Apparently, he was able to prove, but did not allow others to keep copies of the evidence. At best, you can suspect. But how can you conclude so strongly that this was a "scam"?
There's not much wrong with such legacy systems. Unfortunately, they are more like exceptions. Most likely, your legacy systems break and you have no idea how to fix them. Or, you have to hire one guy for life because he is the only one who can maintain and fix your systems. And you pray that he doesn't get sick or hit by the bus.
>Because the future is whatever you're building software in today.
This is a seriously unhealthy attitude for software engineering. This attitude will create legacy systems and legacy systems that will live on forever.
>Intel, Westinghouse, biotech in general, and Dow do a pretty good job on the applied piece IMHO.
Private companies are in a great position to do applied research, but they are often slow or unwilling to distribute the research results for the benefit of everyone.
I believe that whoever is in charge of federal funding in R&D will have to be extremely smart and balanced in his views because R&D plays a great role in the economy and future of this country. The right approach is a balanced between fundamental and applied research. And it's not just balanced but which areas to invest money into.
To be fair, scientists by and large understand the need of replication, and evaluation in general. It's just in certain fields (e.g. psychology) or circumstances with human subjects, it's very expensive or even infeasible to have well controlled repeated experiments.
A similar quote by Michael Jordan: “I've missed more than 9000 shots in my career. I've lost almost 300 games. 26 times, I've been trusted to take the game winning shot and missed. I've failed over and over and over again in my life. And that is why I succeed.”
>Personally, I think the Go community is a little unhealthily obsessed with this particular metric.
Let's not forget that Go was invented to solve Google's problems, one of which is it took hours to compile their C,C++ codes. Compile time of Go 1.7, despite the improvement, is still 2x that of Go 1.4.
Somehow, I feel that the big deal here is the cleverness to use words like "neurons", "deep learning" and "human intuition" (the "intuition" here appears no more than simply taking the route giving max probability of success).
I think you are teasing me, but my answer would be: no, it doesn't count. The Turing test is about "understanding". The filtering of those images has no understanding of what the images are about.
This is not a Turing test. Please correct me if I am wrong, but I bet that none of this was drawn by a computer. Most likely, those that were supposedly "drawn by computers" were pictures drawn by humans but applied various type of filtering (e.g. those by the deep learning algorithms).
The implication is that art and career are mutually exclusive, meaning once you become professional, you can't do art anymore. This assumption is not necessarily true.
Personally, I have never trusted these social network companies in terms of working with/for the platforms. Sure, when they are in trouble, when they need developers, they will say things like this. And when they are strong, they will close up and can't care less about their developers. It's the same with Facebook, etc.
You can't eat the same foods people ate 20 years ago. The cows aren't the same. The chickens aren't same. Cows and chickens today are more obese and eat more antibiotics than they used to be.
Don't know about other countries. But in the USA, the six people who make the team are selected among high schoolers after rounds of competition. It does reflect very much a country's math education. But math education is just part of a much larger equation.