I 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.
Jeff Dean interview: Machine learning trends in 2020
21–30 of 60 posts
Re: Jeff Dean interview: Machine learning trends in 2020
#22Don't fall for the climateChange meme guys.
Re: Jeff Dean interview: Machine learning trends in 2020
#23Earlier quoted context omitted.
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 a…
I 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.
Re: Jeff Dean interview: Machine learning trends in 2020
#24I 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.
Don't worry, in this case we are running in circles.
Re: Jeff Dean interview: Machine learning trends in 2020
#25Does 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?
My current client is a sophisticated AI/ML startup/consultancy, Faculty (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.
Re: Jeff Dean interview: Machine learning trends in 2020
#26This 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.
Re: Jeff Dean interview: Machine learning trends in 2020
#27Earlier quoted context omitted.
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…
Jeff is right. Training a machine learning model for any common task is getting radically cheaper and more energy efficient. This is a combination of both better hardware (eg, Google TPUs) and better optimisation of training techniques (which is mostly done outside Google). Eg, DAWN Bench[1] benchmarks training of ReseNet50 to 93% accuracy. This used to take days. Now the FastAI group has shown you can do it in 18 mi…
Electronic computers have become vastly more energy efficient since they became widely used in the 60s. On net, do we spend more or less energy on computers now than we did then?
That's the paradox. The cheaper energy becomes, the more uses we find for it.
Re: Jeff Dean interview: Machine learning trends in 2020
#28Earlier quoted context omitted.
>> all the stuff we trained in our Google Data Center — the carbon footprint is zero That is a lie unless Jeff Dean lives under different physics laws.
Yeah, unless this is powered entirely by nuclear or renewables, or unless the emitted carbon is then extracted from the atmosphere, the carbon footprint of all this is most definitely not zero. It's like all those celebs buying carbon "offsets" after flying private to climate summits of all sorts. Dude, your carbon footprint is way, way, above zero, and you paying for "offsets" doesn't reduce it one iota.
Even better that the offset projects aren't necessarily helping. Apparently it isn't even clear whether just planting trees anywhere has a net positive influence on combatting the greenhouse effect [0]. Trees may be required to be planted at suitable locations or else they actually contribute to warming.
Re: Jeff Dean interview: Machine learning trends in 2020
#29Unrelated, 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 engi…
- Jeff Dean once caused an AI winter so cold, even humans lost their intelligence.
Re: Jeff Dean interview: Machine learning trends in 2020
#30Unrelated, 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 engi…
> 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!?"