ChatBots that try to do too much and do it worse than real human service reps, like that one that wrongly assured a customer that their airline ticket was refundable
Deluge of low-value generated content taking attention and revenue away from high-value content creators
Have you tried writing code or a paper that’s actually factual with ChatGPT? They are so obviously wrong in many cases that it’s often a hindrance rather than helpful when I’ve tried to use it.
I do love using ChatGPT for fun stuff like “write me a recipe for enchiladas that’s also a country western song.” My kids and I find it hilarious.
We had a remote workshop with GitHub for Copilot. The example was to have it create various functions for a game of Rock, Paper, Scissors. The extra exercise for afterward was to have it add the "Lizard and Spock" options. When I tried to have it do that, it spun its wheels for a little and told me the code it generated violated their responsible use guidelines or whatever.
In retrospect it probably detected it generated something it didn't have the IP rights to give me, but ever since then I've described the state of the art as "like talking through the intercom at a McDonald's drive-thru, but every now and then the attendant says 'sorry, can we start over? I got distracted thinking about killing you.'"
I think people mix up "Improper/indecent/harmful/... uses of AI" with "Troubles made by AI". If we exclude my usage of Copilot in VsCode, my most common exposure to AI is one of my colleagues polluting every slack thread with a low effort, low quality content from ChatGPT that he's most probably not even read once.
But Copilot has revolutionized my coding. I have to code in many languages on a daily basis: Typescript, tsx, Css, Html, Dart, config files (like docker[compose], k8s, Ansible, json configs), c#, python. I'm only fluent in c# and ts. The fact that I do not need to remember the syntax for all the other is a big game changer. I was able to be immediately productive in a new language/framework after reading the documents. Previously it took some time before I ramped up, and then it would be lost after some inactivity. I'm not talking about important concepts, or CS fundamentals. I'm talking about specific ways things can be done in each language/framework. Copilot makes me 1000x more productive in this part. I'm still limited by my mental bandwidth, so I'm probably 2x more productive on an average day.
I also use ChatGPT, and run some models locally just to play with them, but all happen much less frequently than my colleague disrupting discussions with ChatGPT content.
I felt similar at the beginning but then I realized the suggestions were suboptimal, and it happened like 50% of the time. Usually not completely wrong, just imitating something that was already written, but sometimes introducing subtle bugs. So in the end it actually made me less productive because I had to stop my flow and start analyzing if there is no catch in the suggestion. It was a bit tiring and in the end I decided it's easier for me to stay with the flow.
I'll give it a try next year, maybe it improves to the point where the number of suboptimal suggestions falls to 20% or so, it would be much easier then.
Sure - I guess I should say "domain" is an incorrect word, when "use case" is a better phrasing.
LLMs have a tendency to hallucinate at a rate that makes them untrustworthy at scale w/o a human in the loop. The more open ended the prompt, the higher the hallucination rate. Here I mean minor things, like swapping a negative, that can fundamentally change a result.
Thus, any place that we trust computer to perform reliable logic, we cannot trust an LLM because it's error rate is too high.
Methods such as RAG can box in the LLM to keep them on track, but this error rate means that they can never be mission critical, a-la business logic, and keeps them to being a toy.
Where LLMs are game changers are ETL pipelines / data scrapers. I used to work at Clearbit where we built thousands of lines of code just to extract the address of a company's HQ or if a company is owed by another org. LLMs just do that... for free. With LLMs data extraction from free form text is now a solved problem, and thats god damn mindblowing for me.
23% of Americans have used ChatGPT [0], so you’ve got more than a 1 in 5 chance that Joe Public will say he’s used it. Those odds are pretty good for a product that was released less than two years ago. In fact I bet that level of population penetration is higher than even Facebook in its first few years.
> In fact I bet that level of population penetration is higher than even Facebook in its first few years.
If memory serves me correctly, Canadians were the most likely to take an interest in Facebook in the early days. As of 2008, less than two years after being opened to the general public, 32.9% of Canadians were Facebook users. (If my memory fails me, it is possible that some other country had an even higher uptake.)
Only 13.8% of those in the United States were Facebook users at the same time, so if you are referring to the USA specifically then your bet is a pretty safe one. But this also highlights that early interest in technology tends to be highly regional. Relatively high ChatGPT usage in the USA does not imply relatively high usage worldwide.
Which is important as Joe Public hales from the UK. You might have been thinking of John Q. Public, which is the member of the Public family who lives in the USA, when you went to US figures. But I can find no ChatGPT usage data for the UK. The chances of what Joe Public will say are unknown. The odds are probably not as good as what you suggest.
Twitter / X has a very interesting captcha: you get to see 10 objects that have weird colors and are slightly deformed, and then you have to match them (1 at a time) with another row that has the same objects but seen from a different angle.
Of course eventually this will be defeated too, but for now it seems to work pretty well.
Image based or any kind of visual captchas will never be extremely effective. I think we will see more of PoW captchas in the upcoming years (just like cloudflare's turnstile captcha)
I'm not suer about that, can't you give GPT4 a math problem in an image already and have it solve it correctly most of the time?
And these haven't even been trained to defeat captchas/logic problem captchas yet, if it was fine tuned on the general pattern of them I imagine any form of captcha is bust.
D3 is not as ubiquitous as one might think. Outside newsroom graphics departments most visualizations consumed through the browser are published using low-effort crappy BI tools like Power BI and Tableau or some generic charting libraries. So still even in 2023 when you see something that’s been meticulously crafted using a low-level approach (like D3) that allows that slick native feel it’s very impressive.
