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Self-Reflection is essential for humans, can GPT-4 do it?
We reflect on the life of Gordon Moore, Intel’s co-founder
Lex Friedman hosts Sam Altman, CEO of OpenAI, on his podcast
Levi’s takes a look at generated AI models
CAN GPT-4 LEARN FROM ITS MISTAKES?
It depends on how you ask the question.
Our favorite Large Language Model (LLM) learns primarily through human interference, but a better way might be around the corner.
Reflexion is the name of an autonomous agent conceived by researchers at Northeastern University and MIT to account for “dynamic memory” and “self-reflection” in GPT-4.
The way you think is different from the current state of LLMs. As a human, you can reflect on your choices and memory to improve outcomes.
When the world gets abstract, and we can pick any number of roads, any human can make a choice and learn from it for the next time.
These researchers want to build the same qualities into LLMs to make them more efficient.
Now, they used coding for their experiment, but you deserve a simplified example that cuts through the jargon—let’s talk cooking.
Let’s say you’re a personal chef for a family of picky eaters. Their appetites are not evident at first. Meal size, seasoning, and variety are all variables you need to juggle to give them the best meal possible night after night.
Over time, you learn more about the family, and that dynamic improves. Imagine an AI that is capable of the same type of learning.
In their research, the team found that the Reflexion-based GPT-4 model outscored GPT-4 in a coding benchmark 88% to 66%.
Why should you even care? Data is everything.
We’re running low on quality data to train models on. That stagnates the process at the rate we’re going, so giving these LLMs new ways to learn and update without constantly retraining is a massive step up.
Reflexion is a stepping stone on that path.
Imagine a future where it’s not GPT-5, GPT-6, then GPT-7. Instead, it’s GPT-5, GPT-5.1, and GPT-5.2. Then we get to GPT-6.
It’ll save us space and improve efficiency. Think of this as going from a gas-guzzling 8-seater to a hybrid sedan.
We want to get somewhere fast but don’t want to use up all the resources first.
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GORDON MOORE DIES AT AGE 94
Last week, we wrote a piece that discussed Moore’s Law. This past Friday, the man behind that law, Gordon Moore—Intel’s Co-Founder, husband, father, grandfather, and visionary—passed away at his home in Hawaii.
He navigated Intel into a financial and technical success. Moore’s Law challenged people to innovate and prove him right.
It’s been a driving force in this world for decades. His law is what makes artificial intelligence research possible.
Two years ago, Sam Altman wrote a blog post, “Moore’s Law for Everything,” an article tying his work at OpenAI with the continuous impact of Moore’s ideas in our society.
It’s incredible how belief, held with conviction, can spur innovation at a scale we couldn’t even dream of back then.
Moore’s Law is about the shrinking size of computer chips and the compounding growth of computing tech. In the 50+ years since his article was first published, we’re still debating if that initial trend is finally plateauing.
His name is now synonymous with advancement and the drive to become better.
Moore's Law drives every step we’ve taken toward artificial general intelligence and every step left to go.
So thank you, Gordon Moore.
You were right, and that changed everything
Quick Nuggets
📦 Text boxes are cool again. Thanks, ChatGPT
📌 Tips: write better prompts in ChatGPT with advice from WIRED
🪑 Ikea created trippy, generative AI furniture designs that are probably hard to assemble, tbh
🐴 What are you doing here??? Or a look into why all these chatbots sprung up suddenly
🙅♀️ Nope: there’s no such thing as artificial intelligence, according to WaPo
📍 Margaret Mitchell is an AI ethicist you need to learn more about today
💪 The work OpenAI puts into making their models safe is on full display
🦾 AI effects are judgment and decision-making process
🌊 Brain waves help this AI sketch images of your mind
🤫 Secret history: the story behind Elon Musk’s exit from OpenAI
⚔️ A quest can help test GPT-4’s general intelligence benchmarks
🐑 Dolly is DataBrick's shot at OpenAI and marks another LLM in the field
🎙️ Sam Altman went on Lex Friedman’s podcast to discuss OpenAI
👖 Levi’s wants to try on AI-generated models and see if it fits
🍍 Replika is opening back up the adult-only chat feature for some users
👎 Microsoft wants other rival AI researchers to stop using Bing search data
📝 Student essays are falling by the wayside as schools try to adjust
🚪 Backdoors in Diffusion Models are a real possibility and could lead to misuse
🏠 Living Pods are hard to imagine. AI helped this designer make their case
🔥 Fresh Products
WobbleBall - a game made with the help of AI (link)
Chaplin - helps you devise a trading strategy (link)
Riter - UX copy is way easier to make now (link)
Setlist Predictor - what is Taylor Swift playing for her Era’s Tour? (link)
AMBLR - a new AI holiday planner (link)
Prompt generator - for the new Midjourney v5 (link)
Guided - meditations presented to you through AI (link)
Swoon - AI that’s there to be your wingman, unlike Ted Mosby (link)
Galeby - helps customer-facing teams not have to answer repeat questions (link)
Ariana - an AI assistant that runs on WhatsApp (link)
Good Content, Serious Drip
This picture makes me hungry and anxious at the same time. That’s not a winning combo. Also, I have a sudden urge to go to Italy, strange.
That’s it for today. I hope you enjoyed the latest edition of inclined.ai - Davis.
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