I just spent 15m on Amazon trying to prod the recommendation algorithm into finding something I actually wanted to buy so I could get above the "free delivery threshold". Think about that. I wanted to spend money. I wasn't too fussy what it was. Amazon has a decade of my purchasing and browsing history. And they still failed.
Amazon infuriates me. I regularly buy gadget-y bits like electronic components and peripherals. Probably at least once a month. Never see adverts for similar. That ONE TIME I buy a unicorn dress for my 2 year old daughter? That's a lifetime of unicorn related merchandise adverts and recommendations for you!
I suspect Amazon has learned that some feature labels are easy to recognize and correlate (dress color, size, style, etc) and others are hard and lead to useless results (an electronic device's: computer, format, port type, protocol, etc).
So they gave up trying to match CPU with GPU and went back to connecting beer to diapers.
I feel like it’s gotten loads better. You can watch as the voice recognition on your phone changes the words it recognizes to match the context in the sentence. Sometimes it gets a word wrong and fixes it after a half second. Google translate does magical things recognizing common phrasing constructions and bad accents, stuff that Dragon could never do. I built a lipsync pipeline for video games based on Dragon a dec…
Again, just anecdotally, I don't know if its just me, but most of my experience is of google/apple translate and auto-corrects going from the word I want to a wrong/incorrect one. It is one of my most frustrating everyday software experiences. Not only is it not getting better, its actually getting worse, because before I at least had the correct sentence. Now my correct sentence is mangled as it tries to force corre…
That does sound frustrating. I didn’t mean to discount your experience, it certainly is possible that the additional AI has made it worse for some people, especially if there’s an accent involved. Not to mention that the meme of autocorrect mistakes when texting somewhat backs up your experience on a larger scale. I wonder if the scale and complexity of what they’re doing now compared to last decade is the cause of the regressions, like is the problem being solved much harder by trying to factor in and autocorrect based on context, and causing worse results than pure phoneme detection?
I just spent 15m on Amazon trying to prod the recommendation algorithm into finding something I actually wanted to buy so I could get above the "free delivery threshold". Think about that. I wanted to spend money. I wasn't too fussy what it was. Amazon has a decade of my purchasing and browsing history. And they still failed.
Ha. Last time I wanted to buy something on Amazon, their search page kept freezing every time I load it: 100% CPU load. Because I really wanted to buy that thing, I spent 30 mins debugging their silly scripts and found one for-loop that tries to find a non-existent element. Unfortunately, I couldn't figure out a way to enable my fix in a minified script, as reloading the page kept loading the original script.
I read "Why are HR departments apparently so gullible?" and as someone who has worked in a corporate for 20 years I spotted my underwear. The identification of facial recognition as problematic because of accuracy doesn't match my thinking. I believe that the key issue is that given a set of targets facial recognition systems will find near misses from the wider population of all faces offered as candidates, that the…
...I spotted my underwear? What does this mean?
Interpretation 1: they pooped their pants
Interpretation 2: they introduced a non sequitur in their excitement of finding their underwear (perhaps they lost it)
Interpretation 3: when they had their flashback to having worked in corporate 20 years ago they recalled where they misplaced their underwear
This got me thinking - are there any cases of literal snake oil (or any snake-derived products) having better than placebo effects ? (I assume that it's different from snake venom?)
Speaking of jobs and interviews. I am yet to find a job board which does not show JavaScript jobs when searching for Java jobs. Some of them claim to use AI. :)
Let's not forget the job application systems that make you chronologically list every previous employer, their location, your title, and dates of employment, your education history including dates and degrees received, skillsets/technologies you have experience with, etc. All information that is on your CV and/or LinkedIn profile and they want you to manually re-type it into their late 1990's era job application syst…
I guess that is intentional to throttel down on people applying. It works for me: Except, when I desperately need a job, I don't apply. I primarily only know "this" style of applying - It's all big corp companies, though, I must admit.
I don't have time to read the entire paper but I would like to share an anecdote. I worked at a company with a well staffed/funded machine learning team. They were in charge of recommendation systems - think along the lines of youtube up next videos. My team wanted better recommendations (really, less editorial intensive) so the ML team spent weeks crafting 12 or more variants of their recommendation system for our c…
I feel like A/B testing isn’t a great way to determine correctness either.
IMO even in interface designing you should be arguing from first principles rather than relying on telemetry and other empirical data.
The author says "AI is already at or beyond human accuracy in all the tasks on this slide and is continuing to get better rapidly" and one of his examples is "Medical diagnosis from scans". That is an example of precisely the sort of snake oil hype he's berating in the social prediction category. In an extremely narrow sense of pattern recognition of some "image features", i.e. 5% of what a radiologist actually does,…
Author here. I appreciate your criticism. What I had in mind was more along the lines of Google's claims around diabetic retinopathy. I received feedback very similar to yours, i.e. that those claims are based on an extremely narrow problem formulation: https://twitter.com/MaxALittle/status/1196957870853627904 I will correct this in future versions of the talk and paper.
Then I shall write to you directly. I don’t know how you can make the claim that automated essay grading is anything but a shockingly mendacious academic abuse of student’s time and brainpower. To me, this seems far worse than job applicant filtering, firstly because hiring is fundamentally predictive, and secondly because many jobs have a component of legitimately rigid qualifications. An essay is a tool to affect the thoughts of a human. It is not predictive of some hidden factor; it stands alone. It must be original to have value; a learned pattern of ideas is the anti-pattern for novelty. If the grading of an essay can be, in any way, assisted by an algorithm, it is probably not worth human effort to produce. If you personally use essay grading software, or know of anybody at Princeton that does, you have an absolute obligation to disclose this to all of your students and prospective applicants. They are paying for humans to help them become better humans.