Years ago, I made a special long-distance trek to a specific animal rehab center just to hold a baby spider monkey. They took animals from other zoos across the country being shut down for abuses, so they were real careful with their charges.
It was one of the best things I’ve experienced. She was adorable and clingy. And I got to pet (nose) and feed an adult male hippo the same day because they had him penned to clean his enclosure. Fucking terrifying, that; he was super gentle, but if he’d wanted out, that pen wasn’t stopping him.
You put a baby monkey sign out and I’m as good as baited. Every time. I’d probably fall for a lamb or kid (goat kind not human kind) or really any baby animal, as long as mama is chill with it.
The base assumption is that you can tell anything reliable at all about a person from their body language, speech patterns, or appearance. So many people think they have an intuition for such things but pretty much every study of such things comes to the same conclusion: You can’t.
The reason why it doesn’t work is because the world is full of a diverse set of cultures, genetics, and subtle medical conditions. You may be able to attain something like 60% accuracy for certain personality traits from an interview if the person being interviewed was born and raised in the same type of environment/culture (and is the same sex) as you. Anything else is pretty much a guarantee that you’re going to get it wrong.
That’s why you should only ask interviewees empirical questions that can identify whether or not they have the requisite knowledge to do the job. For example, if you’re hiring an electrical engineer ask them how they would lay out a circuit board. Or if hiring a sales person ask them questions about how they would try to sell your specific product. Or if you’re hiring a union-busting expert person ask them how they sleep at night.
I’ve just started doing practical interviews. I basically get really young people with little overall experience and I just want to know if they can do common technical tasks.
So one question is to literally have them explain how to tighten a bolt. One person failed.
To be fair, that’s a very open ended question. I mean, what kind of bolt are we talking about? A standard lag bolt? If so you don’t tighten it! That’d be a trick question! You tighten the nut. Same thing applies with car wheel bolts. Tricky tricky!
Is it a hex bolt that also has a cross head? How tight are we talking?
I’m just going to assume bolts of lightning and Usain Bolt are off the table.
Not really in a bolt tightenning domain, but I have done technical interviews for a lot of devs including junior ones, and them asking all those questions about the task is something I would consider a very good thing.
At least in my domain the first step of doing a good job is figuring out exactly what needs to be done and in what conditions, so somebody who claims to have some experience who when faced with a somewhat open ended question like this just jumps into the How without first trying to figure out the details of the What is actually a bad sign (or they might just be nervous, so this by itself is not an absolute pass or fail thing).
Reminds me of an early application of AI where scientists were training an AI to tell the difference between a wolf and a dog. It got really good at it in the training data, but it wasn't working correctly in actual application. So they got the AI to give them a heatmap of which pixels it was using more than any other to determine if a canine is a dog or a wolf and they discovered that the AI wasn't even looking at the animal, it was looking at the surrounding environment. If there was snow on the ground, it said "wolf", otherwise it said "dog".
Maybe not the hardest, but still challenging. Unknown biases in training data are a challenge in any experimental design. Opaque ML frequently makes them more challenging to discover.
The unknown biases issue has no real solution. In this same example if instead of something simple like snow in the background, it turned out that the photographs of wolves were taken using zoom lenses (since photogs don’t want to get near wild animals) while the dog photos were closeup and the ML was really just training to recognize subtle photographic artifacts caused by the zoom lenses, this would be extremely difficult to detect let alone prove.
The general approach is to use interpretable models where you can understand how the model works and what features it uses to discriminate, but that doesn’t work for all ML approaches (and even when it does our understanding is incomplete.)
So is the example with the dogs/wolves and the example in the OP.
As to how hard to resolve, the dog/wolves one might be quite difficult, but for the example in the OP, it wouldn’t be hard to feed in all images (during training) with randomly chosen backgrounds to remove the model’s ability to draw any conclusions based on background.
However this would probably unearth the next issue. The one where the human graders, who were probably used to create the original training dataset, have their own biases based on race, gender, appearance, etc. This doesn’t even necessarily mean that they were racist/sexist/etc, just that they struggle to detect certain emotions in certain groups of people. The model would then replicate those issues.
Early chess engine that used AI, were trained by games of GMs, and the engine would go out of its way to sacrifice the queen, because when GMs do it, it’s comes with a victory.
You don’t use it for the rule-set and allowable moves, but to score board positions.
For a chess computer calculating all possible moves until the end of the game is not possible in the given time, because the number of potential moves grows exponentially with each further move. So you need to look at a few, and try to reject bad ones early, so that you only calculate further along promising paths.
So you need to be able to say what is a better board position and what is a worse one. It’s complex to determine - in general - whether a position is better than another. Of course it is, otherwise everyone would just play the “good” positions, and chess would be boring like solved games e.g. Tic-Tac-Toe.
Now to have your chess computer estimate board positions you can construct tons of rules and heuristics with expert knowledge to hopefully assign sensible values to positions. People do this. But you can also hope that there is some machine learnable patterns in the data that you can discover by feeding historical games and the information on who won into an ML model. People do this too. I think both are fair approaches in this instance.
All possible moves one step from a given position sure.
But if you then take all possible resulting positions and calculate all moves from there, and then take all possible resulting positions after that second move and calculate all possible third moves from there, and so on, then the possibilities explode so much in number that you can’t calculate them anymore. That’s the exponential part I was refering to.
You can try and estimate them roughly, let’s say you’re somewhere in the middle of the game, there are 12 units of each side still alive. About half are pawns so we take 1.2 possible moves for them, for the others, well let’s say around 8, thats a bit much for horses and the king on average, but probably a bit low for other units. So 6 times 8 and 6 times 1.2, lets call it 55 possibilities. So the first move there are 55 possible positions, for the second you have to consider all of them and their new possibilitues so there are 55 times 55 or 3025, for the third thats 166375, then 9.15 million, 500 million, 27.6 billion, 1.5 trillion etc. That last one was only 7 moves in the future. Most games won’t be finished by then from a given position, so you either need a scoring function or you’re running out of time.
There are more possible chess moves (estimated at 10^120 for an average game) than there are atoms in the observable universe (estimated at 10^80). That is to say the number of possible chess moves has 40 more zeros on the end than the number of atoms in the observable universe.
Can you point to some souce showing how modern hardware can work these out easily?
That’s funny because if I was trying to tell the difference between a wolf and a dog I would look for ‘is it in the woods?’ and ‘how big is it relative to what’s around it?’.
The idea of AI automated job interviews sickens me. How little of a fuck do you have to give about applicants that you can't even be bothered to have even a single person interview them??
That’s more like an excuse to keep those stupid 5, 6, and even more interview round processes. Basically making you work an entire week for free in exchange of a chance of getting an offer. Make the first or second rounds with AI and only bother after that.
But god forbid the applicant didn’t spend hours researching every little detail about a company, writing a perfect letter with information that could have just been bullet points and being able to explain exactly why they absolutely love the company and why it’s been their dream to work there since they were a child. Or even worse: Use AI to write the application.
Cover letters fucking make me hateful. I love generating AI cover letters and sending them. Fuck your cover letters in a market where you need to send 100 applications to get 10 bites
Applicants are expected to dedicated hours of their time to writing their application and performing background research - both of which are becoming increasingly more tedious over time - so the least a company could bloody do is show some basic respect by paying an actual human being to come interview you!
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