 This episode has been supported by Lambda. Dear Fellow Scholars, this is two-minute papers with Karojona Ifaher. In a world where learning based algorithms are rapidly becoming more capable, I increasingly find myself asking the question, so how smart are these algorithms really? I am clearly not alone with this. To be able to answer this question, a set of tests were proposed and many of these tests shared one important design decision. They are very difficult to solve for someone without generalized knowledge. In an earlier episode, we talked about DeepMind's paper where they created a bunch of randomized, mind-bending, or in the case of an AI, maybe silicon-bending questions that looked quite a bit like a nasty, nasty IQ test. And even in the presence of additional distractions, their AI did extremely well. I noted that on this test, finding the correct solution around 60% of the time would be quite respectable for a human where their algorithms succeeded over 62% of the time and upon removing the annoying distractions, this success rate skyrocketed to 78%. Wow! More specialized tests have also been developed. For instance, scientists at DeepMind also released a modular math test with over 2 million questions in which their AI did extremely well at tasks like interpolation, rounding decimals, integers, whereas they were not too accurate at detecting primality and factorization. Furthermore, a little more than a year ago, the glue benchmark appeared that was designed to test the natural language understanding capabilities of these AI's. When benchmarking the state of the art learning algorithms, they found that they were approximately 80% as good as the fellow non-expert human beings. That is remarkable. Given the difficulty of the test, they were likely not expecting human level performance, which you see marked with the black horizontal line which was surpassed within less than a year. So what do we do in this case? Well, as always, of course, design an even harder test. In comes super glue. The paper we are looking at today, which meant to provide an even harder challenge for these learning algorithms. Have a look at these example questions here. For instance, this time around, reusing general background knowledge gets more emphasis in the questions. As a result, the AI has to be able to learn and reason with more finesse to successfully address these questions. Here you see a bunch of examples, and you can see that these are anything but trivial little tests for a baby AI. Not all, but some of these are calibrated for humans at around college level education. So, let's have a look at how the current state of the art AI is fared in this one. Well, not as good as humans, which is good news because that was the main objective. However, they still did remarkably well. For instance, the bull-cue package contains a set of yes and no questions. And these, the AI's are reasonably close to human performance, but on multi-RC, the multi-sentence reading comprehension package, they still do okay, but humans outperform them by quite a bit. Note that you see two numbers for this test. The reason for this is that there are multiple test sets for this package. Note that in the second one, even humans seem to fail almost half the time, so I can only imagine the revelation will have a couple more papers down the line. I am very excited to see that, and if you are too, make sure to subscribe and hit the bell icon to never miss future episodes. If you are a researcher or a startup looking for cheap GPU compute to run these algorithms, check out Lambda GPU Cloud. I've talked about Lambda's GPU workstations in other videos, and I'm happy to tell you that they are offering GPU Cloud services as well. The Lambda GPU Cloud can train ImageNet to 93% accuracy for less than $19. Lambda's web-based IDE lets you easily access your instance right in your browser. And finally, hold onto your papers because the Lambda GPU Cloud costs less than half of AWS and Asia. Make sure to go to LambdaLabs.com, slash papers, and sign up for one of their amazing GPU instances today. Thanks for watching, and for your generous support, and I'll see you next time.