I appreciate the comment, and it’s a point I’ll be making this year in my courses. More than ever, students have been struggling to motivate themselves to do the work. The world’s on fire and it’s hard to intrinsically motivate to do hard things for the sake of learning, I get it. Get a degree to get a job to survive, learning is secondary. But this survival mindset means that the easiest way is the best way, and it’s going to crumble long-term.

It’s like jumping into an MMORPG and using a bot to play the whole game. Sure you have a cap level character, but you have no idea how to play, how to build a character, and you don’t get any of the references anyone else is making.

This is a very output-driven perspective. Another comment put it well, but essentially when we set up our curriculum we aren’t just trying to get you to produce the one or two assignments that the AI could generate - we want you to go through the motions and internalize secondary skills. We’ve set up a four year curriculum for you, and the kinds of skills you need to practice evolve over that curriculum.

This is exactly the perspective I’m trying to get at work my comment - if you go to school to get a certification to get a job and don’t care at all about the learning, of course it’s nonsense to “waste your time” on an assignment that ChatGPT can generate for you. But if you’re there to learn and develop a mastery, the additional skills you would have picked up by doing the hard thing - and maybe having a Chat AI support you in a productive way - is really where the learning is.

If 5 year olds can generate a university level essay on the implications of thermodynamics on quantum processing using AI, that’s fun, but does the 5 year old even know if that’s a coherent thesis? Does it imply anything about their understanding of these fields? Are they able to connect this information to other places?

Learning is an intrinsic task that’s been turned into a commodity. Get a degree to show you can generate that thing your future boss wants you to generate. Knowing and understanding is secondary. This is the fear of generative AI - further losing sight that we learn though friction and the final output isn’t everything. Note that this is coming from a professor that wants to mostly do away with grades, but recognizes larger systemic changes need to happen.

100%, and this is really my main point. Because it should be hard and tedious, a student who doesn’t really want to learn - or doesn’t have trust in their education - will bypass those tedious bits with the AI rather than going through those tedious, auxiliary skills that you’re expected to pick up, and use the AI was a personal tutor - not a replacement for those skills.

So often students are concerned about getting a final grade, a final result, and think that was the point, thus, “If ChatGPT can just give me the answer what was the point”, but no, there were a bunch of skills along the way that are part of the scaffolding and you’ve bypassed them through improper use of available tools. For example, in some of our programming classes we intentionally make you use worse tools early to provide a fundamental understanding of the evolution of the language ergonomics or to understand the underlying processes that power the more advanced, but easier to use, concepts. It helps you generalize later, so that you don’t just learn how to solve this problem in this programming language, but you learn how to solve the problem in a messy way that translates to many languages before you learn the powerful tools of this language. As a student, you may get upset you’re using something tedious or out of date, but as a mentor I know it’s a beneficial step in your learning career.

Maybe it would help to teach students about learning early, and how learning works.

Education has a fundamental incentive problem. I want to embrace AI in my classroom. I’ve been studying ways of using AI for personalized education since I was in grade school. I wanted personalized education, the ability to learn off of any tangent I wanted, to have tools to help me discover what I don’t know so I could go learn it.

The problem is, I’m the minority. Many of my students don’t want to be there. They want a job in the field, but don’t want to do the work. Your required course isn’t important to them, because they aren’t instructional designers who recognize that this mandatory tangent is scaffolding the next four years of their degree. They have a scholarship, and can’t afford to fail your assignment to get feedback. They have too many courses, and have to budget which courses to ignore. The university holds a duty to validate that those passing the courses met a level of standards and can reproduce their knowledge outside of a classroom environment. They have a strict timeline - every year they don’t certify their knowledge to satisfaction is a year of tuition and random other fees to pay.

If students were going to university to learn, or going to highschool to learn, instead of being forced there by societal pressures - if they were allowed to learn at their own pace without fear of financial ruin - if they were allowed to explore the topics they love instead of the topics that are financially sound - then there would be no issue with any of these tools. But the truth is much bleaker.

Great students are using these tools in astounding ways to learn, to grow, to explore. Other students - not bad necessarily, but ones with pressures that make education motivated purely by extrinsic factors than intrinsic - have a perfect crutch available to accidentally bypass the necessary steps of learning. Because learning can be hard, and tedious, and expensive, and if you don’t love it, you’ll take the path of least resistance.

In game design, we talk about not giving the player the tools to optimize their fun away. I love the new wave of AI, I’ve been waiting for this level of natural language processing and generation capability for a very long time, but these are the tools for students to optimize the learning away. We need to reframe learning and education. We need to bring learning front and center instead of certification. Employers need to recognize this, universities need to recognize this, highschools and students and parents need to recognize this.

Hmm… Nothing off the top of my head right now. I checked out the Wikipedia page for Deep Learning and it’s not bad, but quite a bit of technical info and jumping around the timeline, though it does go all the way back to the 1920’s with it’s history as jumping off points. Most of what I know came from grad school and having researched creative AI around 2015-2019, and being a bit obsessed with it growing up before and during my undergrad.

If I were to pitch some key notes, the page details lots of the cool networks that dominated in the 60’s-2000’s, but it’s worth noting that there were lots of competing models besides neural nets at the time. Then 2011, two things happened at right about the same time: The ReLU (a simple way to help preserve data through many layers, increasing complexity) which, while established in the 60’s, only swept everything for deep learning in 2011, and majorly, Nvidia’s cheap graphics cards with parallel processing and CUDA that were found to majorly boost efficiency of running networks.

I found a few links with some cool perspectives: Nvidia post with some technical details

Solid and simplified timeline with lots of great details

It does exclude a few of the big popular culture events, like Watson on Jeopardy in 2011. To me it’s fascinating because Watson’s architecture was an absolute mess by today’s standards, over 100 different algorithms working in conjunction, mixing tons of techniques together to get a pretty specifically tuned question and answer machine. It took 2880 CPU cores to run, and it could win about 70% of the time at Jeopardy. Compare that to today’s GPT, which while ChatGPT requires way more massive amounts of processing power to run, have an otherwise elegant structure and I can run awfully competent ones on a $400 graphics card. I was actually in a gap year waiting to go to my undergrad to study AI and robotics during the Watson craze, so seeing it and then seeing the 2012 big bang was wild.

Oh for sure. And it’s a great realm to research, but pretty dirty to rip apart another field to bolster your own. Then again, string theorist…

For me, it’s the next major milestone in what’s been a roughly decade-ish trend of research, and the groundbreaking part is how rapidly it accelerated. We saw a similar boom in 2012-2018, and now it’s just accelerating.

Before 2011/2012, if your network was too deep, too many layers, it would just breakdown and give pretty random results - it couldn’t learn - so they had to perform relatively simple tasks. Then a few techniques were developed that enabled deep learning, the ability to really stretch the amount of patterns a network could learn if given enough data. Suddenly, things that were jokes in computer science became reality. The move from deep networks to 95% image recognition ability, for example, took about 1 years to halve the error rate, about 5 years to go from about 35-40% incorrect classification to 5%. That’s the same stuff that powered all the hype around AI beating Go champions and professional Starcraft players.

The Transformer (the T in GPT) came out in 2017, around the peak of the deep learning boom. In 2 years, GPT-2 was released, and while it’s funny to look back on now, it practically revolutionized temporal data coherence and showed that throwing lots of data at this architecture didn’t break it, like previous ones had. Then they kept throwing more and more and more data, and it kept going and improving. With GPT-3 about a year later, like in 2012, we saw an immediate spike in previously impossible challenges being destroyed, and seemingly they haven’t degraded with more data yet. While it’s unsustainable, it’s the same kind of puzzle piece that pushed deep learning into the forefront in 2012, and the same concepts are being applied to different domains like image generation, which has also seen massive boosts thanks in-part to the 2017 research.

Anyways, small rant, but yeah - it’s hype lies in its historical context, for me. The chat bot is an incredible demonstration of the incredible underlying advancements to data processing that were made in the past decade, and if working out patterns from massive quantities of data is a pointless endeavour I have sad news for all folks with brains.

I understand that he’s placing these relative to quantum computing, and that he is specifically a scientist who is deeply invested in that realm, it just seems too reductionist from a software perspective, because ultimately yeah - we are indeed limited by the architecture of our physical computing paradigm, but that doesn’t discount the incredible advancements we’ve made in the space.

Maybe I’m being too hyperbolic over this small article, but does this basically mean any advancements in CS research are basically just glorified (insert elementary mechanical thing here) because they use bits and von Neumann architecture?

I used to adore Kaku when I was young, but as I got into academics, saw how attached he was to string theory long after it’s expiry date, and seeing how popular he got on pretty wild and speculative fiction, I struggle to take him too seriously in this realm.

My experience, which comes with years in labs working on creative computation, AI, and NLP, these large language models are impressive and revolutionary, but quite frankly, for dumb reasons. The transformer was a great advancement, but seemingly only if we piled obscene amounts of data on it, previously unspeculated of amounts. Now we can train smaller bots off of the data from these bigger ones, which is neat, but it’s still that mass of data.

To the general public: Yes, LLMs are overblown. To someone who spent years researching creativity assistance AI and NLPs: These are freaking awesome, and I’m amazed at the capabilities we have now in creating code that can do qualitative analysis and natural language interfacing, but the model is unsustainable unless techniques like Orca come along and shrink down the data requirements. That said, I’m running pretty competent language and image models on 12GB of relatively cheap consumer video card, so we’re progressing fast.

Edit to Add: And I do agree that we’re going to see wild stuff with quantum computing one day, but that can’t discount the excellent research being done by folks working with existing hardware, and it’s upsetting to hear a scientist bawk at a field like that. And I recognize I led this by speaking down on string theory, but string theory pop science (including Dr. Kaku) caused havoc in people taking physics seriously.

My cynical guess is that’s what they’re hoping the community will do (“like lemmings, I tell you!” - spez, probably) to drive higher traffic numbers before some announcement or meeting.

Yeah, I really think it’s important to not see Lemmy as one singular community, or a lot of important use cases will go ignored.

Only when it’s intentionally censored and trained to react in a particular way. When it’s not, you remember it was trained on random internet content.

I think it would be a great system to easily donate to instance hosts if it was supported as an instance opt-in feature.

I might be crazy, but I’m wondering if we’ll bypass this in the long run and generate 2D frames of 3D scenes. Either having a game be low-poly grayboxed and then each frame is generated by an AI doing image-to-image to render it out in different styles, or maybe outright “hallucinating” a game and it’s mechanics directly to rendered 2D frames.

For example, your game doesn’t have a physics engine, but it does have parameters to guide the game engine’s “dream” of what happens when the player presses the jump button to produce reproducible actions.

Yeah, I think framing it similar to the old days might help, but I could be wrong. Like, you aren’t signing up for (just to web-equivalent) PHP Fusion or something, you’re signing up for your gaming clan’s forum, or your roleplay group, or your Canadian phreak BB. The difference with Lemmy is just that you also indirectly sign up to receive content from a lot of other places using the same protocol.

IMO, I think the framing/abstraction will make or break the future of the paradigm for mainstream consumption. Not to get into another repeat of the EEE discussion, but assuming nothing nefarious from something like Threads, that would mean people start an account there and then find a niche group with their friends to go hang out on instead.

I also have to push back against the pushback against the paradigm going mainstream, because again IMO a move back toward decentralized platforms is really important for the future of the internet and quite frankly the global economy.

Just editing to expand, but I think maybe there’s a problem in framing Lemmy or Mastodon as communities in themselves, because it really conflicts with the model of instancing and email that is being used to describe them.

Same! If you know of any online courses suitable for postsecondary students looking to build tech skills I would appreciate it, otherwise I might need to try getting a duty reallocation for a bit to put time into building one.

I agree with this, but we’d need to draw lines in the analogy. For example, my CS students struggle with downloading and installing a program and don’t know how to locate find files that they’ve saved in a text editor. We’d be concerned if the people driving didn’t know where their turn signal was, hah.

A lot of students grew up using Chromebooks as their primary computer, so they’re largely limited to app stores and web browsers.

I think the main fear would be that a few really cool communities naturally spark up, even if they’re niche, and could long term create that fracture when you have to choose between keeping with that community (and any corporate backed extensions) or not.