101–110 of 175 posts
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I worked in AWS for a bit and my favorite joke was to flippantly suggest mturk as a solution to some convoluted architecture/process.
That's not a joke, that's a legitimate solution.
But, yes, it would have _worked_ if our requirements weren't so strict :)
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Grocery stores have been tracking your food purchases since the 1990s at least. That's why they have those shopping cards.
Apple Pay and/or cash defeats this, though.
Is it just me or does this article read as a thinly veiled sales pitch to anyone else?
My guess is that since people don't pay for subscriptions and rarely click on ads, Wired and some other publications make most of their money now from paid promotional 'journalism'. I would rather have that than nothing. Everything costs money.
> Today’s artificial intelligence, you see, requires at least some human training. If you want a system to automatically identify cats in YouTube videos, humans must first show it what a cat looks like. The article is written by someone who doesn't know what he's talking about. The "cat videos" story from a while back ostensibly used Unsupervised training, that means, the Google team didn't have to tell the deep neur…
In fact that very network produced also millions of other "concepts", that is, classes of images, that have no direct interpretability in human terms. The "cat neuron" was a fun gimmick, but you're reading way too much into it.
> Today’s artificial intelligence, you see, requires at least some human training. If you want a system to automatically identify cats in YouTube videos, humans must first show it what a cat looks like. The article is written by someone who doesn't know what he's talking about. The "cat videos" story from a while back ostensibly used Unsupervised training, that means, the Google team didn't have to tell the deep neur…
It may have used tags on YouTube videos to identify which had cats. Not sure if that counts as completely unsupervised.
Building high-level features using large scale unsupervised learning http://arxiv.org/abs/1112.6209
From the abstract: Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not.
The Cat detection thing was just a side product of learning to identify features of things in an unsupervised manner, but the news outlets locked on to that with titles such as "How Many Computers to Identify a Cat? 16,000" in NY Times.
Wasn't it amazing that they could distill the concept of cat from images with no help from external labels (human intervention)? They missed the core of the discovery by not understanding that.
The deep learning method is an unsupervised way to process raw input and transform it into useable features. This used to be done by a combination of domain knowledge and supervised training, but they could build an automated way to extract relevant features from images.
This opened the window for hope that one day neural networks will be easily applied to any new domain if there is sufficient raw data to build a deep network for it. In the past there was a need for a large investment in human based data labeling and how to extract the best features from raw data (also described as voodoo magic by the same researchers - it was hard, it was domain locked and expensive).
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If the products match what you need or want, why would you refuse that?
The PA does not work for you, it works for FB. It has only FB's interest's in mind. You are not it's employer as you do not pay for it. It's not in FB's interest to make honest recommendations. If Bob's Burgers is paying $1000/mo in FB ads, but Karen's Burgers keeps being recommended as the "good burger joint", how long before Bob stops buying ads? And why would Karen start buying FB ads since she's getting exposure…
How is this any different than the approach laid out by Google Search? AFAIK, Google isn't suffering in the "search ads" department.
My issue with these services is they always tout these use cases like: > "Can you make me dinner reservations?" or > "Can you help me plan my next vacation?" I'd really love to better understand who is actually asking those types of questions in such a vague fashion, and what their use case is. When I'm picking something as simple as a restaurant, I typically want options, I want to read reviews, I want to consider d…
What is "best" and to whom? Ideally the software would figure this out but I'd always be wondering if it was just going to TripAdvisor and grabbing the first result.
Another problem is that we don't always know what kind of food we want. There's an urban legend that someone actually called a restaurant "I don't care" so that boyfriends would have a place to go when asking their girlfriend for dinner.
My issue with these services is they always tout these use cases like: > "Can you make me dinner reservations?" or > "Can you help me plan my next vacation?" I'd really love to better understand who is actually asking those types of questions in such a vague fashion, and what their use case is. When I'm picking something as simple as a restaurant, I typically want options, I want to read reviews, I want to consider d…
> When I'm picking something as simple as a restaurant, I typically want options, I want to read reviews, I want to consider distance, parking, attire, etc. For me, a lot of what you're doing here is the work that should be done by a machine. Considering "distance, parking, attire, etc." is basically what we have simplex method for. But I agree the questions seem very vague in the context. To run such errands success…
I guess it just seems incredibly inefficient compared to checking a couple boxes and reviewing a list along with a map or other visual aid.
My issue with these services is they always tout these use cases like: > "Can you make me dinner reservations?" or > "Can you help me plan my next vacation?" I'd really love to better understand who is actually asking those types of questions in such a vague fashion, and what their use case is. When I'm picking something as simple as a restaurant, I typically want options, I want to read reviews, I want to consider d…
Agree. Those are too broad. I'm thinking " get me a dinner reservation next sunday with patio seating for 5 in the east village at an upscale tapas place ". As I mentioned elsewhere on this page, my thesis around conversational interfaces isn't that they start off broad and use more Q/A to refine your query. That's slow, and people are visual. Rather, their power lies in the user being able to express a complex query…
It really is all about the interface and the efficiency. I have to wonder though at what point is adding all those filters more involved than checking a couple boxes and glancing at a map or some photos. I'm sure a lot of that depends on context (I can't do those things if I'm driving, but I can use voice recognition).
The other thing I'm unclear about is how such a recommendation engine can best present information about tradeoffs. In theory, each of my filters has a weighting, and that weight might be dynamic based on several other factors. Maybe I really want chinese, but the best match is further away or I know there will be lots of traffic, so I might be willing to compromise on thai, but only if they have that one dish I like. And a lot of it is seeing the options in the moment and making a snap decision. Really curious about the approaches to solve that type of problem.
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Apple Pay and/or cash defeats this, though.
Wouldn't Apple keep a record of your transactions? They even send a push notif with the receipt so they are aware of the location and store.
I'm not sure what the server side component is - but I don't believe they would have itemized data. So Apple/Amex know that you go to Whole Foods, but don't know what you're buying. Obviously, credit card companies have always had that data anyways.