Because AI research is based on a fundamental assumption that has not been scientifically tested—that the human mind can be reduced to a computer—then the research itself cannot be said to be scientific.
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Gödel’s discovery brought back a sense of wonder to mathematics and to the rest of human knowledge. His incompleteness theorem underlies the fact that human investigation can never exhaust all that can be known. Every discovery builds a path to a new discovery.
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According to analytical philosopher Richard Johns, we cannot represent ourselves completely mathematically so we cannot generate fundamentally contradictory thoughts about ourselves. Some part of us lies beyond mathematics. An android would not be so lucky, as Captain Kirk realized in an early Star Trek episode.
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Recent experiments in entanglement of particles in time as well as space show that our entire universe is imbued with final causality within its very fabric. This final causality must come from some source beyond the universe.
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AI can certainly help scientists. But to understand why AI can’t do science on its own, we should take a look at the NP-Hard Problem in computer science. The “Hard” is in the name of the problem for a reason…
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The intensity of my mental processing brought about an observable brain state. The causality did not go in the other direction; the magenta brain state did not increase my conscious process. This type of observation causes a problem for those hoping to duplicate human intelligence in a computer program.
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Contrary to traditional Fisherian hypothesis testing, it is possible to create models after viewing the data and still quantify the generality of the model.
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We want our calculation to demonstrate the notion that if we have high accuracy and a small model, then we have high confidence of generalizing. Intuitively, then, we add the model size to the accuracy and subtract this quantity from the entropy of having absolutely no information about the problem.
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“I can give you a taste of the future even before you undergo the operation,” Eclar explained, “I have a nouspace unit right here with me.”
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Entropy is a concept in information theory that characterizes the number of choices available when a probability distribution is involved. Understanding how it works helps us make better guesses.
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This approach is contrary to Fisher's method where we formulate all theories before encountering the data. We came up with the model after we saw the cards we drew.
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Arctang was one of the few outsiders who knew TruMind's secret. Long ago, his elder brother had entered the fortress-like facility, never to be seen again.
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Google’s quantum supremacy claim is certainly fascinating and controversial, but even if true, it ultimately only amounts to an incremental and even inconsequential improvement in the state of AI and ML, due to the still-unmet need for a halting oracle.
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One technique to avoid data snooping is based on the intersection of information theory and probability: An object’s probability is related to its information content. The greater an object’s information content, the lower its probability. We measure a model’s information content as the logarithmic difference between the probability that the data occurred by chance and the number of bits required to store the model. The negative exponential of the difference is the model’s probability of occurring by chance. If the data cannot be compressed, then these two values are equal. Then the model has zero information and we cannot know if the data was generated by chance or not. For a dataset that is incompressible and uninformative, swirl some tea Read More ›
As long as we can establish that our theories, hypotheses, and/or models are independent of the data, then we can trust that their predictive power will generalize beyond the data we have observed.
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The neat thing about machine learning is that the algorithm can extract general principles from the dataset that can then be applied to new problems. It is like the story that Newton observed an apple fall and then derived from it the general law of gravity that applies to the entire universe.
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While the skeptics said it could not be done, and even industry veterans and the most idealistic AI pioneers had serious doubts, TruMind revolutionized the entire world of technology seemingly overnight with the TruMind capsule.
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Once he’d lit up the entire sequence and it was displayed back to him above the number pad, Johann felt a tremendous euphoric rush of success, the likes of which he'd never felt in his life, even at the close of his greatest deals.
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We want the machine learning algorithms to learn general principles from the data, and not merely little tricks and trivia that that score high but ignore problems.
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He was the richest man in history, having amassed wealth through his inheritance and that of a few other unfortunate people.
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