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Jeff Dean interview: Machine learning trends in 2020
- m0zg 7y agoI 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.
- symplee 7y agoUnrelated, but for those who have never seen the Jeff Dean facts, behold: https://www.quora.com/What-are-all-the-Jeff-Dean-facts 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.
- Keloo 7y ago- After Graham Bell invented the phone he had a missed call from Jeff Dean
- boris 7y ago- Jeff Dean once caused an AI winter so cold, even humans lost their intelligence.
- majewsky 7y ago> Jeff Dean's watch displays seconds since January 1st, 1970. At a Hacker Jeopardy some time ago (I think it was the one at 29C3), the final round ended in a tie, so a tie-breaker was needed. The tie-breaker question was "What is the current Unix timestamp?" The contestants struggled hard, leading the moderator to exclaim "For god's sake, don't you ever check the clock!?"
- narrator 7y agoSomebody tell Jeff about Jevon's Paradox, or actually don't bother.
- deepaksurti 7y agoJevon'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 ... ``` [1] https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox
- wallace_f 7y agoHere, 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.
- roenxi 7y agoI don't quite follow your comment but that isn't about to stop me guessing what you are asking and providing the intuitive model for the paradox. The bad intuition is if X units of a resource are being used to achieve an outcome then providing the option of getting the same outcome with (X - something) will result in less resource use. However, that is ignoring the economic principles of supply and demand to only focus on current use and demand. Reframing it from the supply side: it used to make sense to supply X units to do so much. Now supplying X units can do even more than so much. So the economics of the situation are unlikely to cause a reduction in supply, because if it made sense to supply X units of resource before, it really makes economic sense to supply it now. In fact, since the resource is now more useful (efficiency rose) it probably makes sense to supply even more of it. If Jevons's Paradox actually appears paradoxical, the root cause is a misunderstanding of supply and demand. If efficiency is modelled by moving the supply/demand curves around on a supply/demand chart it is pretty obvious what is going on; efficiency gains are equivalent to moving the supply curve under that model.
- Nashooo 7y agoDoes 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?
- listentojohan 7y agoThere's Tomorrow - and they're hiring. https://www.tmrow.com/ https://www.tmrow.com/
- m00dy 7y agoHi, I'm also working something similar to this. Would you leave your contact info ?
- kot-behemoth 7y agoMy current client is a sophisticated AI/ML startup/consultancy, Faculty (https://faculty.ai https://faculty.ai). They have extensive experience in a variety of areas, and do some cutting-edge stuff. If you're intested, either ping me (email in the profile) and I can connect, or use the website.
- ace32229 7y ago+1 for Faculty - very smart team
- mrischard 7y agoWe're doing some interesting work in this field at Cervest (based in London). https://www.cervest.earth/ https://www.cervest.earth/
- ViralBShah 7y agoI am happy to share my perspective on this. I feel a really good blog on this topic is Bret Victor's blog post - What can a technologist do about climate change? http://worrydream.com/ClimateChange/ http://worrydream.com/ClimateChange/ One of the clear recommendations is - contribute to Julia. A lot of what Bret said in 2015 has actually panned out. Julia has become a powerful language for scientific computing and machine learning. As a result it is being used in climate projects such as Climate Machine (MIT and Caltech). The Julia Lab at MIT participates in this project: Climate Machine: https://clima.caltech.edu/ https://clima.caltech.edu/ Github: https://github.com/climate-machine/ https://github.com/climate-machine/ Julia Lab: http://julia.mit.edu/ http://julia.mit.edu/ Contributions to Julia packages (compiler, stdlibs, math packages, ML packages, parallel computing) will end up finding their way into climate research, because of the extensive reuse of code within the Julia ecosystem. Specifically capabilities such as Zygote.jl (https://github.com/FluxML/Zygote.jl https://github.com/FluxML/Zygote.jl) for differentiable programming have the potential to dramatically make it easy to apply ML techniques to scientific codebases. Compiler contributors are hard to come along, so all contributions to compiler technology are incredibly valuable. The DiffEqFlux.jl ecosystem in Julia is a good example of combining mechanistic models with ML (https://github.com/JuliaDiffEq/DiffEqFlux.jl https://github.com/JuliaDiffEq/DiffEqFlux.jl). Hop on to the Julia slack channel or discourse to dig in deeper. Another thing Bret Victor speaks about in his blog post is working with agencies such as ARPA-E on advanced projects. Julia Computing is participating in an ARPA-E project to bring these capabilities to many energy related simulation and development technologies. This press release gives a broad idea: ARPA-E press release: https://www.energy.gov/articles/department-energy-announces-15-million-development-artificial-intelligence-and-machine https://www.energy.gov/articles/department-energy-announces-... Funded projects: https://arpa-e.energy.gov/sites/default/files/documents/files/DIFFERENTIATE_Project_Descriptions_FINAL.pdf https://arpa-e.energy.gov/sites/default/files/documents/file... Julia Computing press release: https://juliacomputing.com/communication/2019/12/09/arpa-e.html https://juliacomputing.com/communication/2019/12/09/arpa-e.h...
- thundergolfer 7y agoThe 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
- mstokholm 7y agoIs this entire talk up on YouTube somewhere?
- GrryDucape 7y agoI don't like the idea of a computer that can think for itself, I don't like the idea of computers will replace humans jobs, I don't like the way we are heading.
- rimliu 7y agoDon't worry, in this case we are running in circles.
- 2sk21 7y agoYou have nothing to worry about :-) I would recommend that you read the book by Gary Marcus and Ernie Davis for a country viewpoint.
- cookie_monsta 7y agoI wouldn't like the way it was heading either if Netflix could manage to recommend a movie that I might actually like. As is, I'm not that worried.
- a_imho 7y agoHype does not care about whether it works as advertised on the tin. As long as it generates money you will be served buzzword whether you like it or not and you will pay for it. If lucky, the setback will only be financial.
- lonelappde 7y agoSay hello to your rickshaw carrier for me, since you don't use cars, buses, or trains.
- AllasAskBar 7y agoDon't fall for the climateChange meme guys.
- rurban 7y agoThis the first nonsense talk by Jeff Dean. AI doesn't help at all it battling climate change, only politics do help. The models are accurate enough for centuries, AI would only help in hard forecasting in the usual 2 weeks window on local events. Long term on global scale there's no AI needed at all. So it looks like he ran out of topics to entertain himself. Or he went politician. Which would be a welcoming change.
- thundergolfer 7y agoI’d agree that Jeff there is a man with only a hammer walking around telling us that Climate Change is a nail. Engineers can’t fix everything. Of course the engineering is important, but politics is 1000% the road block on climate action and that is where efforts should be focused,
- natalyarostova 7y ago> that is where efforts should be focused Are all humans fungible? You don't think it's valuable to have some errant geniuses trying to think of weird and creative ways to solve climate change through unconvential engineering? It's not as though they'd be good politicians.
- thundergolfer 7y agoOf course yes, that's valuable. But the political focus shouldn't be lost in the messaging. Google needs to take a political stand on Climate Change, first and foremost. The engineering investment is secondary. We already have the technology to solve our climate problems. We don't need the fancy AI solutions. We just need to implement what we have already right now. That needs to be made abundantly clear to everybody. If Google is asked "what are you doing to address Climate Change?" and we hear predominantly engineering talk, then they're being disingenuous.
- panarky 7y agoPerhaps the Singularity can figure out how to clean up the political mess we've made.
- outside1234 7y agoWhy is there never a Google talk entitled "Building profiles on people with Machine Learning?"
- summerlight 7y agohttps://arxiv.org/abs/1906.05433 https://arxiv.org/abs/1906.05433 This paper seems relevant to the climate change part. This is more about what to do rather than technologies.