ChatGPT is my primary search engine now. (I just wish it would accept a URL query parameter so it could be launched straight from the browser address bar.)
Just type the tech question, start refining into what is needed and get a snippet of code tailored for what is needed. What previously would take 30 to 60 minutes of research and testing is now less than a couple of minutes.
Which may be why I’ve been very underwhelmed by GPT so far. It’s not terrible at programming, and it’s certainly better than what I can find on Google, but it’s not better than simply looking up how things work. I’m really curious as to why it hasn’t put a more heavy weight on official documentation for its answers, they must’ve scraped that a long with all the other stuff, yet it’ll give you absolutely horrible suggestions when the real answer must be in its dataset. Maybe that would be weird for less common things, but it’s so terrible at JavaScript that it might even be able to write some of those StackOverflow answers if we’re being satirical, and the entire documentation for that would’ve been very easy to flag as important.
Such luxury is increasingly rare for software developers nowadays.
The most extreme I can think of is when I want to find when a show comes out and I have to read 10 paragraphs from 5 different sites to realize no one knows.
I found that you can be pretty sure no one knows if it’s not already right on the results page. And if the displayed quote for a link on the results page is something like “wondering when show X is coming out?”, then it’s also a safe bet that clicking that link will be useless.
You learn those patterns fast, and then the search is fast as well.
I wish MLs were more useful than search engines, but they have still a long way to go to replace them (if they ever do).
What you’re describing as “clinging to an old way of things” is how every single thing has been, ever, new or old.
Yeah, I find that queries which can be answered in a sentence are the worst to find answers from search engines because all the results lengthen the response to an entire article, even when there isn't an answer.
Both have their uses.
"Hey Google, why do ____ happen?" "I'm sorry, I don't know anything about that"
But you're GOOGLE! Google it! What the heck lol
So yeah, ChatGPT being able to hear what I say and give me info about it would be great! My holdup has been wakewords.
Our REST endpoint can talk to whatever you want and we’ll have native ChatGPT soon.
I used Bing yesterday and it was able to parse out exactly what I wanted, and then give me idiot-proof steps to making the recipe in-game. (I didn't need the steps, but it gave me what I wanted up front, easily.) I tried it twice and it was awesome both times. I'll definitely be using it in the future.
You mean these? Took me a few seconds to find, not sure how an LLM would make that easier. I guess the biggest benefit of LLM then is for people who don't know how to find stuff.
Bing made it even easier.
Also, I've found some of those lists to be missing some recipes.
Imagine if iOS had something like apple script and all apps exposed and documented endpoints. LLMs would be able to trivially solve problems that the best voice assistants today cannot handle.
Then again none of the current assistants can handle all that much. "Send Alex P a meeting invite tomorrow for a playdate at the Zoo, he's from out of town so include the Zoo's full address in the invite".
"Find the next mutual free slot on the team's calendar and send out an invite for a zoom meeting at that time".
These are all things that voice assistants should have been doing a decade ago, but I presume they'd have required too much one off investment.
Give an LLM proper API access and train it on some example code, and these problems are easy for it to solve. Heck I bet if you do enough specialized training you could get one of the tiny simple LLMs to do it.
Still can’t quite make it work. I feel like I could learn a lot if I could have random conversations with GPT.
+ bonus if someone else in the car got excited when I see cows. Don’t care if it’s an AI.
The other day I asked it about the place I live and it made up nonsense, I was trying to get it to help me with an essay and it was just wrong, it was telling me things about this region that weren't real.
Do we just drive through a town, ask for a made up history about it and just be satisfied with whatever is provided?
I have tried to use it many times to learn a topic, and my experience has been that it is either frustratingly vague or incorrect.
It's not a tool that I can completely add to my workflow until it is reliable, but I seem to be the odd one out.
I find this highly concerning but I feel similar.
Even "smart people" I work with seem to have gulped down the LLM cool aid because it's convenient and it's "cool".
Sometimes I honestly think: "just surrender to it all, believe in all the machine tells you unquestionably, forget the fact checking, it feels good to be ignorant... it will be fine...".
I just can't do it though.
I think the current state of AI trustworthiness (“very impressive and often accurate but occasionally extremely wrong”) triggers similar mental pathways to interacting with a true sociopath or pathological liar for the first time in real life, which can be intensely disorienting and cause one to question their trust in everyone else, as they try to comprehend this type of person.
I don't like being told lies in the first place and having to unlearn it.
It doesn't help that I might as well have just gone straight to the "verification" instead.
It's smart but can also be very dumb.
It's the same issue with Google Search, any web page, or, heck, any book. Fact checking gets you only so far. You need critical thinking. It's okay to "learn" wrong facts from time to time as long as you are willing to be critical and throw the ideas away if they turn out to be wrong. I think this Popperian view is much more useful than living with the idea that you can only accept information that is provably true. Life is too short to verify every fact. Most things outside programming are not even verifiable anyway. By the time that Steve Jobs would have "verified" that the iPhone was certainly a good idea to pursue, Apple might have been bankrupt. Or in the old days, by the time you have verified that there is a tiger in the bush, it has already eaten you.
When I spend time on something that turns out to be incorrect, I would prefer it to be because of choice I made instead of some random choice made by an LLM. Maybe the author is someone I'm interested in, maybe there's value in understanding other sides of the issue, etc. When I learn something erroneous from an LLM, all I know is that the LLM told me.
People should be able "throw the ideas away if they turn out to be wrong" but the problem is these ideas unconsciously or not help build your model of the world. Once you find out something isn't true it's hard to unpick your mental model of the world.
Intuitively, I would think the same, but a book about education research that I read and my own experience taught me that new information is surprisingly easy to unlearn. It’s probably because new information sits at the edges of your neural networks and do not yet provide a foundation for other knowledge. This will only happen if the knowledge stands the test of time (which is exactly how it should be according to Popper). If a counterexample is found, then the information can easily be discarded since it’s not foundational anyway and the brain learns the counterexample too (the brain is very good in remembering surprising things).
The benefit is that I got a quick look at various solutions and quickly satisfied a curiosity, and decided if I’m interested in the concept or not. Without AI, I might just leave the idea alone or spend too much time figuring it out. Or perhaps never quite figure out the terms of what I’m trying to discover, as it’s good at connecting dots when you have an idea with some missing pieces.
I wouldn’t use it for a conversation about things as others are describing. I need a way to verify its output at any time. I find that idea bizarre. Just chatting with a hallucinating machine. Yet I still find it useful as a sort of “idea machine”.
I think even if an AGI was created, and humans survived this event. I'd still have trouble trusting it.
The quote "trust but verify" is everything to me.
I think it's because Americans, more than nearly all other cultures, love convenience. It's why the love for driving is so strong in the US. Don't walk or ride, drive.
Once I was walking back from the grocer in Florida with 4 shopping bags, and people pulled over and asked if my car had broken down and if I needed a ride, people were stunned...I was walking for exercise and for the environment...and I was stunned.
More evidence of this trend can be seen in the products and marketing being produced:
Do you need to write a wedding speech? Click here.
Do you need to go get something from the store? get your fat ass in the car and drive, better yet, get a car that drives for you? Better than this, we'll deliver it with a drone...don't move a muscle.
Don't want to do your homework? Here...
Want to produce art? Please enter your prompt...
Want to lose weight? We have a drug for that...
Want to be the authority on some topic? We'll generate the facts you need.
As convenience in a domain becomes ubiquitous or at least expected among consumers, they quickly readjust their evaluation of "having time for X" around the new expectation of the convenient service, treating all alternatives as positive opportunity cost. This would explain a lot of those folks who are upset when it's suggested that they don't need Amazon, Instacart, etc. in their lives if they are to do something about their contributions to mass labor exploitation.
Of course these conveniences quickly become ubiquitous in large economies with a glut of disposable income, which encourages VCs to dump money into these enterprises so they're first to market, and also to encourage the public to believe that the future is already here and there's no reason to worry about backsliding or sustainability of the business model. Yet in every single case we see prices eventually rise, laborers squeezed, etc. A critical mass of people haven't yet acknowledged this inevitability, in no small part due to this fixation on convenience at the expense of more objective, reasoned understandings (read: post-truth mindset).
They're only good on universal truths. An amalgam of laws from around the globe doesn't tell me what the law is in my country, for example.
This. I hate being told the wrong information because I will have to unlearn the wrong information. I would rather have been told nothing.
Rather than asking it about facts, I find it useful to derive new insights.
For example: "Tell me 5 topics about databases that might make it to the front page of hacker news." It can generate an interesting list. That is much more like the example they provided in the article, synthesizing a bed time story is not factual.
Also, "write me some python code to do x" where x is based on libraries that were well documented before 2022 also has similarly creative results in my experience.
A person who uses ChatGPT must have the understanding that it's not like Google search. The layman, however, has no idea that ChatGPT can give coherent incorrect information and treats the information as true.
Most people won't use it for infotainment and OpenAI will try its best to downplay the hallucination as fine print if it goes fully mainstream like google search.
no I will not give the public credit, most people have no grounding to discern wtf a language model is and what it's doing, all they know is computers didn't use to talk and now they do
It seems to be able to speak on history, sometimes it's even right, so there's a use case that people expect from it.
FYI I've used GPT4 and Claude 2 for hundreds of conversations, I understand what its good and bad at; I don't trust that the general public is being given a realistic view.
Like talking to most people you mean?
You will now be able to feed it images and responses of the customers. Give it a function to call complementaryDrink(customerId) Combine it with a simple vending machine style robot or something more complex that can mix drinks.
I'm not actually in a hurry to try to replace bartenders. Just saying these types of things immediately become more feasible.
You can also see the possibilities of the speech input and output for "virtual girlfriends". I assume someone at OpenAI must have been tempted to train a model on Scarlett Johansson's voice.
If people are treating LLMs like a random stranger and only making small talk, fair enough, but more often they're treating it like an inerrable font of knowledge, and that's concerning.
That's on them. I mean, people need to figure out that LLMs aren't random strangers, they're unfiltered inner voices of random strangers, spouting the first reaction they have to what you say to them.
Anyway, there is a middle ground. I like to ask GPT-4 questions within my area of expertise, because I'm able to instantly and instinctively - read: effortlessly - judge how much to trust any given reply. It's very useful this way, because rating an answer in your own field takes much less work than coming up with it on your own.
All human interactions from all of history called and they …
I verify just about everything that I ask it, so it isn’t just a general sense of improvement.
Take a look at https://chat.openai.com/share/41bdb053-facd-448b-b446-1ba1f1... for example.
Context: had a bunch of photos and videos I wanted to share with a colleague, without uploading them to any cloud. I asked GPT-4 to write me a trivial single-page gallery that doesn't look like crap, feeding it the output of `ls -l` on the media directory, got it on first shot, copy-pasted and uploaded the whole bundle to a personal server - all in few minutes. It took maybe 15 minutes from the idea of doing it first occurring to me, to a private link I could share.
I have plenty more of those touching C++, Emacs Lisp, Python, generating vCARD and iCalendar files out of blobs of hastily-retyped or copy-pasted text, etc. The common thread here is: one-off, ad-hoc requests, usually underspecified. GPT-4 is quite good at being a fully generic tool for one-off jobs. This is something that never existed before, except in form of delegating a task to another human.
It used to be more reliable when web browsing worked, but it's still pretty reliable.
https://chat.openai.com/share/338e7397-0201-44f4-a2c3-75b733...
I use ChatGPT for all sorts of things - looking into visas for countries, coding, reverse engineering companies from job descriptions, brainstorming etc etc.
It saves a lot of time and gives way more value than what you pay for it.
Then it makes stuff up far less frequently.
If the next version has the same step up in performance, I will no longer consider inaccuracy an issue - even the best books have mistakes in them, they just need to be infrequent enough.
> Then it makes stuff up far less frequently.
Now there's a business model for a ChatGPT-like service.
$1/month: Almost always wrong
$10/month: 50/50 chance of being right or wrong
$100/month: right 95% of the time
Ah yes, I dont understand how to talk to people either!
Comments like yours make me think that no one cares about this...and judging by a lot of the other comments, I guess they don't.
Probably going to be people, wading through a sea of AI generated shit, and the individual is supposed to just forever "apply critical thinking" to it all. Even a call from ones spouse could be fake, and you'll just have to apply critical thinking or whatever to workout if you were scammed or not.
I feel like using LLM today is like using search 15 years ago - you get a feel for getting results you want.
I'd never use chatGPT for anything that's even remotely obscure, controversial, or niche.
But through all my double-checking, I've had phenomenal success rate in getting useful, readable, valid responses to well-covered / documented topics such as introductory french, introductory music theory, well-covered & non-controversial history and science.
I'd love to see the example you experienced; if I ask chatGPT "tell me about Toronto, Canada", my expectation would be to get high accuracy. If I asked it "Was Hum, Croatia, part of the Istrian liberation movement in the seventies", I'd have far less confidence - it's a leading question, on a less covered topic, introducing inaccuracies in the prompt.
My point is - for a 3 hour drive to cottage, I'm OK with something that's only 95% accurate on easy topics! I'd get no better from my spouse or best friend if they made it on the same drive :). My life will not depend on it, I'll have an educationally good time and miles will pass faster :).
(also, these conversations always seem to end in suffocatingly self-righteous "I don't know how others can live in this post-fact free world of ignorance", but that has a LOT of assumptions and, ironically, non-factual bias in it as well)
I've seen the hallucination rate of LLMs improve significantly, if you stick to well covered topics they probably do quite well. The issue is they often have no tells when making things up.
I don't think it's quite the same.
With search results, aka web sites, you can compare between them and get a "majority opinion" if you have doubts - it doesn't guarantee correctness but it does improve the odds.
Some sites are also more reputable and reliable than others - e.g. if the information is from Reuters, a university's courseware, official government agencies, ... etc. it's probably correct.
With LLMs you get one answer and that's it - although some like Bard provide alternate drafts but they are all from the same source and can all be hallucinations ...
Yes and no. If the LLM is repeating the same thing on multiple drafts then it's very unlikely to be a hallucination.
It's when multiple generations are all saying different things that you need to take notice.
LLMs hallucinate yes but getting the same hallucination multiple times is incredibly rare.
"In particular, we find that LMs often hallucinate differing authors of hallucinated references when queried in independent sessions, while consistently identify authors of real references. This suggests that the hallucination may be more a generation issue than inherent to current training techniques or representation."
https://arxiv.org/abs/2303.08896
"SelfCheckGPT leverages the simple idea that if a LLM has knowledge of a given concept, sampled responses are likely to be similar and contain consistent facts. However, for hallucinated facts, stochastically sampled responses are likely to diverge and contradict one another."
and that wouldn't eliminate hallucinations just tell you if large details have likely been hallucinated.
But it's a method some research has used.
P.S. Also aren’t LLMs deterministic if you set their “temperature” to zero? Are there drafts if the temperature is zero? If not, then that’s the same as removing the randomness no?
>Just do the comparison on the user’s machine if the LLM provider is that cheap.
This is not possible. Users don't have the resources to run these gigantic models. LLM inference is not cheap. Open ai, Google aren't running profit on free cGPT or Bard.
>P.S. Also aren’t LLMs deterministic if you set their “temperature” to zero? Are there drafts if the temperature is zero? If not, then that’s the same as removing the randomness no?
It's not a problem of randomness. a temp of 0 doesn't reduce hallucinations. LLMs internally know when they are hallucinating/taking a wild guess. randomness influences how that guess manifests each time but the decision to guess was already made.
I never said it did. If it did we wouldn’t be complaining about hallucinations as we can just turn them off at will by setting the temp to zero.
> LLMs internally know when they are hallucinating/taking a wild guess.
No they don’t. If they did we would be able to program them to not do so.
I would argue that wild guesses are all LLMs are doing. They practically statistically guess their way to an answer. It works surprisingly well a lot of the time but they don’t really understand why they are right/wrong.
P.S. LLMs are kind of like students who didn’t study for the test so they use “heuristics” to guess the answer. If the test setter is predictable enough, the student might actually get a few right.
why be mad at a hammer if you hit your thumb with it?
The two biggest features I want are for the voice assistants to read something for me, and to do something on google/Apple Maps hand free. Neither of these ever work. “Siri/ ok google add the next gas station on the route” or “take me to the Chinese restaurant in Hoboken” seem like very obvious features for a voice assistant with a map program.
The other is why can I tell Siri to bring up the Wikipedia page for George Washington but I can’t have Siri read it to me? I am in the car, they know that, they just say “I can’t show you that while you’re driving”. The response should be “do you want me to read it to you?”
I say "ok google, add a stop for gas" a lot, and it works well for me.
If you were riding with someone, you could say "hey, let's stop at X on the way?"
Voice assistants today require you to conform to an unknown strict protocol that only works if you say the right words in the right order. I prefer to avoid the frustration.
Just today I asked it about a historical figure "hey Google, who was X?" and it replied. I was shocked and felt lucky so I asked "What is ChatGPT?" and it gave me the usual response "sorry, I didn't understand". Seriously, you didn't understand? It could have said something like I couldn't find it in my search results (odd but okay), or that it can't look it up online, etc.
I have to say "asian restaurant" and it finds a bunch of places (many aren't asian at all). But if I say "hey Google, what are the Asian restaurants near neighborhood X with 4 stars or more?" it craps its pants hard.... even though Google Search is totally capable of answering that question.
Me: “OK Google, take me to the Chinese restaurant in Hoboken”
Google Assistant: “Calling Jessica Hobkin”.
"I'd like an iced tea" "An icee?" "No an iced tea" "Hi-C?"
The pattern for current world's voice assistants is: ${brand 1}, ${action} ${brand 2} ${joiner} ${brand 3}.
So, "OK Google, take me to Chinese restaurant in Hoboken using Google Maps".
Which is why I refuse to use this technology until the world gets its shit together.
Example from a couple days ago:
Me, in the shower so not able to type: "Hey Siri, add 1.5 inch brad nails to my latest shopping list note."
Siri: "Sorry, I can't help with that."
... Really, Siri? You can't do something as simple as add a line to a note in the first-party Apple Notes app?
They did telegraph it, they showed the multimodal capabilities back in the GPT4 Developer Livestream[0] right before first releasing it.
I think the only place where plugins will make sense are for realtime things like booking travel or searching for sports/stock market/etc type information.
That will be the real use case for plug ins.
It would be interesting to know if this really changed anything for anyone (competitors, VCs) for that reason. It's like the efficient market hypothesis applied to product roadmaps.
And the ability ingest images was a highlight and all the hype of the GPT-4 announcement back in March: https://openai.com/research/gpt-4
I wouldn’t count out focused, revenue-oriented players with Meta’s shit in their pocket out just yet.
what do you think they’re missing? i was trying to build a diaper but it would be impossible to compete with these guys
BTW, I expect these technologies to be democratized and the training be in the hands of more people, if not everyone.
1. Domain-specific AI - Training an AI model on highly technical and specific topics that general-purpose AI models don't excel at.
2. Integration - If you're going to build on an existing AI model, don't focus on adding more capabilities. Instead, focus on integrating it into companies' and users' existing workflows. Use it to automate internal processes and connect systems in ways that weren't previously possible. This adds a lot of value and isn't something that companies developing AI models are liable to do themselves.
The two will often go hand-in-hand.
why wouldn’t a company do that themselves e.g. how inter come has vertically integrated AI? any examples?
Just look at Salesforce AppExchange - it's a marketplace of software built on top of Salesforce, a large chunk of which serves to integrate other systems with Salesforce. LLMs open up the ability to build new types of integrations and to provide a much friendlier UI to non-developers who need to work on integrating things or dealing with data that exists in different places.
Maybe not if you rely on models that can be ran locally.
OpenAI is big now, and will probably stay big, but with hardware acceleration, AI-anything will become ubiquitous and OpenAI won’t be able to control a domain that’s probably going to be as wide as what computing is already today.
The shape of what’s coming is hard to imagine now. I feel like the kid I was when I got my first 8-bit computer in the eighties: I knew it was going to change the world, but I had little idea how far, wide and fast it would be.
any pertinent examples?
You will be eaten if you do this imo.
Because past history shows that the first out of the gate is not the definitive winner much of the time. We aren't still using gopher. We aren't searching with altavista. We don't connect to the internet with AOL.
AI is going to change many things. That is all the more reason to keep working on how best to make it work, not give up and assume that efforts are "doomed" just because someone else built a functional tool first.
also, I did not know until today's thread that OpenAI's stated goal is building AGI. which is probably never going to happen, ever, no matter how good technology gets.
which means yes, we are absolutely looking at AltaVista here, not Google, because if you subtract a cult from an innovative business, you might be able to produce a profitable business.
most of them accurately detect it is a sunk cost fallacy to continue but it looks like a form of positive thinking... and that's the power of community!
Rather than die, why not just pivot to doing multi-modal on top of Llama 2 or some open source model or whatever? It wouldn’t be a huge change
A lot of businesses/governments/etc can’t use OpenAI due to their own policies that prohibit sending their data to third party services. They’ll pay for something they can run on-premise or in their own private cloud
I can already see "Alexa/Siri/Google Home" replacement, "Google Image Search" replacement, ed-tech startups that were solving problems with AI using by taking a photo are also doomed and more to follow.