The video discusses the role of algorithms in shaping our digital experiences on the internet. It explains that algorithms are used to decide what we see on platforms like TweetBook, what videos we watch, what prices we pay, and even whether a transaction is fraudulent. The video also delves into the concept of "algorithmic bots" - machines that use algorithms to solve complex problems.
The video then explores the evolution of these bots. Initially, humans built them by giving them simple instructions like "if this, then that". However, with the complexity of modern problems, this approach is no longer feasible. The video introduces the concept of a "builder bot" that creates bots, and a "teacher bot" that tests them. The builder bot starts by connecting the wires and modules in the bot brains almost at random, leading to "special" student bots that are sent to the teacher bot for testing.
The teacher bot tests the student bots by comparing their performance to a set of "bee" photos and "three" photos. The student bots that perform best are kept aside, while the others are recycled. The builder bot then makes copies of the best bots with changes in new combinations, and the process repeats. Over time, a student bot emerges that can tell a bee from a three in a photo it's never seen before. However, the human overseer, the teacher bot, and the student bot itself cannot understand how this bot does this.
The video concludes by emphasizing that while we are increasingly in a position where we use tools that no one, not even their creators, understand, we can only hope to guide them with the tests we make. The video ends with a call to action, asking viewers to like, comment, subscribe, and share the video to increase watch time for the bots overseeing their actions.
1. Algorithms are prevalent on the internet and are responsible for various actions such as recommending videos, identifying fraudulent transactions, and setting prices.
2. These algorithms are often built by humans, who provide instructions in the form of "if this, then that" rules.
3. However, many problems are too complex for humans to write simple instructions for, leading to the need for more advanced algorithms.
4. Algorithmic bots are valuable assets for companies, with their internal workings often considered trade secrets.
5. The current state of algorithmic bot technology is often described as "I hope you like linear algebra", with the specifics of how they work often unknown.
6. One approach to building bots is to create a bot that builds bots and a bot that teaches bots.
7. These bots' brains are simpler, something a smart human programmer can make.
8. The builder bot connects the wires and modules in the bot brains almost at random, leading to some very "special" student bots.
9. The teacher bot can't teach, but it can test. The student bots are tested on a large scale, with the best performing bots being kept and the rest being discarded.
10. This process is repeated as many times as necessary until a student bot emerges that can perform the task well.
11. The wiring in the student bot's head becomes incredibly complicated after keeping many useful random changes.
12. The student bot is very good at only the kinds of questions it's been taught to, and can be improved by giving it more questions.
13. Companies are obsessed with collecting data because more data equals longer tests equals better bots.
14. The bot is trying to be good at the test to survive, but what it is thinking or how it thinks is not really knowable.
15. The student bot gets to be the algorithm because it's point one percent better than the previous bot at the test the humans designed.
16. There are tests to increase user interaction, set prices just right to maximize revenue, or pick the posts from all your friends you'll like the most, or articles people will share the most, or whatever.
17. If it's testable, it's teachable. A student bot will graduate from the warehouse to be the algorithm of its domain.
18. We are increasingly in a position where we use tools, or are used by tools, that no one, not even their creators, understand.
19. We can only hope to guide them with the tests we make, and we need to get comfortable with that, as our algorithmic bot buddies are all around, and not going anywhere.