Please do so. I don't think that case is going to be as strong as you think it is. After all, many of fundamental ideas behind today's multi-core CPUs are from the 60s: Out Of Execution (1967) Multi-cores and SIMD (1966)

There is clearly a difference in scale in speed, but is there a fundamental advantage? Many of the key concepts behind GPUs were already known in the 1960s: SIMD (see above), the CDC6000 series used switching between threads like GPU do to compensate latency, vector processors also developed in 1960s also invented some of the concepts used by todays GPUs.

You don't have a clue. There are many other issues. At the moment most successful AI is using supervised learning and needs tons of labeled data in order to train the network. We still don't have a clue how to train an AI using only very small sample. Humans can easily learn from very small sets of examples, often a single example is good enough, ANNs needs tons of examples, especially the very deep and powerful ones. We don't know how the brain works yet, ANNs are only inspired by the brain, they are not a proper simulation. We still have to understand tons of things until we can build a simulation of the brain. And with semiconductor scaling slowing down, it might take really long until we get the processing power we would need even if would know what exactly needs to be simulated.

And what would we gain? Sure, you can also train a ANN to sort some rows in a spreadsheet or sum some numbers together, but it is something that conventional algorithms are already very good at, we don't needs ANNs to do that and they are not going to be efficient at it.

I don't think it is that simple. You can build an ASIC for Etherium, but different from Bitcoin it requires a good external memory interface. The memory requirements for Etherium are just too high for using only internal memory. Your ASIC architecture would likely look a lot like a GPU but remove many things that are not required for ETH mining.

You should be able to implement something easy in 30 minutes even in an interview situation. And most Hackerrank are not about implementing linked lists or trees, but actually using data structures such as trees or hashes, so its perfectly fine to use the data structures (and algorithms) included the standard libraries of C++, Java or Python. Take something such as Marc's Cakewalk. It easily can be implemented using a few lines of code (e.g.: 10 lines of non-golfed python). It shows if you are able to spot the trivial greedy algorithm and are somehow able to sort a small amount of numbers, but the number is so small that even bubblesort would work well.

I disagree. That someone is doing well in competitive programming (CP) type of questions does not tell you that that person is a good programmer. However, it also doesn't measure pointless stuff and proper knowledge of data structures and algorithms is a skill every good developer should have. CS101 can sometimes be pointless because many people cannot transfer this knowledge to any other problem. Just being able to tell that Quicksort is average O(nlog n) and worst-case O(n^2) is not useful if that doesn't mean anything to you. A bigger CPUs is not going to fix that O(n^3) algorithm that worked fine when the developer unit tested it with 10 elements, but somehow struggles when trying to run it on 100k elements in the production system.

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.

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.

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.

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

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.

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?

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.

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.

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.

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.