Not this fantastic espresso.
That will tell us where we need to put research effort, and where that will lead to progress towards our Super Intelligence. The seven capabilities that I have selected below start out as concrete, but get fuzzier and fuzzier and more speculative as we proceed.
It is relatively easy to see the things that are close to where we are today and can be recognized as things we need to work on. When those problems get more and more solved we will be living in different intellectual world than we do today, dependent on the outcomes of that early work.
So we can only speak with conviction about the short term problems where we might make progress.
And by short term, I mean the things we have already been working on for forty plus years, sometimes sixty years already. And there are lots of other things in AI that are equally hard to do today. I just chose seven to give some range to my assertion that there is lots to do. Real perception Deep Learning brought fantastic advances to image labeling.
Many people seem to think that computer vision is now a solved problem.
But that is nowhere near the truth. Below is a picture of Senator Tom Carper, ranking member of the U. He is showing what is now a well known particular failure of a particular Deep Learning trained vision system for an autonomous car.
The stop sign in the left has a few carefully placed marks on it, made from white and black tape. The system no longer identifies it as a stop sign, but instead thinks that is a forty five mile per hour speed limit sign. But really how could a vision system that is good enough to drive a car around some of the time ever get this so wrong?
Stop signs are red!
Speed limit signs are not red. Surely it can see the difference between signs that are red and signs that are not red? We think redness of a stop sign is an obvious salient feature because our vision systems have evolved to be able to detect color constancy. The data sets that are used to train Deep Learning systems do not have detailed color labels for little patches of the image.
And the computations for color constancy are quite complex, so they are not something that the Deep Learning systems simply stumble upon. We can see it is and say it is a checkerboard because it is made up of squares that alternate between black and white, or at least relatively darker and lighter.Oct 02, · Malcolm Gladwell “Small Change: Why the revolution will not be tweeted” In this article, Malcolm Gladwell argues that it is the real-world action of dedicated individuals united by a common belief, cause, or goal that brings about true change or revolution.
Consolidating the data is something that’s needed to be done for a while. Most surveys on the book selling industry totally ignored/underestimated self publishing sales. Twitter, Inc. (/ ˈ t w ɪ t ər /) is an American online news and social networking service on which users post and interact with messages known as "tweets".
Tweets were originally restricted to characters, but on November 7, , this limit was doubled for all languages except Chinese, Japanese, and Korean. Registered users can post, like, and retweet tweets, but unregistered users can.
Aug 21, · benjaminpohle.com | The New Yorker writer and author Malcolm Gladwell made headlines around the Internet when his essay "Small Change: Why . Dear Erin, I am so sorry to read this post, but I understand. I think you’re right: we should mourn our & your loss. I too am sorry we won’t have you as a colleague in the way we had hoped, but I think that if you’re unable or unwilling to keep VAPing or adjuncting, then moving on is the best way forward.
Whatever Gamergate may have started as, it is now an Internet culture war. On one side are independent game-makers and critics, many of them women, who advocate for greater inclusion in gaming.