I've seen many companies attempt to use AI/ML as a business model.<p>Personally speaking, I left my own job earlier this year due in part to deep disagreements about how my previous company was utilizing and marketing itself as AI-based, even changing its name to include "AI".<p>But when I left fintech to work in the healthcare industry (totally different role), people started coming to me to pitch their products/solutions. I started getting pitched AI products (harness the power of AI & predictive analytics!), and the use of buzzwords was pretty outrageous. I started wondering...do these companies have any real customers? Who are buying these products, and are they seeing a sufficient-enough ROI to justify these purchases?
I find it hard to wrap my head around this question because "AI" is a relative (and moving) target.<p>Alexa would be considered advanced AI in the 90s, wouldn't it? Google Search or Facebook ad targeting might be, too. They certainly fit some definition of ML.<p>It sounds like you're thinking of companies that are doing simple data analysis and calling it AI, right?
The most difficult thing to do is to pitch a <i>product</i> instead of a <i>technology</i>. This is more accentuated by the fact that we're mostly engineers and we're not good at pitching benefits vs features.<p>But I can assure you that customers do appreciate the AI component, especially when explained to them. I do a LOT of education in commercial meetings and demos. Some of our customers we invited to our offices for a more in-depth explanation of our technology.<p>My experience is that customers' interest is piqued by buzzwords, but if you are able to explain the tech in plain words, they are astonished and the sale is much easier.<p>To summarize, if you are raising money it may be worth it to buzzword the hell of your deck, but when selling the product the key is to focus on benefits + education.<p>Disclaimer: Am an AI startup founder (<a href="https://optimusprice.ai" rel="nofollow">https://optimusprice.ai</a>)
In the first startup I created, we made ML-based underwriting and credit scoring models for several banks and insurance companies. The results were astonishing. For instance, one insurance company improved its profit by ~70M$/year, thanks to our model (according to our client's report from actual data, not simulation).<p>But, it was actually tough to sell, because of several factors. 1) The internal risk team did not really like a technology that they could not understand. 2) It was hard to give an explanation of why it would work to the executives.<p>In the end, we were getting 2-4 big clients per year.
AI is not a business model. It's usually just a component in a larger solution. You need to get a lot of other things right for AI to produce value, notably around the data you gather and pre-process. For most startups, that data is hard to access, and without it, their algorithms won't have much to train on.<p>While there are a few horizontal ML/AI/data science vendors, most AI is invisible, a component in some vertical solution, and consumed by people and businesses that don't really care how the results are produced, as long as it works.<p>Very few companies are good at applying AI across the board, like we see at Google, but the number of businesses using predictive models at least in some capacity is growing.
We use DL for our grammar checker in Zoho Writer (<a href="https://zohowriter.com" rel="nofollow">https://zohowriter.com</a>). We don't advertise our product as "The next generation intelligent word processor".<p>It's just one of many features that's best done with AI. I like it that way. Pitching utility is so much better than pitching how we built something with blockchain, AI and IOT combined ;)
Right now the whole AI/ML is basically just a buzz word everyone uses because it is popular to use.<p>In reality, a true AI/ML company is basically just using AI/ML to create services/solutions for existing issues.<p>Case in point: google is using ai/ml to improve their speech to text and text to speech services.
What do you mean exactly ? I've been running a machine learning / reinforcement / optimization service company since 2015 and our customers range from the largest EU corps to small and mid size startups. If you mean how many customers use AI/DL/RL in production right now, it's a smaller percentage of our customers, around 25% I'd say, but growing, slowly. Most is about research roadmaps and testing this testing that, though these can be yezr long projects.<p>DL integration into or as a replacement to pieces of software stacks, plus long term model updates, measurements, testing and avoiding regressions (very hard, think of ensuring all past samples are similarly predicted as before after a model update, that's beyond simply improving the accuracy) is difficult and standard practice is building up slowly.
I know Seldon have real paying customers:
<a href="https://www.seldon.io" rel="nofollow">https://www.seldon.io</a><p>I can’t speak for them specifically or any other AI company but from cursory chats with folk in the industry suggests a lot of work is consultancy and proof of concept type stuff with bigger companies which they use to partially fund product dev on their own internal projects - some of which see the light of day.
We use DL at my company. It's used to help and suggest the people in data input what some of the inputs should be. It's been getting better over time as it collects more data points.<p>Apart from that, I'm also personally responsible for implementing DL for data anomaly detection.<p>So yes, ML/DL is very much over-hyped right now, but it has real world tangible benefits. It is being used in multiple fields and I believe will grow quite fast.
I run a company that does financial portfolio optimizations and runs regressions on financial time-series data. These tools are in some sense the very first widespread use of "machine learning" techniques (at least in finance). I've noticed the same effects that the OP mentions: a handful of companies in this sector using the same tools may slap "ML and AI" all over their product and marketing. They're not incorrect - it just feels like a bit of a stretch to do so.
I was at an AI company at my last job. They had customers, but it was largely speculative. I cannot say the product was worth what was being paid for it. That being said, there is certainly a problem domain that AI could be good for, if it ever lives up to the hype. Much more effective is human-in-the-loop AI, which is what Google, Facebook, etc. use and make lots of money from.
We are an AI company working out of Hyderabad / India, We have a very fast Classification algorithm as a product. and currently been used by 3 paying customers.<p><a href="http://www.alpes.ai/" rel="nofollow">http://www.alpes.ai/</a><p>We are releasing our API next week. The idea being you can quickly try out the algorithm and see Results and Training time on your own datasets.
I think legal contract analysis is an area where machine learning is actually needed for core features to work.<p>Some products like <a href="https://www.contractstandards.com/" rel="nofollow">https://www.contractstandards.com/</a> are beginning to appear. They need document classification, text sentence level classification, entailment, clustering are all necessary for making contract analysis easier for the user.<p>And these products need good domain experts to identify pain points where ml can help.<p>In ml as API side, one potentially valuable product suite I have seen is <a href="https://aylien.com/text-api/" rel="nofollow">https://aylien.com/text-api/</a> . Even they are building additional products like news analysis.<p>But I am little sceptical on whether ml as API can scale in terms of addressing specific task-specific nuances needed for products building upon them.
I have seen some specialized image classifiers find a market in analyzing scientific instrument data in academia, outside of that literally nothing else.<p>But there sure are a lot of stories about adversarial bayesian multi-model deep net retention markov cycle consistent custom asic demystified robust perturbations.<p>The speech recognition and predictive text on my phone is still awful.
My general rule of thumb is that any AI/ML is a result of it being a solution to a pain point within existing product(s). Not having a product with a necessary scale or require the technical sophistication of AI/ML but pitching it as if it has it is imo a red flag. This I call premature optimization.<p>Another point is marketing. A ride sharing service like Uber or Lyft could easily market themselves as "using AI/ML to solve last mile transportation". There's reason to believe they have some ML teams that do amazing work to support the ride sharing product. Both above have paying customers.
A lot of compagnies do basically an externalisation of ml: they are providing the team the compagny would build if they had to do the job themselves.<p>Usually their website is buzzwordy but they are not necearily too much vocal because they find their customers through direct contact. And they do have customers... theres a real market<p>In healthcare there are not ao much killer application yet in ai/ml and its true that a lot are running on investor funds, but in my opinion this will change soon.
At my company we try to sell a product that solves a customers need. Some of the things we’re doing as part of the solution we chose for the customers product could be called AI. But honestly, our customers don’t really care _how_ we solve their problem. They would be happy buying magic wands if that would solve their problem.<p>So sibling comments mentioned that some companies struggle with selling products not technologies. I agree.
Customers (mostly businesses) are pretty much queueing to use our product, Datavoyant by Amplyfi (<a href="https://www.amplyfigroup.com/" rel="nofollow">https://www.amplyfigroup.com/</a>). We have real paying customers.
AI and ML techniques are great for both our product, as well for our marketing.
Yes they do, it's generally smoke and mirrors. But by being buzzword complient, party like it is 1999. Ai/ml is a cute way to fleece clients for 99.999 percent of the time. Ml/ai from the consumer side is a joke.
Most people that balk at the term A.I. seem to presume that intelligence is some capacity unique to humans.<p>Intelligence is merely pattern matching and goal oriented planning, exactly what machine learning is doing today.<p>Stop worrying and learn to love the bomb.
I think the buzzword problem is rooted in sales mentality. Selling to VCs and selling to customers. Humans have to make decisions with incomplete information so we "pattern match" and "gut instinct" and "FOMO". So blame it on capitalism for systemically ensuring that entrepreneurs behave like this, or whatever, but frankly they are the only rational actors in this system.