It’s tough as a computer science professor from a related perspective. Lots of students arbitrarily hating anything AI related because of this, including all of the traditional techniques from the 60 years prior to the rise of LLMs and diffusion models, and others misconstruing or discounting any AI class that isn’t LLM or diffusion related.

I never like to say technology is inevitable, as the inevitability argument is one of the best marketing tools major companies have to justify their poor ethics and business models (see: the gig economy founders, the “Momentum” mindset). It’s clear, though, that there is quite a paradigm shift occuring.

Not op but https://www.cbc.ca/news/politics/poilievre-to-run-in-alberta-byelection-1.7525104

Basically he asked a con in the highest % of con votes to step down to trigger a by-election. It’s an area where the other parties don’t even campaign, they just hand it to the cons.

There are already talks of “liberals rigged the election” so that he can deflect and not make it a personal failing that he lost a riding that’s historically always been conservative and lost a 25 point lead in the polls in a few months.

Yeah, I wasn’t a fan of the visual scripting, but I do consider composing nodes in the editor, connecting signals, modifying field values with sliders, having global variables in a separate editor, visual curve editors, file managers, etc. to be a form of visual scripting by a different name, and I do quite like that.

I’ve been curious how this sort of editor would work for non-game code, like making a CLI in C, C++, Kotlin, etc. Where you primarily interact with nodes and inspectors for data organization and scripts for behaviour implementation. I need to go back to Smalltalk to see some of the ideas there for alternative code organization structures.

Maybe I’m an old fogey, but I usually hear more pushback against visual languages as being too finicky to actually create anything with and I usually advocate for a blending of them, like working in Godot and having nodes to organize behaviour but written scripts to implement it.

I really appreciate the talks from Bret Victor, like Inventing on Principle (https://youtu.be/PUv66718DII), where he makes some great points about what sorts of things our tooling, in addition to the language, could do to offload some of the cognitive load while coding. I think it’s a great direction to be thinking, where it’s feasible anyways.

Also, one reason folks new to programming at least struggle with text code is that they don’t have the patterns built up. When you’re experienced and look at a block of code, you usually don’t see each keyword, you see the concept. You see a list comprehension in Python and instantly go “Oh it’s a filter”, or you see a nested loop and go “Oh it’s doing a row/column traversal of a 2d matrix”. A newbie just sees symbols and keywords and pieces each one together individually.

Insane compute wasn’t everything. Hinton helped develop the technique which allowed more data to be processed in more layers of a network without totally losing coherence. It was more of a toy before then because it capped out at how much data could be used, how many layers of a network could be trained, and I believe even that GPUs could be used efficiently for ANNs, but I could be wrong on that one.

Either way, after Hinton’s research in ~2010-2012, problems that seemed extremely difficult to solve (e.g., classifying images and identifying objects in images) became borderline trivial and in under a decade ANNs went from being almost fringe technology that many researches saw as being a toy and useful for a few problems to basically dominating all AI research and CS funding. In almost no time, every university suddenly needed machine learning specialists on payroll, and now at about 10 years later, every year we are pumping out papers and tech that seemed many decades away… Every year… In a very broad range of problems.

The 580 and CUDA made a big impact, but Hinton’s work was absolutely pivotal in being able to utilize that and to even make ANNs seem feasible at all, and it was an overnight thing. Research very rarely explodes this fast.

Edit: I guess also worth clarifying, Hinton was also one of the few researching these techniques in the 80s and has continued being a force in the field, so these big leaps are the culmination of a lot of old, but also very recent work.

Lots of good comments here. I think there’s many reasons, but AI in general is being quite hated on. It’s sad to me - pre-GPT I literally researched how AI can be used to help people be more creative and support human workflows, but our pipelines around the AI are lacking right now. As for the hate, here’s a few perspectives:

• Training data is questionable/debatable ethics,
• Amateur programmers don’t build up the same “code muscle memory”,
• It’s being treated as a sole author (generate all of this code for me) instead of like a ping-pong pair programmer,
• The time saved writing code isn’t being used to review and test the code more carefully than it was before,
• The AI is being used for problem solving, where it’s not ideal, as opposed to code-from-spec where it’s much better,
• Non-Local AI is scraping your (often confidential) data,
• Environmental impact of the use of massive remote LLMs,
• Can be used (according to execs, anyways) to replace entry level developers,
• Devs can have too much faith in the output because they have weak code review skills compared to their code writing skills,
• New programmers can bypass their learning and get an unrealistic perspective of their understanding; this one is most egregious to me as a CS professor, where students and new programmers often think the final answer is what’s important and don’t see the skills they strengthen along the way to the answer.

I like coding with local LLMs and asking occasional questions to larger ones, but the code on larger code bases (with these small, local models) is often pretty non-sensical, but improves with the right approach. Provide it documented functions, examples of a strong and consistent code style, write your test cases in advance so you can verify the outputs, use it as an extension of IDE capabilities (like generating repetitive lines) rather than replacing your problem solving.

I think there is a lot of reasons to hate on it, but I think it’s because the reasons to use it effectively are still being figured out.

Some of my academic colleagues still hate IDEs because tab completion, fast compilers, in-line documentation, and automated code linting (to them) means you don’t really need to know anything or follow any good practices, your editor will do it all for you, so you should just use vim or notepad. It’ll take time to adopt and adapt.

As someone who researched AI pre-GPT to enhance human creativity and aid in creative workflows, it’s sad for me to see the direction it’s been marketed, but not surprised. I’m personally excited by the tech because I personally see a really positive place for it where the data usage is arguably justified, but we either need to break through the current applications of it which seems more aimed at stock prices and wow-factoring the public instead of using them for what they’re best at.

The whole exciting part of these was that it could convert unstructured inputs into natural language and structured outputs. Translation tasks (broad definition of translation), extracting key data points in unstructured data, language tasks. It’s outstanding for the NLP tasks we struggled with previously, and these tasks are highly transformative or any inputs, it purely relies on structural patterns. I think few people would argue NLP tasks are infringing on the copyright owner.

But I can at least see how moving the direction toward (particularly with MoE approaches) using Q&A data to support generating Q&A outputs, media data to support generating media outputs, using code data to support generating code, this moves toward the territory of affecting sales and using someone’s IP to compete against them. From a technical perspective, I understand how LLMs are not really copying, but the way they are marketed and tuned seems to be more and more intended to use people’s data to compete against them, which is dubious at best.

Not to fully argue against your point, but I do want to push back on the citations bit. Given the way an LLM is trained, it’s not really close to equivalent to me citing papers researched for a paper. That would be more akin to asking me to cite every piece of written or verbal media I’ve ever encountered as they all contributed in some small way to way that the words were formulated here.

Now, if specific data were injected into the prompt, or maybe if it was fine-tuned on a small subset of highly specific data, I would agree those should be cited as they are being accessed more verbatim. The whole “magic” of LLMs was that it needed to cross a threshold of data, combined with the attentional mechanism, and then the network was pretty suddenly able to maintain coherent sentences structure. It was only with loads of varied data from many different sources that this really emerged.

My guess was that they knew gaming was niche and were willing to invest less in this headset and more in spreading the widespread idea that “Spatial Computing” is the next paradigm for work.

I VR a decent amount, and I really do like it a lot for watching TV and YouTube, and am toying with using it a bit for work-from-home where the shift in environment is surprisingly helpful.

It’s just limited. Streaming apps aren’t very good, there’s no great source for 3D movies (which are great, when Bigscreen had them anyways), they’re still a bit too hot and heavy for long-term use, the game library isn’t very broad and there haven’t been many killer app games/products that distinct it from other modalities, and it’s going to need a critical amount of adoption to get used in remote meetings.

I really do think it’s huge for given a sense of remote presence, and I’d love to research how VR presence affects remote collaboration, but there are so many factors keeping it tough to buy into.

They did try, though, and I think they’re on the right track. Facial capture for remote presence and hybrid meetings, extending the monitors to give more privacy and flexibility to laptops, strong AR to reduce the need to take the headset off - but they’re first selling the idea, and then maybe there will be a break. I’ll admit the industry is moving much slower than I’d anticipated back in 2012 when I was starting VR research.

I get both sides of the argument here. I think we need to have this big reaction because companies have held so much power over employees for so long - I’ll avoid ranting about worker-owned cooperatives here - but the past few years I’ve surprised myself by moving into a bit of a “slippery slope” camp with these things. Not to say it shouldn’t happen, but that we need to be prepared for the follow-up.

Hopefully related example, in education: There were some really big push backs recently where I am over bad treatment of the students in highschool, all legit. The school board ignored it for a long time, it got bad, they finally took it seriously. Then they overcorrected and stopped believing teachers at all and started jumping straight to firing at almost any complaint. Then students started weaponizing complaints, and now teachers are getting fired for trying to enforce deadlines and for giving low marks because students are complaining about how deadlines, grades, and meeting grading requirements are detrimental to mental health and well-being, and now there are a bunch of these students from this board in my university classes failing hard and filing complaints about courses being too difficult and other things despite them having glowing reviews just a few years prior.

I guess what I’m getting at: I think it’s fair for someone to choose not to hire people like this because it’s possible that the people willing to stand up and make an important fuss over these things might not know where the line stands between a worthwhile complaint and a non-worthwhile one, and might make a company look badexternally even though it’s doing good internally, just not to someone new to the workforce’s expectations.

I also think it’s fair to go the opposite direction, because ultimately we need major change in the way companies/everything are structured that lead to these nasty layoffs and poor conditions and if someone does raise issues where there aren’t, hopefully we are prepared enough and in the right enough to take it seriously, but weather it and act in everyone’s best interests.

That too, and I can really efficiently manage the items going into bags given I backpack my groceries and want pretty specific configurations…

Totally agree. I forgot about those, as I’ve only encountered the weighing ones once in the past very long time and it was a mess, I can totally get hate if weighing ones are the only experience with them.

I almost exclusivity self-checkout for groceries, and it had drastically sped up my checkout time as most people in my area opt to use traditional checkout and the stores are still keeping lots of lanes open (just closing the express lanes). The last 3 times I’ve used a non-self checkout, each time I was double charged for items or didn’t have reduced prices applied and didn’t notice because I was bagging.

Hah, yeah I learned I wasn’t so much a night person as much as someone who indulges in revenge sleep procrastinating and doesn’t get enough alone time in the day.