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Mind Matters Reporting on Natural and Artificial Intelligence

Eric Holloway

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Current Artificial Intelligence Research Is Unscientific

The assumption that the human mind can be reduced to a computer program has never really been tested

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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Math Shows Why the Mind Is Not Just a Formula

The Liar’s Paradox shows that even mathematics cannot be reduced to a fixed set of axioms

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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A Philosopher Explains Why Thinking Matter Is Impossible

He’s right but Captain Kirk tumbled to it before him. So did a medieval poet

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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Quantum Mechanics Shows That Our Universe Has Purpose

Not only can two physically separated particles influence each other, they can influence each other through time

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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Will an AI Win a Nobel Prize for Science All by Itself One Day?

No, but Support Vector Machines (SVMs) can allow scientists to frame questions so that a comprehensible answer is more likely

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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Playing Tetris Shows That True AI Is Impossible

Here’s a look inside my brain that will show you why

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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What Vehicle Would Bob Buy?

Both empirical generalized information (EGI) and the Gini metric can generate useful information

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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Machine learning: Harnessing the Power of Empirical Generalized Information (EGI)

We can calculate a probability bound from entropy. Entropy also happens to be an upper bound on the binomial distribution

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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Arctang Knows He Must Choose

Arctang stood aghast. “How could you believe any of this? Do you really think you are just a program on a computer?" —Trumind Serial, Part 8

“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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Missing piece

Machine Learning: Decision Trees Can Solve Murders

Decision trees increase our chance at a right answer. We can see how that works in a mystery board game like Clue

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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Machine Learning: Using Occam’s Razor to Generalize

A simple thought experiment shows us how

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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Empath—the Ultimate Technology—Cares About You

Arctang discovers how empathy can be manufactured—Trumind Serial, Part 6

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 vs. IBM?: Quantum Supremacy Isn’t the Big Fix Anyway

If human thought is a halting oracle, then even quantum computing will not allow us to replicate human intelligence

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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Occam’s Razor Can Shave Away Data Snooping

The greater an object's information content, the lower its probability.

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 ›

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Machine Learning, Part 3: Don’t Snoop on Your Data

You risk using a feature for prediction that is common to the dataset, but not to the problem you are studying

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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Machine Learning, Part 2: Supervised Learning

Machine learning isn’t hard to understand; it’s just different. Let’s start with the most common type

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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Finally… the Ultimate Smart Machine

Suddenly, out of nowhere, a small startup called TruMind made the AI dream a reality—Trumind Serial, Part 6

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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Mindtrap

Is immortality worth risking the unthinkable? — Trumind serial, part 5

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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The Challenge of Teaching Machines to Generalize

Teaching students simply to pass tests provides a good illustration of the problems

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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A Singular Space Adventure Takes A Twist…

The old man could buy anything but youth. Until now, maybe. —TruMind serial, part 4

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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