Category: Technology

AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic

David Leave a comment

2024-07-28 Gary Marcus

My strong intuition, having studied neural networks for over 30 years (they were part of my dissertation) and LLMs since 2019, is that LLMs are simply never going to work reliably, at least not in the general form that so many people last year seemed to be hoping. Perhaps the deepest problem is that LLMs literally can’t sanity-check their own work.

…

Since LLMs inevitably hallucinate and are constitutionally incapable of checking their own work, there are really only two possibilities: we abandon them, or we use them as components in larger systems that can reason and plan better, much as grownups and older children use times tables as part of a solution for multiplication, but not the whole solution.

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The idea is to try to take the best of two worlds, combining (akin to Kahneman’s System I and System II), neural networks, which are good at kind of quick intuition from familiar examples (a la Kahneman’s System I) with explicit symbolic systems that use formal logic and other reasoning tools (a la Kahneman’s System II).

https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt

These bubbles kill cancer

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2023-10-06 Jim Lynch

The process [of histotripsy] uses a transducer—which converts electricity into sound—to deliver ultrasound waves to a malignant mass at a precise location. When the waves hit gasses inside cancerous cells, they generate clouds of tiny bubbles through a process known as cavitation.

Pulsing sound waves causes the millimeter-sized bubble clouds to repeatedly grow and collapse. On an ultrasound monitor, it can look like bubbles from boiling water—quickly rising and falling along the surface in your pot.

In the past, researchers saw the creation of bubbles through ultrasound as “uncontrollable,” something to be avoided. Histotripsy, however, generates mechanical energy to activate those bubble clouds and break up the tumor cells’ structure, turning it into a liquid called acellular lysate.

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histotripsy foils cancer’s cloaking efforts by destroying its cell walls, leaving the tumor antigens in plain sight for the body’s immune system.

https://news.engin.umich.edu/2023/10/these-bubbles-kill-cancer/

What Are Dreams For?

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2023-08-31 Amanda Gefter

In a series of papers, Blumberg articulated his theory that the brain uses REM sleep to “learn” the body. You wouldn’t think that the body is something a brain needs to learn, but we aren’t born with maps of our bodies

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In 2013, Blumberg published a paper in Current Biology titled “Twitching in Sensorimotor Development from Sleeping Rats to Robots.” In it, he asked, “Can twitching, as a special form of self-generated movement, contribute to a robot’s knowledge about its body and how it works?”

https://www.newyorker.com/science/elements/what-are-dreams-for

Password advice

David Leave a comment

There have been news stories about how the Office of Inspector General, U.S. Department of the Interior found weak passwords in the Department of Interior’s Active Directory accounts:

The OIG report:
P@s$w0rds at the U.S. Department of the Interior: Easily Cracked Passwords, Lack of Multifactor Authentication, and Other Failures Put Critical DOI Systems at Risk
It has password advice on page 8:

NIST SP 800–63 recommends using passphrases instead of passwords …

Password vs. Passphrase Examples
Password = 5pr1ng*Ish3re
Passphrase = DinosaurLetterTrailChance

I believe the passphase words have to be chosen randomly from a large word list to be effective, but it is easier to remember than a complex password.

Geoffrey Hinton’s Forward-Forward Algorithm Charts a New Path for Neural Networks

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2022-12-08 by Synced

there is increasing interest in whether the biological brain follows backpropagation or, as Hinton asks, whether it has some other way of getting the gradients needed to adjust the weights on its connections. In this regard, Hinton proposes the FF [Forward-Forward] algorithm as an alternative to backpropagation for neural network learning.

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It aims to replace the forward and backward passes of backpropagation with two forward passes: a positive pass that operates on real data and adjusts weights “to improve the goodness in every hidden layer,” and a negative pass that operates on externally supplied or model-generated “negative data” and adjusts weights to deteriorate the goodness.

https://syncedreview.com/2022/12/08/geoffrey-hintons-forward-forward-algorithm-charts-a-new-path-for-neural-networks/

The Forward-Forward Algorithm: Some Preliminary
Investigations by Geoffrey Hinton
https://www.cs.toronto.edu/~hinton/FFA13.pdf

Using the Forward-Forward Algorithm for Image Classification
Includes sample Python code using the Keras Python library.
https://keras.io/examples/vision/forwardforward/

A Thought on the Lovelace Test

David Leave a comment

The Lovelace test demands of a computing machine that it not only produce an artifact that is by conventional standards amazing, but it leaves everyone looking at it
stupefied as to how it does what it does: including in this stupefaction the creators and designers of the machine.
– Selmer Bingsjord [1]

 

An artificial agent, designed by a human, passes the [Lovelace] test only if it originates a “program” that it was not engineered to produce. The outputting of the new program—it could be an idea, a novel, a piece of music, anything—can’t be a hardware fluke, and it must be the result of processes the artificial agent can reproduce. Now here’s the kicker: The agent’s designers must not be able to explain how their original code led to this new program. [2]

 

My thought is if God made humans, can a human use creativity to make an artifact, so that God can’t explain how it was made?

 

[1]
Artificial Intelligence: Will Machines Take Over? (Science Uprising, Ep. 10)
2022-09-21 YouTube channel: “Discovery Science”
https://youtu.be/suuxAZbDCYE?t=362

[2]
Forget Turing, the Lovelace Test Has a Better Shot at Spotting AI
2014-07-08 Jordan Pearson
https://www.vice.com/en/article/pgaany/forget-turing-the-lovelace-test-has-a-better-shot-at-spotting-ai