I've been using LLMs pretty extensively in a professional capacity and with the proper grounding work it becomes very useful and reliable.
LLMs on their own is not the world changing tech, LLMs+grounding (what is now being called a Cognitive Architecture), that's the world changing tech. So while LLMs can be vulnerable to bullshitting, there is a lot of work around them that can qualitatively change their performance.
"It has already started to be a problem with the current LLMs that have exhausted most easily reached sources of content on the internet and are now feeding off LLM-generated content, which has resulted in a sharp drop in quality."
Do you have any sources to back that claim? LLMs are rising in quality, not dropping, afaik.
And hopefully this will allow them to follow the 80/20 rule where the AI can do 80% of the grunt work and the human can concentrate on the 20% creative part.
Agreed. It's such a disingenuous argument. It's the usual casting of poor people as lazy, and what they need is a good lashing to get them to work.
Like... No. People want dignity. People want to feel satisfied in their lives. UBI trials have shown that they use that money to get the life/jobs that they want. They're just not gonna be forced into shitty jobs as you said. This last bit is the part not said out loud.
I summarized the two readings of the bill. (Claude AI did, really)
Senator Pate gave a speech introducing Bill S-233, which would create a national framework to implement a guaranteed livable basic income program in Canada. She argued that poverty is a major social issue that needs to be urgently addressed. The COVID-19 pandemic has exacerbated income inequality and disproportionately affected marginalized groups. A guaranteed livable income could improve health, social, and economic outcomes for low-income Canadians.
The speech outlined how poverty puts people at greater risk of poor health, food insecurity, and homelessness. COVID-19 has spotlighted these vulnerabilities, as lower-income groups have suffered higher mortality rates. Senator Pate cited research showing guaranteed income pilots reduced hospital visits and improved participants' health. She argued a national program is feasible, building on existing supports like the Canada Child Benefit. Costs could be offset by reducing other programs and realizing savings in areas like healthcare.
There is growing momentum for guaranteed income, with support across party lines. Public opinion also favors it. Senator Pate positioned the bill as responding to decades of calls to action on poverty reduction. She appealed to fellow Senators to stop perpetuating myths about poverty and act boldly to implement this long-overdue policy. The speech was a compelling case for guaranteed income as a powerful tool for promoting equity and dignity.
Senator MacDonald responded to Senator Pate's speech introducing Bill S-233, which would create a framework for a guaranteed basic income (GBI) program in Canada. He commended Senator Pate's advocacy for the poor, but expressed concerns about the bill's lack of detail and fiscal implications.
Senator MacDonald outlined analyses questioning the affordability and sustainability of a GBI program. He cited research suggesting it could cost hundreds of billions annually, require tax increases, and reduce work incentives. Senator MacDonald also noted provincial studies concluding GBI is too costly and ineffective for poverty reduction compared to targeted measures.
Given Canada's debt and deficits, Senator MacDonald argued the country cannot realistically consider implementing GBI currently. He contended the solution is generating wealth through natural resource development, not expanding welfare states. Senator MacDonald suggested Conservatives could support GBI to replace current programs if fiscal conditions improve under a future Conservative government.
In conclusion, Senator MacDonald maintained Conservatives oppose Bill S-233. While GBI aims are laudable, he believes the bill's lack of detail and Canada's finances make it unrealistic presently. He advocated defeating the bill or sending it to committee for further scrutiny.
I'll put up a summary of the transcript once it becomes available or if I can extract it from the video.
Looks like maybe the note is down? I just get an empty list :(
There's lots of alternate, free and open source syncing solutions. I use syncthing myself.
But the main result is achieved anyway, right? The picture that the system tried to download did not make it into the training set.
For one thing: when you do it, you're the only one that can express that experience and knowledge. When the AI does it, everyone an express that experience and knowledge. It's kind of like the difference between artisanal and industrial. There's a big difference of scale that has a great impact on the livelihood of the creators.
I don't think that Sarah Silverman and the others are saying that the tech shouldn't exist. They're saying that the input to train them needs to be negotiated as a society. And the businesses also care about the input to train them because it affects the performance of the LLMs. If we do allow licensing, watermarking, data cleanup, synthetic data, etc. in a way that is transparent, I think it's good for the industry and it's good for the people.
Essentially, you don't ask them to use their internal knowledge. In fact, you explicitly ask them not to. The technique is generally referred to as Retrieval Augmented Generation. You take the context/user input and you retrieve relevant information from the net/your DB/vector DB/whatever, and you give it to an LLM with how to transform this information (summarize, answer a question, etc).
So you try as much as you can to "ground" the LLM with knowledge that you trust, and to only use this information to perform the task.
So you get a system that can do a really good job at transforming the data you have into the right shape for the task(s) you need to perform, without requiring your LLM to act as a source of information, only a great data massager.