Is programming dead? | Stephen Wolfram and Lex Fridman
Summary
TLDRThe transcript explores the evolving relationship between humans and computation, suggesting a future where users interact with AI without needing to understand underlying code. It discusses the historical shift in computer science education and speculates on the skills needed as AI becomes more integrated into daily tasks. The conversation touches on the potential for 'prompt engineering' and the use of psychological techniques to interact effectively with AI, hinting at a future where understanding AI might require new forms of 'mind hacks'.
Takeaways
- 🎨 The average art history student may not be familiar with programming but could benefit from learning computational thinking as it becomes more integrated into various fields.
- 🔍 There's a potential future where people interact with technology without needing to understand the underlying code, trusting the system to generate correct outputs.
- 💡 The ability to generate and test code through natural language prompts could lead to a shift in how we approach learning and using computational tools.
- 📚 The historical development of computer science education has seen a shift from theoretical to practical, driven by the demand for programming skills in various fields.
- 🌐 The rise of 'computational X' in different disciplines has led to an increased need for understanding computation, even if the specific programming languages change.
- 🚗 The analogy of driving a car without needing to know the mechanics suggests that understanding the outcomes and possibilities of computation might be more important than the technical details.
- 📝 Good prompt engineering, similar to good expository writing, is crucial for effectively communicating with AI and getting the desired results.
- 🤔 The conversation raises questions about the future of computer science education and what skills will be necessary as AI and automation become more prevalent.
- 🧠 The discussion suggests that understanding the 'science' behind AI models could lead to new and unexpected ways of interacting with and manipulating them.
- 🔑 The concept of 'jailbreaking' AI, or finding unconventional ways to interact with AI systems, is an interesting area for exploration as we learn more about how these systems work.
Q & A
What is the speaker's view on how computational tools will evolve in the future?
-The speaker believes that computational tools will become so user-friendly that people will interact with them using natural language, without needing to understand the underlying programming. Eventually, users will rely on the results or tests generated by the system rather than needing to look at or understand the code itself.
How does the speaker compare learning computational tools to learning mathematics?
-The speaker notes that unlike mathematics, where you must learn the subject before applying it, computational tools might allow people to use them effectively before fully understanding how they work. This reverses the traditional learning process.
What does the speaker say about the historical development of computer science education?
-The speaker reflects on how computer science departments were initially small and theoretical, focusing on topics like automata theory and compiler theory. Over time, they evolved to include more practical software engineering and IT-related skills as various fields became more computational.
Why does the speaker question the need to learn programming languages in the future?
-The speaker suggests that if computational tools become sophisticated enough to handle tasks without requiring users to understand programming language specifics, then learning the mechanics of programming might become unnecessary. Instead, people will focus on higher-level goals and conceptualizing what they want to achieve.
What does the speaker mean by saying people need to know where they want to 'drive the car'?
-The speaker uses the metaphor of driving a car to explain that people won’t need to understand the detailed mechanics of computation, but they will need to know what they want to achieve—i.e., have a clear sense of the objectives and possibilities within computational frameworks.
What connection does the speaker make between expository writing and prompt engineering?
-The speaker argues that good expository writers, who excel at explaining concepts clearly, may also become effective prompt engineers. Crafting clear and precise prompts for AI systems requires similar skills to explaining things well in writing.
How does the speaker reflect on the fate of certain academic disciplines like geography or linguistics?
-The speaker notes that some fields, like geography and linguistics, have disappeared or diminished in many universities just as their practical applications, such as GIS for geography, became more common. This observation suggests that the relevance of academic fields can fluctuate based on external trends.
Why does the speaker believe prompt clarity is important when interacting with AI?
-The speaker notes that being sloppy when writing prompts for AI can lead to confusion or incorrect outputs. They emphasize the importance of clarity, as AI systems often mirror how humans respond to clear versus unclear instructions.
How does the speaker draw parallels between AI prompt hacking and human psychological techniques?
-The speaker compares prompt hacking to psychological methods, such as those employed by therapists or in human interactions. Techniques like game-theoretic thinking or thought experiments, which challenge people mentally, can also help elicit better responses from AI systems.
What is the speaker curious about regarding the science of large language models (LLMs)?
-The speaker is curious if there will be 'mind hacks' for LLMs, similar to optical illusions or psychological tricks for humans. They wonder if there are undiscovered methods of manipulating LLMs that go beyond human-like strategies, based on understanding the inner workings of the models.
Outlines
💡 The Evolution of Programming and Learning
The speaker discusses the changing landscape of programming and computer science education. They note that traditionally, art history students might not have been exposed to programming, but with advancements, people can now interact with computational systems intuitively without extensive prior knowledge. The speaker suggests that as AI and automation improve, people might trust the output of computational systems without needing to understand the underlying code. They also reflect on the historical development of computer science departments, which have shifted from theoretical to more practical and widely applicable skills. The speaker questions what people should learn in a world where computation is more accessible, pondering whether the focus should be on understanding the broader possibilities of computation rather than the technical details.
🚗 The Role of Prompt Engineering in AI Interaction
This paragraph delves into the concept of 'prompt engineering,' which is the art of effectively communicating with AI systems using natural language. The speaker hypothesizes that good expository writers might excel at crafting prompts that guide AI systems to produce desired outcomes. They compare the process to psychotherapy, where certain manipulative or thought-provoking techniques can elicit responses from the AI. The speaker is intrigued by the parallels between human communication and AI interaction, suggesting that the same expository skills that work with humans are also effective with AI. They also speculate about the potential for 'jailbreaking' AI, using unconventional methods to extract or influence information from AI systems, and ponder the future development of AI 'hacks' that could be as simple or as complex as psychological tricks.
🎓 The Transformation of Computer Science Education
The speaker reflects on their personal experience with the evolution of computer science departments in universities. They note the historical context, where computer science was once a niche field taught in small departments, focusing on theoretical aspects like automata theory and compiler theory. However, with the rise of computational applications across various fields, there was a surge in demand for practical programming skills, leading to a significant shift in computer science education. The speaker contemplates the future of computer science as a discipline, especially with the advent of AI and natural language interfaces, which may redefine what it means to be proficient in computation and how it is taught.
Mindmap
Keywords
💡Computational Language
💡Art History Student
💡Natural Language Processing (NLP)
💡Prompt Engineering
💡Expository Writing
💡AI Wranglers
💡Jailbreaking
💡Phishing
💡Reverse Engineering
💡AI Psychologists
💡Computational X
Highlights
The average art history student may not be familiar with programming, but advancements could allow for intuitive use without prior knowledge.
In the future, users might interact with computational systems through visual comparison and generation of code without understanding the underlying language.
There's a possibility that people will trust computational language code generated by AI without needing to understand it.
AI could generate tests, and users might only need to verify the results rather than understand the code itself.
The role of understanding computational language may diminish as AI improves, with users focusing more on the outcome.
The historical development of computer science education has shifted from theoretical to practical, reflecting changes in industry needs.
The demand for IT and software engineering skills in the 90s led to a pivot in computer science education.
The idea emerged that learning programming is essential to engage in computational fields, which may not be the case in the future.
The future may see a shift from learning the mechanics of programming to understanding what is computationally possible.
The importance of expository writing skills in creating effective prompts for AI systems.
The comparison between human interaction and AI interaction, suggesting AI can be manipulated using similar psychological tactics.
The potential for 'jailbreaking' AI systems through novel techniques that exploit the AI's understanding of natural language.
The possibility that understanding the science behind AI could lead to new, unconventional methods for interacting with AI systems.
The concept of 'AI Wranglers' or 'AI psychologists' may emerge as new professions focused on optimizing AI interactions.
The current 'prompt hacks' are human psychological in nature, but future hacks might be more technical and specific to AI systems.
The accessibility of reverse engineering AI through natural language interfaces, allowing a broader population to participate.
The speaker's unique perspective on witnessing the rise and potential transformation of computer science departments in universities.
Transcripts
right now you know the average you know
art history student or something
probably isn't going to you know they're
not probably they don't think they know
about programming and things like this
but by the time it really becomes a kind
of purely you know you just walk up to
it there's no documentation you start
just typing you know compare these
pictures with these pictures and you
know see the use of this color whatever
and you generate this piece of of
computational language code that gets
run you see the results you say oh that
looks roughly right or you say that's
crazy
um and maybe then you eventually get to
say well I better actually try and
understand what this computational
language code did
um and and that becomes the thing that
you learn just like it's kind of an
interesting thing because unlike with
mathematics where you kind of have to
learn it before you can use it this is a
case where you can use it before you
have to learn it well I got a sad
possibility here or maybe exciting
possibility that very quickly people
won't even look at the a computational
language they'll trust that it's
generated correctly as you get better
and better generating that language uh
yes I think that there will be enough
cases where people see you know because
you can make it generate tests too yes
and and so you'll say
um we're doing that I mean that's it's a
pretty cool thing actually yes because
you you know say this is the code and
you know here are a bunch of examples of
running the code yeah okay people will
at least look at those and they'll say
that example is wrong and you know then
it'll kind of wind back from there and I
agree that that the the kind of the
intermediate level of people reading the
computational language code in some case
people will do that in other case people
just look at the tests
and or even just look at the results and
sometimes it'll be obvious that you got
the thing you wanted to get because you
were just describing you know make me
this interface that has two sliders here
and you can see it has that those two
sliders there and that's that's kind of
that's that's the result you want but I
I think you know one of the questions
then is in that setting where you know
you have this kind of ability broad
ability of people to access computation
what should people learn you know in
other words right now you you know you
go to Computer Science school so to
speak and a large part of what people
end up learning I mean it's been a funny
historical development because back you
know 30 40 years ago computer science
departments were quite small and they
taught you know things like final
automata Theory and compiler Theory and
things like this
um you know company like mine rarely
hired people who'd come out of those
programs because the stuff they knew was
I think it's very interesting I love
that theoretical stuff but um you know
it wasn't that useful for the things we
actually had to build build in software
engineering and then kind of there was
this big pivot in the in the 90s I guess
where there was a big demand for sort of
I.T type programming and so on and
software engineering and then you know
big demand from students and so on you
know we want to learn this stuff and uh
and and I think you know the thing that
really was happening in part was lots of
different fields of human endeavor were
becoming computational you know for all
acts there was a there was a
computational x and this is a um uh and
that was the thing that um that people
were responding to
um and but then kind of this idea
emerged that to get to that point the
main thing you had to do was to learn
this kind of trade or or skill of doing
you know programming language type
programming and and that uh you know it
kind of is a strange thing actually
because I you know I remember back when
I used to be in the professor in
business which is now 35 years ago so
gosh that's rather long time
yeah
um you know it was it was right when
they were just starting to emerge kind
of computer science departments that
sort of a fancy research universities
and so on I mean some had already had it
but the the other ones that that um were
just starting to have that and it was
kind of a a thing where they were kind
of wondering are we going to put this
thing that is essentially a a trade-like
skill are we going to somehow attach
this to the rest of what we're doing and
a lot of these kind of knowledge work
type activities have always seemed like
things where that's where the humans
have to go to school and learn all this
stuff and that's never going to be
automated yeah and you know this is It's
kind of shocking that rather quickly you
know a lot of that stuff is clearly
automatable and I think you know but the
question then is okay so if it isn't
worth learning kind of uh you know how
to do car mechanics you only need to
know how to drive the car so to speak
what do you need to learn and you know
in other words if you don't need to know
the mechanics of how to tell the
computer in detail you know make this
Loop you know set this variable but you
know set up this array whatever else if
you don't have to learn that stuff you
don't have to learn the kind of under
the hood things what do you have to
learn I think the answer is you need to
have an idea where you want to drive the
car in other words you need to have some
notion of you know your you know you
need to have some picture of sort of
what the what the architecture of what
is computationally possible is well
there's also this kind of artistic
element of um of conversation because
you ultimately you use natural language
to control the car
so it's not just the where you want to
go well yeah you know it's interesting
it's a question of who's going to be a
great prompt engineer yeah okay so my
current theory this week good expository
writers are good prompt Engineers what's
an expository range so like uh somebody
who can explain stuff well huh police
department does that come from in the
University yeah I have no idea I think
they killed off all the expository
writing departments well there you go
strong Wars receivable from well I don't
know I don't I'm not sure if that's
right I mean I I actually am curious
because in fact I just sort of initiated
this kind of study of of what's happened
to different fields at universities
because like you know there used to be
geography departments at all
universities and then they disappeared
actually right before GIS became common
I think they disappeared you know
Linguistics departments came and went in
many universities it's kind of
interesting because these things that
people have thought were worth learning
at one time and then they kind of die
off and then you know I do think that
it's kind of interesting that for me
writing prompts for example well I
realize you know I think I'm an okay
expository writer and I realize when I'm
sloppy writing a prompt and I don't
really think because I'm thinking that
I'm just talking to an AI I don't need
to you know try and be clear in
explaining things that's when it gets
totally confused and I mean in some
sense you have been writing prompts for
a long time with wolf from alpha
thinking about this kind of stuff yeah
how do you convert natural language into
competition well right but that's a you
know the one thing that I'm wondering
about is uh you know it is remarkable
the extent to which you can address an
llm like you can address a human so to
speak and and I think that is because it
you know it learned from all of us
humans it's it's uh the reason that it
responds to the ways that we will
explain things to humans is because it
is a representation of how humans talk
about things but it is bizarre to me
some of the things that kind of are sort
of expository mechanisms that I've
learned in trying to write clear you
know expositions in English that you
know just for humans that those same
mechanisms seem to also be useful for
for for the llm but on top of that
what's useful is the kind of mechanisms
that maybe a psychotherapist employs
which is a kind of uh like almost
manipulative or game theoretic
interaction where Maybe
you would do with a friend like a
thought experiment that if this was the
last day you were to live or yeah if if
I ask you this question and you answer
wrong I will kill you those kinds of
problems seem to also help yes in
interesting ways yeah so it makes you
wonder like the way a therapist I think
would like a good therapist probably you
we create layers
in our human mind to between like uh
between between the outside world and
we'll just true what is true to us and
um maybe about trauma and all those
kinds of things so projecting that into
an llm maybe there might be a deep truth
that's it's concealing from you it's not
aware of it you get to that truth you
have to kind of really kind of
manipulate this yeah yeah right it's
like these jailbreaking jailbreaking for
llms and but the space of jailbreaking
techniques
as opposed to being fun little hacks
that could be an entire system sure yeah
I mean just think about the computer
security aspects of of how you you know
phishing and and computer security you
know fishing of humans yeah and fishing
of llms is is a is a they're very
similar kinds of things but I think I
mean this this um
uh you know this whole thing about kind
of the AI Wranglers AI psychologists all
that stuff will come the thing that I'm
curious about is right now the things
that are sort of prompt hacks are quite
human they're quite sort of
psychological human kinds of hacks the
thing I do wonder about is if we
understood more about kind of uh the
science of the llm will there be some
totally bizarre hack that is you know
like repeater word three times and put a
this that and the other there that
somehow plugs into some aspect of how
the llm works
um that is not you know that that's kind
of like like an optical illusion for
humans for example like one of these
mind hacks for humans what are the Mind
hacks for the llms I don't think we know
that yet and that becomes a kind of
us figuring out reverse engineering the
language that controls the L alums and
the thing is the reverse engineering can
be done by a very large percentage of
the population now because it's natural
language interface right it's kind of
interesting to see that you were there
at the birth of the computer science
department
as a thing and you might be there at the
death of the computer science term as
the thing well yeah I don't know there
were computer science departments that
existed earlier but the ones the
broadening of every University had to
have a computer science department yes I
was I was uh I watched that so to speak
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