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Anyone else witnessing a panic inside NLP orgs of big tech companies?

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Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#331
post #89

Earlier quoted context omitted.

If you go into industry you’ll be given a chance to deploy these models and rush them into products. You’ll also make good money. If you go into academia (or research, whether it’s in academia or industry) you’ll be given the chance to try to understand what they’re doing. I can see the appeal of making money and rushing products out. But it wouldn’t even begin to compete with my curiosity. Makes me wish I was younge…

This is as likely to happen as that someone will fully understand how the brain works. I don't think you're missing much out in academia

We can’t isolate individual neurons in a functioning brain or train custom models (“probes”) inside of a living human brain that lets us see what they’re feeling on specific inputs. The scope to understand how these models work is incredible: the more intelligent they get, the more we can learn about intelligence works.

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#332
post #172
post #135

Earlier quoted context omitted.

> I think a lot of our current sociological problems, problems associated with wealth inequality, etc., I see where you’re coming from, but is this really the main source of the inequality? Based on numbers relating to workers’ diminishing share of profits, it seems to be that the capital class has been able to take a bigger piece of the profit pie without sharing. In the past, companies have shared profits more wide…

IMO the "main source of inequality" is that tech allows a small number of people to use technological and fiscal leverage to make an outsized impact on society as a whole. Anyone who has a job that produces value in a 1:1 way is positioned to be 'disrupted'. NLP, etc, just provides more tools for companies to increase their leverage in the market. My bet is that GPT-4 is probably better at being a paralegal than at l…

This is exactly what has happened to commercial and investment banking (market/trading) in the last 30 years. Computers and mass automation. Even if your profits only grow with inflation (in reality, they grow much faster), but you can reduce costs each year (less labour required), then return on equity continues to rise. It is crazy to me that most big commercial banks still have so many physical branches. I guess they exist for regulatory purposes -- probably _very_ hard to close a branch to avoid "banking deserts".

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#333

Earlier quoted context omitted.

> There is no good reason this should not work everywhere else, in exactly the same way. Take for example a large retailer who has a large internal knowledge base. Train an LLM on that corpus, ask the knowledge base any question. Since LLM’s can’t scope themselves to be strictly true or accurate, there are indeed good reasons, like liability for false claims and added traditional support burden from incorrect guidanc…

If they are accurate for ~80% of the questions, they will be as accurate as any 1st or 2nd line help desk.

Hmm. Hypothetically if a human on first line help desk gives advice that is so completely bad as to be a crime, are they liable or the company? Because I guess a chat-bot would definitely not be liable.

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#334
Here's my two cents, as I work with NLP in a tech company, mostly with Dialogflow and Rasa, in my current project, we are using Chat GPT (and previously GPT 3) to summarize articles, and I see that it can really handle FAQ questions really well. One of most common requirements was to train our bot to handle FAQ type question apart from complex conversation stories/flows,but this thing can straight up take the content from an article,summarize it neatly and send a response back.

We have had some issues and complaints with the API,( mostly with GPT 3 as the fine tuning was only open for the base model and that had some trouble with some questions). Also there is a finicky response time, despite using having paid access. Response time varies from 10 seconds to even a minute (during some downtime that occured a few days ago, and a few days even before that there was a complete outage).

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#335
post #251

Earlier quoted context omitted.

Now 90% of humanity can toil for 12 hours a day in the fields to support the 10% who own all the machines. Super awesome!

When 90% of people are toiling in fields, there won’t be as many complaints I’d think because there would be a lot more equality among the 90%, vs the 50/50 split that we have today.

You're describing feudalism

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#336

As one of the comments on reddit posts - it's not just big tech companies, but also entire university teams which feel the goalposts moving miles ahead all of a sudden. Imagine working on your PhD on chat bots since start of 2022. Your entire PhD topic might be irrelevant already...

We are just building a chatbot to assist students that study (that study AI itself, to be precise).

We could not "just use ChatGPT" because the answers have to be correct, or students could take legal action against the university in situations where they'd give an answer in an exam that the chatbot gave them and that might be wrong. It's exciting for sure to compare e.g. ChaptGPT with other approaches, for a set of controlled benchmark questions such as:

  -- snip --
  Q: What is the A* algorithm?

  A: The A* algorithm is a popular heuristic search algorithm used in pathfinding and graph traversal. It is an extension of Dijkstra's algorithm, but uses a heuristic function to guide the search towards the goal node, which can make it more efficient than Dijkstra's algorithm in certain situations.
  The algorithm works by maintaining a priority queue of nodes to be explored, where the priority of each node is the sum of its actual cost from the starting node (g-score) and an estimate of the cost to reach the goal node (h-score), based on a heuristic function. The heuristic function must be admissible, meaning it never overestimates the actual cost to the goal node.
  At each step, the algorithm selects the node with the lowest priority from the queue and expands it, generating its neighboring nodes and computing their priority scores. The algorithm then adds these nodes to the queue if they have not been explored before or if a better path to them has been found.
  If the goal node is reached, the algorithm terminates and returns the path from the starting node to the goal node. If the queue becomes empty before the goal node is reached, then there is no path from the starting node to the goal node.
  Overall, the A* algorithm is a powerful and widely used algorithm that has many applications in fields such as robotics, video games, and logistics.
  -- snip --

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#337
I would have never guessed that menial jobs would be the hardest ones to automate. I realized that humans have some incredible skills that were vital to our survival, and were honed over billions of years. But we take these skills for granted because most humans are born with these natural abilities, which are actually very impressive in the grand scheme of things. A janitor might not get paid very much relative to other jobs, but it took billions of years of evolution to hone their brain, muscles, balance, hand eye coordination, energy efficiency, information processing, etc. We're actually very impressive creatures!

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#338
post #305
post #126

Earlier quoted context omitted.

https://portal.konjer.xyz/

Incredible results to my questions. Do these work by finding similar pieces of text from a vector DB, and then embedding those similar pieces of text in the prompt? The answers I'm getting seem to be comprehensive, as if it has considered large amounts of book text, curious how this works as there's an OpenAI token limit. I've heard this is what tools like langchain can help with, so maybe I should play around with t…

For more context, hn post by maker here: https://news.ycombinator.com/item?id=34635338

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#339
post #293

Wow - this is just wild. I've seen lots of arguments around "AI won't take everyone's job, it will just open up new areas for new jobs." Even if you take that with the benefit of the doubt (which I don't really think is warranted): 1. You don't need to take everyone's job. You just need to take a shitload of people's jobs. I think a lot of our current sociological problems, problems associated with wealth inequality,…

Something curious that I've noticed is that the people who I see that are MOST excited are in tech. When I show ChatGPT to a lay person they don't really care. When I show it to a professional copywriter they say that if they submitted this content to a client they would lose the client. I'm reminded of when my son was learning to talk and everything he said seemed brilliant and coherent to me. To any stranger it sou…

Perhaps those in tech can simply see further out. It reminds me of the advent of the internet, the lay person also didn't care, until websites and web apps were made that catered to their needs. But the people who made those sites and apps were precisely the tech people who could see beyond the lay person's idea of what the internet was. So too with AI.

Re: Anyone else witnessing a panic inside NLP orgs of big tech companies?

#340

Earlier quoted context omitted.

how much of that work requires physical labor? when I think of the work to be done, I think of like, building roads and houses and factories, transitioning to renewable energy, staffing hospitals with doctors and nurses, picking up trash on the street, cleaning up oceans, research in biology and pharmacology. I don't really think of filing paperwork, or writing code, or meetings in general. It's the stuff that requir…

> building roads and houses and factories, transitioning to renewable energy, staffing hospitals with doctors and nurses, picking up trash on the street, cleaning up oceans, research in biology and pharmacology All that takes knowledge work. Physical too, but most of the cost today is knowledge work.

Exactly. A future where humans are the drones while big AI does all the thought work is the inverse of the techno-futuristic dreams of the 80s-00s
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