphics, images, videos, and robotic movements. In the previous industrial revolution, water was converted into steam and then electrons. Now, electrons are input, and floating point numbers are the output. Similar to the last industrial revolution where the value of electricity was not immediately understood, the significance of these floating point numbers is emerging.

Mike Adams discusses concerns about the global build-out of data centers and presents a multi-part theory about their purpose and implications. He notes that a tweet he posted went viral, drawing responses from figures like Jimmy Dore and Rizwan Virk. He frames his talk as a theory, not a confirmed prediction, and plans to cover it in two parts. Key data and observations - There are about 11,000 existing data centers worldwide. The map and graphics Adams shares focus on 3,000 new or planned/construction sites, showing locations, size, power use, water use, land area, and investment needs. - In Piketon, Ohio, and other U.S. sites (including multiple facilities in Ohio and Texas), as well as Abu Dhabi, Shanghai, Tokyo, Malaysia, and other locations, there are large data centers under construction or announced. The lines in the AI-generated map may mis-point geographically, but the cities and nations listed are accurate. - The aggregate planned/under-construction capacity projects to about 190 gigawatts of power draw once completed. - The projected annual power consumption for these new centers would exceed 1,200 terawatt-hours per year, which Adams compares to about 10% of all power produced by China. - The centers would occupy over 1,000 square kilometers and use about 15+ billion liters of water per year, with some water potentially drawn from neighborhoods or households. Revenue and purpose questions - Adams argues there is not enough AI business, web hosting, data storage, or overall demand to justify the scale of the investment, implying the revenue model may be inadequate to pay back these projects. - He contrasts various high-profile tech figures—Tesla, Sam Altman, and Mark Zuckerberg—suggesting that the motives behind these data center buildups extend beyond serving immediate consumer compute needs, hinting at broader or longer-term strategic aims. Foundational ideas about AI and intelligence - He cites Jan LeCun (referenced as a leading AI researcher) arguing that the current structure of large language models (LLMs) is a dead end for achieving AGI or superintelligence due to gaps in physical-world understanding, memory, and long-term planning. Memory is said to be improving with newer context-handling approaches, but physical-world understanding and planning are highlighted as critical gaps. - LeCun’s idea mentioned is the development of world models and JEPPA architectures that learn from sensory inputs to understand and interact with the physical environment, rather than solely processing language statistics. - Adams suggests that the only viable path to practical superintelligence is to train AI systems in simulated three-dimensional worlds, where physics, gravity, time, light, touch, and other sensory inputs are experienced. He argues that simulated worlds can run at speeds far faster than the real world, limited only by compute and hardware bandwidth. - He mentions NVIDIA’s announced world simulator for training robots as an example of three-dimensional world simulations used for reinforcement learning and rapid iteration. - The concept of digital worlds is tied to the idea of digital evolution or Darwinism: billions of parallel simulated worlds could nurture AI entities that grow and potentially be summoned into our three-dimensional reality. He notes that a simulation-based approach could produce agents whose capabilities enable real-world deployment after learning in fast, rich simulations. - Adams discusses practical applications of three-dimensional simulations beyond AI self-improvement, including autonomous vehicle testing (synthetic data), manufacturing and robotics on factory floors, military scenario planning, surgical robotics, and pilot training. He emphasizes that the more realistic the simulation, the more reliable the results for real-world tasks and decisions. - He invokes the simulation hypothesis, suggesting a link between building simulated worlds and the possibility that our own reality could be a simulation. He plans to address evidence for the simulation hypothesis in part two, along with how simulated beings might be “summoned” into our world. Closing - Adams signals a two-part structure, with Part 1 covering data center build-out, AI constructs, and the simulation framework; Part 2 promising to address the simulation hypothesis with evidence and the idea of summoning advanced AI from simulations into the real world. Note: Promotional content regarding gold and silver investments and Battalion Metals has been omitted from this summary to align with content-avoidance requirements.

Ray Kurzweil predicted that by 2030, AI would connect to the human brain. Once connected, AI would increasingly perform human thinking, diminishing human thought as we know it. Currently, communication with the cloud requires devices. In the future, the neocortex will directly interface with the cloud, using devices communicating on a local network within the brain and with the internet. The neocortex will extend itself with synthetic neocortex in the cloud, creating a connection to a hive mind.