Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Ask HN: Where is AI/ML actually adding value at your company?

news.ycombinator.com

Post reply on HN
Print view View original

161–170 of 200 posts

Re: Ask HN: Where is AI/ML actually adding value at your company?

#161

Post by strebler »

dchuk wrote: post #140
strebler wrote: post #28

We're a computer vision company, we do a lot of product detection + recognition + search, primarily for retailers, but we've also got revenue in other verticals with large volumes of imagery. My co-founder and I both did our thesis' on computer vision. In our space, the recent AI / ML advances have made things possible that were simply not realistic before. That being said, the hype around Deep Learning is getting pr…

Can you expand more on "we do a lot of product detection + recognition + search, primarily for retailers" please? Is that something like identifying products in social media images or something?

We have several products, each of which serves different departments within retailers.

The exact things we do depends entirely on which department(s) are licensing it. Basically, anywhere there's a product image (from their own inventory to mobile to social) and we can provide some kind of help, we do. Every department needs totally different things, so it varies quite a bit...but it's all leveraging our core automated detection + recognition + search APIs.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#162

Post by thecourier »

malisper wrote: post #86

One of my coworkers used basic reinforcement learning to automate a task someone used to have to do manually. We have two data ingestion pipelines. One that we ingest immediately, and a second for our larger customers which is throttled during the day and ingested at night. For the throttled pipeline, we initially had hard coded rate limits, but as we made changes to our infrastructure, the throttle was processing a…

the decision tree for sql analysis sounds great

Re: Ask HN: Where is AI/ML actually adding value at your company?

#163

Post by rsrsrs86 »

collyw wrote: post #113
jorgemf wrote: post #38

Earlier quoted context omitted.

> but those aren't clearly different from "use some statistics to make a prediction" You can reduce 90% of ML to this. Even neural networks are based on statistics. If I have to draw a line between statistics and ML is that ML learns, it means it can predict things, however statistics only gives you information about the data you have. But for sure statistics and ML overlap a lot.

Predicting things seems to be the primary purpose of statistics in many cases.

I'd say describing uncertain process and measures is. If you have a good description you might be able to predict values as well.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#164

Post by rsrsrs86 »

jngiam1 wrote: post #70

From Coursera - we use ML in a few places: 1. Course Recommendations. We use low rank matrix factorization approaches to do recommendations, and are also looking into integrating other information sources (such as your career goals). 2. Search. Results are relevance ranked based on a variety of signals from popularity to learner preferences. 3. Learning. There's a lot of untapped potential here. We have done some res…

Are you just weighing different scores on 2? That would be heuristics more precisely. Not really learning; Unless you update the weights my minimizing some cost function.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#165

Post by nimchimpsky »

ekarulf wrote: post #106
brianwawok wrote: post #44

Earlier quoted context omitted.

So is this like the Amazon "feature" where I buy a coffee table on Amazon, then I get suggested to buy a coffee table EVERY DAY for 3 months. Literally row after row of coffee table? Because there must be a big pool of people who buy 1 coffee table buying more coffee tables immediately after?

It's a hard problem to determine the repeat purchase cadence of a product. At one end of the bell curve you have items re-purchased frequently, e.g. diapers or grocery, and on the other end you have items that are rarely repurchased. I haven't looked at coffee tables specifically, but I know when I've looked at home products in the past I've been surprised at how frequently people will buy two large items, e.g. TVs o…

"It's a hard problem to determine the repeat purchase cadence of a product."

I don't think it is.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#166

Post by paulsutter »

brianwawok wrote: post #44
ekarulf wrote: post #35

Amazon Personalization. We use ML/Deep Learning for customer to product recommendations and product to product recommendations. For years we used only algorithms based on basic statistics but we've found places where the machine learned models out perform the simpler models. Here is our blog post and related GitHub repo: https://aws.amazon.com/blogs/big-data/generating-recommendat... https://github.com/amznlabs/amazo…

So is this like the Amazon "feature" where I buy a coffee table on Amazon, then I get suggested to buy a coffee table EVERY DAY for 3 months. Literally row after row of coffee table? Because there must be a big pool of people who buy 1 coffee table buying more coffee tables immediately after?

Advertising is trained against ROI, not against what will "seem right" to the user.

Maybe in-market* furniture shoppers tend to spend a lot of money. Maybe furniture is a very profitable category. Even if the system is smart enough to assume there's only a 20% chance that you're in the process of significant furniture purchases, furniture ads may still be a better use of the ad slot than a lower value item where you have an 80% chance of being in-market.

Then why show the same damn coffee table over and over? Maybe that's more likely to return your attention to your furniture purchasing? I have no idea. Most likely, they don't know exactly either. Most likely, that's just what the highest-scoring current algorithm decided.

*The duration of "in-market" varies by category. Some product categories have a long consideration phase. For example car shoppers tend to spend 2-3 months considering alternative brands and models before they spend a few weeks narrowing down on a specific car configuration and exact pricing.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#167

Post by LolWolf »

anlif wrote: post #145
amelius wrote: post #87

Earlier quoted context omitted.

This sounds like a pretty standard use of ML to me. No need to feel guilty, this stuff just isn't very difficult from a user's perspective, especially if you use the right libraries. It helps if you maintain a good bookkeeping of your experiments, so you have a good picture of what works and what doesn't. By the way, control engineering for industry used to be very difficult (but is paid very well), and requires know…

Care to elaborate?

I disagree with the above, but I think I can shed light on what they might mean. Usually, control theory (which is used in most manufacturing processes) requires quite a bit of background knowledge on the processes at hand along with fairly powerful (mathematical/physical) tools to both approximate and model such processes, along with creating systems that use these models to perform the desired task.

I believe that the parent post means that with current simulation-based tools and large amounts of data generated from manufacturing processes, one can work directly with abstract machine learning models instead of creating physical models or approximations thereof---thus being able to dispose of the mathematical baggage of optimization/control theory and work with a black-box, general approach.

I disagree since we have very few guarantees about machine learning algorithms relative to well-known control approximations with good bounds; additionally, I think it's quite dangerous to be toying with such models without extensive testing in industrial processes, which, to my knowledge is rarely done in most settings by experts, much less people only recently coming into the field. Conversely, you're forced to consistently think about these things in control theory, which I believe, makes it harder to screw up since the models are also highly interpretable and can be understood by people

This is definitely not the case in high-dimensional models: what is the 3rd edge connected to the 15th node in your 23rd layer of that 500-layer deep net mean? Is it screwing us over? Do we have optimality guarantees?

Re: Ask HN: Where is AI/ML actually adding value at your company?

#168

Post by slmyers »

mattmanser wrote: post #155
andrewchambers wrote: post #150

Earlier quoted context omitted.

I hope you get a decent chunk of those cost savings as a reward for your effort, great job.

D'Angelo Barksdale: Nigga, please. The man who invented them things? Just some sad-ass down at the basement at McDonald's, thinkin' up some shit to make some money for the real players. Malik 'Poot' Carr: Naw, man, that ain't right. D'Angelo Barksdale: Fuck "right." It ain't about right, it's about money. Now you think Ronald McDonald gonna go down in that basement and say, "Hey, Mista Nugget, you the bomb. We sellin…

Oh, a Wire reference on HN... my life is one step closer to completion.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#169

Post by icelancer »

antognini wrote: post #18

At Persyst we use neural networks for EEG interpretation. Our latest version has human-level performance for epileptogenic spike detection. We are now working on bringing the seizure detection algorithm to human-level performance.

We use them for EMG data/interpretation as well.

Re: Ask HN: Where is AI/ML actually adding value at your company?

#170

Post by IndianAstronaut »

brockf wrote: post #129

At our data science company, we're building a marketing automation platform that uses deep reinforcement learning to optimize email marketing campaigns. Marketers create their messages and define their goals (e.g., purchasing a product, using an app) and it learns what and when to message customers to drive them towards those goals. Basically, it turns marketing drip campaigns into a game and learns how to win it :)…

I was doing something similar in email marketing. Used decision tree models with a lot of feature engineering to help predict email open rates.
Post reply on HN