Brookings: "Gauging investment in self-driving cars"

This is the report that was in the queue to be linked a few weeks ago. It's also in the TreeHugger piece.
It's about big money. and for what it's worth Brookings is quite gung-ho on autonomous vehicles.

Two quick notes up front though, contrary to the assertion below, that autonomous vehicles are  the driving force behind artificial intelligence, inference, and machine learning research, the fact is it might be research in other applications that is coalescing into vehicle applications.

Not to dismiss the use of AI in cars but using Google as an example they were pursuing big data applications long before Waymo and with their TPU's will be chugging along whether or not level 4 and 5 autonomous vehicles become a reality.

Secondly, for a non-specialist blog we have an astounding number of posts, so many posts on AI, going back to 2012 that a Google search of the site just for "Machine Learning" is almost overwhelming.

Here's one from 2014 that might mark the point we got really serious about this stuff:

Deep Learning is VC Worthy

Or maybe it was 2013's "Why Is Machine Learning (CS 229) The Most Popular Course At Stanford" 

From the Brookings Institution:
Monday, October 16, 2017

A CNN story late last year declared 2016 “a tipping point for excitement in self-driving cars.” In fact, the year saw reports of major investments by Ford, Mercedes, Apple, Intel, Delphi Automotive, and venture capitalists, among numerous players jockeying for position in the emerging market for autonomous vehicles. There may be debate about the time it will take the technology to adapt enough for wide deployment, but there is consensus that this time will come in a matter of years rather than decades.

To date, there are no public estimates of how large this surge in investment in autonomous vehicle technology is in the aggregate. PWC’s 2016 Connected Car Study, its fourth annual report on the sector, says the top five original equipment manufacturers spent $46 billion in research and development in 2015, and there are numerous reports that catalogue investments, acquisitions, and other activities in the growing ecosystem that supports self-driving cars. We set out to estimate the aggregate investment across this entire area.

The inquiry provides a useful window onto the state of play in development of autonomous vehicle technology, as entities from the major auto manufacturers to startups scramble to take the lead. It is also a measure of what it takes to reach a tipping point in development of sophisticated artificial (or augmented) intelligence. As Qi Lu, the star Microsoft engineer who moved to Beijing to become Baidu’s chief operation officer, recently put it, “In autonomous systems, the car is the first major commercial application that is going to land.” Investment in self-driving cars appears to be the leading edge for AI development.

Indeed, as progress accelerates on vehicles interest is increasing in broader artificial intelligence. A Forrester Research report predicted that 2017 will the year that “Artificial intelligence (AKA cognitive computing) technologies will be rapidly assimilated into analytics practices,” driving new insights from big data, and investment in artificial intelligence has been touted by popular investor news sites like The Motley Fool and a column on Fortune.com. If 2016 was a tipping point for investment in self-driving cars, 2017 could turn out to be a tipping point for investment in artificial intelligence more generally.

The Autonomous Vehicle Landscape
The ecosystem for self-driving cars has numerous layers. It encompasses not just the machine-learning that operates the vehicles, but also the array of sensor and navigation technology needed, from refinements to the advanced braking or lane-keeping assistance common in today’s vehicles, advances in more granular and adaptive mapping, and vehicle-to-vehicle and vehicle-to-infrastructure communications systems.

Because lives and safety are at stake, autonomous vehicles require a high degree of reliability and need to adapt to widely diverse contexts. In Austin, for example, one of Google’s autonomous vehicles was befuddled by a cyclist rocking back and forth in a track stand at a red light and, in Australia, Volvo engineers found it a challenge to predict the bounding of kangaroos. As a result, in order to train self-driving artificial intelligence systems, the players involved are stretching to accumulate vehicle miles and investing untold hours of engineering time....MUCH MORE

The Google site search mentioned above returns 1400 hits for site:climateerinvest.blogspot.com "machine learning"  and while many of those are dups or other sites linking in there are still hundreds of posts.
Alternatively, using the 'search blog' box top left yields page after page of results just for machine learning. I'm afraid to try "AI" for fear of getting nothing else done today.

Recently:
"$80 billion has been spent on self-driving cars with nothing to show for it."
Just To Be Clear: Some Good Has Come From The $80 Billion Ploughed Into Autonomous Vehicles