Sadly our marketing tests show that when we say our product uses ML we get far less engagement than when we say AI. I don't know that net-conversion is better with AI but ML sure doesn't capture people's imagination. Sigh.
Btw, reminds me of the old joke that goes something like this:
AI for marketing, ML for recruiting, Regression for design, multiplication for implementation.
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.
This is not generally true. When you overtly brand products with machine learning or AI, there’s often a customer perception that it’s overly complicated or something they could never understand, which often drives them away.
For example, you don’t see too many of the most successful or long-term quant finance products touting machine learning or AI. They do use these techniques (sometimes directly for trading, sometimes only for auxiliary problems), but don’t make a big deal out of it.
I worked previously in both adtech & quant finance, and have seen products where ML is the absolute core of the whole thing, yet it wasn’t marketed that way (even recently). I’ve also seen products fail to gain market traction due to overly aggressive ML/AI branding that customers (mostly other enterprise business marketing teams) found alienating and overly complex.
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.
I find it interesting that this discussion takes any interest in the underlying technology, to me that seems utterly irrelevant. Machine Learning largely seemed to me to become a term used by academics and engineers to avoid having that discussion about AI, because 5 years ago if you told anyone that you worked in AI they'd think you were building Wall-e. As the ML research paid off there were suddenly some hype around it and that's where marketing came in. You actually have to sell the product. No longer was it enough to tell people you were recognizing cats on the internet - you were developing a DL ML AI CNN for feline categorization. It's like your 4K UHD HDR AMOLED HDTV.
When marketing gets involved it's actually quite positive for them to have those associations with the bicentennial man.
But the point of ML to begin with is likely often to appeal not by a better product but by appealing to investors or managers. If you create a better product but it doesn’t have “AI” in it then it failed in that aspect. What’s needed is a set of things that can be sold as AI or ML but isn’t.
Because ML/AI are feature enablers. They make a good product better, but they won't make a product successful. It's a signal that people are more interested in solving technical problems than solving business/product problems.
You can improve lead generation by 6.8% without ML too. A/B testing a few changes can easily accomplish the same thing.
The AI could be optimizing to attract people who are willing to give out their email address than actual potential customers so it doesn't help the business. We tried using Google ads to optimize for leads before and we saw ads were on sketchy websites promising people free stuff.
Lead generation was a bad KPI to use but it was chosen can't optimize for sales because the volume is to low to train the ML models. This is the reality of many businesses that experiment in AI and they produce junk.
ML does work well for certain domains where you have the scale of data to pull it off, but now the hype has spread where it is not appropriate.
In fact, I am skeptical of AI and ML in general for its effectiveness, purpose, and applications.
Agreed. It's all about the application and purpose (and the hubris of some of its practitioners), not the theory (or even the demonstrated effectiveness in certain areas).
When we see these disconnects at "work", on the ground -- in terms of lack of relevance / applicability to what the business actually needs -- they are sometimes quite startling, in fact.
Making 90% of your income off of this tech over the last n years is different than that tech being successful. I work at a very large company that is trying to use ML and AI in all kinds of places. The trend I am seeing is that most of that effort is falling flat, really flat, in fact. They have success in places where regular algorithms would also succeed, but just having regular developers design matching systems and do pretty basic statistics isn't sexy in terms of marketing, so they hush it all up and pretend that ML and neural nets and things are the only way forwards. I don't think it is. Our problem is that we have too many ETL robots who aren't very intelligent people and not very forward-thinking themselves!
The cutting edge NLP stuff just showcased at my company was pretty lame, too. I barely saw any statistically significant results at all and yet they rather unscientifically proclaim success because they got any effect at all. Some of what we do in our field doesn't matter because it comes down to whether a customer got a 2nd call back and got converted to some minor sale or added to a program for them. It's throw away and creates good will at conferences and talks. We make a big deal out of it.
We are spending hundreds of millions on projects, trying to save money on generating leads, reducing interactions with customers and vendors through staffed phone banks, and so on. My company has hired all kinds of academics and research type people and has given them titles of "Distinguished this" and "Principal that" and honestly there's not that much to show for it, maybe zero direct outcomes so far. What galls me the most is in all the conferences and demos they are showing off things like High School robotics vehicles and AI parlor tricks and astonishingly little has translated into the business we do. Meanwhile, there are people in the company who do know how to reduce costs and get more done and have outstanding outcomes, but their techniques are not sexy and thus unimportant to the PT Barnum MBAs running our company. I'm sure that's true most everywhere, of course.
These Principals and Distinguisheds all keep proclaiming success while cashing fat paychecks. Meanwhile this year, our stock has had a tough go of it, so I'm curious whether these attempts will continue. The market takes no prisoners. Sure we get a lot of mileage out of looking cool for the recent grad crowd purposes of recruiting--kids want sexy, cool tech projects to work on and words like "insurance" turn them off, so there's that, I guess.
My take on that is that it won't be long before all those new recruits will figure out they got bait and switched pretty bad and that they aren't going to get to work on any of this sexy ML and AI stuff anymore than I am in my role. I got lured in by Data Science (because PhD), which just shows how gullible I am, but at least some of that traditional statistical modeling is having an impact here and there. The problem again is that even that is overblown by a couple of orders of magnitude! In my project, we're simply trying to get more real-time data out to people who need it without having to call in to get it and that is ridiculously difficult because of all the systems we try to knit together and how overall terrible our data quality is. And now my boss wants to build out an "analytics engine" to capture some of this sexy ML and AI stuff. It leads me to believe that the people involved are most interested in getting promoted and not much more.
Anyways, it is cool tech, but American taxpayers and people who are forced to buy our products are paying for it and I rather think they would prefer to spend their money in some better fashion.
AI is the buzzword, while Machine Learning is what's truly driving the shift, indeed. But, I have to admit: it's even harder to sell wtih the words 'Machine Learning'.
Here is my annoyance with AI Hype: people seeking extra tailwinds pitch their startups as "ML companies" even with the most tangential usage of ML. It drowns out real ML companies. Most people cannot tell the difference.
At a hackathon recently, someone pitched their app as an "ML-driven app" though the only ML in there was some 1-line language translation feature they were consuming off Watson REST services for a tangential feature on their app.
Meanwhile, my submission actually used a self-trained CNN on a custom dataset using TensorFlow and changes to start/end layers on the NN. The image classification features were the core of the app and it wasn't something that was just a wrapper over an Off-the-shelf API. We actually tried multiple networks and went thru the trouble of parameterizing everything.
At the end, I wonder how many judges actually understood the difference in effort/value to the two attempts at ML.
I've been at a few startups and ML has typically meant shoddy rules-based logic or some out of the box model. Only one company applied it with rigor and even then it was a slog - lackluster results, tinkering with different models, poring through research papers to figure out where the cognitive gap came from. The rest of the company thought we were brilliant as did prospective clients. Funny thing is it's possible (though less common) to get paid just as much if not more doing data analysis / engineering than vaunted ML/AI work. Businesses are swimming in data but it's siloed or dirty. I don't really see that and the actual analysis being automated away - too much messiness (human error inputting data into systems like Salesforce, ETL breaks in prod leading to gaps, etc).
Btw, reminds me of the old joke that goes something like this: AI for marketing, ML for recruiting, Regression for design, multiplication for implementation.
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