Machine Learning is a technology the same way blockchain is. Customers buy products and solutions, not your tech stack so it doesn't make sense for any company to sell themselves as a machine learning company, unless you're selling APIs.
Alexa / Siri / Google Assistant would not be possible without machine learning. Same goes for Snapchat's filters and any other form of AR. Google's Pixel phones wouldn't have such a great camera without computational photography / machine learning.
Thanks for your reply, though I'm not sure if I get your comment correctly. As in, I agree that marketing is super important but if I had an idea about a certain SaaS product that requires a certain ML model, I need to decide either to build it myself, or using somebody else's APIs.
> We found that as our AI got worse, our product got better.
To me great business innovations needs 2 equal forces: 1) The marketing and customer oriented drive 2) The scientific, technological and engineering side.
If you only have one of each, any success is surely fickle and will quickly dissolve.
If one force (Marketing in this case) overwhelms the other, then of course you have 'Blockchains' to track food. Not saying that there isn't a need for this, but it smells funny. I'm surprised there isn't a 'Deep Neural Network' used also to make things look even more 'cutting edge' ....
Or their applications of AI to their products are so transparent to the level where you cannot recognize? I'm pretty sure that their revenue will drop by more than half without their machine learning infrastructure.
I think you're over-thinking it. This is all just marketing hype and when someone says deep learning and AI you can just substitute automated data mining without loss of generality. Automated data mining doesn't sound as sexy and so the marketing departments are not presenting it that way.
Most of marketing at traditional compnaies can be automated by ML. This is already happening, but there are huge F500 companies where the employees have an incentive not to automate.
Is it the new gold rush though. I work in a large organisation that has a lot of data and inefficient processes, and we haven’t bought anything.
It hasn’t been for a lack of trying. We’ve had everyone from IBM and Microsoft to small local AI startup try to sell us their magic, but no one has come up with anything meaningful to do with our data that our analysis department isn’t already doing without ML/AI. I guess we could replace some of our analysis department with ML/AI, but working with data is only part of what they do, explaining the data and helping our leadership make sound decisions is their primary function, and it’s kind of hard for ML/AI to do that (trust me).
What we have learned though, is that even though we have a truck load of data, we can’t actually use it unless we have someone on deck who actually understands it. IBM had a run at it, and they couldn’t get their algorithms to understand anything, not even when we tried to help them. I mean, they did come up with some basic models that their machine spotted/learned by itself by trawling through our data, but nothing we didn’t already have. Because even though we have a lot of data, the quality of it is absolute shite. Which is anecdotal, but it’s terrible because it was generated by thousand of human employees over 40 years, and even though I’m guessing, I doubt we’re unique in that aspect.
We’ll continue to do various proof of concepts and listen to what suppliers have to say, but I fully expect most of it to go the way Blockchain did which is where we never actually find a use for it.
With a gold rush, you kind of need the nuggets of gold to sell, and I’m just not seeing that with ML/AI. At least no yet.
AFAIK their promise of "we use machine learning to..." never panned out even remotely. All the processes ended up being mostly manual, with all the tradeoffs that entails.
With the money they raised, after spending so much on marketing, I assume they downsized, lost some talent, and pivoted mostly to a sales-driven recruiting business for their top clients.
When machine learning was a buzz word then companies started using it to describe almost everything they do. Recently I have also noticed that more companies (and PMs) are using AI in its place, at least in their marketing speak.
Often the AI or machine learning that is being sold to their customers (or if it’s a startup, to their investors) is in fact a team or a group of teams creating static rules. If it is image recognition the they will often have a large team of manual reviewers. Yes there maybe some AI or machine learning models that are assisting in the decision making, but they are usually much less effective than people realize.
Today whenever I hear AI or machine learning, my default is to assume it is marketing speak.
I don’t doubt there are models, I just doubt their effectiveness. But saying “we use AI to do X”, sounds much better than saying, “we have a team of experts who help us do X really well”
With that said I have worked and continue to work with some amazing data scientist who really are pushing the limits of what can be done with machine learning.
In the context of the business area that Palantir is in, though, they absolutely do need machine learning to maximize the value of their product. Imagine trying to build a program to beat AlphaGo, or a search engine better than Google, or a best-in-breed image classifier, without using machine learning. You can't do it. Machine learning is a critically important technique that can solve problems better than anything else can. Palantir's work contains many such problems. Not using the best tool for the job, and not even having a roadmap to do so, means they're ripe for disruption by some other company coming along doing the work better.
Machine learning isn't just some buzzword used to attract VC money. It really, truly, does work.
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