> “The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.” ~ Edsger W. Dijkstra
The point of the Turing Test is that if there is no extrinsic difference between a human and a machine the intrinsic difference is moot for practical purposes. That is not an argument to whether a machine (with linear algebra, machine learning, large language models, or any other method) can think or what constitutes thinking or consciousness.
I kind of agree but I think the point is what people mean by words is vague, so he said:
>Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.
which is can you tell the AI answers from the humans ones in a test. It then becomes an experimental result rather than what you mean by 'think' or maybe by 'extrinsic difference'.
The Chinese Room is a pretty useless thought exercise I think. It's an example which if you believe machines can't think seems like an utterly obvious result, and if you believe machines can think it's just obviously wrong.
People used to take it surprisingly seriously. Now it's hard to make the argument that machines can't understand say Chinese when you can give a Chinese document to a machine and ask it questions about it and get pretty good answers.
> For example, if a user with appropriate privileges mistakenly runs ‘rm -rf / tmp/junk’, that may remove all files on the entire system. Since there are so few legitimate uses for such a command, GNU rm normally declines to operate on any directory that resolves to /. If you really want to try to remove all the files on your system, you can use the --no-preserve-root option, but the default behavior, specified by the --preserve-root option, is safer for most purposes.
That was added in 2006, so didn’t exist for a good half of its life (even longer if you count pre-GNU). I remember rm -rf / being considered just one instance of having to double-check what you do when using the -rf option. It’s one reason it became common to alias rm to rm -i.
> An AACS encryption key (09 F9 11 02 9D 74 E3 5B D8 41 56 C5 63 56 88 C0) that came to prominence in May 2007 is an example of a number claimed to be a secret, and whose publication or inappropriate possession is claimed to be illegal in the United States.
This is a silly take for anyone in tech. Any binary sequence is a number. Any information can be, for practical purposes, rendered in binary [1].
Getting worked up about restrictions on numbers works as a meme, for the masses, because it sounds silly, but is tantamount to technically arguing against privacy, confidentiality, the concept of national secrets, IP as a whole, et cetera.
> Any piece of digital information is representable as a number; consequently, if communicating a specific set of information is illegal in some way, then the number may be illegal as well.
There is thought-stopping satire and thought-provoking satire. Much of it depends on the context. I’m not getting the latter from a “USA land of the ‘free’” comment.
> It depends on where you live. In many places, collecting rainwater is completely legal and even encouraged, but some regions have regulations or restrictions.
United States: Most states allow rainwater collection, but some have restrictions on how much you can collect or how it can be used. For example, Colorado has limits on the amount of rainwater homeowners can store.
Australia: Generally legal and encouraged, with many homes using rainwater tanks.
UK & Canada: Legal with few restrictions.
India & Many Other Countries: Often encouraged due to water scarcity.
I think so; I joined Reddit when it was in tech news as people left Digg after the big redesign. I'm not sure when the exodus started. I left Fark over the hd-dvd mess.
In both cases, legality depends entirely on repercussions, i.e. if there's someone to enforce the ban. I suspect that in the "illegal numbers" case there might be.
> The question of whether a computer can think is no more interesting than the question of whether a submarine can swim. ~ Edsger W. Dijkstra
LLMs / Generative Models can have a profound societal and economic impact without being intelligent. The obsession with intelligence only make their use haphazard and dangerous.
It is a good thing court of laws have established precedent that organizations deploying LLM chatbots are responsible for their output (Eg, Air Canada LLM chatbot promising a non-existent discount being responsibility of Air Canada)
Also most automation has been happening without LLMs/Generative Models. Things like better vision systems have had an enormous impact with industrial automation and QA.
The conclusion of the article admits that in areas where stochastic outputs are expected these AI models will continue to be useful.
It’s in area where we demand correctness and determinism that they will not be suitable.
I think the thrust of this article is hard to see unless you have some experience with formal methods and verification. Or else accept the authors’ explanations as truth.
- Inserts its own affiliate link (even when no discount is found, uses strategies to push for interaction like adding a dismiss/pay with paypal link that adds the affiliate association)
- Adds a very small kickback from the affiliate payment they receive as a rewards program. (Which, while scraps, makes content creators "lose" in economic terms in the affiliate offerings)
- Promises to consumers to find the best discounts available
- Promises to vendors to allow control of the discounts offered and the offer rate of said discounts
- Previous both promises are contradictory yet simultaneously offered
- An extra/upcoming claim around forcing non-affiliated stores to affiliate.
These people care little of the potential consequences, they operate from a particular mental model in that regard [1]. The harm will be done. When they swan dive from a reputational and legal perspective, step back and the problem solves itself.
> 4. Rewarding employees who make value for the business and think like founders/equity owners, not employees.
That is simple to do but not something many companies want to do. Just give employees equity via mutualisation. (Real ownership not discourse ownership)
Why would you think that? Lobbying organizations exist to advance the interests of their members. Their members in this case are businesses. This will restrict the control businesses have over their former employees. Therefore, they don’t like it.
The point of the Turing Test is that if there is no extrinsic difference between a human and a machine the intrinsic difference is moot for practical purposes. That is not an argument to whether a machine (with linear algebra, machine learning, large language models, or any other method) can think or what constitutes thinking or consciousness.
The Chinese Room thought experiment is a compliment on the intrinsic side of the comparison: https://en.wikipedia.org/wiki/Chinese_room