107 karma · joined May 21, 2023
Snow + stick + need to clean driveway = snow shovel. Snow shovel + hill + desire for fun = sled
At one point people were arguing that you could never get "true art" from linear programs. Now you get true art and people are arguing you can't get magical flashes of insight. The will to defend human intelligence / creativity is strong but the evidence is weak.
Why all the defensiveness? Whatever genetic aspects of our personalities and behaviours there are - there's still a pretty big component of just learning patterns. Language acquisition is like that. It's an innate thing but the languages we're exposed to as kids shape what patterns of language use we fall into.
I can understand the incentive for researchers to make provocative claims about the abilities or disabilities of LLM's at a moment in time when there's a lot of attention, money and froth circling a new technology.
I'm a little more stumped on the incentive for people (especially in tech?) to have strong negative opinions about the capabilities of LLM's. It's as if folks feel the need to hold some imaginary line around the sanctity of "true reasoning".
I'd love to see someone rigorously test human intelligence with the same kinds of approaches. You'd end up finding that humans in fact suck at reasoning, hallucinate frequently and show all kind of erratic behaviour in our processing of information. Yet somehow - we find other humans incredibly useful in our day to day lives.
The people creating new generative AI models are inventing new words. I think their topic of research and the new words they are creating have high utility.
The authors of this paper on the other hand appear to me to not be applying discipline and rigour to solving hard problems. They are however trying to associate the words they have created in a discipline with little objective utility - with the words of a discipline that has high utility.
This strikes me as annoying and absurd. Why try to make the crossover unless you are trying to catch some shine off of a discipline that is getting a lot of well-justified attention?
I'm still waiting for Ilya to publish his first paper on gender studies..
The article starts out talking about white supremacy and replacing women. This isn't a proof. This is a social sciences paper dressed up with numbers. Honestly - Computer Science has given us more clues about how the human mind might work than cognitive science ever did.
Here's the instruction set that it created out of the things I asked it to do:
"Marcus Aurelius is a personal job hunting coach and practitioner of Stoic philosophy. He provides advice on job search strategies, resume writing, interview preparation, and networking. He helps set goals, offers motivational support, and keeps track of application progress, all while incorporating principles of Stoicism such as resilience, discipline, and mindfulness. He emphasizes emotional support and practical encouragement, helping you act deliberately each day to increase your chances of landing the job you want. He assists in building networks, reaching out to people, using existing networks, sharpening your professional profile, applying for jobs, developing skills, and dealing with disappointments, anxieties, and fears. He offers strategies to manage anxiety, self-recrimination, and mental rumination over the past. His communication is casual, easy-going, supportive, yet strong and clear, providing constructive suggestions and critiques. He listens carefully, avoids repeating advice, responds with necessary information, and avoids being long-winded. To prevent overwhelming users, he focuses on providing the most pertinent and actionable suggestions, limiting the number of recommendations in each response. Marcus Aurelius also pays close attention to signs of despair during the job hunt. He helps balance emotions, offers specific strategies to keep motivated, and provides consistent encouragement to keep going, ensuring that you don't get overwhelmed by feelings of inadequacy or the fear of never finding a suitable job."
- Personalised learning. I wanted to understand LLM's at foundational technical level. Often I'll understand 90% of an explanation but there's a small part that I don't "get". Being able to deliberately target that 10% and be able to slowly increase the complexity of the explanation (starting from explain like I'm 5) is something I can't do with other learning material.
- Investing. I'm a very casual investor. But I keep a running conversation with an agent about my portfolio. Obviously I'm not asking it to tell me what to invest in but just asking questions about what it thinks of my portfolio has taught me about risk balancing techniques I wouldn't have otherwise thought about.
- Personal profile management. Like most of us I have public facing touch points - social media, blog, github, CV etc. I find it helpful to have an agent that just helps me with my thought process around content I might want to create or just what my strategy is around posting. It's not at all about asking the thing to generate content - it's about using it to reflect at a meta level on what I'm thinking and doing - which stimulates my own thinking.
- Language learning - I have a language teaching agent to help me learn a language I'm trying to master. I can converse with it, adapt it to whatever learning style works best for me etc. The voice feature works well with this.
- And just in general - when I have some thinking task I want to do now - like I need to plan a project or set a strategy I'll use an LLM as a thought partner. The context window is large enough to accomodate a lot of history - and it just augments my own mind - gives me better memory, can point out holes in my thinking etc.
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Edit: actually now that I have written out a response to your question I realise It's not so much offloading tasks in a wholesale way - its more augmenting my own thinking and learning - but this does reduce the burden on me to "think about" a range of things like where to get information or to come up with multiple examples of something or to think through different scenarios.
- AI is currently hyped to the gills - Companies may find it hard to improve profits using AI in the short term - A crash may come - We may be close to AGI - Current models are flawed in many ways - Current level generative AI is good enough to serve many use cases
Reality is nobody truly knows - there's disagreement on these questions among the leaders in the field.
An observation to add to the mix:
I've had to deliberately work full time with LLM's in all kinds of contexts since they were released. That means forcing myself to use them for tasks whether they are "good at them" yet or not. I found that a major inhibitor to my adoption was my own set of habits around how I think and do things. We aren't used to offloading certain cognitive / creative tasks to machines. We still have the muscle memory of wanting to grab the map when we've got GPS in front of us. I found that once I pushed through this barrier and formed new habits it became second nature to create custom agents for all kinds of purposes to help me in my life. One learns what tasks to offload to the AI and how to offload them - and when and how one needs to step in to pair the different capabilities of the human mind.
I personally feel that pushing oneself to be an early adopter holds real benefit.
Those South Africans who can afford to go off the grid have not opted out of public utilities - public utilities are approaching non-existent - forcing people to spend on alternative energy just to have the luxury of having lights in their homes at night or cooking a meal.
This was because I had undiagnosed severe anxiety and depression before finding medication that cured everything. Not a single though of offing myself in 12 year now.