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Apart from that, I'm also personally responsible for implementing DL for data anomaly detection.
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.
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
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.
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.
In the end, we were getting 2-4 big clients per year.
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$/year, thanks to our model (according to our client's report from actual data, not simulation). But, it was actually tough to sell, because of several factors. 1) The internal risk team did not r…
While there are a few horizontal ML/AI/data science vendors, most AI is invisible, a component in some vertical solution, and consumed by people and businesses that don't really care how the results are produced, as long as it works.
Very few companies are good at applying AI across the board, like we see at Google, but the number of businesses using predictive models at least in some capacity is growing.
Most people that balk at the term A.I. seem to presume that intelligence is some capacity unique to humans. Intelligence is merely pattern matching and goal oriented planning, exactly what machine learning is doing today. Stop worrying and learn to love the bomb.
All you've done is taken what machines do today and claimed that is what intelligence is.
In the first startup I created, we made ML-based underwriting and credit scoring models for several banks and insurance companies. The results were astonishing. For instance, one insurance company improved its profit by ~70M$/year, thanks to our model (according to our client's report from actual data, not simulation). But, it was actually tough to sell, because of several factors. 1) The internal risk team did not r…
And then what happened to the start-up?
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). But, it was actually tough to sell, because of several factors. 1) The internal risk team did not r…
And then what happened to the start-up?
Earlier quoted context omitted.
And then what happened to the start-up?
The AI fired all the humans and became an Etherium contract.
It is just an ML model, it just reads some inputs, then outputs some score.