Do you really think that humans are so special as to encode all their knowledge in language? Watch a movie. Listen to a song. Examine a piece of art. Feel sculpture. Play a guitar. Dance.
There is a segment of the software community that is highly language centric/adept. But that community is often blind to other forms of understanding.
Just look at the language of Shakespeare. Much of the language is visual and experiential. How much would you actually understand without your senses and imagination? Your knowledge encompasses your being.
The real mechanistic view drops language as a special case and just says their is no knowledge, only behavior.
Well, actually, yes, they do. Many animals have elaborate languages encompassing many concepts. Crows can explain to one another what a human looks like, for example.
If there were two versions of this poem that started the same way, it would pick between the variations in the corpus randomly. In other cases it might choose based on the style of prose or other stuff like that.
GPT-3 can get some trivia right, but it's only because the editors of Wikipedia already came to consensus about it and Wikipedia was weighted more. It doesn't have a way of coming to a consistent conclusion on its own.
Without consistency, how can it be said to know or believe anything? You might as well ask what a library believes. Sure, the authors may have believed things, but it depends which book you happen to pick up.
But I do think you're missing the point just a bit. When we speak and think, we use all kinds of metaphors that express judgements about the world, usually without realizing it. In other words, the way we use language encodes concepts in a deep way.
To borrow an example from George Lakoff, we, in English, use war-metaphors to talk about arguments. Of arguments and of wars you can say things like "he's marshalling his forces," "they're ceding their territory," or "she's girding her defenses". In fact, almost anything you can say about a war you can also say about an argument. In American politics, with regard to partisan squabbling and the filibuster, we talk about "the nuclear option". The fact that these metaphors make sense to us indicates a judgement, something like "arguments are like wars". That judgement shows up in billions of lines of English scraped from the internet and can be fed into a model, allowing GPT-3 to "make that connection" via purely statistical methods.
Yes, this is a bit like asking "what a library believes". But a lot of these metaphors show up in our languages and, in a way, they express judgements, which is something akin to a belief. Does that mean a library has beliefs? Is this all knowledge is? I wouldn't go that far. But the argument is an interesting one and worth raising.
But a sophisticated understanding of metaphors could be used to tell the truth or to lie. In the case of GPT-3, it doesn't know the difference. Telling the truth and lying come out of the same autocompletion process.
If you consider the use of a metaphor to be showing judgement, it means that a particular metaphor seems to be appropriate to use in a particular context.
> We’re very far from having machines that can learn the most basic things about the world in the way humans and animals can do. Like, yes, in particular areas machines have superhuman performance, but in terms of general intelligence we’re not even close to a rat. This makes a lot of questions people are asking themselves premature. . That’s not to say we shouldn’t think about them, but there’s no danger in the immediate or even medium term. There are real dangers in the department of AI, real risks, but they’re not Terminator scenarios.
That's pretty measured overall, but he doesn't know that there's no existential AI risk in the medium term. No one does, and that's the problem. Experts simply suspect that it's unlikely. Stuart Russell and him have debated similar topics [2].
To tie back to your point: I keep seeing LeCun brush over tricky questions like yours and the ones at [2] with an arrogant confidence. I wish that he would be more careful, and I hope that I have a skewed view of him.
[1] https://www.theverge.com/2017/10/26/16552056/a-intelligence-...
[2] https://www.lesswrong.com/posts/WxW6Gc6f2z3mzmqKs/debate-on-...
Except people are bad at exponential processes. Yet when economics drives us we are suddenly good at making them happen. And this combo seems to be what makes these existential risks. (Like climate change, or other manifestations of the coordination problem.)
In our heads, language is a combination of words and concepts, and knowledge can be encoded by making connections between concepts, not simply words. If there is no concept or idea backing up the words, it can hardly be called knowledge. Consider the case of the man who did not speak French, yet memorised a French dictionary, and subsequently went on to win a Scrabble competition. Just because he knows the words, would you say he knows the language?
A language model such as GPT-3 operates only on words, not concepts. It can make connections between words on the basis of statistical correlations, but has no capacity for encoding concepts, and therefore cannot "know" anything.
Great point.
> A language model such as GPT-3 operates only on words, not concepts. It can make connections between words on the basis of statistical correlations, but has no capacity for encoding concepts, and therefore cannot "know" anything.
Are you sure? Aren't "concepts" encoded in how language is used, at least to some degree?
LeCun does say that models that explicitly attempt represent knowledge perform better than GPT-3 in terms of answering questions. I'm no expert but I believe him.
Good point and I think this shows up to the extent different languages might affect how we express particular concepts.
However I think it is more accurate to say that language solidifies and gives form to how we express concepts and the “concepts” themselves are independent of languages. Only our “expression” of these “concepts” depends on language.
For anyone interested in art and art history, this distinction was the central focus of the French surrealist painter Rene Magritte.
Logical empiricism was ultimately a dead end as the criteria for even verifying empirical truth has long been contentious philosophically, and was further critiqued by contemporaries such as Quine who attacked the premise of the analytic/synthetic distinction (think Hume's fork, which Kant tried to solve) and Popper who cited the problem of induction to critique the fundamental premises of the positivists verificationism.
Wittgenstein is an interesting case, as the Tractatus is considered an early work of his, profoundly influential to analytic philosphy at the time, yet his later work, Philosophical Investigations is sometimes seen to retract the dogmatism found in the Tractatus. I tend to take the view that it's a continuation of his thought, rather than a retraction of his earlier work. Crudely, whereas his former thought represented a narrowly axiomatic definition of language and its truth value, PI investigates, among many other ideas, language as an activity, or game, that has meaning dependent on the context of its use, languages as families. Granted, Wittgenstein is a complex thinker and these are simply my interpretations.
It's also curious to note that as positivism was beginning to fall out of favor around the time of the second world war, a continental thinker such as Heidegger, whose thought luxuriated in the kind of metaphysical questions the positivists necessarily eschewed, rose to prominence and was infamously sanctioned by the NSDAP to philosophize about their presumed "destiny". Bit of a tangent, but I think the historical context is relevant, as often philosophical movements are birthed from pre- and post-war attitudes.
Of course, you could always attempt to define knowledge such that it is purely verbal, or alternatively define whatever is going on in the brain of an animal to be language, but is either approach useful? In common usage, we recognise, as knowledge, various things that cannot be communicated by language, such as knowing how to ride a unicycle on a tightrope (I doubt you can learn it just from a book) and the infamous qualia which supposedly prove that the mind is dualistic. And what about the knowledge of how to use language? How does that get bootstrapped?
This can lead to some confusion. In Frank Jackson's Knowledge Argument[1], dualists say that qualia are knowledge, knowledge is language, and so if Mary could not learn qualia by studying science, then materialism is false. This argument trades on being inconsistent over what it means to have knowledge of something.
Most expressions of language that survived from a few thousand years ago are centered around myths, and while those myths may have contained certain moral or ethical lessons (that were and are subject to interpretation) they certainly weren't tightly coupled to reality in an objective sense.
Training on expressions of language (I separate the concept of language itself from its expression in the form of writing, speaking, etc) certainly has use cases but can GPT-3 recognize a previously unknown analogy and correlate it with the proper piece of applicable "knowledge" it has? If not then it really has no understanding.
1. i get where you're coming from
2. yes, language is bottlenecked by human perception, as are all things
3. even the notion of myth and fiction is encoded in language. language is self-descriptive and self-aware and you can separate sense from non-sense.
4. i'm not talking about knowledge or understanding, but of addressing the question of why training on language let's GPT-3 make human-like predictions as if it knows about reality? either it's a fluke, or it's because language as a whole is a model that approximates reality.
There's little doubt that GPT-3 could hold its own quite well in a bout of polite conversation and/or small talk that features in many social situations but that's more a commentary on the limited area of knowledge and behavior that etiquette expects for interactions in such settings.
It could also be trained on the canon of Shakespeare and behave as a prior work of Shakespeare, but if you left one play out of that canon and then attempted to have GPT-3 generate that missing work it wouldn't... at all. It doesn't approximate how Shakespeare thought, it approximates the literature he produced that you used for training.
None of this is to denigrate the achievement that GPT-3 represents, it is merely to point out that it is unfair to GPT-3 to attempt to hold it to the standards of AGI.
If it's only philosophical, then me saying that Hacker News Website itself has 'knowledge' of everything we discuss about is also philosophical. Same can be applied to plain paper books.
How about any web application? A for loop? anything which can generate something for you?
> GPT-3 doesn't have any knowledge of how the world actually works.
I think this is a philosophical question. There is a view that, basically, there is no such thing as knowledge, just language (or, at least, there is no distinction between knowledge and language). In this view, all there really is is language, which is mostly composed of metaphors and, ultimately, metaphors only refer to other metaphors, i.e. language is circular. In this view, not only is the ultimate, physical, concrete world beyond us but also we can't even talk about it. From this perspective, GPT-3 is not substantively different than what our minds are doing.
That view makes some strong claims (I don't find it convincing), but it's out there. A slightly different claim, though, is that "knowledge of how (we think) the world actually works" is encoded in language. To me, that seems trivially true. So, again, how you take this quote from LeCun depends on what you think knowledge is and your view of the relationship between knowledge and language.