Obviously this isn't all "AI" in the historical CS sense of the word. It's a mix of progress in things like signal processing, computer vision, NLP, neural nets and transformers, and the raw computer engineering that has made all of that practical on modern hardware.
Are you and I sticks-in-the-mud for not just going along with calling this "AI"? Is there actual worthwhile nuance by calling things their proper names rather than just kowtowing to the word that normal people have latched onto?
2) Audio and image identification is indeed an area where great strides have been made, and if those are relevant to your business, then you’re in great luck, and you should absolutely leverage that. But this goes back toward my “it should be aimed at a specific use case” statement; image identification is much much more narrow than how AI is being hyped.
3) Personally I’m less than impressed with the progress that voice assistants have made; they present great, but they have utterly failed to make a dent in my day to day living. It seems like end users regularly oscillate between “this is amazing”, “this is stupid”, and “this is incredibly creepy”. In my opinion this area saw a great big leap forward about 5-6 years ago and progress has basically stopped.
If you read back about older AI bubbles, what you’ll find is that there are bits and pieces left over from those bubbles that are unquestionably improvements on what came before. But critically these improvements fell well short of the grandiose promises made by AI boosters at the time. It’s entirely probable that this AI boom will follow a similar pattern; a massive cycle of hype and collapse that leaves behind useful techniques and tools that nonetheless fall well short of what was promised during the cycle.
As far as what is and is not considered to be “AI”; that definition has changed readily over the years. Ground breaking techniques are regularly called “AI” until they stop being called that and get new names. We’re already seeing this process work now with ML.
I think there are the AI leaders, and then there is everyone else. What is the difference?
The leaders are mostly big tech, who have driven the step-change advances you describe. This, IMHO, was due to their pre-existing mastery of data. They already had a ton of well-organized data, because they were engineering cultures, and data was the lifeblood of their business (ads, search, shopping). Once ML/AI came into the picture, it was full steam ahead.
Most others are (blind) followers, and cannot tease apart the engineering bit from the (data) science hype. They get fixated on the latter (data science and ML/AI) and forget the engineering, or "scale" part.
The first question should not be about AI/ML, but on the other hand, do you have solid (data) engineering where your data is easily accessible to any data scientist? By now it should be apparent that "data is the new oil" and will be useful even if you don't plan to do deep learning.
If you don't have solid (data) engineering and "data at scale" for anyone, anywhere, then your ML/AI efforts are doomed.
Data first, only then ML/AI. See "Data Science Hierarchy of Needs": https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc00...
In the traditional CS sense, the things you mention could all be considered weak AI.
I predict as we continue to transition to stronger AIs with more advanced capabilities we will continue to see a shift in what people think of as “AI”.
[1]https://en.m.wikipedia.org/wiki/Artificial_general_intellige...
It's just that these results are also EXTREMELY unevenly distributed - and good luck breaking into anything that can use applied ML and not get crushed by GAAFM.
However I agree with your point here:
>If an executive or similar is talking about "AI" or "Machine Learning" without the ability to identify specific use cases that they're hoping to implement, then that is a huge red flag.
So it's not as simple as "AI is Hype" - it's not hype - it's just that most organizations will struggle to actually implement it because all the data/talent/compute etc... sits in GAAFM.
It’s unquestionable that there are certainly areas that ML is delivering in spades, but it’s nowhere near as ubiquitous as the hype implies.
Do you have any examples of domains where a FAANG has operationalized AI/ML outside of consumer products?
"DeepMind AI Reduces Google Data Centre Cooling Bill by 40%"
https://deepmind.com/blog/article/deepmind-ai-reduces-google...
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)
https://sustainability.google/progress/projects/machine-lear...
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.
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.
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.
Personally I think this will be another “AI winter”, where we will gain some improvements in narrow domains but fall far short of what was promised, leading to disillusionment and a reduction in research budgets.
We'll see. Maybe with Moore's law alikes pushing the processing capacities and a separate 10x improvement in the mechanics (ie 'how' AI learns).
Two years on and you can't find any mention of it on any of the internal websites, all the PPT's and videos have been scrubbed from the NAS drives they were once promoting people to go watch and learn from. All the supposed "partnerships" have been dissolved, or never took off in the first place.
Two years later and blockchain is nothing but a memory at the company I work at. Just as fast as it arrived, it disappeared without so much as a trace of it ever existing in the first place.
I could see the triple ledger being promising in freight for example.
And VR.
Whats more, it's one of those games that could conceivably run on a very cheap VR headset in the next few years.
It has however become a very good metric for spotting low quality technical leadership. If an executive or similar is talking about "AI" or "Machine Learning" without the ability to identify specific use cases that they're hoping to implement, then that is a huge red flag. AI, as much as it actually exists, is a tool to be applied to an end, not magic pixie dust to be sprinkled onto your product to make it more profitable.