A number of people are talking about implications of AI to schools. I spoke about some of my thoughts to a school board earlier, some highlights:
1. You will never be able to detect the use of AI in homework. Full stop. All "detectors" of AI imo don't really work, can be defeated in various ways, and are in principle doomed to fail. You have to assume that any work done outside classroom has used AI.
2. Therefore, the majority of grading has to shift to in-class work (instead of at-home assignments), in settings where teachers can physically monitor students. The students remain motivated to learn how to solve problems without AI because they know they will be evaluated without it in class later.
3. We want students to be able to use AI, it is here to stay and it is extremely powerful, but we also don't want students to be naked in the world without it. Using the calculator as an example of a historically disruptive technology, school teaches you how to do all the basic math & arithmetic so that you can in principle do it by hand, even if calculators are pervasive and greatly speed up work in practical settings. In addition, you understand what it's doing for you, so should it give you a wrong answer (e.g. you mistyped "prompt"), you should be able to notice it, gut check it, verify it in some other way, etc. The verification ability is especially important in the case of AI, which is presently a lot more fallible in a great variety of ways compared to calculators.
4. A lot of the evaluation settings remain at teacher's discretion and involve a creative design space of no tools, cheatsheets, open book, provided AI responses, direct internet/AI access, etc.
TLDR the goal is that the students are proficient in the use of AI, but can also exist without it, and imo the only way to get there is to flip classes around and move the majority of testing to in class settings.
Don't think of LLMs as entities but as simulators. For example, when exploring a topic, don't ask:
"What do you think about xyz"?
There is no "you". Next time try:
"What would be a good group of people to explore xyz? What would they say?"
The LLM can channel/simulate many perspectives but it hasn't "thought about" xyz for a while and over time and formed its own opinions in the way we're used to. If you force it via the use of "you", it will give you something by adopting a personality embedding vector implied by the statistics of its finetuning data and then simulate that. It's fine to do, but there is a lot less mystique to it than I find people naively attribute to "asking an AI".
This is one of the cleverest stories in startups. For years we'd been worrying about how to finance the airliner. Then Boom realized that if they could build jet engines, they could build gas turbines, and fund the airliner with the profits.
23 años de la última vez que un piloto argentino compitió en F1. El domingo @FranColapinto va a estar representando a 🇦🇷, y por supuesto que @Globant va a estar acompañándolo como desde el principio.
Muy emocionante ser parte de esto junto con @williamsf1#FranColapintoaF1
Ariadna Trueba will do an Introduction to AI Integration in Software Quality Testing.
Are you ready?
👉https://t.co/aZQjoT1Ec9
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Come on in! We are talking with Aaron Evans, Benjamin Bischof, Grigoriy Goldshteyn & Vlad Zeciu about Overcoming the Loopholes while Using Gen AI for Testing
👉https://t.co/aZQjoT1Ec9
#GlobantNXTConference#QuaNTANXT #💚