Dude, I'm battlin' zombies and all I got is luv pow3r from a n3wb magical grrl.
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.I have been in 'Lock-Down' mode for the past few weeks. My team respects my shut door and knows that I am deep in the design process.
The KWI will be our first Integrated amp and adds a greater level of functionality, control and enhanced user interface with digital displays and minimal controls. Latest Forum Threads.
Is there a way to marry a foreign woman without fear of divorce rape? Autonomous Vehicles And The Rise Of Mobile Sex Workers. Home Essays Apollo Shoe Case Study. Apollo Shoe Case Study.
Topics: Invoice, I’ve started a flowchart and listed some strengths and weaknesses, but had to leave before I could finish it. See if you need to add any more strengths and/or weaknesses that you find from the ICQ and narrative descriptions of the revenue cycle.
Investigating Possible Conspiracies and Cover-ups – JFK, The Moon Landings, etc. By Wade Frazier. Revised June Introduction. Gary Wean and the JFK Assassination. See if you need to add any more strengths and/or weaknesses that you find from the ICQ and narrative descriptions of the revenue cycle.
lausannecongress2018.come a bridge working paper (ICC-1) for the audit of Apollo Shoes as of December 31, , addressing this issue. Port Manteaux churns out silly new words when you feed it an idea or two.
Enter a word (or two) above and you'll get back a bunch of portmanteaux created by jamming together words that are conceptually related to your inputs.. For example, enter "giraffe" and you'll get .