What is AI? How should we do Future Research for Application?

Excellent look at what has been achieved in AI, and how it relates to what we call intelligence.  Are we taking the right approach to look for yet better solutions?  Nicely presented, and good descriptions of the dilemma of how research and results are funded and perceived by the public, academia  and industry.  I particularly like that he does not abandon 'classical' AI vs Neural approaches.  Supporting summarizing, non technical video on human vs animal intelligence: https://vimeo.com/288403370

ACM Summary:  The recent successes of deep learning  have revealed something very interesting about the structure of our world, yet this seems to be the least pursued and talked
about topic today.

In Al, the key question today is not whether we should use model-based or function-based approaches but how to  integrate and fuse them so we can realize their collective benefits.

We need a new generation of Al  researchers who are well versed in and appreciate classical Al, machine learning,  and computer science more broadly while also being informed about Al history.

Adnan Darwiche discusses "Human-Level Intelligence or Animal-Like Abilities?" 

Communications of the ACM, October 2018, Vol. 61 No. 10, Pages 56-67
10.1145/3271625

"The vision systems of the eagle and the snake outperform everything that we can make in the laboratory, but snakes and eagles cannot build an eyeglass or a telescope or a microscope." —Judea Pearl

The recent successes of neural networks in applications like speech recognition, vision, and autonomous navigation has led to great excitement by members of the artificial intelligence (AI) community, as well as by the general public. Over a relatively short time, by the science clock, we managed to automate some tasks that have defied us for decades, using one of the more classical techniques due to AI research.

The triumph of these achievements has led some to describe the automation of these tasks as having reached human-level intelligence. This perception, originally hinted at in academic circles, has gained momentum more broadly and is leading to some implications. For example, some coverage of AI in public arenas, particularly comments made by several notable figures, has led to mixing this 
excitement with fear of what AI might bring us all in the future (doomsday scenarios). 

 Moreover, a trend is emerging in which machine learning research is being streamlined into neural network research, under its newly acquired label "deep learning." This perception has also caused some to question the wisdom of continuing to invest in other machine learning approaches or even other mainstream areas of AI (such as knowledge representation, symbolic reasoning, and planning). ... "