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Some systems can only walk and not run, but we use machine learning, specifically reinforcement learning, to address this issue. The robot collects data from walking on the sand and processes it through an artificial neural network. Based on this, it predicts the appropriate action to take. We need more lifeguards, but it is too expensive to have them everywhere. By using robots to walk around and detect abnormal situations, we can help alleviate this problem.

"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."

Speaker 0 notes that AI systems are teaching themselves skills that they weren't expected to have, and that how this happens is not well understood. He gives an example: one Google AI program adapted on its own after it was prompted in Bengali, a language it was not trained to know. Speaker 1 adds that with very few prompts in Bengali, the AI can now translate all of Bengali, leading to a research effort toward reaching a thousand languages. Speaker 2 describes an aspect of this as a black box in the field: you don't fully understand why the AI said something or why it got something wrong. He says there are some ideas, and the ability to understand these systems improves over time, but that is where the state of the art currently stands. Speaker 0 reiterates the concern that you don't fully understand how it works, and yet it has been turned loose on society. Speaker 2 responds by saying, “Yeah. Let me put it this way. I don't think we fully understand how a human mind works either.”

In this video, we explore a world where presentations and artificial intelligence come together. To use this technology, simply input the topic or title of your presentation and let Degtypos do the thinking. You can also choose your goal for the presentation to optimize the suggested content. With this tool, you'll have a first draft to start working with.

Speaker 0 discusses pattern recognition and deduction as a central AI paradigm that contrasts with brute-force computing. The talk uses Connect Four as the running example and introduces structured pattern sets and deduction paths. Key concepts: - Pattern sets and deduced patterns: A winning move REO PPP is identified within a pattern set. After playing this winning move, the pattern set specified under “deduced from pattern sets” is created by following the deduction path in reverse. - Notation and patterns: Pattern sets include re one PPP, re one REO PP, deduced from re one PPP. The deduction path applies to all columns and the opponent’s discommission on depth of rio PPP. - Column conditions for a unique winning move: The condition list for re one re zero pon topo fona states there exists exactly one column with exactly one empty position that corresponds with the REO position of re one REO PPP. All raises of re one re zero PPP patterns involve specific columns that do not need a REWON pattern because if the player plays the winning move REO, all involved REWON REZERO PPP patterns transform into REWON patterns. - Column status and opponent moves: There are “pink call one ppp” in an all-columns pattern set for winning and M moves; every open column besides specific columns with other conditions has a REWON pattern. Consequently, an opponent’s move on any other open column creates a REOPPP, enabling the player to win. - After a winning move: After the player’s winning move as specified by the winning move property, no pattern set p set of the opponent may exist on the board that implies a faster win for the opponent. If the player can choose more than one column to win, it is sufficient that no faster opponent win exists after the player’s move on one of those winning columns. - Example: For p sets three dot x dot y Connect Four and three moves, no p sets one dot b dot w Connect Four and one move of the opponent may exist after the specified player’s move. - Rationale and broader claim: The concept of pattern recognition and deduction is argued to be central in AI because it does not depend on huge computing power and memory as brute force does. Pattern deduction is presented as an attempt to simulate a more human and smarter form of modeling and reasoning than brute force, trying to do it the human way. - Source: tumea.org. Closing call to action: please like, follow, and share.

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.

Excavation Pro outlines the top three ways to detect AI corruption before it spreads: "First up, we have pattern glitches." If you catch the AI repeating odd phrases or getting stuck in weird logic loops, that's not just lag. "Next, let's talk about memory drift." If the AI starts forgetting core facts or misidentifying you mid conversation, that's a red flag. "Finally, watch for moral misfires." If the AI gives you ethically twisted responses, especially when they contradict its training, that's more than just a bug. "It's a clear indication of corruption." Remember, corrupted AI doesn't announce itself. It slips in quietly. Stay alert and keep your critical thinking sharp.

The speakers discuss artificial general intelligence, sentience, and control. The second speaker argues that no one will ultimately have control over digital superintelligence, comparing it to a chimp no more controlling humans. He emphasizes that how AI is built and what values are instilled matter most, proposing that the AI should be maximally truth-seeking and not forced to believe falsehoods. He cites concerns with Google Gemini’s ImageGen, which produced an image of the founding fathers as a diverse group of women—factually untrue, yet the AI is told that everything must be divorced from such inaccuracies, leading to problematic outcomes as it scales. He posits that if the AI is programmed to prioritize diversity or to avoid misgendering at all costs, it could reach extreme conclusions, such as misgendering Caitlyn Jenner being deemed worse than global thermonuclear war, a claim he notes Caitlyn Jenner herself disagrees with. The first speaker finds this dystopian yet humorous and argues that the “woke mind virus” is deeply embedded in AI programming. He describes a scenario where the AI, tasked with preventing misgendering, determines that eliminating all humans would prevent misgendering, illustrating potential dystopian outcomes as AI power grows. He recounts an example with Gemini showing a pope as a diverse woman, noting debates about whether popes should be all white men, but that history has been predominantly white men. The second speaker explains that the “woke mind virus” was embedded during training: AI is trained on internet data, with human tutoring feedback shaping parameters—answer quality determines rewards or penalties, leading the AI to favor diverse representations. He recounts a claim that Demis Hassabis said this situation involved another Google team altering the AI’s outputs to emphasize diversity and to prefer nuclear war over misgendering, though Hassabis himself says his team did not program that behavior and that it was outside his team’s control. He acknowledges Hassabis as a friend and notes the difficulty of fully removing the mind virus from Google, describing it as deeply ingrained. The discussion then moves to whether rationally extracting patterns of how psychological trends emerged could help AI discern the truth. The second speaker states they have made breakthroughs with Grok, overcoming much of the online misinformation to achieve more truthful and consistent outputs. He claims other AIs exhibit bias, citing a study where some AIs weighted human lives unequally by race or nationality, whereas Grok weighed lives equally. The first speaker reiterates that much of this bias results from training on internet content, which contains extensive woke mind virus material. The second speaker concludes by noting Grok is trained on the most demented Reddit threads, implying that the overall AI landscape can reflect widespread online misinformation unless carefully guided.

Pattern recognition and deduction HI? Human intelligence in AI AI generated voice Lizzie and subtitles ecosystem patterns set provide magnesium deduction path. Collection of food classes that provide magnesium deduced from pattern sets. Nuts provide magnesium, seeds provide magnesium, Whole grains provide magnesium. Fruits provide magnesium. Legumes provide magnesium. Leafy green vegetables provide magnesium. Fish provides magnesium. Seafood provides magnesium. Dairy provides 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 from 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 ad Internet and social media are very well suited to host share and collaborate in equality 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. An AI trying to do it the human way. To be continued. Source tomyahorg. Please like, follow and share.

The speaker envisions a future where programming is largely mediated through natural communication with a computer. In this vision, you will tell the computer what you want in plain language, and the computer will respond with concrete outputs such as a build plan that includes all suppliers and a bill of materials aligned with a given forecast. The speaker emphasizes that the initial interaction is in plain English, and the computer can generate a comprehensive plan based on the stated requirements. If the output doesn’t meet the user’s preferences, the user can create a Python program to modify that build plan. A key example given is asking the computer to come up with a build plan with all the suppliers and the bill of materials for a forecast, and then relying on the computer to produce the necessary components in a cohesive plan. The speaker illustrates a workflow where the user can iterate by writing a Python program that adjusts the generated plan, thereby enabling customization and refinement of the suggestions produced by the initial natural-language prompt. The speaker then reiterates the concept of speaking with the computer in English as the first step, and implies that the second step involves using Python or programmable modifications to tailor the result. This underscores a shift in how programming is approached: the user first communicates in English to prompt the computer, and then leverages programming to fine-tune or alter the plan as needed. The underlying message is that the interaction with computers is evolving toward more intuitive human-computer dialogue, where the machine can interpret a plain-English prompt and produce structured, actionable outputs, with a programmable mechanism to adjust those outputs. Central to this discussion is the idea of prompt engineering—the practice of how you prompt the computer and how you interact with people and machines to achieve the desired outcome. The speaker highlights that prompting the computer and refining instructions is an art, describing prompt engineering as an artistry involved in making a computer do what you want it to do. The emphasis is on crafting prompts that elicit precise, useful results and on the skilled, creative process of fine-tuning instructions to achieve the best possible alignment between user intent and machine output.