[jskdvsksnb]

Even for the behemoth companies that are able to harness AI, it seems like the domains are a) heavily constrained b) fault tolerant. For example, voice assistants - they have very limited capabilities and consumers will accept pretty poor performance. Look at the errors in Google's attempts to automatically answer questions in searches.

Do you have any examples of domains where a FAANG has operationalized AI/ML outside of consumer products?

[AndrewKemendo]

Data Center optimization:

"DeepMind AI Reduces Google Data Centre Cooling Bill by 40%"

https://deepmind.com/blog/article/deepmind-ai-reduces-google...

[bumby]

Any insight into the actual methodology? I couldn't find specifics, but I would be curious what their baseline condition is.

I wonder if the baseline case is "no control optimization" or if it was based on current control best-practices. For example, one article claims it produces cooler water temperature than normal based on outside conditions. This is a best practice in good energy management through wet-bulb outdoor air temperature reset strategies without using ML. If their 40% savings was above and beyond these best practices, that's a pretty big accomplishment. If it's based on the static temperature setpoint scenario (i.e. non best practice), it's less so.

Edit: after skimming [1], it seems like their baseline condition was the naive/non-best practice approach. I'm not discounting the potential for ML, but I think a more accurate comparison should use traditional "best practice" control strategies, not a naive baseline condition. In some cases, it seems like the ML approach identified would be less advantageous than current non-ML best-practices (e.g., increasing cooling tower water by a static 3deg rather than tracking with a wet-bulb temperature offset)

[1] https://research.google/pubs/pub42542/

[cinntaile]

I read/heard somewhere that this isn't actually used in practice, but I can't find a source. Anyone at Google willing to shine some light on this?

[rasz]

is that the one where "AI" told them to just turn off unused cloud instances?

[bumby]

"In fact, the model’s first recommendation for achieving maximum energy conservation was to shut down the entire facility, which, strictly speaking, wasn’t inaccurate but wasn’t particularly helpful either."

https://sustainability.google/progress/projects/machine-lear...

[andreilys]

“they have very limited capabilities and consumers will accept pretty poor performance”*

The limited capabilities is a stretch... if you showed a google home to someone in 1980s they would be absolutely floored.

” Do you have any examples of domains where a FAANG has operationalized AI/ML outside of consumer products?”

Operations and supply chain is a pretty obvious one. Amazon is clearly leading the pack here.

[baja_blast]

> if you showed a google home to someone in 1980s they would be absolutely floored.

I am not so sure about that. If you came from a time machine and said "This is an AI from the year 2020", they would try and converse with it and quickly realize it's unable to converse. People from the 80's would probably assume by the year 2020 they'd have sentient robots and be disappointed when all it can do is turn on the lights when asked a specific way.

[mellavora]

additional support: Wallmart became dominant because it was one of the first companies to computerize their supply chain management.

[Veedrac]

> outside of consumer products

Well FAANG all produce consumer products, so that wipes out a bazillion legitimate applications, but you've still got that Facebook and Google sell ads, which uses AI for targeting. Data centre cooling was already mentioned, but did you know lithography now uses ML? There's even work on using ML for place and route.

[3minus1]

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