Yelp is crazy unethical. Even before I heard about this nonsense, I worked at a small business in San Francisco whose customer traffic was directly influenced by their cesspool of a site.
Anyway, my supervisor and I worked hard to make sure every customer was happy. And we were succeeding! We had a perfect 5 star rating on Yelp! It was amazing! Then one day we got a 1 Star Rating on our Yelp Page. Someone from Pennsylvania left a nasty review on our site. It was scathing.
Now, that’s not something that’s too far out of the realm of possibility for my job. While I sold mattresses in a brick and mortar, we also sold mattresses via Amazon and our online store and people from all over the country purchased mattresses from us. But I digress. The reason this is important is, well, where it gets dicey for Yelp. Because sure enough, Yelp sent us an email telling us that if we paid some fee they would push all the bad reviews off the site. They were extorting money out of us!
And here’s where it gets really interesting. My supervisor contacted the customer to see if there was anything we could do to make them happy with their purchase, so they can change their review. But the customer in question had literally never heard of our company and obviously never purchased anything from us.
Yelp literally committed fraud, and it was only when we threatened to sue that they took the fraudulent review off of our page.
Yelp is awful.
I worked for a solar company (smaller, not one of the big national ones) and my friend worked in marketing, so part of that was looking over the Yelp reviews and reaching out to people. Someone left this scathing one star review for us, but the person wasn’t a customer. The working theory around the office was it was a fake by a competitor or just a really confused person. But because it was bad the head-honchos got on my friend’s ass to get in touch with Yelp to get it removed or whatever. I think they ended up paying to have it moved or did some incentive with customers to get them to flood the page with 5 stars to balance it out. The way that Yelp scams is gross.
actually i can’t find the link but a bunch of social scientists were like … hmmm about this. And they created a fake frozen yogurt shop with a random address typed into google (i think it was a NJ city) and sure enough 1 star and 2 star reviews came pouring in about how their frozen yogurt was melted, or tasted bad or there were rats but the place never existed :)
What do machines dream of? New images released by Google give us one potential answer: hypnotic landscapes of buildings, fountains and bridges merging into one.
They were created by feeding a picture into the network, asking it to recognise a feature of it, and modify the picture to emphasise the feature it recognises.
That modified picture is then fed back into the network, which is again tasked to recognise features and emphasise them, and so on. Eventually, the feedback loop modifies the picture beyond all recognition.
At a low level, the neural network might be tasked merely to detect the edges on an image. In that case, the picture becomes painterly, an effect that will be instantly familiar to anyone who has experience playing about with photoshop filters:
But if the neural network is tasked with finding a more complex feature – such as animals – in an image, it ends up generating a much more disturbing hallucination:
Ultimately, the software can even run on an image which is nothing more than random noise, generating features that are entirely of its own imagination.
Here’s what happens if you task a network focused on finding building features with finding and enhancing them in a featureless image:
The pictures are stunning, but they’re more than just for show. Neural networks are a common feature of machine learning: rather than explicitly programme a computer so that it knows how to recognise an image, the company feeds it images and lets it piece together the key features itself.
But that can result in software that is rather opaque. It’s difficult to know what features the software is examining, and which it has overlooked. For instance, asking the network to discover dumbbells in a picture of random noise reveals it thinks that a dumbbell has to have a muscular arm gripping it.
The solution might be to feed it more images of dumbbells sitting on the ground, until it understands that the arm isn’t an intrinsic part of the dumbbell.
“One of the challenges of neural networks is understanding what exactly goes on at each layer. We know that after training, each layer progressively extracts higher and higher-level features of the image, until the final layer essentially makes a decision on what the image shows. For example, the first layer may look for edges or corners. Intermediate layers interpret the basic features to look for overall shapes or components, such as a door or a leaf. The final few layers assemble those into complete interpretations – these neurons activate in response to very complex things such as entire buildings or trees,” explain the Google engineers on the company’s research blog.
“One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation,” they add. “Say you want to know what sort of image would result in ‘banana’. Start with an image full of random noise, then gradually tweak the image towards what the neural net considers a banana.”
The image recognition software has already made it into consumer products. Google’s new photo service, Google Photos, features the option to search images with text: entering “dog”, for instance, will pull out every image Google can find which has a dog in it (and occasionally images with other quadrupedal mammals, as well).
So there you have it: Androids don’t just dream of electric sheep; they also dream of mesmerising, multicoloured landscapes.
All the events in the Harry Potter series from book 3 and on only happened because Fudge was carrying a newspaper in Azkaban on the day it had Scabbers’ picture.
“I’m frankly surprised by the show. There’s stupid things – there’s – sorry people who write the show and everybody who works on it and everything, but there’s stupid things on the show that they shouldn’t do. Like, why do they have to say “bitch” and kill all the women? You know? Because there are certain small ways in which the show is sort of gratuitously misogynistic when it doesn’t need to be. When I read the scripts, I cringe sometimes. Yeah, there’s a million other things you could say, you don’t need to do this. Or, um, you have killed every other female character who had more than a two-episode arc. Do you have to take this one? Charlie’s still around! Although she’s not a threat to the boys as a romantic interest because she’s gay.”
