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.

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.

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.

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

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?

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.

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.

German law allows non-compete agreements but puts some serious restrictions on them. They are limited to a maximum of two years, can be expensive for the former employer because the former employer is required to pay compensation to the former employer while the non-compete agreement is in place. These compensations can often be between 50-100% of the former salary and do not get the former employer anything other than the non-compete. And these non-compete clauses can be hard to add to existing contracts. So it is not unlikely that non-compete clauses wouldn't apply to all the German engineers that were part of Grohman engineering when Tesla purchased them.

Nope. Training can help people to learn about a new language, a new operating system, etc. But if people lack the talent for abstract thought, can't write something as simple as FizzBuzz in any language of their choice, then no amount of training is going to enable them to write complex software. The issue is that Universities do not want to tell people early that they lack talent and should switch to a different profession. Then they somehow finish their CS degree and cannot find a job.

A very small number of them will, but most won't. H1Bs are abused, this needs to stop. H1Bs are not needed to bring in people with bad to mediocre skills. There is no shortage of those people. But not every usage of H1Bs is abusive and training can only help people that have the required talent.

There is no shortage of people with an CS degree. But there is certainly a shortage of people that can actually write good code for non-trivial tasks. Proper CS is hard, you need to know tons of things about very different topics from algorithms and maths, to hardware details and interfaces. In addition problem solving and abstract thinking skills are required. Only a small fraction of people is able to do that and even if people have the talent, but are only into CS for the money, they will likely never learn enough.

The issue with H1B is that they are justified with the real shortage of really good people, but are used to keep wages down for people doing routine, trivial tasks that can be done even by people with only so-so education and skills.

Why? They are making good money in Germany and racists are a small minority. They would still make a lot of money, even if they have to remove these posts.

Sorry, but that is just plain bullshit. You fully allowed to criticize migrants and immigration in Germany. What is not allowed is to incite hatred or violence against them and Jailterm based on this law rarely happens.

They still want to sell ads to German companies and to German customers. They also want to get paid for these ads. Volkswagen is a German company, but they still need to abide American laws while selling cars in the US. They could let their American subsidy go bankrupt and stop selling cars instead of paying the fine. But instead they will pay the fine and keep their access to the US market, because this is likely going to pay off in the long term. The same is likely true for Facebook. They don't like to employ a lot of people to check posts that were flagged for hatespeech because that takes a lot of time and reduces the amounts of ads they can sell to racists. But if they have to choose between operating slightly less profitable in Germany or not at all, they will likely go for slightly less profitable.

She's number 4 on the list of most popular politicians in Germany. Her party is still 14 percentage points in front of every other party. 50% think she is doing a good job.(Source) Yes, there is a vocal minority that hates her and calls the media "Lügenpresse" or lying press and vote for the new right-wight populist party "AfD"(12%) but far more people either support Merkel's party (36%) or center to left-wing parties (22%+10%+10%). And this isn't just MSM, many of the federal states of Germany had elections this year and while AfD had impressive gains, it is nowhere near a majority anywhere.

Some weighting depending on area could be a good idea, but needs additional measures to prevent abuse. Otherwise consulting companies will apply for H1Bs in a cheap area with very low wages and then move people to silicon valley soon after the visa has been granted. It also seems a good idea to give more H1Bs to areas with higher wages as these often indicate real shortages. Stricter limits on working hours are also needed, otherwise companies will cheat the system by paying a high monthly salary that is actually a low hourly salary because of the insane working hours demanded. One potential solution for that issue could be rules that require paid overtime for H1Bs and allow workers to sue for their overtime payment even several years later.