Most companies developing AI capabilities have yet to gain significant benefits
sloanreview.mit.edu
sloanreview.mit.edu
“The people that are really getting value are stepping back and letting the machine tell them what they can do differently,” says Sam Ransbotham, a professor at Boston College who coauthored the report. He says there is no simple formula for seeing a return on investment, but adds that “the gist is not blindly applying” AI to a business’s processes."
As Robin Hanson puts it, 'automation as colonization wave'. Just like electricity or computers or telephones or remote working: they always underperform initially because of the stickiness of organizations and bureaucracies.
Per Conway's law, no organization wants to reorganize itself to use a new technology like software, they want to make the new technology an imitation of itself, old wine in 5% more efficient new skins. It takes either intense near-death-experiences (see: remote working/corona) to force a shift, or starting up new businesses or units to be born-digital.
Those who undergo the birth trauma, like Google, are sitting on a river of AI gold and putting AI into everything; those who fail will whine on surveys about how AI is a scam and overhyped and an AI winter will hit Real Soon Now Just You Wait (it will pop any second now, just like how the media has regularly reported since 2009 about how the Big Tech bubble would pop)...
isn't this very similar to the logic you used a paragraph above though when you spun it as "all great things have birth problems, just wait?". The born-again AI company rhetorically sounds more like conversation to a Christian cult than a business strategy.
This issue of huge promises of the digital revolution followed by very meagre productivity gains actually has played out not just in 'AI' but a lot of sectors over the last three decades at this point.
Even for self-proclaimed AI companies like Google, how much of Google's financial bottom line is this new-agey wave of AI, and how much of it is pagerank, a ton of backend engineering and selling ads?
Why a Christian cult? How is AI hype more related to Christianity than any other cult?
They do, however, practice the zeal of the converted, which is I think what they really were aiming it. And that by itself would qualify as "cultlike" by some definitions.
Its just cultish insanity that happens to also be one of the bloodiest ever civil wars.
Seems like they would be making the same money either way, so the ML advantage might not be the major factor.
Conway's Law really does seem to show here.
If you replace occurrences of AI in the article with Natural Intelligence that is what so many companies really need to implement more of beforehand.
That way any decision-making that is delegated to AI afterward will not have the same limitations that the organization already had.
You usually don't want a business model with an anti-growth pattern baked into an even more opaque and unchangeable feature.
Problem is, deep application of the NI is going to get you most of the way you want to go business-wise, after that the AI might be even more difficult to justify.
Industrial-wise, with unique & complex equipment & data, where an operator gains skill through familiarity with both, a machine can be trained to gain some of that skill.
But there will always need to be someone better trained than the machine in order to get the most out of the machine.
The best investment is often going to be in a system to leverage their ability rather than try to operate without them at all.
Adwords exploits automated auctions (from Multi-agent systems)
Maps uses planning
Translate uses deep networks
Search uses ??? (dunno, but it ain't map reduce for sure)
I wonder if that is the reason I feel Google search has gotten so much more dull.
From the article: "The authors say the most successful companies learn from early uses of AI and adapt their business practices based on the results."
This sounds exactly like the advice that ERP companies used to recommend to organizations. Don't try to customize the ERP, adapt your business to the out-of-the-box best practices. Those who don't often face huge costs and pain trying to adapt the tool to the business, rather than the other way around.
Interestingly, this might say more about organizational culture that encourages adaptation than anything else. It has been said (I think Steven Hawking may have been the one) that "adaptability is intelligence" [1].
From your comment: "Per Conway's law, no organization wants to reorganize itself to use a new technology like software, they want to make the new technology an imitation of itself, old wine in 5% more efficient new skins. It takes either intense near-death-experiences (see: remote working/corona) to force a shift, or starting up new businesses or units to be born-digital."
Conway's Law is about how the design of an organization's information systems often mirror their communication patterns. I have seen this myself to be quite realistic, anecdotally... although.. isn't this really kind of sad in a way? First of all, I see this as an excuse to write off groups of people as "legacy" and "outdated" - for example, digital transformation seems to be as much about a generational shift as it is a technological one. Or perhaps it is just showing the way - change culture/communication - value adaptability - to change systems.
Laws were meant to be broken, so why the belief that because of Conway's law, it always has to be this way, or that Conway's law is a constraint that cannot be broken? Certainly, we won't change things if we aren't willing to adapt.
Second, regarding the recognition of it taking near-death experiences to force change - doesn't that seem true for the broader human population, not just organizations? For that matter, why do we seem to be so stubborn and unable to flip these odds in our favor? That part is frustrating.
Next, to starting new business or units to be "born-digital" - of course, I understand why people think this way, why it probably works, and why it is a go-to strategy. But there is some part of me that this is part of disposable culture, it is partly exactly what is wrong with how we approach things - instead of incremental improvement, it is burn things down or start from scratch. Because we can't adapt the designs we implement, we have to start all over.. and it seems like a wasteful exercise. Where is our logical "exnovation" (the opposite of innovation)?
Lastly, about the gap between those who become "AI-enabled" companies and actually achieve success, and those who do not. In full recognition of the reality that those who do not get with the times, often get left behind, when it comes to technology - this gap worries me more. I'm thinking here of intellectual property, and that what is the best AI model will most likely always be locked and controlled by the profit motive - and thinking, what then?
I suppose this is why some thinkers are so worried about an AI-enabled future (ex. Elon Musk or Stephen Hawking) - it's not the positive potential that scares them, it's what happens to humans [2].
[1] http://www.actinginbalance.com/intelligence-is-adaptability/
[2] https://www.washingtonpost.com/news/innovations/wp/2018/04/0...
In the 90s every washing machine had "fuzzy logic", it was the new hyped thing. Of course there are legitimate applications of fuzzy logic, but you don't have to apply it everywhere. It quickly died down once people noticed this fact.
Right now we're in the phase of the hype cycle where clueless managers ask their engineers to apply AI to anything, because they read about the AI revolution everyday and fear being left behind. But some problems have good non-AI solutions where AI won't reap much benefits, while the data collection, the experts, the time to develop, and the necessary restructuring to become an AI company costs a lot.
I wouldn't call Google as sitting on a river of AI gold.
It is using AI to make more gold from the same mine, instead of the obvious gold, it scratches and extract gold from the dirt and rocks. But it's still doing it from that same mine.
And that's exactly what AI is, garbage in, garbage out. I used to be a machine learning engineer in a company that is about as old as the dinosaur. They wanted to apply that gold digging AI machine google had on a landfill, which didn't quite pan out the way they liked it.
Anyway, I think AI is a sustaining innovation, not a disruptive innovation. It makes existing businesses work better, but it doesn't create new markets where none previously existed like the Internet did. Google makes a ton of money off AI; the core of the ads system is a massive machine-learning model that optimizes ad clicks like no human could. But that only works because they already have the traffic and the data, which they got by positioning themselves as the center of the Internet when it was young.
I do agree that companies need to adjust their processes to take full advantage of AI rather than expecting it to be a magic bullet, but I don't know if I really think "birth trauma" is the right metaphor. More like adolescence; it's a painful identity shift, and some people never successfully make the leap. Those who can't don't die, though, they just become emotionally stunted adults that never reach their full potential.
Does search actively use AI as well? Is it fully dependent on a NN without manual algorithms?
I work on Assistant now, since recently rejoining Google, and it uses AI for the usual suspects: speech and NLP.
Is there a place that doesn't do that? That's entry requirement right?
In other applications like forecasting, there is so much hype, and when you put the (non-interpretable) solutions to the test you end up disappointed (versus a pure stats approach).
There are of course fields where ML is pretty much the only viable solution, but trying to blame corporate bureaucracy to explain its failures in other fields does not help.
Instead AI is thought of magic, something you hire a data scientist and they just do. Few in product, leadership, and other disciplines can think critically about different approaches to provide the right kind of data driven (and data skeptical) leadership.
I think it’s similar to how software was treated 20 years ago. You hired for it and effectively delegated it. You could supposedly manage software devs without technical knowledge. Now code literacy is heavily sought after in many disciplines. We need to get to the same place with data.
I worked for a data analytics consultancy for about 6 months, filled with data scientist types - the statistics and decision trees type.
One of the bigger projects was looking at computer vision to get a robot to detect when a pipe was busted underwater.
I asked the CTO about it and they said they were going to do it by teaching a mini off the shelf robot to locate a beer bottle in the office and transfer learn from there. I mentioned that would be highly unlikely for several reasons and was quickly ushered away from their pet project.
Suffice to say they eventually had to negotiate the final project scope down just a bit to offline binary image classification (broken vs. not broken).
So even a company filled with so called data scientists can fall into the trap of believing the snake oil.
Addendum: not necessarily believing their own snake oil I suppose, totally possible that they were just naive about the capabilities after reading too many wired/ars articles.
Culture eats strategy, after all.
Edit: I only joined the company because I thought they were above all the snake oil, turned out their oil was just a different color.
This is entirely the case in too many places, and them trying to solve a problem that pressure gauges solved about 100 years ago.
And even if you don't get useful data, at the very least you can soften out a lot of the issues with integrating AI in a product. A few of the AI products I've seen give way too little control to the users and instead rely on opaque algorithms. Which is a very frustrating experience especially because those products will often charge a premium for the AI and try to create products where they wall you into their ecosystem and so it's very difficult to maybe export to a different solution that might give the user more control.
Like other popular terms (“Big Data”, “Blockchain” ...) companies fear being left behind so they scrape up whatever they were already doing and get the marketing team to just say “now with AI” and carry on doing what they always did/sold.
After three months of discussions explanations and meetings and all the jazz, I asked them for a summary of the proposed solutions on their side and the costs as we were already making no progress and the cost estimates they were throwing around were massive. At the end, the cost estimate was huge, and the solution was basically "we're gonna try doing X and at the end it might work, or it might not work because what we do is magic".
So when talking to the CEO of the company whether we should embark on this expensive journey and decided against it. But because they had no other solution, they went with the vizualization tool I added and suddenly they realized it fits all their needs. Since half a dozen years, they've been using just that tool and extracting insane value from it.
I guess I lost my train of thought there, but to conclude, I believe until execs and managers learn to use the tools already available at their disposal, and generate awesome reports with almost 0 SQL knowledge - that can really cover pretty much every traditional business scenario I've encountered so far -, AI and ML are very much outside of their grasp and is more useful for technology businesses.
Way back, I was getting to know about stock market analysis. I concluded that the recommended (technical) analysis was, by any individual recommender, just outside their area of mathematical competence. Someone with very little math would be impressed with (say) moving averages, but someone who understood some statistics would denigrate moving averages, but be impressed by Bollinger bands, etc.
Since no one understands neural networks :-) everyone was impressed by them.
Seems like a similar thing with AI/ML -- "Hey, this is beyond my level of understanding, it must be magic! Buy buy buy!"
Having successfully built that, those capabilities could be applied at scale and then we started experiments with more advanced analyses, this time more successful since both we had much more data and the customer became familiar with the data-intensive development.
Maybe one solution is finding the AI equivalent of Microsoft Office, ie a group of cheap tools that are so powerful, flexible and integrated that they can be used by individual employees and teams across all industries and business needs.
https://en.wikipedia.org/wiki/AI_effect
I feel like I use a ton of tools everyday that would have been considered "AI" 10 years ago, like content-aware fill in Photoshop, translation and correcting software, etc.
Of course, once you actually start to get good at it you want to switch back to using code, but it's a good way to start.
As usual, the hardest problems are outside of the code.
From my experience working in an industrial plant which has been involved in several machine learning trials a lot of the time there are attempts made to use complex modeling techniques to make up for a lack of measurements.
Something I question is whether the outcome would have been better if the money which was invested into hiring AI consultants was spent on better plant instrumentation.
Industrial Instruments are not cheap something like a PGNAA analyser (https://en.wikipedia.org/wiki/Prompt_gamma_neutron_activatio...) is an expensive capital purchase and I suspect some people have unrealistic expectations that AI and machine learning can replace things like this.
I think there is some middle ground where AI complements better sensors (maybe instrument companies should be pushing this). I've yet to see any of the data experts push back and say something like "actually you need to measure this better first before we can model it."
I think if neural networks or SVMs are AI, then linear regression is as well. Neural Turing machines and other recent developments I think are closer to the layperson's idea of "AI," though.
I think (particularly with DL) it would probably be more accurate to claim it boils down to nonlinear logistic regression rather than linear regression. To your point, both are relatively old techniques
First - there is much more data now than 5 or 10 years ago; it is generated by every process and is easy to store.
Second - there is a greater art and capability to aggregate and manipulate data. It's simply faster, but also there is a lot of supporting technology in the form of workflows and tooling.
Third - there are more algorithms now; these are often derived from AI research (DNN, RNN, Bayesian things..)
The first two definitely mean that linear regression can generate much more value than 10 years ago.
The third one is a product of the frustration with linear regression and many other "traditional" algorithms. In many domains (speech, images, text processing) the community smashed its head on the wall for 30 years before the computational resources and algorithmic tricks that came out in 2010->now came on stream. You just can't do much with TFIDF or similar with text - I tried very very hard; on the other hand using a transformer is like bloody magic.
I’ve done my share of experiments with ML/AI and where I’ve seen the most interesting value has been NLP applications (such as categorizing customer comments or assigning categories to products based in description) and finding “factors that influence behavior x” which then can be turned into either a model or a few simple rules.
Deming, from Out of the Crisis (1986):
People with master's degrees in statistical theory accept
jobs in industry and government to work with computers. It is
a vicious cycle. Statisticians do not know what statistical
work is, and are satisfied to work with computers. People
that hire statisticians likewise have no knowledge about
statistical work, and somehow suppose that computers are the
answer. Statisticians and management thus misguide each other
and keep the vicious cycle rolling. (p. 133)
Statisticians could help us do our work better, but not the way they did it. Data scientists could help us do our work better, but not the way they did it. AI will have the same problem for most businesses who choose to follow trends and fads rather than evaluate the actual value and utility of the technology and subject matter. And will be of greatest benefit to those who hire experienced people. This was the problem with both statisticians and data scientists for many companies. They'd hire to fill a slot, not for expertise.
In many ways it's mirrored in companies trying to transition to DevOps as well. Filling a slot. Rather than comprehending whether the approach is valid for them, what the approach actually entails, and properly evaluating who should fill the relevant positions.
Still, probably beats blockchain. There was a time when I heard a blockchain project being awarded almost every other month at the companies I worked for and I still wait for ANY single one of them to do anything useful.
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.
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.
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.
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?
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...
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...
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.
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.
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.
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...
https://sustainability.google/progress/projects/machine-lear...
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)
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.
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.
Or is the article literally suggesting the 10% of companies that profit off of AI are the 10% “learning” by retraining their models on newer data? To me that is akin to saying companies that maintain their websites tend to be more profitable, it’s not that much of a revelation.
I don't have the patience to suss out whether they're talking about a deep broad concept or just being too vague.
For instance:
> Organizational learning with AI is demanding. It requires humans and machines to not only work together but also learn from each other — over time, in the right way, and in the appropriate contexts. This cycle of mutual learning makes humans and machines smarter, more relevant, and more effective. Mutual learning between human and machine is essential to success with AI. But it’s difficult to achieve at scale.
I feel this is talking about a problem at my company -- I had a lot of stumbling blocks implementing AI predictions because the size of packages are stored as strings, weight information is inconsistent, etc. etc. so it's very hard to categorize things based on what's in the database.
Perhaps that's what they mean from "humans learning from machines and machines learning from humans" -- actually following decent data standards because you have to if you want to do anything with it -- but damn, just say that.
I feel this is a silly way of saying "maybe we should have actually listened to the programmers from the very beginning when they were talking about doing things the right way."
This helps answer the questions: is this worth investing in? What returns can I expect, and when? What types of operating model is needed to put this all together? How can this play alongside everything else I'm running/building/exploring?
Lord knows they don't listen to their technical staff, because they read in a glossy magazine that ML is the future, and only suckers don't have an "AI play".
Heard too many stories of companies trying to use it to predict equipment failures or something along those lines. Did they have failures in their datasets? No. Obviously they did not succeed.
They then declared AI/ML a failure.
Yes, this is true, but a lot of this poor understanding can be tracked down to the over-enthusiasm (and straight overselling speak, mind you) of most of the AI scientists.
The company I work for (mining) hired a top-notch AI guy to "change the fundaments of data management and processing in the company, using the latest AI technology". Guy left six months later... I still wonder why he lasted that long.
Not saying anything bad here it is just an interesting observation of 'history repeating'.
Though the true benefit is all of those companies that relabeled their trusty optimization algorithms to AI and got funded for having the correct buzzwords.
Yes, most companies probably do not know how to effectively leverage AI knowledge from 2012-2020 (deep learning era). But, classical ML/AI tools such linear regression, basic clustering, A* search, bayes nets etc. are older tools that many industries are now using for the first time.
It's 3 people - smart, but fairly typical CS Masters level. They've been working on AI/Machine Learning models for 3 years, with a truly unreasonably large budget for hardware/software, and very little to show for it.
If the models they were developing actually worked, there would be no reason for my company to continue their primary business, because we would effectively predict the next 20 years of stock performance in a fairly large segment of the market.
At least the CFD people have gotten some good use out of the giant stack of GPUs they bought.
It's very difficult to have a transformative impact by adding features or making a process 5% more efficient. It seems like we're very far away from AI enabling the average person to have new opportunities and experiences in the same way as past technological innovations have.
example: email enables me to communicate instantly and asynchronously with someone on the other side of the world. what does ml enable me to do?
Here’s one for ya:
Communicate instantly and asynchronously with someone who speaks a different language.
In any case, I agree with the thread’s larger point that the buzz and hype around the field is exhausting, and certainly not always warranted. Nothing makes my eyes roll more than reading a post from a data “thought-leader” on LinkedIn.
For example, with ML blind people can manage much better than before.
In the 1980s companies poured lots of money into personal office computers with no measured increase in employee productivity. This was called thd productivity paradox.
https://cs.stanford.edu/people/eroberts/cs201/projects/produ...
I think the answer is that it's arranged that way because that arrangement makes it easier for humans to interpret. Because humans prefer to see data in such 2D arrangements, file formats like PDF were developed so that 2D layouts could be defined and preserved across platforms.
Hence the fundamental problem here is that we want computers to be able to read data that is designed for human readability. That is not an easy problem and it is in fact a large part of what people mean when they talk about "AI".
Though as a counterpoint, separating content from presentation is already a fairly common practice.
A lot of the text we encounter in the world does depend to a significant extent on 2D layout to facilitate its comprehension. Examples include many (perhaps most) websites, product labels, tax forms and receipts.
None of that is the fault of the PDF format which is intended to faithfully represent 2D layouts. As you mention, if the goal is accurate labeling of content other formats exist which are more suitable to that purpose (though I think the concept of separating content from presentation has always worked better in theory than in practice).
-driverless -robotic piece part manufacturing -new approaches to graphics production (games/hollywood)
Any others? I know there are other things being tackled, but not sure if they will scale to be world beating.
e.g. highly detailed feature reports of properties and landscapes, applied to millions of acres of data.
No company has had any benefit from AI.
These pathetic statements from MIT articles have no value. It's just blog spam.
If you want to be pedantic, some companies have had benefit.
Scammy AI companies. Other parts of the pyramid scheme like training organisations. Chess websites Massive data companies like Google and Baidu in only some areas of operations.
The rest is just data collection and people doing data science lying that they are doing AI, often with a ROI that's a loss.