161–170 of 219 posts
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That was my point, you just went back further in history than I did :p
Then your point is wrong. What's so novel about CPUs? We've had sand since the Earth was first formed! I swear the tech industry has more luddites than the Amish.
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"engineering before physics" is exactly wrong. No one did Engineering before a sophisticated understanding of Physics was achieved. They built bridges and towers, Engineering enables statements to be made about the performance of machines and buildings; it will survive a wind like x, you can do n cycles, do not load the wings in this way.
I guess the Romans didn’t have any engineers building siege engines and fortifications, then.
Take the best Roman engineer.
Translate a first year engineering paper on structures into Latin.
Ask Roman to sit said paper.
What will happen and why? The Roman chap will look very confused and will make statements (in Latin) about how stupid this stuff is and how it has nothing to do with proper engineering. The Roman will score 0. The why is that the understanding of structures and materials in the ancient world was artizanal, based on trade knowledge (often secret and hard to reproduce) and not systematic, based on the scientific method and inspectable or testable.
Currently we accept that knives, cabinets and sheds may be built or made using artisanal knowledge, we do not accept that apartment blocks, aircraft or automobiles are built this way. Society insists that these are built using systematic knowledge because otherwise they sometimes fall down or crash.
The systematic approach to aircraft is the best example - think how much civil air traffic there is now, and how rare air crashes are. The issues of subsonic flight have been systematically accounted for, right up to the point where we now see 1:2,000,000 crashes per flight.
Mechanical, aeronautical and civil engineering proceed in this way. Issues are discovered with mechanisms or structures or materials, these are characterized with scientific investigation, the characterizations lead to constraints and parameters that are required to be accounted for in future designs and old designs are re-evaluated in the light of the new knowledge.
Stating that you will build a new building in a certain way because domes are strong and concrete is strong would not cut the mustard in the modern world... The parthenon has stood for 2000 years, but how many similar structures collapsed after a few months?
Finally a realistic view of where “artificial intelligence” currently stands. I wish I knew where guys like Elon Musk are seeing this other artificial intelligence I’m just not seeing. The current AI we have is just fancy linear regression.
Nope. Linear regression will not reconstruct images, nor will it create and generate new images that have not existed previously from databases of images. Nor will it translate colloquial Chinese into colloquial English, nor will it deconstruct a question into a set of queries and generate an answer. The issue isn't the availability of fantastic algorithmics, it's the availability of people who can see them and apply…
Nature succeeded in creating human-level intelligence with one trick and no understanding, so clearly it can be done. It did take a while though. More tricks and more understanding would probably help speed things up.
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It's not that bridges before Newton were bad, it's that Newton gave us the ability to design the strongest possible bridge of a given shape with the materials at hand - using not just calculus but calculus-of-variations, a subject nearly as old as Newtonian mechanics [1]. With this knowledge, what happens when one adds one or two columns to a bridge is now longer "news" the way it might have been before Newtonian mec…
> It requires lots memorizing, trial-and-error and experts that often can't fully explain their reasoning. You just described all of software engineering.
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It's been a long time since I got my BE in Mechanical Engineering, but I still remember being struck by the difference between well-understood engineering and rule-of-thumb engineering. Bridge building is mostly well-understood engineering. When you study Static Mechanics [0] you learn all sorts of Physics equations, including Newtonian Mechanics, that completely describe the forces and motions of a structure based o…
These topics bring me back. The "rule of thumb" engineering that you speak of made me remember the different constants that were taught we should just accept as is because, well, it is considered constant. Nevermind where the guy in the book got it from, this is what works and this is what people in the industry has accepted to be standard.
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Last I checked bridges came before Newtonian Mechanics and it seems strange to argue this wasn't a good thing. Admittedly paper writing wasn't the main mechanism of transmitting knowledge but it's fairly common for human engineering to come before the full theoretical foundations as opposed to after.
Cathedrals regularly fell down, and domes, and no doubt bridges too.
https://en.wikipedia.org/wiki/List_of_structural_failures_an...
Yus - bridges.
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Not really, no. Saying we are at year five of Deep RL is about as informative as saying we are at year five of deep learning. Reinforcement learning as a field goes back decades.
But now we have GPUs, which makes it entirely different. /s And it kinda does, but in an engineering way rather than a statistics way. Like reinforcement learning from pixels is pretty new (i would be really interested if you have 10+year old citations), and pretty amazing. I've been looking at RL (through OpenAI gym) and realising that I "just" need to annotate a bunch of images and then train a network that will pr…
Using high speed hardware can allow someone to do 10's or scores of runs a day. If you are doing one every 2 weeks or so then it's really, really hard to make any progress at all because you daren't take risks. So the productivity of 80 a day vs 2 per month isn't just 100x it's lots and lots more.
Also as you say it's lowered the bar which means that teams can onboard grad students and interns and get them to do something that's useful - it may be trivial - but it's useful.
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Nope. Linear regression will not reconstruct images, nor will it create and generate new images that have not existed previously from databases of images. Nor will it translate colloquial Chinese into colloquial English, nor will it deconstruct a question into a set of queries and generate an answer. The issue isn't the availability of fantastic algorithmics, it's the availability of people who can see them and apply…
In fact linear regression could be used to do all of those things, most of them poorly but some of them pretty well (see dictionary learning). If you believe otherwise then you fundamentally misunderstand how neural networks work.
I can use bricks to build a boat, if you think I can't then you don't understand floating.