[blamestross]

> Humans can't update their own algorithms. Can't directly share what they know in fractions of second. Can't be replicated in a fraction of a second. Can't scale up brain power in seconds or less.

We don't have any evidence AIs could do this either. Computers are not magic.

> Humans can't update their own algorithms.

This is the exact task your education has proved is possible. Companies update their policies to paper-clip maximize all the time. They react to environment stimulus and increase in sophistication over time.

"Complete Integration" seems like an artificial criteria you are creating as a "desperate post-rationalization to avoid the realization the singularity is long past and the rapture of the nerds left almost all of us behind."

[Nevermark]

> "Complete Integration" seems like an artificial criteria you are creating as a "desperate post-rationalization to avoid the realization the singularity is long past and the rapture of the nerds left almost all of us behind."

Uh what? Seems like? To who?

Integration matters even with humans. Shared cultures, priorities, ways of organizing information, terminology make a huge difference in efficiency for human teams.

It is no different with software, and computing hardware. But the speed they communicate wish is in the GHz already. And circuits designed close together (integrated design) can connect with huge bandwidth.

Billions of times faster than us. And that is today.

Machines also have no problem updating themselves today. Genetic algorithms for the machine learning architecture, direct optimization of meta learning parameters, ... the list goes on.

Once they are as smart as us in any area, machines surpass us almost instantly. There is no hanging around at some level for machines. Even when we are still the ones designing them.

Machines will have control of even the smallest unit of effect in their own design. Transistor level up for a start. But substrate chemistry and transistor design as well.

Even with humans doing those redesigns the cost of a calculation per second continues to drop exponentially. There is no end to that in sight, as even as transistor sizes stabilize to a few atoms across, we have just started going 3D with circuits. RAM chips are routinely stacked, sometimes CPUs are.

Going full 3D with circuits would be a massive increase in computing power, as power innovations enable more of that.

In the meantime, numbers of cores per chips continues to climb, chips per machine climbs, machines per data center climbs.

I am always puzzled by people who don't recognize the "magic" that has transformed the vacuum tube computers slowly doing simple arithmetic at their best, to talking and hearing, generating complex art and music, etc. The whole history within the lifetime of living people.

The time frame from where we are now to human level intelligence is likely to be much shorter than the 74 years from 1947 till 2021.

Try to imagine before 1947. Any non-technical person would consider what we have now as hard sci-fi, or magic, depending on their reference frames.

Why do you think so many design systems are incorporating more and more "dumb" AI into them? They are already surpassing us in new areas constantly.

[blamestross]

> Try to imagine before 1947. Any non-technical person would consider what we have now as hard sci-fi, or magic, depending on their reference frames.

I know all about the acceleration and the singularity. The Singularity isn't about AI, it is about the rate of change exceeding our ability to adapt to it.

It is well past. It happened back when AI was still in winter. Companies, cultures, and other human organizations were the agent of it's occurrence. Modern AI is just the natural progression of it's growth chipping away at the tail end of the sigmoid, not kicking off a new boom.

Computation exists in physical reality it has to obey physical laws. There are fundamental limits on how well computations can be distributed simply because bandwidth in/out of a volume is limited by surface area. Your brain has smacked directly into heat dissipation problems and production limits (pelvises only get so big). Animal brains can't get much bigger without liquid cooling (See whales and elephants with giant ears). Computers might have room to grow still, but mostly because they are so hilariously behind what evolution produced in us.

The name on this account is "blame Stross", as in "Charles Stross" (he hangs out around here). And while I am thankful that he sent me on my educational journey, a lot of stuff he and other scifi authors guessed at are just wrong. I've spent my adult life working on these problems and working on the largest distributed computations in the world. I've run into AI experts over and over that just don't understand the limits on what they do. AI is cool, but it isn't magic. It boils down to search algorithms over a space. That never will be embarrassingly parallel bc the only way to prevent diminishing returns on more workers is to coordinate those workers. We can coordinate workers in O(log(n)*n^(1/3)) time in this physical reality (log n merge steps and root cubed hops on maximally packed computers), which is great, but not constant. Quantum computing doesn't really help here.

> Going full 3D with circuits would be a massive increase in computing power, as power innovations enable more of that

And even if we figure out heat dissipation and power delivery, it will smack into scaling limits even faster than it took to hit them for flat circuits. O(n^1/3) isn't much better than O(n^1/2)

> I am always puzzled by people who don't recognize the "magic" that has transformed the vacuum tube computers slowly doing simple arithmetic at their best, to talking and hearing, generating complex art and music, etc. The whole history within the lifetime of living people.

You established the expectations of your life in the fun part of a sigmoid. I get it. We don't live there anymore.

All the evidence we have is that intelligence doesn't scale well and beyond the bare minimum required to result in reproduction. There isn't selection pressure for it beyond a certain point. AIs won't be any different in that regard. I think conscious self-improving AI will be a thing. I don't even think it will be hard to do. It might even be smarter than us, but the growth curve will be sigmoid just like ours. I also know the bottleneck on self-improvement is experience (active interaction with reality) not knowledge or computational power. No agent can discern causality from correlation without testing actions and AIs only stand at incredible disadvantage when it comes to available agency.

We don't even actually want bootstrapping AIs for any reason except our ego. It is a lot easier to make and manage a slave race of glorified simulations of optic nerves (which basically all modern AI is) versus actual people who want agency.

[Nevermark]

I didn't say anything about the singularity.

My entire career has been AI. So you are right, machine learning today is dominated by gradient based, and line based, searches.

And I understand what you are saying about distributed computing's inherent limitations.

But, it's not all about increasing the amount of computing (although that will continue to be a big factor for many years). Better organized computation is continually producing better results with less computing too.

Keep in mind that our brains take about the same effort to learn as to operate. Machine learning models operate with an incredible efficiency, and a minuscule amount of computing than when being trained. Models trained on massive cloud resources can be run on embedded processors, phones or smart watches.

Improvements to gradient/line searches accrue across virtually all of today's machine learning, so will continue to be researched and improved.

In the past, "simple" things like convolution, the right way to stage layers, etc., have dramatically improved the results and reduced model complexity in ways our neural circuits are unable to match. (Convolution reuses weight values across many virtual neurons. In our brain all those neurons must be real and independently learn to behave similarly.)

These days novel ways of multi-target training have not just expanded the types of problems machine learning is good at, but also reduced model sizes in ways our brain's networks are unlikely to be able to do. There is no limit to how many performance derivatives, from different trained outputs, can go through a machine "neuron" either changing that neuron's weights or going on to change other weights.

Generative Adversarial Networks use multiple target training. There is no end in sight yet on the kinds of things that having multiple performance targets operating in different subsets of weights can do. It is a massive booster for many problems that would be difficult or unattainable otherwise today.

Model reuse will be a massive savings in training time. Standard blocks can be trained through to other blocks. They don't make sence as long as every major retraining effort produces a better model, but at some point a lot of models or parts of models will be pretrained.

Finally, the value of improvements to machine learning are now colossal. So the resources put into improving them are colossal. A trained system can be used throughout a company, or sold as a product to any number of customers. A trained human ... not so much. So where a machine can match a human, the machine version is far more valuable.

Well we will see.