I just tried a few AI plugins for #figma and they were all bad. This domain might be a great test for #LLMs . I predict these failings are unlikely to be fixed any time soon:
Layout was poor
They can't create components
Laughably complex object hierarchies (everything was enclosed in a frame)
Of course things will improve, but I expect fixing these deep structural problems are a function of many new constraints, likely beyond what today's LLMs are actually capable of. @simon ?
"The biggest question raised by a future populated by unexceptional A.I., however, is existential. Should we as a society be investing tens of billions of dollars, our precious electricity that could be used toward moving away from fossil fuels, and a generation of the brightest math and science minds on incremental improvements in mediocre email writing?" (From an NYT article. See original thread.)
Do you REALLY want to get a feel for how GPT-4o does what it does? Just complete this poem — by doing so, you’ll have performed a computation similar to the one it does when you feed it a text-plus-image prompt.
Just FYI, if you have older parents or other family members, set up some sort of shibboleth with them so they know what to ask you if you ever call them asking for something. These new generative models are going to be extremely convincing, and the idiots in charge of these companies think they can use guardrails to stop it being used inappropriately. They can't. #genAI#LLMs#chatgpt
“React and the component model standardises the software developer and reduces their individual bargaining power excluding them from a proportional share in the gains”. An amazing write-up by @baldur about the de-skilling of developers to reduce their ability to fight back against their employers.
“The general problem of mixing data with commands is at the root of many of our computer security vulnerabilities.” Great explainer by security researcher Bruce Schneier on why large language models may not be a great choice for tasks like processing your emails. https://cacm.acm.org/opinion/llms-data-control-path-insecurity/
I just issued a data deletion request to #StackOverflow to erase all of the associations between my name and the questions, answers and comments I have on the platform.
One of the key ways in which #RAG works to supplement #LLMs is based on proven associations. Higher ranked Stack Overflow members' answers will carry more weight in any #LLM that is produced.
By asking for my name to be disassociated from the textual data, it removes a semantic relationship that is helpful for determining which tokens of text to use in an #LLM.
If you sell out your user base without consultation, expect a backlash.
"When the Singaporean government asked local writers if they would agree to having their work used to train a large language model, it probably did not expect the country’s tiny literary community to react so fiercely."
i used an analogy yesterday, that #LLMs are basically system 1 (from Thinking Fast and Slow), and system 2 doesn’t exist but we can kinda fake it by forcing the LLM to have an internal dialog.
my understanding is that system 1 was more tuned to pattern matching and “gut reactions”, while system 2 is more analytical
i think it probably works pretty well, but curious what others think
Just came up with a new analogy I'm rather proud of: LLMs are digital compost heaps. They decompose whatever you hurl in and turn it into artificial excrement.
Also I'm moving from StackExchange to Codidact. If I'm going to do any more unpaid labour it's going to be for a not-for-profit, rather than a for-profit company. Feeding that work into a digital compost heap is the push I needed.
Eventually, people may stop writing, stop filming, stop composing—at least for the open, public web. People will still create, but for small, select audiences, walled-off from the content-hoovering AIs.
If we continue in this direction, the #web—that extraordinary ecosystem of knowledge production—will cease to exist in any useful form.
The TinyChart-3B LLM answers questions about data visualizations. It can also generate underlying data from a dataviz and Python code to re-create a similar chart.