Tier3r · HackerTrans

2 lata temudiscuss

The feeling I'm getting is management took such a huge stance (the capex, the name change, etc) on it it'd be humiliating to turn 180 and ditch it now (they've spent 50 billion on Reality Labs so far). So they're quietly pushing it aside and one day when people aren't looking, they'll take it to the back of the house and shoot it in the head. Probably call it an "internal re-org" or something like that.

Tier3r

2 lata temudiscuss

The only reason I can think that this atrocity got deployed was from an engineering perspective, creating AI profiles to engage theoretically gives FB a powerful lever to increase engagement, because you'd have this entire new field to collect data, experiment and test people's responses. In other words, it would turn engagement into a form tractable to optimisation algorithms.

Tier3r

2 lata temudiscuss

I'd argue that it hasn't come to pass, but it is not wrong that its a better way to teach. Virtually all education has been set up in an industrial line style of pre-u education -> university -> companies -> profits -> some value to society. Every part has been attempted to be structured to optimise for the subsequent stage - companies optimisie for profits, universities optimise for employment rate and salary, high schools optimise for college entrance rates, etc. Yet because of Goodhart's and organizational incompetence, each part optimises badly, so education gets exponentially disconnected from each. His proposals are valuable to society, but the system is structurally against it.

The only way this works if it skips the assembly line right down to being of value to society, ie you set up a system which explicitly transforms that teaching into things like startups, research, nonprofits etc.

Tier3r

2 lata temudiscuss

In intelligence/performance. It's admittedly a fuzzy notion. Most benchmarks will probably show decreasing gains between generations. Similar to time/space complexity, trying to debate about what performance/intelligence is will get into a million definitions, caveats and technicalities. But a relative comparison between inputs and outputs is gives us useful information.

The inputs - data, compute and parameters - going into training these models have grown by many orders of magnitude between each gen. There's a lot of fuzziness about how much better each gen has gotten, but clearly 4 is not many orders of magnitude better than 3 by any reasonable definition. This mental model isn't useful to say how good each gen is, but it is quite useful to see the trend and make long term predictions.

Tier3r

2 lata temudiscuss

We are likely going to get there. Similar to the steam/combustion engines (and other core technologies like computers, wireless transmission etc) there's first a massive rush to increase the power of it, at the cost of efficiency and effectiveness for more niche use cases. Then it is specialised to various use cases with large improvements in efficiency and effectiveness. My own prediction for where most gains will now come is

1) Creating new "harnesses" for models that connect to various systems, APIs, frameworks, etc. While this sounds "trivial", a lot of gains can come from this. Similar to how the voice version of ChatGPT was (apparently) amazing, all you really had to do was create an additional voice to text layer and another text to voice layer.

2) Increasing specialisation of models. I predict over time that end user AI companies (e.g those that just use models and not develop them), will use more and more specialised models. The current, almost monolithic, system where every service from text summary to homework help is plugged into the same model will slowly change.

Tier3r

2 lata temudiscuss

From the amount of data each successive generation used (which grew many orders of magnitude each time) to the decreasing, logarithmic performance, it's quite clear the steam is running out on shoving more data into it. If one plots the data to performance graph, its horribly logarithmic. In another perspective, the ability of LLMs to transfer learning actually decreases exponentially the larger they and the data sets get. This fits into the how humans have to specialise in topics because the mental models of one field is very difficult to transfer to another.

Tier3r

2 lata temudiscuss

I fear not the man who has written 10,000 features once, but the man who has written 1 feature 10,000 times

Tier3r

2 lata temudiscuss

That is false equivalence, there's a class of things that should rightfully be discouraged from military use (chemical/biological weapons, land mines, phosphorous weapons) because of the significant harm/side effects they cause beyond some definition of acceptable.

Tier3r

2 lata temudiscuss

That seems like a good idea. I am puzzled by what benefit the RL has in OP. It seems like a well defined constraint optimisation problem that could be done without RL, for example in the way you mentioned.

Tier3r

2 lata temudiscuss

Google is getting enshittified. It's already visible in many small ways. I was just using Google maps and in the route they called X (bus) Interchange as X International. I can only assume this happened because they are using AI to summarise routes now. Why in the world are they doing that? They have exact location names available.

Tier3r

2 lata temudiscuss

I'm also wondering this. Two possibilities. One, find the first principles/root node/glue that holds many disparate concepts together in some causative way. Two, the specific procedure/step/concept that you keep reusing across multiple problems.

Tier3r

2 lata temudiscuss

The initial cram is an interesting concept. If you insert new things to learn at a constant rate, the repetition burden grows logarithmically. Assuming you have some fixed amount of time you can devote everyday optimally your repetition burden should be constant. So the solution is making new things to learn not constant, but front loading a lot of it.

Tier3r

2 lata temudiscuss

I'm dubious of its scale and effects. Tech creates exponential value because of aggressive focus, iteration and critical mass of talent. The reserves seems very far from this.

Tier3r

2 lata temudiscuss

I suspect the actual dollar value of AI from "companionship" far outstrips the value created from the endless junk like Gemini summaries.

Tier3r

2 lata temudiscuss

Starting from the basics, Ramanujan was known to spend huge amounts of time in the library pouring over mathematical texts. He was also personally and spiritually obsessed with mathematics, thinking it was an expression of divinity. So its quite probable a significant chunk of his memories were already mathematical and random accesses to it were the same.

Tier3r

2 lata temudiscuss

As much as I love Anki there is enormous friction in creating the cards, especially as a beginner. And you very easily get overfitting when you keep seeing the same question.

Tier3r

2 lata temudiscuss

That is IMO already done by domain experts in the form of textbooks, courses, university classes etc. And with formal, tested relationships between various islands of knowledge. I'm not too sure how much I would trust an LLM to do this

Tier3r

2 lata temudiscuss

Same for Anki, Notion, Zettelkatsen etc. Even ignoring the setup cost, it costs as much effort to insert in something as it does to retrieve it. The value prop tends to be low for individuals.

Tier3r

2 lata temudiscuss

Perplexity has a dubious idea based around harvesting user chats -> making service better -> getting more user prompts. I am quite unconvinced that user prompts and stored chats will materially improve an LLM that is trained on a trillion high quality tokens.

The second idea being kicked around is synthetic data will create a new fountain of youth for data that will also fix its reasoning abilities.

Tier3r

2 lata temudiscuss

I'd chime in on a somewhat lesser known part of networks that in practice the real determinant of performance: congestion control. The effects can be on the scale of an order of magnitude or two. The problem of how decentralised nodes in a network can use finite network resources optimally is fundamentally unsolvable (proved by Jaffe, intuitively nodes never have perfect information). Most congestion control algos use a congestion window (the amount of data in-flight) to limit themselves, this window is divided at a packet loss (aka congestion event) and increased when a successful round trip is made (in modern algorithms logarithmically if below a steady state assumption, cubic if above). There's a general consensus that these algorithms perform extremely poorly on long, fat networks because size of window growth depends on the round trip. Put another way, increasing bandwidth far from directly improves throughput. Latency in worst case is limited by the speed of light, and is in the order of the 150ms to 250ms range in the time it takes for light to travel alone. So there are fundamental limits on the latency that Starlink can provide.

Some back of the envelop calculations = it takes about 7ms in extra latency to travel up and down to the satellite vertically on a round trip, and modeling fiber optic vs starlink as two spheres of different radii starlink will take ~16% more time. Performance will be also extremely affected by the reliability of the medium ie packet loss from errors transmitting through the atmosphere vs fiber optic. Most congestion controls believe latency and packet loss comes from congestion and will aggressively back off. When I tested this on my own 866Mbps WiFi network with iperf and linux qc, with a 100ms latency and 1% packet loss my throughput was 1.86Mbps. At 5ms latency and 0.01% packet loss it was 58Mbps.

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