Coding assistants today are useful, image generation is useful, speach recognition/generation is useful.

All of these can support businesses, even in their current (early) state. Those businesses have value in funding even 1% improvements in engineering/science.

I think that this is different than before, where even in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable.

Where as in the past, hopes for the technology waned and funding for research dropped off a cliff, today's stuff is useful now, and so companies will continue to spend some amount on the research side.

Anyway, considering all these things can be done on device, where is the long term business prospect of which you speak?

Now try to mute a video on youtube and understand what's being said from the automatic subtitles.

If you do it in english, be aware that it's the best performing language and all others are even worse.

1. It's overwhelmingly more useful than the [no text] it was replacing, particularly for the deaf or if you want to search for keywords in a video.

2. When it fails, it tends to do so in ways that trigger human suspicion and oversight.

Those aren't necessarily true of some of the things people are shoehorning LLMs into these days, which is why I'm a lost more pessimistic about that technology.

https://news.ycombinator.com/item?id=41199567#41201773

I expect that YouTube will up their transcription game soon, too.

Basically it's kinda useful to put time tags, but I need to manually fix each and every sentence. Sometimes I need to fix the time tags as well.

I just spoke about youtube because it's more popular and easy to test.

I've come to notice a correlation between contemporary AI optimism and having effectively made the jump to coding with AI assistants.

I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants.

For that past few years most of the code I've written has been solving fairly niche quantitative problems with novel approaches and I've found AI coding assistants to range from useless to harmful.

But on a recent webdev project, they were much more useful. The vast majority of problems in webdev are fundamentally not unique so a searchable pattern library (which is what an LLM coding assistant basically is) should be pretty effective.

For other areas of software, they're not nearly as useful.

I think this is true and also why you see some "older devs just don't like AI" type comments. AI assistants seem to be great at simple webdev tasks, which also happens to be the type of work that more junior developers do day to day.

I have also found them useful with that and I keep one active for those types of projects because of the speed up, although I still have to keep a close eye on what it wants to inject. They also seem to excel at generating tests if you have already developed the functions.

Then there are more difficult (usually not webdev) projects. In those cases, it really only shines if I need to ask it a question that I would previously have searched on SO or some obscure board for an answer. And even then, it really has to be scrutinized, because if it was simple, I wouldn't be asking the question.

There is def. something there for specific types of development, but it has not "changed my life" or anything like that. It might have if I was just starting out or if I only did webdev type projects.

If this thing also conferred an actual productivity advantage that would be one thing, and it might motivate me to get past the horrible UX, but I haven't seen any evidence yet.

But after that we ran into a kind of loop, where you put my feelings into much better words than I could. If I had stopped after iteration 3, I probably would have finished what I wanted to do in half a day

This has follow-on consequences for a shattering phase transition between “persuasive demo” and “useful product”.

We can now make arbitrarily convincing demos that will crash airplanes (“with no survivors!”) on the first try in production.

This is institutionalized by the market capitalizations of 7 companies being so inflated that if they were priced accurately the US economy would collapse.

Really? I work in AI and my biggest concern is that I don't see any real products coming out of this space. I work closer to the models, and people in this specific area are making progress, but when I look at what's being done down stream I see nothing, save demos that don't scale beyond a few examples.

> in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable.

This is literally all I see right now. There's some really fun hobbyist stuff happening in the image gen area that I think is here to stay, but LLMs haven't broken out of the "autocomplete on steroids" use cases.

> today's stuff is useful now

Can you give me examples of 5, non-coding assistant, profitable use cases for LLMs that aren't still in the "needed just a bit more research to be commercially viable" stage?

I love working in AI, think the technology is amazing, and do think there are some under exploited (though less exciting) use cases, but all I see if big promises with under delivery. I would love to be proven wrong.

LLMs can be used to generate high-quality, human-like content such as articles, blog posts, social media posts, and even short stories. Businesses can leverage this capability to save time and resources on content creation, and improve the consistency and quality of their online presence.

2. Customer Service and Support:

LLMs can be integrated into chatbots and virtual assistants to provide fast, accurate, and personalized responses to customer inquiries. This can help businesses improve their customer experience, reduce the workload on human customer service representatives, and provide 24/7 support.

3. Summarization and Insights:

LLMs can be used to analyze large volumes of text data, such as reports, research papers, or customer feedback, and generate concise summaries and insights. This can be valuable for businesses in fields like market research, financial analysis, or strategic planning.

4. HR Candidate Screening:

Use case: Using LLMs to assess job applicant resumes, cover letters, and interview responses to identify the most qualified candidates. Example: A large retailer integrating an LLM-based recruiting assistant to help sift through hundreds of applications for entry-level roles.

5. Legal Document Review:

Use case: Employing LLMs to rapidly scan through large volumes of legal contracts, case files, and regulatory documents to identify key terms, risks, and relevant information. Example: A corporate law firm deploying an LLM tool to streamline the due diligence process for mergers and acquisitions.

Spam isn't a feature. See also, this whole message that could just have been the headlines.

> 2. Customer Service and Support:

So… less clear than the website and not empowered to do anything (beyond ruining your reputation) because even you don't trust it?

> 3. Summarization and Insights:

See 1, spam isn't a feature. This is just trying to undo the damage from that (and failing).

> 4. HR Candidate Screening:

> 5. Legal Document Review:

If it's worth doing, it's worth doing well.

>Content Generation

I'm working on AI tools for teachers and I can confidently say that GPT is just unbelievably good at generating explanations, exercises, quizes etc. The onus to review the output is on the teacher obviously, but given they're the subject matter experts, a review is quick and takes a fraction of the time that it would take to otherwise create this content from scratch.

So sorry, we're back to spam generator. Even if it's "good spam".

a bit dramatic. there has to be an adjustment of teaching/assessing, but nothing that would "ruin" anyone's life.

>So sorry, we're back to spam generator. Even if it's "good spam".

is it spam if it's useful and solves a problem? I don't agree it fits the definition any more.

Teachers are under immense pressure, GPT allows a teacher to generate extension questions for gifted students or differentiate for less capable students, all on the fly. It can create CBT material tailored to a class or even an individual student. It's an extremely useful tool for capable teachers.

Who said generating an essay is useful sorry ? What problem does that solve?

Your comments come accross as overly optimistic and dismissive . Like you have something to gain personally and aren’t interested in listening to others feedback.

>Who said generating an essay is useful sorry ? What problem does that solve?

Useful learning materials aligned with curriculum outcomes, taking into account learner needs and current level of understanding is literally the bread and butter of teaching.

I think those kinds of resources are both useful and solve a very real problem.

>Your comments come accross as overly optimistic and dismissive . Like you have something to gain personally and aren’t interested in listening to others feedback.

Fair point. I do have something to gain here. I've given a number of example prompts that are extremely useful for a working teacher in my replies to this thread. I don't think I'm being overly optimistic here. I'm not talking vague hypotheticals here, the tools that I'm building are already showing great usefulness.

If you don't have the power to just change your mind about what the entire curriculum and/or assessment context is, it can be a workload increase of dozens of hours per week or more. If you do have the power, and do want to change your entire curriculum, it's hundreds of hours one-time. "Lives basically ruined" is an exaggeration, but you're preposterously understating the negative impact.

> is it spam if it's useful and solves a problem?

Whether or not it's useful has nothing to do with whether or not it's spam. I'm not claiming that your product is spam -- I'll get back to that -- but your reply to the spam accusation is completely wrong.

As for your hypothesis, I've had interactions where it did a good job of generating alternative activities/exercises, and interactions where it strenuously and lengthily kept suggesting absolute garbage. There's already garbage on the internet, we don't need LLMs to generate more. But yes, I've had situations where I got a good suggestion or two or three, in a list of ten or twenty, and although that's kind of blech, it's still better than not having the good suggestions.

I think it has a lot to do with it. I can't see how generating educational content for the purpose of enhancing student outcomes with content reviewed by expert teachers can fall under the category of spam.

>As for your hypothesis, I've had interactions where it did a good job of generating alternative activities/exercises, and interactions where it strenuously and lengthily kept suggesting absolute garbage.

I like to present concrete examples of what I would consider to be useful content for a k-12 teacher.

Here's a very quick example that I whipped up

https://chatgpt.com/share/ec0927bc-0407-478b-b8e5-47aabb52d2...

This would align with Year 9 Maths for the Australian Curriculum.

This is an extremely valuable tool for

- A graduate teacher struggling to keep up with creating resources for new classes

- An experienced teacher moving to a new subject area or year level

Bear in mind that the GPT output is not necessarily intended to be used verbatim. A qualified specialist teacher with often times 6 years of study (4 year undergrad + 2 yr Masters) is the expert in the room who presumably will review the output, adjust, elaborate etc.

As a launching pad for tailored content for a gifted student, or lower level, differentiated content for a struggling student the GPT response is absolutely phenomenal. Unbelievably good.

I've used Maths as an example, however it's also very good at giving topic overviews across the Australian Curriculum.

Here's one for: elements of poetry:structure and forms

https://chatgpt.com/share/979a33e5-0d2d-4213-af14-408385ed39...

Again, an amazing introduction to the topic (I can't remember the exact curriculum outcome it's aligned to) which gives the teacher a structured intro which can then be spun off into exercises, activities or deep dives into the sub topics.

> I've had situations where I got a good suggestion or two or three, in a list of ten or twenty

This is a result of poor prompting. I'm working with very structured, detailed curriculum documents and the output across subject areas is just unbelievably good.

This is all for a K-12 context.

Also, each of your examples had at least one error, did you not see them?

I didn't could you point them out?

>There are countless existing, human-vetted, designed on special purpose, bodies of work full of material like the stuff your chatgpt just "created". Why not use those?

As a classroom teacher I can tell you that piecing together existing resources is hard work and sometimes impossible because resource A is in this text book (which might not be digital) and resource B is on that website and quiz C is on another site. Sometimes it's impossible or very difficult to put all these pieces together in a cohesive manner. GPT can do all that an more.

The point is not to replace all existing resources with GPT, this is all or nothing logic. It's another tool in the tool belt which can save time and provide new ways of doing things.

Every year there are thousands of graduate teacher looking for tools to help them teach better.

>good teachers I know can generate them on the spot

Even the best teacher can't create an interactive multiple choice quiz with automatic marking, tailored to a specific class (or even a specific student) on the spot.

I've been teaching for 20+ years, I have a solid grasp of the pain points.

Neither can "AI" though, so what's the point here?

here's an example of a question and explanation which aligns to Australian Curriculum elaboration AC9M9A01_E4 explaining why frac{3^4}{3^4}=1, and 3^{4-4}=3^0

https://chatgpt.com/share/89c26d4f-2d8f-4043-acd7-f1c2be48c2...

to further elaborate why 3^0=1 https://chatgpt.com/share/9ca34c7f-49df-40ba-a9ef-cd21286392...

This is a relatively high level explanation. With proper prompting (which, sorry I don't have on hand right now) the explanation can be tailored to the target year level (Year 9 in this case) with exercises, additional examples and a quiz to test knowledge.

This is just the first example I have on hand and is just barely scratching the surface of what can be done.

The tools I'm building are aligned to the Austrlian Curriculum and as someone with a lot of classroom experience I can tell you that this kind of tailored content, explanations, exercises etc are a literal godsend for teachers regardless of experience level.

Bear in mind that the teacher with a 4 year undergrad in their specialist area and a Masters in teaching can use these initial explanations as a launching pad for generating tailored content for their class and even tailored content for individual students (either higher or lower level depending on student needs). The reason I mention this is because there is a lot of hand-wringing about hallucinations. To which my response is:

- After spending a lot of effort vetting the correctness of responses for a K-12 context hallucinations are not an issue. The training corpus is so saturated with correct data that this is not an issue in practice.

- In the unlikely scenario of hallucination, the response is vetted by a trained teacher who can quickly edit and adjust responses to suit their needs

“Here are some sharepoint locations, site Maps, and wikis. Now regurgitate this info to me as if you are a friendly call center agent.”

Pretty cool but not much more than pushing existing data around. True AI I think is being able to learn some baseline of skills and then through experience and feedback adapt and be able to formulate new thoughts that eventually become part of the learned information. That is what humans excel at and so far something LLMs can’t do. Given the inherent difficulty of the task I think we aren’t much closer to that than before as the problems seem algorithmic and not merely hardware constrained.

Which is extremely valuable!

>Pretty cool but not much more than pushing existing data around.

Don't underestimate how valuable it is for teachers to do exactly that. Taking existing information, making it digestable, presenting it in new and interseting ways is a teacher's bread and butter.

Are you suggesting a chatbot is a suitable replacement for a teacher?

No I'm saying that a chatbot can be a superhuman teacher's assistant.

Which all takes valuable time us teachers are extremely short on.

I've been a classroom teacher for more than 20 years, I know how painful it is to piece together a hodge podge of resourecs to put together lessons. Yes the information is out there, but a one click option to gather this into a cohesive unit for me saves me valuable time.

>95% of my time an mental capacity in this situation goes for deciding what makes sense in my particular pedagogical context? What wording works best for my particular students?

Which is exactly what GPT is amazing at.Brainstorming, rewriting, suggesting new angles of approach is GPTs main stength!

>Explanations are even harder.

Prompting GPT to give useful answers is part of the art of using these new tools. Ask GPT to speak in a different voice, take on a persona or target a differnt age group and you'll be amazed at what it can output.

> I find out almost daily that explanations which worked fine in last year, don't work any more

Exactly! Reframing your own point of view is hard work, GPT can be an invaluable assistant in this area.

No, it isn't. It just increases noise. I don't need any more info, I need just to make decisions "how?".

> Prompting GPT to give useful answers is part of the art of using these new tools. Ask GPT to speak in a different voice, take on a persona or target a differnt age group and you'll be amazed at what it can output.

I'm not amazed. At best it sounds like some 60+ year old (like me) trying to be in the "age group" 14 while after only hearing from someone how young people talk. Especially in small cultures like ours here (~1M people).

We specifically don’t do programming prompts/responses nor advanced college to PHD level stuff, but it’s really mediocre at this level and these subject areas. Programming might be another story, I can’t speak to that.

All I can go off is my experience but it’s not been great. I’m willing to be wrong.

Is the output of average C students not commercially valuable in the listed fields? If AI is competing reliably with students then we've already hit AGI.

1. content: Nope, except for barrel-bottom content sludge of the kind formerly done by third world spam spinning companies, most decent content creation stays well away from AI except for generating basic content layout templates. I work as a writer and even now, most companies stay well away from using GPT et al for anything they want to be respected as content. Please..

2. Customer service: You've just written a string of PR corporate-speak AI seller bullshit that barely corresponds to reality. People WANT to speak to humans, and except for very basic inquiries, they feel insulted if they're forced into interaction with some idiotic stochastic parrot of an AI for any serious customer support problems. Just imagine some guy trying to handle a major problem with his family's insurance claim or urgently access money that's been frozen in his bank account, and then forced to do these things via the half-baked bullshit funnel that is an AI. If you run a company that forces that upon me for anything serious in customer service, I would get you the fuck out of my life and recommend any friend willing to listen does the same.

3. This is the one area where I'd grant LLMs some major forward space, but even then with a very keen eye to reviewing anything they output for "hallucinations" and outright errors unless you flat out don't care about data or concept accuracy.

4. For reasons related to the above (especially #2) what a categorically terrible, rigid way to screen human beings with possible human qualities that aren't easily visible when examined by some piece of machine learning and its checkbox criteria.

5. Just, Fuck No... I'd run as fast and far as possible from anyone using LLMs to deal with complex legal issues that could involve my eventual imprisonment or lawsuit-induced bankruptcy.

Also these all tend to have an option where you simply ask it and it will redirect you to a person.

Those agents deal with the same queries all day, despite what you think your problem likely isn't special, in most cases may as well start calling the agents "stochastic parrots" too while you're at it.

We just don't have that.

We have autocomplete on steroids and many people are fooling themselves that if you just take more steroids you will get better and better results. The metaphor is perfect because if you take more and more steroids you get less and less results.

It is why in reality we have had almost no progress since April 2023 and chatGPT 4.

I think that none of the initials are boolean; things can be degrees of artificial, degrees of general, and degrees of intelligent.

I think most would assert that humans count as a "general" intelligence, even if they disagree about most of the other things I've put in this comment.

I am probably through some ETF an investor in MS, so I do hope the openai API usage is showing a more stable and upward trend.

[1]: https://explodingtopics.com/blog/chatgpt-users

Not sure how they plan on making money in the long-term, eventually the investors and shareholders will start asking when they will be seeing the returns on their investment.

You can see this in action with multiplication. Much like humans when asked to guess the answer, they'll get it wrong, unless they know the answer from rote learning multiplication tables, this System-1 thinking. In many cases when asked they can reason further and solve it, by breaking it down and solving it step by step, much like a human, this is system-2 thinking.

In my opinion, it seems nearly everything is there for it it to take the next leap in intelligence, it's just putting it all together.