This post argues that beating human benchmarks on tests do not mean that the AI is actually better at the task. This seems to go well with recent controversies around BERT but is more fundamental than that.

Core argument (details and evidence in post):

• Reports that AI beat humans on certain benchmarks or very specialised tasks don’t mean that AI is actually better at those tasks than any individual human.
• They certainly don’t mean that AI is approaching the task with any of the same understanding of the world people do.
• People actually perform 100% on the tasks when administered individually under ideal conditions (no distraction, typical cognitive development, enough time, etc.) They will start making errors only if we give them too many tasks in too short a time.
• This means that just adding more of these results will NOT cumulatively approach general human cognition.
• But it may mean that AI can replace people on certain tasks that were previously mistakenly thought to require general human intelligence.
• All tests of artificial intelligence suffer from Goodhart’s law.
• A test more closely resembling an internship or an apprenticeship than a gameshow may be a more effective version of the Imitation Game.
• Worries about ‘superintelligence’ are very likely to be irrelevant because they are based on an unproven notion of arbitrary scalability of intelligence and ignore limits on computability.

I thought you guys might find this interesting. I slightly modified an image classifier to take audio, then recorded myself sticking keys and picks into locks for 45 minutes respectively. This was in order to create my dataset. I broke those long clips into 5 second segments, which left me with about 1000 clips for training. After 5 minutes of training and 15 epochs, I achieved a little more than 90% accuracy on my training and validation set, which is good enough for a fun project like this.

What this means is that I can put my microphone next to a lock, then detect in live time whether that lock is being picked or if a key is being inserted. I can then record the time the event happened and save the audio clip that triggered the event.

Here is a video demonstration of the project.

Here is an article I wrote where you can see the code.

For anyone that’s into lock picking, I created my training data on Sargent, Corbin Russwin, and Schlage mortise cylinders. I used both single pin picking and raking. I might play around with bumping in the future, if I come back to the project.

So I trained a VAE on 1-D Sparse data, and I am attempting to use the encoded latent variables for a similarity metric. However, the latent space has an extra dimension that I have no idea where it came from, and I am not sure which variable to use. I am attempting to use z_mean as my latent variables. But the shape of the output from the z_mean layer is somehow (8*512), even though my latent size was 512. Can someone help me understand what is going on here? Thank you!

I’m not sure if this is the right place to ask this. What are your views on this? Thank you

I know that there are a bunch of simple self help/mental health apps in which you input your “data”, such as, how you are feeling that day, what you have eaten, what you have done and then highlights things which supposedly maybe makes you feel worse/anxious/good/happy or what not. Is there any app or something which uses regression analysis/machine learning which makes better correlations between input data and visualize them? I at least imagine that using machine learning, you could make a lot better personalized assumptions about which things are important to your well being and which things make you feel x way (of course, it also depends on the quality of input data, but i think it would still be a lot better than these simple apps)

P.S. I know that i could have written this better, but i am quite tired and will go to sleep soon and want to ask this here before i have forgotten about it lol

How to find if a table with more than a million records contains a rule (if A=… then B=C) where A, B , C are column names and if the rule is present then which columns satisfy this condition.

To find this out, we looked at 300 DS vacancies from StackOverflow, AngelList, Dice, and similar websites. Here’s what the data — and experts like Carla Gentry, Andriy Burkov, Dr. Ganapathi Pulipaka, and Lon Riesberg — say.

Surprising or expected?

https://towardsdatascience.com/enhancing-satellite-imagery-with-deep-multi-temporal-super-resolution-24f08586ada0

Paper: https://arxiv.org/abs/1907.06490

Code: https://github.com/diegovalsesia/deepsum