Ethan Mollick, Professor at @Wharton, is a brilliant educator on how AI is impacting the classroom.
He breaks down how AI is creating the “Homework Apocalypse” and how to handle each assignment type 👇
ESSAYS:
1. Back to in-class essays: doesn’t take advantage of AI.
2. Keep outside essays, forbid AI: impossible to control.
3. Keep outside essays, require AI: burden is on instructor to QA.
4. Active learning in class: best long-term option, requires structural change.
READINGS:
1. Only test on readings where AI sucked: more instructor leg work, not sustainable.
2. Do not disclose discussion topics until students are in class: might not be as accessible.
3. Use AI as a reading partner: requires experimentation.
PROBLEM SETS:
Might perform middle of the road today, but assume AI will continue to improve and will become more multimodal and can even answer geometry questions like the below.
Keep testing your questions in ChatGPT GPT-4 and Bing creative mode.
Changes are coming fast. This is the summer to jump in.
I’ve worked with several schools on their AI policies. Here are my 20 essential tips for educators on AI in the classroom: https://t.co/G0Ac0G8yq9linkedin.com/feed/update/ur…
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Note: There will be some overlap in these categories (ChatGPT could be in almost all of these) and inclusion on this list is not an endorsement.
It started with ChatGPT, which attracted more users than the entire population of Vietnam in under two months.
Since that launch, we’ve seen AI explode (40K+ AI companies), trend (like OpenAI CEO’s boomerang and Google Gemini chaos), and make its mark (see: Nvidia’s stock 📈)
I went through all of my notes, emails, posts, texts (and occasionally vivid nightmares) to recap all of the 2023 AI wins across 10 major categories.
2023 was momentous for AI - it was the hardest year yet to pick (runners up and winners were neck-and-neck in some categories!)
Here are NINE AI podcast tools you should bookmark and test for your next episode.
Let’s dive in 👇
Podcastmarketing.ai: This tool transcribes your podcast and then uses artificial intelligence to distill it into show notes, episode summaries, and more.
Castmagic: This AI-powered technology helps podcasters save time by transforming audio into text prepared for publication, including transcriptions, show notes, summaries, highlights, quotations, social media postings, and more.
“But large language models aren’t anything special, they’re just predicting the next token/word 😝”
That is drastically underrepresenting the power, value, and threat of LLMs.
Just because LLMs are predicting the features and probability of the next token/word/phrase does not mean it has no value or is never accurate.
GPT-4 scored 90th percentile on the bar exam…it may or may not “know” law, but that doesn’t mean it can’t get a high score, or, even outside of the exam, partially replace a lawyer’s list of duties.
There is going to be an absurd amount of B2B ChatGPT-esque apps in the next 12 months.
Here are just some of my top tips, having worked on both sides of the AI table for 10+ years 👇
Enterprise: Business Strategy 🏢
Prep your procurement teams (6+ months won’t cut it). Start small, maintain trust and transparency with end users. Consider internal-only use cases first (like Morgan Stanley and GPT-4).
Enterprise: Finance 💰
Assume it’s a paid PoC (to anchor, VC-backed startups will aim for min $100k/yr and prefer $250k/yr for production). Keep your CVC team in the loop for potential strategic investments. Budget for ML optimization. Buffer 20-40% in for trial and error.