Are we surprised? Not really no... you don't own any of the data you're feeding it. Keep it away from your secrets.
So transformer models produce things that look plausible... and that's it. What would it look like if we started to make hybrid models in which a transformer model is also tied to proper computation tools with general knowledge? This piece is a good illustration of what it could provide.
For all the conversations about how chat GPT might displace jobs, there's a big untold: how much of copyright is violated in the process? It's also very concerning about how much data it collects when interacted with.
Or why you can't trust large language model for any fact or knowledge related tasks...
It's limits and biases are well documented. But, what about the ideologies of the people behind those models? What can it tell us about their aims behind those models? Questions worth exploring in my opinion.
Interesting work, trying to get back to the source material used by a generative model. This is definitely necessary as well.
A few interesting points in there. Too much hype and important points are glanced over, we'd all benefit from them being more actively explored.
Very nice summary of the architecture in the latest trend of transformer models. Long but comprehensive, a good way to start diving in the topic.
Such generative models are getting more and more accessible. You can play with them using a few lines of python now.
The human labor behind AI training is still on going. This is clearly gruesome and sent over to other countries... ignoring the price for a minute this is also a good way to hide its consequences I guess.
Very good piece about that dangerous moment in the creation of the latest large language models. We're about to drown in misinformation, can we get out of it?
A few compelling arguments for the impact of the latest strain of generative neural networks. The consequences for the eroded trust about online content are clear. I'm less convinced about some of the longer term predictions this piece proposes though.
There are a few reasons to worry about the latest strain of generative neural networks. One of them is the trust we can place in new generated content. The other one is indeed the impact on our culture. There's been already a trend at focusing on what sells rather than what's truly novel or avant-garde. This could well push it further. We'll we drown in mediocre content?
Interesting tool to for the automatic transcription and translation of videos using off the shelf components. Seems to work nicely.
Don't worry, so called AI isn't going to take away your jobs. But do worry though, this marks the end of trusting any pictures or texts you see in the media. Everything needs to be challenged, even more so now.
Interesting reverse engineering job of Copilot's client side to have a better idea at which information it actually feeds to the model. A couple of funny tricks to prepare the prompt are involved. Obviously some telemetry involved as well, again with interesting heuristics to try to figure out if the user kept the suggestion or not.
At least a good balanced post about Generative AI and programming. It's not overestimating abilities of the latest trend in large language models and moves away from the "I'll loose my job, developers will be replaced with AI" stance.
A few months old but a good piece to put things in perspective after the recent craze around large language models in general and GPT in particular. Noteworthy is the "wishful mnemonics" phrase mentioned and how it impacts the debate. Let's have less talks about AIs and more about SALAMIs please?
Nice article, gives a few clues to get a grasp on how GPT-3 works.
Words of caution regarding the use of language models for producing code. This can derail fairly quickly and earlier than you'd expect... without noticing it.