Jeff Dean interview: Machine learning trends in 2020
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Jeff Dean interview: Machine learning trends in 2020
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Re: Jeff Dean interview: Machine learning trends in 2020
#2I hope one of those trends will be fixing (or abandoning) TensorFlow, because it's really disorganized, full of legacy and mysterious behaviors, and in some places just plain badly engineered.
Re: Jeff Dean interview: Machine learning trends in 2020
#3Unrelated, but for those who have never seen the Jeff Dean facts, behold: https://www.quora.com/What-are-all-the-Jeff-Dean-facts
Some highlights include:
-Jeff Dean's PIN is the last 4 digits of pi.
-He once shifted a bit so hard it ended up on another computer.
-He wrote an O(n^2) algorithm once. It was for the Traveling Salesman Problem.
-Jeff Dean once implemented a web server in a single printf() call. Other engineers added thousands of lines of explanatory comments but still don't understand exactly how it works. Today that program is known as GWS.
-There is no 'Ctrl' key on Jeff Dean's keyboard. Jeff Dean is always in control.
-Jeff Dean's watch displays seconds since January 1st, 1970. He is never late.
-Jeff's code is so fast the assembly code needs three HALT opcodes to stop it.
Re: Jeff Dean interview: Machine learning trends in 2020
#4Somebody tell Jeff about Jevon's Paradox, or actually don't bother.
Re: Jeff Dean interview: Machine learning trends in 2020
#5Does anyone have tips on how a European based developer with machine learning expertise can get involved with projects battling climate change like Jeff is talking about here?
Re: Jeff Dean interview: Machine learning trends in 2020
#6The 2020 trends question is right at the end. Summary:
- much more multitask learning and multimodal learning
- more interesting on-device models — or sort of consumer devices, like phones or whatever — to work more effectively.
- AI-related principles-related work is going to be important.
- ML for chip design
- ML in robots
Re: Jeff Dean interview: Machine learning trends in 2020
#7Somebody tell Jeff about Jevon's Paradox, or actually don't bother.
Jevon's Paradox [1]
An example from [1]:
```
Jevons observed that England's consumption of coal soared after James Watt introduced the Watt steam engine, which greatly improved the efficiency of the coal-fired steam engine from Thomas Newcomen's earlier design. Watt's innovations made coal a more cost-effective power source, leading to the increased use of the steam engine in a wide range of industries. This in turn increased total coal consumption, even as the amount of coal required for any particular application fell. Jevons argued that improvements in fuel efficiency tend to increase (rather than decrease) fuel use ...
```
Re: Jeff Dean interview: Machine learning trends in 2020
#8Somebody tell Jeff about Jevon's Paradox, or actually don't bother.
Is Jevons paradox really a problem when your carbon footprint is zero?
> VentureBeat: One of the things that’s come up a lot lately, you know, in the question of climate change — I was talking with Intel AI general manager Naveen Rao recently and he mentioned this idea [that] compute-per-watt should become a standard benchmark, for example, and some of the organizers here are talking about the notion of people being required to share the carbon footprint of the model that they trained for submissions here.
> Dean: Yeah, we’d be thrilled with that because all the stuff we trained in our Google Data Center — the carbon footprint is zero.
Re: Jeff Dean interview: Machine learning trends in 2020
#9Somebody tell Jeff about Jevon's Paradox, or actually don't bother.
Jevon's Paradox [1] An example from [1]: ``` Jevons observed that England's consumption of coal soared after James Watt introduced the Watt steam engine, which greatly improved the efficiency of the coal-fired steam engine from Thomas Newcomen's earlier design. Watt's innovations made coal a more cost-effective power source, leading to the increased use of the steam engine in a wide range of industries. This in turn…
Here, I know your meaning is about market adoption of AI, but also on the topic of AI this made me wonder why it intuitively feels to me like Jevon's model would be inadequate, but do I only have this intuition because of my education? I think there's another way: critically test the statement:
>Jevons argued that improvements in fuel efficiency tend to increase (rather than decrease) fuel use
In other words, I can ask: does aggregate fuel usage depend on fuel efficiency? If I state the question this way and ask myself to answer it, it starts to become obviously an incomplete model to use.
This may seem so pedestrian but it is so confusing for me to think about in terms of conceptualizing what structure was needed to recognize a poor prediction model and considering a better one. This seems so different than what AI is today.
Re: Jeff Dean interview: Machine learning trends in 2020
#10Somebody tell Jeff about Jevon's Paradox, or actually don't bother.
He probably does know about it because he seems he’s a well-read guy, but I think is too late for him and for people like him now: the pay they receive is too good for them to leave it all for some “principles” and on top of that I think the’ve also managed to acquire come cognitive dissonance traits that allow them to get out of bed in the morning and go to work without feeling guilty.
Otherwise I cannot understand how he can really think that “more AI” is going to help with deforestation, as in “more AI” probably means less overall costs for bad people in the Amazon (where your major costs are people-related, it’s phisically very demanding cutting down trees in an Equatorial climate) which in turn means more trees being cut down. And this is just the beginning of it.