That's nothing, you should read Harvard Business Review......a periodical for people who don't like to read, but want to impress their coworkers by having a copy of Harvard Business Review on their desk. The articles inside get really inane sometimes.
free university education for my 40% income tax and 25% sales tax
TBH that's probably not worth it. Hard to be sure because obviously the 40% + 25% goes to other things as well. You've probably spent a lot extra in taxes than you would have if you'd just paid for college.
Do you deny that even that limited system is still at least close to a self-driving car? Admit you were wrong, and improve, that is your best scenario here.
To quote Wikipedia again,
the project was a journey of 1,200 miles (1,900 km) over six days on the motorways of northern Italy dubbed Mille Miglia in Automatico ("One thousand automatic miles"), with an average speed of 56 miles per hour (90 km/h).[43] The car operated in fully automatic mode for 94% of its journey, with the longest automatic stretch being 34 miles (55 km). The vehicle had only two black-and-white low-cost video cameras on board and used stereoscopic vision algorithms to understand its environment.
"Anomaly Detection" is still fairly vague, and a large number of techniques could be used, depending on the details. In the worst case, statistics is just a semester long class in college, and so is linear algebra. If you apply yourself, then within four months you could be quite good at both of those topics.
There was nothing close to SDCs in the mid nineties.
Wow, can't you at least be bothered to read Wikipedia? Relieve yourself of ignorance before posting, we have a vast network of information queriable in seconds and somehow we still get these kinds of falsehoods. We've had things "like" self driving cars since the 1950s, and in to quote Wikipedia, "In 1995, Carnegie Mellon University's Navlab project completed a 3,100 miles (5,000 km) cross-country journey, of which 98.2% was autonomously controlled, dubbed 'No Hands Across America."
The article is too negative. If you listen to the AlphaGo programmers, they have logs explaining why certain moves were made or not made at each step. They look through the logs and try to understand. The real problem isn't "we don't understand," it's that the logs have mountains and mountains of data. Figuring out why one move was chosen over another when the computer performed a billion operations is hard. That's a lot of logs to look through, a lot of connections to consider.
You know what is scary? Humans are not predictable at all. Once the neural network is trained, it is deterministic, and given a question will return the same answer every time. If you want to determine how it will respond in a certain scenario, or replay that scenario for debugging, you can do so. With effort it might even be possible to prove that certain behaviors are impossible. That is far more than you can do with a human.
Most likely they'll make deals with content companies, much like Youtube has done. It's the smaller websites that really need to worry, because they have no negotiation power.
When the media talks about AI, they usually mean strong AI, getting confused.
When researchers talk about AI, they usually mean weak AI. Because they have no clue how to achieve strong AI.
Except the part where he says AlphaGo uses deep neural networks that are an attempt to emulate the way real neural networks in our brains work. Our brains don't really work like that (we are Turing Complete, whereas AlphaGo's nn is not; among other things). The AlphaGo creators know (and knew from the beginning) that neuralNetworks we have today are not like our brains. They said, "Well that's ok, we can still use them to solve this problem."
This is in contrast to chess AIs that analyse every single turn in order to find the optimal solution according to some predefined heuristics.
This is essentially what AlphaGo is, except the heuristic is a trained neural network, instead of something hand-coded (and with a domain-specific priority queue of what move to investigate next).
Near where I live there's an elevated overpass, with a location almost directly under the overpass. Google maps suggests driving directly off the edge of the overpass to get there. So you know, get there, if you don't mind the 10m drop.
but a person who asks "how I startup" isn't interested in doing thankless work for free. Only APPS!
This is a little too harsh, there is a lot of gold to mine in B2B automation, and there are a lot of device startups like NEST IoT or whatever. In B2C the world is more bleak, I admit.
Mostly though, if you don't have the technical chops to pick up a new technology easily, and you're not full-stack, then you're just not attractive to a startup. They are looking for people who can do it all, and be self-managed.
Slashdot Mirror
"Do you deny that even that limited system is still at least close to a self-driving?" Actually, that is exactly what I am denying.
OK, you're a moron.
Your original statement was "Two decades later the rate of success of SDCs has improved only a fraction of a percent".
No it wasn't, ltr. I'd like to hear you say again that we didn't have self-driving cars in the 90s. It's entertaining.
That's nothing, you should read Harvard Business Review......a periodical for people who don't like to read, but want to impress their coworkers by having a copy of Harvard Business Review on their desk. The articles inside get really inane sometimes.
free university education for my 40% income tax and 25% sales tax
TBH that's probably not worth it. Hard to be sure because obviously the 40% + 25% goes to other things as well. You've probably spent a lot extra in taxes than you would have if you'd just paid for college.
To quote Wikipedia again,
the project was a journey of 1,200 miles (1,900 km) over six days on the motorways of northern Italy dubbed Mille Miglia in Automatico ("One thousand automatic miles"), with an average speed of 56 miles per hour (90 km/h).[43] The car operated in fully automatic mode for 94% of its journey, with the longest automatic stretch being 34 miles (55 km). The vehicle had only two black-and-white low-cost video cameras on board and used stereoscopic vision algorithms to understand its environment.
Yeah, that's what I tried to say, but I guess I wasn't very clear. Oh well >S
"Anomaly Detection" is still fairly vague, and a large number of techniques could be used, depending on the details. In the worst case, statistics is just a semester long class in college, and so is linear algebra. If you apply yourself, then within four months you could be quite good at both of those topics.
There was nothing close to SDCs in the mid nineties.
Wow, can't you at least be bothered to read Wikipedia? Relieve yourself of ignorance before posting, we have a vast network of information queriable in seconds and somehow we still get these kinds of falsehoods. We've had things "like" self driving cars since the 1950s, and in to quote Wikipedia, "In 1995, Carnegie Mellon University's Navlab project completed a 3,100 miles (5,000 km) cross-country journey, of which 98.2% was autonomously controlled, dubbed 'No Hands Across America."
If you specifically want to learn neural networks, then yes, statistics and linear algebra are important. If you aren't so picky, then this book will teach you a lot of good techniques.
The article is too negative. If you listen to the AlphaGo programmers, they have logs explaining why certain moves were made or not made at each step. They look through the logs and try to understand. The real problem isn't "we don't understand," it's that the logs have mountains and mountains of data. Figuring out why one move was chosen over another when the computer performed a billion operations is hard. That's a lot of logs to look through, a lot of connections to consider.
You know what is scary? Humans are not predictable at all. Once the neural network is trained, it is deterministic, and given a question will return the same answer every time. If you want to determine how it will respond in a certain scenario, or replay that scenario for debugging, you can do so. With effort it might even be possible to prove that certain behaviors are impossible. That is far more than you can do with a human.
Most likely they'll make deals with content companies, much like Youtube has done. It's the smaller websites that really need to worry, because they have no negotiation power.
When the media talks about AI, they usually mean strong AI, getting confused.
When researchers talk about AI, they usually mean weak AI. Because they have no clue how to achieve strong AI.
Except the part where he says AlphaGo uses deep neural networks that are an attempt to emulate the way real neural networks in our brains work. Our brains don't really work like that (we are Turing Complete, whereas AlphaGo's nn is not; among other things). The AlphaGo creators know (and knew from the beginning) that neuralNetworks we have today are not like our brains. They said, "Well that's ok, we can still use them to solve this problem."
Estimates at the time were 5-10 years for that level of play in Go. Google threw a lot of hardware at the problem, and got there faster. Once you realize how much hardware they actually threw at the problem, it doesn't seem as impressive.
This is in contrast to chess AIs that analyse every single turn in order to find the optimal solution according to some predefined heuristics.
This is essentially what AlphaGo is, except the heuristic is a trained neural network, instead of something hand-coded (and with a domain-specific priority queue of what move to investigate next).
Near where I live there's an elevated overpass, with a location almost directly under the overpass. Google maps suggests driving directly off the edge of the overpass to get there. So you know, get there, if you don't mind the 10m drop.
This is why we can't have good things.
SO........who is putting down the other stones? Yourself?
That is actually fascinating.
Rare that you'll get that kind of discount unless you're buying something big.
For an individual consumer, the best strategy is *still* to try to maximize whatever reward payback from the cards.
I'll bet they'll recover
No that's backend and kind of low-level. Cool though.
The web programming world is all Javascript. Wake me up when someone builds a web page out of FORTH.
Poe's law.
but a person who asks "how I startup" isn't interested in doing thankless work for free. Only APPS!
This is a little too harsh, there is a lot of gold to mine in B2B automation, and there are a lot of device startups like NEST IoT or whatever. In B2C the world is more bleak, I admit.
Mostly though, if you don't have the technical chops to pick up a new technology easily, and you're not full-stack, then you're just not attractive to a startup. They are looking for people who can do it all, and be self-managed.