61–70 of 228 posts
While not exactly aligned to the research, I've been surprised how poor Nest Thermostat's learning feature is. The main selling point for Nest is having a "learning thermostat". Perhaps my schedule is just not predictable enough, but the auto-generated temperature schedules it generates after its "learning" period is not even close to what I would manually set up on a normal thermostat. Maybe I'm just an "edge case"…
"Why am I sweating right now? Oh, the Nest set the temperature too high again!" And then after a few instances, I just turn off all the automation and set up a schedule like normal. Same with the "away from home" which seems to randomly think I'm away and I have no idea why. Oh, and the app doesn't show me filter reminders, only the actual device, which I never touch all the way downstairs. There's not even any statu…
If you have a fairly regular life I would think a schedule would outdo ML pretty much all the time, because you know exactly what that schedule should be. ML might be useful for a secret agent whose life is so erratic that a schedule would be useless.
That is to say ML is maybe better than falling back to nothing.
It is the same on Netflix. I have phases where I watch a certain genre for a few weeks and then move on. For example after a few Scandi crime series it is time for something else. However, at the same time my daughter loves Animé and pretty only watch that. It is really hard for an ML algorithm to grab these nuances.
Netflix makes a far more obvious sin: not having “who is watching” as boolean choices. If I am watching with my partner, I want both of our accounts to mark that series as viewed. And I really want Netflix to tell me what I’m watching with her so that I don’t continue watching it without her because I will be single if that happens (again).
Are you sure you want to watch this without your partner ?
Yes ? We recommend the following service for finding temporary accommodation on short notice
It is the same on Netflix. I have phases where I watch a certain genre for a few weeks and then move on. For example after a few Scandi crime series it is time for something else. However, at the same time my daughter loves Animé and pretty only watch that. It is really hard for an ML algorithm to grab these nuances.
Always interesting to see outsiders writing papers about this, using anecdote and unrelated data (mostly political and real world purchase data in this case) to argue that ML doesn't make useful predictions. Meanwhile I look at randomized controlled trial data showing millions of dollars in revenue uplift directly attributable to ML vs non-ML backed conversion pipelines, offsetting the cost of doing the ML by >10x. I…
Translation: Computers can't read minds.
A bigger generalization is that, whenever a software feature becomes essentially mind reading; someone's either feeding a hype engine or letting their imagination run away.
The best things to do in that case is to pop the bubble if you can, or walk away. I will often clearly state, "Computers can't read minds. You're making a lot of assumptions that will most likely prove false."
Always interesting to see outsiders writing papers about this, using anecdote and unrelated data (mostly political and real world purchase data in this case) to argue that ML doesn't make useful predictions. Meanwhile I look at randomized controlled trial data showing millions of dollars in revenue uplift directly attributable to ML vs non-ML backed conversion pipelines, offsetting the cost of doing the ML by >10x. I…
Are you really sure you're not just fooling yourselves with your randomized controlled trials? As Feynman famously said, the easiest person to fool is yourself. And in business even more than science, you might even like the results. Have you ever put this data up against something similar to the peer review system in academia, where several experts from a competing deparment (or ideally competing company) try to pic…
Always interesting to see outsiders writing papers about this, using anecdote and unrelated data (mostly political and real world purchase data in this case) to argue that ML doesn't make useful predictions. Meanwhile I look at randomized controlled trial data showing millions of dollars in revenue uplift directly attributable to ML vs non-ML backed conversion pipelines, offsetting the cost of doing the ML by >10x. I…
I regularly buy the same brand of toilet paper, socks, and sneakers. Machine learning can predict that.
But, machine learning can't predict that I spent the night at my parents house, really liked the fancy pillow they put on the guest bed, and then had to buy one for myself. (This is essentially the conclusion in the abstract.)
Such a prediction requires mind reading, which is impossible.
Always interesting to see outsiders writing papers about this, using anecdote and unrelated data (mostly political and real world purchase data in this case) to argue that ML doesn't make useful predictions. Meanwhile I look at randomized controlled trial data showing millions of dollars in revenue uplift directly attributable to ML vs non-ML backed conversion pipelines, offsetting the cost of doing the ML by >10x. I…
If your ML model is able to predict what consumers are going to buy, the revenue lift would be zero. Let's say I go to the store to buy milk. The store has a perfect ML model, so they're able to predict that I'm about to do that. I walk into the store and buy the milk as planned. So how does the ML help drive revenue? The store could make my life easier by having it ready for me at the door, but I was going to buy it…
Always interesting to see outsiders writing papers about this, using anecdote and unrelated data (mostly political and real world purchase data in this case) to argue that ML doesn't make useful predictions. Meanwhile I look at randomized controlled trial data showing millions of dollars in revenue uplift directly attributable to ML vs non-ML backed conversion pipelines, offsetting the cost of doing the ML by >10x. I…
> Always interesting to see outsiders writing papers about this, using anecdote and unrelated data (mostly political and real world purchase data in this case) to argue that ML doesn't make useful predictions. Meanwhile I look at randomized controlled trial data showing millions of dollars in revenue uplift directly attributable to ML vs non-ML backed conversion pipelines, offsetting the cost of doing the ML by >10x.…
Also ML can predict that, BTW. Facebook knows you are connected to your parents. If the pillow seller tells Facebook that your parents bought the pillow, then Facebook knows and may choose to show you an ad for that pillow.