131–140 of 331 posts
"Set this as a CRON that fires at 2AM everyday, period with less activity and traffic. People wake up to emails reminding them about their abandoned carts" Hah I wondered why I got so many notifications in the middle of the night. Now I know that it's from people who think they're helping - not realising that it actually sours my opinion on their company/product.
My startup was approached by a corporate VC that wanted to make a strategic investment. Based on the attendee list from our meeting, which included very high up folks from the company, I felt good going in. They expressed interest in our technology that makes reading on screen easier [1], but they were surprised to learn that we didn't use machine learning to accomplish this. I indicated that it was actually quite ef…
Earlier quoted context omitted.
I went to your website, and I can't figure out what your product is or does. I had to click in to individuals before I figured out what it does. Why not put "BeeLine Reader makes reading on-screen easier, faster, and more enjoyable. We use a simple cognitive trick — an eye-guiding color gradient — to pull your eyes from one line to the next." on your front page? Maybe you guys have already done some kind of testing a…
Appreciate the feedback. We recently redesigned the website to offer silos, and one of the goals was to make the landing page not offer too many specifics that would make some audiences bounce. But perhaps your suggestion is just what we need to do. Thanks for taking the time to share! One lesson learned: I should have linked to the /individual page instead of the generic landing page in my comment. Updating link now…
As someone who sells both of these services, I can only add that it depends, and if you have a good dataset, it's trivial to write either one. But once you start having to account for noise or seasonality or autoregression or dynamic weights or non linear kernel spaces, pure SQL really starts to fall down on the job.
Curious. OP gave a few examples for Ecommerce where SQL will do fine. Can you give a few where ML will do something otherwise impossible or harder with SQL?
My startup was approached by a corporate VC that wanted to make a strategic investment. Based on the attendee list from our meeting, which included very high up folks from the company, I felt good going in. They expressed interest in our technology that makes reading on screen easier [1], but they were surprised to learn that we didn't use machine learning to accomplish this. I indicated that it was actually quite ef…
You may have pitched a perfectly fine product to somebody in the market for a perfectly fine acquihire.
They are of course for companies that make so much money that they can afford to spend 6-digit pays for a Marketing Manager who doesn't know sh*t about his job who in turn is spending millions on randomizing-diagram generators so it seems like he is working hard.
Who on earth are these people describing ? I've never heard of anyone hiring expensive Data Scientists, spinning up Spark/H2O clusters, building a data lake, doing a database offload to S3/HDFS all for a "select from orders table where basket size is the biggest" query. AI/ML doesn't even work like this. It's simply not designed for giving 100% accurate answers to highly structured queries.
My startup was approached by a corporate VC that wanted to make a strategic investment. Based on the attendee list from our meeting, which included very high up folks from the company, I felt good going in. They expressed interest in our technology that makes reading on screen easier [1], but they were surprised to learn that we didn't use machine learning to accomplish this. I indicated that it was actually quite ef…
Can someone explain the dynamic that’s going on here? I somehow can believe that VCs are so stupid to be purely hype driven, so what is pushing them to make this their investment focus? Do they just jump on every bandwagon in the hope that one of them might pan out?
Earlier quoted context omitted.
I do ML/NLP, and recently have been doing color related things. I think you are underestimating how useful NLP could be in your application. I tried out one of your test cases, and I did find it useful. However, I think that blending colors based on entity recognition along with your line based system could focus attention on important parts. Have a look at [1] (sorry for the long URL) and imagine the colors blending…
That sounds like a surefire way to annoy and distract the reader. Imagine if every entity was in bold, in a piece of text. It wouldn't draw your attention to important information, it would just disorient you completely. "Why is 'John' in bold? I already know this sentence is about him!".
So I guess test it and variations?