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Comment Re:HackerRank is useless (Score 2) 66

In the most popular challenges in Hackerrank speed is not an issue. You only need to be a speed champion if you are competing for the top 5% or so. Otherwise speed is not an issue but figuring out the right algorithm and being able to write it down without making tons of mistakes that require long debug sessions. Taking your time to think properly about the problem and then carefully writing correct code is actually the way to go, as debugging can easily use a lot more time than coding itself. Many of the easier tasks are just a few lines of code in C++, Python or Java without any code golfing.

Comment Re:Easy (Score 2) 66

Proper abstraction can actually help efficiency, e.g.: the templated c++ sort is a lot faster than the C qsort function because the compiler can optimize the code for each datatype and inline the compare function, while the C qsort has to use indirect calls to the compare function via function pointers and can't do static optimizations based on things such as size of the elements.

Efficiency is also often a question of using a proper algorithm. Most of the time that O(n) DP-algorithm coded in Python is going to be much faster than a O(2^n) bruteforce algorithm handcoded in assembly.

Comment Re:I'm with Zuckerberg and Facebook's Yann LeCun h (Score 1) 318

Sure, but if you look at the limitation of current technology it is easy to figure out that there is still a huge number of problems to solve, many of them where nobody so far has any clue how to solve them. It's likely not just a matter of a few more years of research and throwing even bigger datasets and computers at the problem. Sure, you can make up any projections about the future and no matter how crazy they seem, we won't know that they are wrong until we are in the future. But are Elon Musk's style projections about the future of AI likely? No, not really. Yann LeCun is clearly an expert in AI, while Musk is a business man. Hypeing AI helps to finance Musk business and keeps the stock price high.

Comment I'm with Zuckerberg and Facebook's Yann LeCun here (Score 4, Informative) 318

I think Elon Musk is the one that has either a limited understanding of current AI technology or just hypes AI on purpose, while being fully aware that AI still has major limitations and they are unlikely to disappear within the next few years. Important and very important progress has been made, but General AI is likely still very far away.
Facebook's director of AI Yann LeCun gave a very good interview to IEEE spectrum: Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter

Comment Re:Because they can rather than because its needed (Score 1) 100

You need more than a single driver per vehicle. The driver will operate 40h per week, but your bus service is likely operating something such as 7*16h=112h per week and the bus driver can't drive 8h straight without breaks. Even at minimum wage the cost for 3-4 drivers is pretty significant. Within a single year you be able to get back the extra money required for expensive sensors, compute modules and software.

Out of date also doesn't really matter as long as it can still do its job. This first generation self-driving trolley might only work within environments that are easy to handle and in a few years you might have a second generation trolley that can handle more complex environments and drive faster, but that doesn't mean that first gen. trolleys cannot continue to fulfil their limited roles.

The speed of a local bus is slow, so limit of 45 km/h is not going to make a significant difference. This could make journey time shorter by increasing the frequency of the buses and thus reducing waiting times. With self-driving buses cities can easily go use a high number of smaller trolleys instead of large buses at a low frequency.

More people would use public transport instead of their own car. While bus drivers will lose their job, new jobs will be created elsewhere, e.g.: When people save money by not owning a car, they will likely spend that money elsewhere, e.g.: eating at restaurants more often.

Comment Re:Why this when Apples sysem is WORSE? (Score 1) 192

The issues here is the bundling of the google applications. A manufacturer can't decide that it wants to install e.g.: maps and gmail but not google search. Either full AOSP without maps, etc. or full blown google. This seems pretty similar to the Microsoft Windows / IE bundling things. On the other hand, Microsoft was charging money for windows, while google gives away Android and the apps for free. I wonder if they could actually do something like gapps+gsearch+forced chrome default->free, gapps without gsearch+chrome $20?

Comment Re:No, it's not. (Score 3, Insightful) 83

The issue here is not deep pockets or not, the issue are networking effects that create an defacto monopoly. If you got such an defacto monopoly many people are forced into doing business with you. If people don't like your terms, that can't just switch to your competition, because your competition isn't offering the same network. A company with such a defacto monopoly is not allowed to abuse this monopoly. They are not allowed to use it extend their market share other areas or force terms on users that they wouldn't accept if healthy competetion was present.

Comment Re:Here It's Pay to Lose (Score 3, Interesting) 106

Serious candidates need to have publications in journals that those in the field know about and have a good impact factor and the area experts generally read a few of the papers. Having a large number of papers in a dodgy, predatory journal will kill any chance of being hired.

I would even go further: A single paper in a dodgy journal on your CV can easily kill your career in science. It is a red flag that shows, that you lack one of the most basic skills any researcher should have. You show that you are unable to tell the difference between a real and a predatory journal and often it even shows that even your advisor was unable to do so. A PhD from a clueless advisor is almost worthless.
Quantity over quality is not a valid excuse. There are plenty of non-predatory, real lower rank conferences that will happily publish anything with only the slightest bit of scientific value.

Comment Re:Kelly picked the wrong myths to debunk. (Score 1) 284

I think you have a too simplistic view of evolution. Easily getting stuck in a local minimum would be a major obstacle for long term survial of any species. So you can expect that tools against that were among the first traits to evolve. Also things are apperantly encoded in a way, where what might seem to be a large structural change is actually only small changes to the DNA, e.g.: a mutation in a single gene can get people an extra finger. And the description of any revolutionary AI algorithm that people will be able to come up with in the next few years, will easily fit within 1 GB. The evolution had millions of year on a massively parallel computer to optimize. Likely we won't be able to come with something better within a short time frame. And: Many AI algorithms have even bigger issues with getting stuck in local minima and some AI tools such as dropout are even build based on evolutionary ideas.

Comment Re:Kelly picked the wrong myths to debunk. (Score 1) 284

You still have to pay for at least food and the buildings, basically the same things that you are paying for when you pay for a basic income or similar system. And while incarcerated people won't buy stuff and their productivity will be very low, if they work at all. And such a system isn't unlikely to quickly result in a revolution. And even mass incarceration isn't very effective in preventing crime. At some time people are going to be released and will potentially commit the next crime. The US has incarcerated a very high percentage of their population, yet their crime level is much higher than in "socialist" european countries such as Sweden or Germany. Paying for a certain amountl of welfare and social security is actually helping the richer. It makes their lives saver, makes revolutions, wars and other uprisings unlikely that could easily destroy or redistribute large parts of the wealth of the rich. And last but not least: It helps the economy because all these people can buy stuff.

Comment Re:How quickly some forget... (Score 1) 284

Sure, smarter algorithms that are able to work with tiny datasets are likely possible. But when will they be available? We just don't know. We have already tried for a long time and didn't find anything that worked well, so it is clearly not a trivial problem. Your "let the robot play with the cat proposal" would likely end up with a robot that is able to recognize that cat extremely well, but fails to recognize that a cat with a different fur color is also a cat. It is also just a different way of generating more data, while humans are actually able to learn from very few examples, even without any way of generating more examples.

So there are many problems that need to be solved first and likely many other roadblocks that we don't know about yet. You can't make a prediction based on that. It is not impossible, that we will have superhuman general artificial intelligence in a few years, but it is also very possible, that in a 10-20 years we will know that our current tools are useful, but limited to a specific set of applications and general intelligence and other application areas are still searching for algorithms that work.

Yes, AI made impressive progress in the last few years, but there is not reason to believe all hard problems are solved now and that would be just a matter of year years until we get to superhuman general intelligence. At the moment we are nowhere near that.

Comment Re:How quickly some forget... (Score 1) 284

You are repeating a mistake that is often made when recent breakthroughs in one area of technology happen: Things are currently moving fast, so they are expect that things will continue to move fast. But if you look at the history, you see that a while after a breakthrough things are hitting a road block and are moving much slower. Some of these road blocks are already visible: Conventional semiconductor technology is close to its physical limits and good training of large network requires bigger and bigger datasets, which are harder and harder to get. It is not impossible to get around these road blocks and the existance of humans demonstrate it is possible. But there is no way to tell how many road blocks are still in front of us and how long it is going to take to find a way around them.

Comment Re:Kelly picked the wrong myths to debunk. (Score 2) 284

Brains are not magic! The existence of human intelligence proves that intelligence is possible, everything else is just details.

Details can be damn hard to figure out and it is not so unlikely that evolution already found something that damn close to optimum if you consider factors such as energy to build and operate. It's tradeoffs are likely already getting tuned to changed environment, where high intelligence helps a lot and starvation isn't as big of an issue as it has been for millions for years. Potentially genetic engineering can make these changes quicker.

No, there will not be basic income or anything decent like that. There will be mass incarceration as people turn to crime to survive.

Mass incarceration is expensive and inefficient. It is likely much cheaper to pay for an basic income or a similar welfare system.

Comment Re:How quickly some forget... (Score 1) 284

You are missing the point here. An improvement by 6x in fuel efficiency is great sure. However, it is nowhere near the improvements that would be expected by exponential growth. The plane that flies around the world with solar cells instead of fuel, is very slow, very expensive to build and has a very small payload. If you consider how much fossile energy was likely required to build this plane, you see that this isn't a breakthrough. You need to consider economics as well. The speed of the regular commericial air plane almost did not improve within the last 60 years. A regular Boeing 707 designed in 1957 would already go 607 miles per hour in regular cruise operation, while a modern 787 has a cruise speed of 587 mph. The Concorde could do 1354 mph, but was basically phased out because the high fuel consumption and the noise made it economically infeasible to operate. If you consider only planes that are economically feasible to operate, the growth is really slow. All current passenger planes are staying below the sound barrier, because of the fuel and noise issues. They are limited by physics. You could go faster, but not without a lot of extra noise and fuel.

It is not unlikely that a simlar thing will happen to AI: At some point human like AI might be feasible, but what is the point if operating such an AI is $100M/year, if you can hire a human for $100k/year?

Comment Re:He's wrong, and the smart people are right (Score 4, Interesting) 284

Unfortunately Sam Harris is bad at math. He claims "It's crucial to realize that the rate of progress doesn't matter, because any progress is enough to get us into the end zone. We don't need Moore's law to continue. We don't need exponential progress. We just need to keep going.". It seems he has never seen a monotonically increasing, yet asymptotically bounded function. However, that is exactly the kind of progress we are seeing in older technologies, e.g.: Airplanes stay at almost exactly same speed (because going past the sound barrier would use lots of energy) and get slightly more efficient each year, but will never get to the point where they can operate almost without any fuel or other large energy source, simply because the laws of physics don't allow that kind of progress.

But even if the possible progress is not bounded, it is still not guaranteed that we will get there. It can still take so long, that it never going to happen before human civilization is completely destroyed by some disaster. Or it could simply be stopped by economics as further improvements can easily get so expensive or tiny, that the likely benefits from pushing the research further can not offset the cost.

Harris also seems to think that general AI is ineviatable, because we want to make progress towards things such as things such as cureing cancer or Alzheimer. But it is not clear that such an achievement actually requires general superhuman intelligence. It likely requires superhuman intelligence, e.g.: the computers that simulate protein folding way better than any human could ever do, but not necessary general intelligence. Specialized artificial intelligence seems to be much easier to achieve and is at the same time likely almost as good as general intelligence for topics such as those. You don't need to develop an artificial general intelligence to cure cancer, if you already developed a specialized artificial intelligence that is able to find a cure.

Imagine what could happen when a huge neural net is applied.

The problem with huge neural nets is training them. The more possiblities a network has, the harder it becomes to train it. Large parts of the progress in the last few years were made by finding clever constraints on the network in order to make them easier to train.

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