AI worse than humans in every way at summarising information, government trial finds
AI worse than humans in every way at summarising information, government trial finds
A test of AI for Australia's corporate regulator found that the technology might actually make more work for people, not less.
Artificial intelligence is worse than humans in every way at summarising documents and might actually create additional work for people, a government trial of the technology has found.
Amazon conducted the test earlier this year for Australia’s corporate regulator the Securities and Investments Commission (ASIC) using submissions made to an inquiry. The outcome of the trial was revealed in an answer to a questions on notice at the Senate select committee on adopting artificial intelligence.
The test involved testing generative AI models before selecting one to ingest five submissions from a parliamentary inquiry into audit and consultancy firms. The most promising model, Meta’s open source model Llama2-70B, was prompted to summarise the submissions with a focus on ASIC mentions, recommendations, references to more regulation, and to include the page references and context.
Ten ASIC staff, of varying levels of seniority, were also given the same task with similar prompts. Then, a group of reviewers blindly assessed the summaries produced by both humans and AI for coherency, length, ASIC references, regulation references and for identifying recommendations. They were unaware that this exercise involved AI at all.
These reviewers overwhelmingly found that the human summaries beat out their AI competitors on every criteria and on every submission, scoring an 81% on an internal rubric compared with the machine’s 47%.
Well, not every metric. I bet the computers generated them way faster, lol. :P
And for a much much smaller paycheck.
All corporate gives af about.
It might be all I care about. Humans might always be better, but AI only has to be good enough at something to be valuable.
For example, summarizing an article might be incredibly low stakes (I’m feeling a bit curious today), or incredibly high stakes (I’m preparing a legal defense), depending on the context. An AI is sufficient for one use but not the other.
This is a really valid point, especially because it's not only faster but dramatically cheaper.
The thing is, summaries which are pretty terrible might be costly. If decision makers are relying on these summaries and they're inaccurate, then the consequences might be immeasurable.
Suppose you're considering 2 cars, one is very cheap but on one random day per month it just won't start, the other is 5x the price but will work every day. If you really need the car to get to work, then the one that randomly doesn't start might be worse than no car at all.
Are we sure it's cheaper though? I mean it legitimatly might not be. I have some friends who work in tech and they use an AI model for, amongst other things, summarizing information on their internal documentation. They've told me what their company is paying for the license to use this thing, and it's eyewatering. also, uhh last time I checked, the company they got that license from does not turn a profit... so it appears to be too cheap at the moment.
It might really be the case that it isn't cheaper than just paying someone a normal salary to do that work, and it probably isn't cheaper than just jamming the work being done by the AI now back onto preexisting employees (which is what they did before ~2 years ago anyway).
The other thing that makes me feel this might not be unreasonable is that everyone on the team likes the tool, except their manager, who has thrown out the idea to cut it twice now (that I know of).
Right? That’s the entire point.
LLMs == AGI was and continues to be a massive lie perpetuated by tech companies and investors that people still have not woken up to.
Who is claiming that LLMs are generally intelligent? Is it just "they" or can you actually name a company?
I think the idea is that every company is dumping money into LLMs and no other form of alternative AI development to the point that all AI research is LLM based and therefore to investors and those involved, it’s effectively the only only avenue to AGI, though that’s likely not true
The fact that we even had to start using the term AGI when in common parlance AI always meant the same up until recently, shows how goal posts are being moved.
What people mean by AI has been changing for as long as the term has been used. When I was studying CS in the 80s, people said the holy grail was giving a computer printed English text and having it read it aloud. It wasn't much later that OCR and text to speech software was commonplace.
Generally, when people say AI, they mean a computer doing something that normally takes a human, and that bar goes up all the time.
To a degree, but, like, video game ai has been called that for decades, I don't think anyone ever thought it was agi. It's a more specific term, and it saw use before the big LLM craze started
The thing with 'common parlance' is that it's used by people without a deep understanding of the subject. Among AI researchers, there’s never been confusion about this. We have different terms for different things for a reason. The term AGI has been around since the early 2000s.
It's like complaining about the terms jig, spoon, spinner, and fly, and saying that back in the day, we just called them fishing lures. They are fishing lures, but these terms describe different types. Similarly, AGI is a form of AI, but it refers to a specific kind.
"Just one more training on a social network"
Can't wait for the bouble to burst.
We shouldn't wait, it is already basically illegal to sample the works of others so we should just pull the plug now.
Ten ASIC staff, of varying levels of seniority, were also given the same task with similar prompts.
This is the key line here. These are likely university educated staff with significant experience in writing and summarising information and they were specifically tasked with this. However, within the social media landscape (Lemmy, reddit, etc) AI is already better at summarising information than humans because most human social media users are fucking retarded and spend their time either a) not reading properly/at all or b) cherrypicking information to fit whatever flavour of impassioned narrative they are trying to sell to everyone else.
Just some very recent examples I've seen of Lemmy users proving they are completely incapable of parsing relevant information are that article about an alternative, universal and non-proprietary database called GetGee which everyone seemed to think was an article about whether TikTok should be banned (because the word TikTok was in the title and that tricked their monkey brains) or the update to the 404 Media story on "active listening" in which people responded as if this technology exists and is in use when 404 Media still haven't been able to confirm either of these things. The second one was particularly egregious because it got picked up by all kinds of tech-related YouTube channels and news sites and regurgitated by their viewers and readers without a single one of these people ever bothering to read the source material properly.
Lemmy users proving they are completely incapable of parsing relevant information
To be fair, you need to actually read the article to be able to summarize it.
I had the same thought. Most people I encounter online and in person are not great at summarizing information regardless of the context.
For example: those who don't summarize the content of a conversation and instead poorly and inaccurately act out the entire encounter, "word by word ". Ughhhhh.
human social media users are fucking retarded
I feel attacked
The most promising model, Meta’s open source model Llama2-70B, was prompted to summarise the submissions
Llama 2 is insanely outdated and significantly worse than Llama3.1, so this article doesn't mean much.
On July 18, 2023, in partnership with Microsoft, Meta announced Llama 2 On April 18, 2024, Meta released Llama-3
L2 it’s one year old. A study like that takes time. What is your point? I bet if they would do it with L3 and the result came back similar, you would say L3 is „insanely outdaded“ as well?
Can you confirm that you think with L3, the result would look completely opposite and the summaries of the AI would always beat the human summaries? Because it sounds like you are implying that.
We know the performance of L2-70b to be on par with L3-8b, just to put the difference in perspective. Surely they models continue to improve and we can only hope the same improvements will be found in L4, but I think the point is that models have improved dramatically since this study was run and they have put in way more attention in the fine-tuning and alignment phase of training, specifically for these kinds of tasks. Not saying this means the models would beat the human summaries everytime (very likely not), but at the very least the disparity between them wouldn't be nearly as large. Ultimately, human summaries will always be "ground truth", so it's hard to see how models will beat humans, but they can get close.
Can you confirm that you think with L3, the result would look completely opposite and the summaries of the AI would always beat the human summaries? Because it sounds like you are implying that.
Lemmy users try not to make a strawman argument (impossible challenge)
No, that's not what I said, and not even close to what I was implying. If Llama 2 scored a 47% then 3.1 would score significantly better, easily over 60% at least. No doubt humans can be better at summarizing but A) It needs someone that's very familiar with the work and has great English skills and B) It needs a lot of time and effort.
The claim was never that AI can summarize better than people, it was that it can do it in 10 seconds and the result would be "good enough". People are already doing AI summaries of longer articles without much complaints.
This is pretty much every study right now as things accelerate. Even just six months can be a dramatic difference in capabilities.
For example, Meta's 3-405B has one of the leading situational awarenesses of current models, but isn't present at all to the same degree in 2-70B or even 3-70B.
Just a few more tens of millions of dollars, and it’ll be vastly improved to “pathetic” and “insipid”.
Did LLama3.1 solve the hallucination problem?
I bet we would have heard if it had, since It's the albatross hanging on the neck of this entire technology.
Not a stock market person or anything at all ... but NVIDIA's stock has been oscillating since July and has been falling for about a 2 weeks (see Yahoo finance).
What are the chances that this is the investors getting cold feet about the AI hype? There were open reports from some major banks/investors about a month or so ago raising questions about the business models (right?). I've seen a business/analysis report on AI, despite trying to trumpet it, actually contain data on growing uncertainties about its capability from those actually trying to implement, deploy and us it.
I'd wager that the situation right now is full a lot of tension with plenty of conflicting opinions from different groups of people, almost none of which actually knowing much about generative-AI/LLMs and all having different and competing stakes and interests.
"What are the chances..."
Approximately 100%.
That doesn't mean that the slide will absolutely continue. There may be some fresh injection of hype that will push investor confidence back up, but right now the wind is definitely going out of the sails.
The core issue, as the Goldman - Sachs report notes, is that AI is currently being valued as a trillion dollar industry, but it has not remotely demonstrated the ability to solve a trillion dollar problem.
No one selling AI tools is able to demonstrate with confidence that they can be made reliable enough, or cheap enough, to truly replace the human element, and without that they will only ever be fun curiosities.
And that "cheap enough" part is critical. It is not only that GenAI is deeply unreliable, but also that it costs a truly staggering amount of money to operate (OpenAI are burning something like $10 billion a year). What's the point in replacing an employee you pay $10 an hour to handle customer service issues with a bot that costs $5 for every reply it generates?
Yeah we are on the precipice of a massive bubble about to burst because, like the dot com bubble magic promises are being made by and to people who don’t understand the tech as if it is some magic that will net incredible profits just by pursuing it. LLMs have great applications in specific things, but they are being thrown in every direction to see where they will stick and the magic payoff will come
Yea, the "cheaper than droids" line in Andor feels strangely prescient ATM.
What are the chances that this is the investors getting cold feet about the AI hype?
Investors have proven over and over they’re credulous idiots who understand sweet fuck-all about technology and will throw money at whatever’s in their face. Creepy Sam and the Microshits will trot out some more useless garbage and prize a few more billion out of the market in just a little while.
NVIDIA has been having a lot of problems with their 13th/14th gen CPU's degrading. They are also embroiled in an anti-trust investigation. That coupled with the "growing pains of generative AI" has caused them a lot of problems where 2 months ago they were one of the world's most valuable companies.
Some of it is likely the die-off of the AI hype but their problems are farther reaching than the sudden AI boom.
Thanks!
I would expect "faster" to be a way
or cheaper
"I can easily do it on my phone" is also good.
The important thing here isn't that the AI is worse than humans. It's than the AI is worth comparing to humans. Humans stay the same while software can quickly improve by orders of magnitude.
LLMs as they stand are already approaching the improvement flatline portion of the sigma curve due to marginal data requirements increasing exponentially.
It's a known problem in the actual AI research field that nobody in private industry likes to talk about.
If it scores 40% this year it'll marginally increase by 10% next year then 5% 3 years later and so on.
AI doesn't follow Moore's law.
So far "more data" has been the solution to most problems, but I don't think we're close to the limit of how much useful information can be learned from the data even if we're close to the limit of how much data is available. Look at the AIs that can't draw hands. There are already many pictures of hands from every angle in their training data. Maybe just having ten times as many pictures of hands would solve the problem, but I'm confident that if that was not possible then doing more with the existing pictures would also work.* Algorithm design just needs some time to catch up.
*I know that the data that is running out is text data. This is just an analogy.
And at rather ridiculously fast paces, as demonstrated by comparing the different versions of Midjourney
The difference in being able to generate realistic humans is even more striking.The question is where do the current LLMs fit in that kind of a timeline.
Theoretically that's true. Can you tell techbros and the media to shut up about AI until it happens though?
Shut up about it ; )
The AI we have today is the worst it'll ever be. I can only think of two possible scenarios where AI doesn't eventually surpass human on every single cognitive task:
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There's something fundamentally different about computer made of meat (our brains) that cannot be replicated in silica. I personally don't see this as very likely since both are made of matter and matter obeys the laws of physics.
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We destroy ourselves before we reach AGI.
Otherwise we'll keep improving our technology and inching forward. It may take 5 years or 50 but it wont stop unless either of the scenarios stated above is true.
It would be odd if AI somehow got worse. I mean, wouldn't they just revert to a backup?
Anyway, I think (1) is extremely unlikely but I would add (3) the existing algorithms are fundamentally insufficient for AGI no matter how much they're scaled up. A breakthrough is necessary which may not happen for a long time.
I think (3) is true but I also thought that the existing algorithms were fundamentally insufficient for getting to where we are now, and I was wrong. It turns out that they did just need to be scaled up...
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This is an old study, they tested University level adults against the standard Llama2-70B.
Kinda absolete now, the model has completely fallen out of use, for the newer and far better 3 and 3.1 Versions. It also wasnt fine tuned for summarization, and while base L2-70B was OK, it wasnt great at anything without fine tuning.
This clickbait title also sounds like self gratification, the abysmal reading comprehension in the Internet is directly counter to it. The average human found on the Internet doesnt approch the level of literary capabilities, that those ten human testers showed in the study.
This reminds me. What happened to that tldr bot? I did appreciate the summaries, even if they weren't perfect.
Meanwhile, here's an excerpt of a response from Claude Opus on me tasking it to evaluate intertextuality between the Gospel of Matthew and Thomas from the perspective of entropy reduction with redactional efforts due to human difficulty at randomness (this doesn't exist in scholarship outside of a single Reddit comment I made years ago in /r/AcademicBiblical lacking specific details) on page 300 of a chat about completely different topics:
Yeah, sure, humans would be so much better at this level of analysis within around 30 seconds. (It's also worth noting that Claude 3 Opus doesn't have the full context of the Gospel of Thomas accessible to it, so it needs to try to reason through entropic differences primarily based on records relating to intertextual overlaps that have been widely discussed in consensus literature and are thus accessible).
My guess ist that even if it would be better when it comes to generic text, most of the texts which really mean something have a lot of context around them which a model will know nothing about and thus will not know what is important to the people working with this topic and what is not.
"AI" or Large Language Models, are designed by definition to give averaged answers. So they're not just averaging on the text you give them, they're averaging it with all general text of the training model, to create a probabilistically average result based on all of it.
There's no way around this, because it's simply how such systems work. It's their lifeblood to produce a "best guess" across large amounts of training data ...which is done by averaging out all that language. A large amount of language... Hence the name.
Are we talking 10% worse and 95% cheaper? Or 50% worse and 10% cheaper? Or 90% worse and 95% cheaper?
Because that last one is good enough for fiscal conservatives. Hell, the second one is good enough for fiscal conservatives.
The linked pdf lists the deficiencies of the LLM responses. They are varied and it sometimes misses the mark completely or cant grasp vital context.
Still pretty useless comparison, they testet 10 university level humans against Llama2-70B. The model has fallen out of use completely by now and was never really great at summarization. The study didnt fine tune it either, so this isnt really representative of the current situation.
There are far better models out, that were either especially trained for summarization or can be easily fine tuned to excel at it. Not to mention the Llama3 and 3.1 series, with the crazy 405B model.
Knowing this it seems like a very low quality study. They should probably redo this with multiple conditions.
- Base Llama 3
- Tuned Llama 3
- Untrained human summarizer
- trained/professional human summarizer
Artificial intelligence is worse than humans in every way at summarizing documents
In every way? How about speed? The goal is to save human time so if AI is faster and the summary is good enough, then it is a success. I guarantee it is faster. Much faster.
If you make enough mistakes, speed is a detriment not a benefit. Increasing speed allows you to produce more summaries but if you still need to correct and edit them all you've done is add a step where a human has to still read the document to the level where they could summarize it and edit the AI summary. Therefore the bottleneck of a human reading the document and working on a summary is still there. It would only potentially make it slightly easier if the corrections needed are small and obvious.
47% is a fail. 81% is an A-... Sure the AI can fail faster than a human can succeed, but I can fail to run a marathon faster than an athlete can succeed.
I guess by the standards we use to judge AI I'm a marathon runner!
If I want to get a better sense of lemmy than headlines, that 47% success at summarizing all the posts is good enough and much faster than I can even skim
If I want to code a new program, that 47% is probably pretty solid at structure and boilerplate so good enough. It can save me a lot of time
If I want to summarize the statuses of my entire team, that 47% may be sufficient for a Slack update to keep everyone up to speed but not enough to send to management
If I’m writing my thesis, that 47% is abject failure
A summary is useless if it’s not accurate.
Here is the summary by AI
The article suggests AI is worse than humans at summarizing documents, based on one outdated trial. But really, Crikey is just feeling threatened. AI is evolving fast, and its ability to handle vast amounts of data without the human biases Crikey often exhibits is undeniable. While they nitpick AI’s limitations, they ignore how much better it will get—probably even better than their reporters. Maybe they’re just jealous that AI could do in seconds what takes humans hours!
From my experience that was the case. However it was with gpt 3, and I am a sample of 1.
Nice to have though, would likely skip or half-ass a lot of stuff if I didn't have a tool like AI to do the boring parts. When I can get started on a task really quickly, I don't care what the quality is, I'll iterate until it meets my standards.
To all of you AI haters out there, stay away from the two minute papers yt channel. You'll get very sad at the actual state of AI.
Also beware the AI Explained channel, where the creator is full-time investigating and evaluating cutting edge development in AI. You might even glimpse what's coming.
No shit.
Intelligence vs non intelligence: intelligence is superior... Who would have thunk it lol
Artificial intelligence is worse than humans in every way
As if capitalists have ever cared about that...
