That’s not what case-by-case means. Wiktionary:
Separate and distinct from others of the same kind; treated individually.
Case-by-case implies that each treatment is different and is not generalisable; but the fact that they use a patient’s own tissue does not make each individual treatment different. If you want to extend the logic, you might call vaccination a case-by-case treatment as well, since they use different needles for each person.
it was done on a case-by-case basis. Each person has their own therapy tailored for them. This does not appear to be a mass-solution.
I’m not sure what you are expecting for something to be considered a cure? What they are describing is a treatment procedure which uses the patient’s own tissue. How does that make it case-by-case?
It can at least get one unstuck, past an indecision paralysis, or give an outline of an idea. It can also be useful for searching though data.
If this works, it’s noteworthy. I don’t know if similar results have been achieved before because I don’t follow developments that closely, but I expect that biological computing is going to catch a lot more attention in the near-to-mid-term future. Because of the efficiency and increasingly tight constraints imposed on humans due to environmental pressure, I foresee it eventually eclipse silicon-based computing.
FinalSpark says its Neuroplatform is capable of learning and processing information
They sneak that in there as if it’s just a cool little fact, but this should be the real headline. I can’t believe they just left it at that. Deep learning can not be the future of AI, because it doesn’t facilitate continuous learning. Active inference is a term that will probably be thrown about a lot more in the coming months and years, and as evidenced by all kinds of living things around us, wetware architectures are highly suitable for the purpose of instantiating agents doing active inference.
I don’t know about google because I don’t use it unless I really can’t find what I’m looking for, but here’s a quick ddg search with a very unambiguous and specific question, and from sampling only the top 9 results I see 2 that are at all relevant (2nd and 5th):
In order to answer my question, I need to first mentally filter out 7/9 of the results visible on my screen, then open both of the relevant ones in new tabs and read through lengthy discussions in order to find out if anyone has shared a proper solution.
Here is the same search using perplexity’s default model (not pro, which is a lot better at breaking down queries and including relevant references):
and I don’t have to verify all the details because even if some of it is wrong, it is immediately more useful information to me.
I want to re-emphasise though that using LLMs for this can be incredibly frustrating too, because they will often insist assertively on falsehoods and generally act really dumb, so I’m not saying there aren’t pros and cons. Sometimes a simple keyword-based search and manual curation of the results is preferred to the nonsense produced by a stupid language model.
Edit: I didn’t answer your question about malicious, but I can give some example of what I consider malicious and you may agree that it happens frequently enough:
etc.
Maybe I can share some insight into why one might want to.
I hate searching the internet. It’s a massive mental drain for me to try figure out how I should put my problem into words that others with similar ideas will have done before me - it’s my mental processing power wasted on purely linguistic overhead instead of trying to understand and learn about the problem.
I hate the (dis-/mis-)informational assault I open myself to by skimming through the results, because the majority of them will be so laughably irrelevant, if not actively malicious, that I become a slightly worse person every time I expose myself.
And I hate visiting websites. Not only because of all the reasons modern websites suck, but because even if they are a delight in UX, they are distracting me from what I really want, which is (most of the time) information, not to experience someone’s idiosyncratic, artistic ideas for how to organise and present data, or how to keep me ‘engaged’.
So yes, I prefer stupid a language model that will lie about facts half the time and bastardise half my prompts if it means I can glance a bit of what the internet has to say about something, because I can more easily spot plausible bullshit and discard it or quickly check its veracity than I can magic my vague problem into a suitable query only to sift through more ignorance, hostility, and implausible bullshit conjured by internet randos instead.
And yes, LLMs really do suck even in their domain of speciality (language - because language serves a purpose, and they do not understand it), and they are all kinds of harmful, dangerous, and misused. Given how genuinely ignorant people are of what an LLM really is and what it is really doing, I think it’s irresponsible to embed one the way google has.
I think it’s probably best to… uhh… sort of gatekeep this tech so that it’s mostly utilised by people who understand the risks. But capitalism is incompatible with niches and bespoke products, so every piece of tech has to be made with absolutely everyone as a target audience.
We’re all living in amerikka
koka kola
santa klaus
That is a different car brand, though?
Global population? You say “the”, so you obviously mean the one we have in common.
That argument extends to any realistic recreation of events. It’s not wrong, I’m just not sure what could be done about it.
Don’t do that.
Yeah, I don’t know why anyone knowledgeable would expect them to be good at chess. LLMs don’t generalise, reason or spot patterns, so unless they read a chess book where the problems came from…
Not well, apparently.
Firstly, I’m willing to bet only a minority of users regularly use those buttons. Secondly, you’re talking about the most popular LLM(s) out there. What about all the other LLMs almost nobody is using but are still being developed/researched? Where do they find humans willing to sit and rate all the garbage their LLM puts out?
I know LLMs are used to grade LLMs. That isn’t solving the problem, it’s just better than nothing because there are no alternatives. There aren’t enough humans willing to endlessly sit and grade LLM responses.
For that you need a program to judge the quality of output given some input. If we had that, LLMs could just improve themselves directly, bypassing any need for prompt engineering in the first place.
The reason prompt engineering is a thing is that people know what is expected and desired output and what isn’t, and can adapt their interactions with the tool accordingly, a trait uniquely associated with adaptive complex systems.
Like a completely mad or autistic artist that is creating interesting imagery but has no clue what it means.
Autists usually have no trouble understanding the world around them. Many are just unable to interface with it the way people normally do.
It’s a reflection of our society in a weird mirror.
Well yes, it’s trained on human output. Cultural biases and shortcomings in our species will be reflected in what such an AI spits out.
When you sit there thinking up or refining prompts you’re basically outsourcing the imaginative visualizing part of your brain. […] So AI generation is at least some portion of the artistic or creative process but not all of it.
We use a lot of devices in our daily lives, whether for creative purposes or practical. Every such device is an extension of ourselves; some supplement our intellectual shortcomings, others physical. That doesn’t make the devices capable of doing any of the things we do. We just don’t attribute actions or agency to our tools the way we do to living things. Current AI possess no more agency than a keyboard does, and since we don’t consider our keyboards to be capable of authoring an essay, I don’t think one can reasonably say that current AI is, either.
A keyboard doesn’t understand the content of our essay, it’s just there to translate physical action into digital signals representing keypresses; likewise, an LLM doesn’t understand the content of our essay, it’s just translating a small body of text into a statistically related (often larger) body of text. An LLM can’t create a story any more than our keyboard can create characters on a screen.
Only once/if ever we observe AI behaviour indicative of agency can we start to use words like “creative” in describing its behaviour. For now (and I suspect for quite some time into the future), all we have is sophisticated statistical random content generators.
Yeah a real problem here is how you get an AI which doesn’t understand what it is doing to create something complete and still coherent. These clips are cool and all, and so are the tiny essays put out by LLMs, but what you see is literally all you are getting; there are no thoughts, ideas or abstract concepts underlying any of it. There is no meaning or narrative to be found which connects one scene or paragraph to another. It’s a puzzle laid out by an idiot following generic instructions.
That which created the woman walking down that street doesn’t know what either of those things are, and so it can simply not use those concepts to create a coherent narrative. That job still falls onto the human instructing the AI, and nothing suggests that we are anywhere close to replacing that human glue.
Current AI can not conceptualise – much less realise – ideas, and so they can not be creative or create art by any sensible definition. That isn’t to say that what is produced using AI can’t be posed as, mistaken for, or used to make art. I’d like to see more of that last part and less of the former two, personally.
It’s not so much the hardware as it is the software and utilisation, and by software I don’t necessarily mean any specific algorithm, because I know they give much thought to optimisation strategies when it comes to implementation and design of machine learning architectures. What I mean by software is the full stack considered as a whole, and by utilisation I mean the way services advertise and make use of ill-suited architectures.
The full stack consists of general purpose computing devices with an unreasonable number of layers of abstraction between the hardware and the languages used in implementations of machine learning. A lot of this stuff is written in Python! While algorithmic complexity is naturally a major factor, how it is compiled and executed matters a lot, too.
Once AI implementations stabilise, the theoretically most energy efficient way to run it would be on custom hardware made to only run that code, and that code would be written in the lowest possible level of abstraction. The closer we get to the metal (or the closer the metal gets to our program), the more efficient we can make it go. I don’t think we take bespoke hardware seriously enough; we’re stuck in this mindset of everything being general-purpose.
As for utilisation: LLMs are not fit or even capable of dealing with logical problems or anything involving reasoning based on knowledge; they can’t even reliably regurgitate knowledge. Yet, as far as I can tell, this constitutes a significant portion of its current use.
If the usage of LLMs was reserved for solving linguistic problems, then we wouldn’t be wasting so much energy generating text and expecting it to contain wisdom. A language model should serve as a surface layer – an interface – on top of bespoke tools, including other domain-specific types of models. I know we’re seeing this idea being iterated on, but I don’t see this being pushed nearly enough.[1]
When it comes to image generation models, I think it’s wrong to focus on generating derivative art/remixes of existing works instead of on tools to help artists express themselves. All these image generation sites we have now consume so much power just so that artistically wanting people can generate 20 versions (give or take an order of magnitude) of the same generic thing. I would like to see AI technology made specifically for integration into professional workflows and tools, enabling creative people to enhance and iterate on their work through specific instructions.[2] The AI we have now are made for people who can’t tell (or don’t care about) the difference between remixing and creating and just want to tell the computer to make something nice so they can use it to sell their products.
The end result in all these cases is that fewer people can live off of being creative and/or knowledgeable while energy consumption spikes as computers generate shitty substitutes. After all, capitalism is all about efficient allocation of resources. Just so happens that quality (of life; art; anything) is inefficient and exploiting the planet is cheap.
For example, why does OpenAI gate external tool integration behind a payment plan while offering simple text generation for free? That just encourages people to rely on text generation for all kinds of tasks it’s not suitable for. Other examples include companies offering AI “assistants” or even AI “teachers”(!), all of which are incapable of even remembering the topic being discussed 2 minutes into a conversation. ↩︎
I get incredibly frustrated when I try to use image generation tools because I go into it with a vision, but since the models are incapable of creating anything new based on actual concepts I only ever end up with something incredibly artistically compromised and derivative. I can generate hundreds of images based on various contortions of the same prompt, reference image, masking, etc and still not get what I want. THAT is inefficient use of resources, and it’s all because the tools are just not made to help me do art. ↩︎
I reply to people on lemmy on a case-by-case basis. I decide how to eat food on a case-by-case basis. But if you give me a deck of cards and tell me to shuffle them, I generally do not decide how to shuffle on a case-by-case basis; it doesn’t matter whose cards they are.