There are also some really obvious limitations. Like we told it what the reward function is, and we gave it a very nicely shaped reward function saying'you've gotten a little bit closer,' and that's something that you don't get in the real world, the real world doesn't tell you how well you're doing at a certain task. So that was one obvious limitation. And another thing was that a lot of the tasks we would have trial and error where the robot would try the task and then we would put the robot back into the previous scene and then it would try again and oftentimes I would be kind of resetting the scene after every trial. And that's also something that's not really scalable if you want robots to leverage large amounts of data. And then the last thing was that the robot learned a cool skill, but it learned something very specific to the objects that it was seeing in the scene that it was in. And ultimately, if we wanna put robots into the world, we can't have them just work for one scene and one object.

One billion photos were used to train Meta's latest photo-recognition algorithm, a powerful demonstration of the current appetite for data. For those companies without access to platforms like Instagram, there is another answer: synthetic data. Synthetic data is artificially created by a computer, rather than collected from the real world. These computer-generated images can be automatically annotated by the machine that creates them. Annotation is an important part of AI training and is a process where important points in a photo, such as people or objects, are labelled to help the machine learning models understand what the image depicts.

Machine Learning and AI are all the buzz. In the last year, IDC reports that 37.5 billion dollars will be spent on machine learning and AI investments, increasing to close to $100 billion by 2023. Yet organizations still struggle to get value out of their machine learning and AI investments. It's widely known that 80% of any data science project is spent wrangling the data. To compound this fact, machine learning and AI models require high quality data in order to be effective.