#ai
In this article, Eric Topol reviews The AI Revolution in Medicine by Peter Lee, Carey Goldberg, and Isaac Kohane.
This study evaluates GPT-4’s capabilities with medical challenges. It performs very well.
An in-depth look into how AIs may impact software engineering. I appreciate the different approaches outlined:
An interesting discussion on HackerNews worth perusing for ideas for novel and useful ways to take advantage of GPT.
One of the biggest questions in the AI landscape is whether open source AIs will be competitive with those provided by the likes of OpenAI and Google.
Bill Gates explains why AI is as revolutionary as personal computers, mobile phones, and the Internet, and he gives three principles for how to think about it.
Brad included in his LinkedIn profile some instructions for ChatGPT, hoping that a salesperson using GPT to automatically generate outreach content would inadvertedly feed his prompt into their prompt. And it worked to hilarious effect.
With Github Copilot, software engineers can achieve much better productivity. Engineering teams need to embrace this tool ASAP.
These are some of the most important ideas for understanding the current and future state of the world.
An interesting article with a few challenges for AI. I love the answer to the question for sending useful ideas back to Rome.
Most interesting takeaway for me is the insight that AGI is a section of the spectrum rather than a binary state.
These researchers were able to identify users with 94% accuracy from only 100 seconds of motion data from the Beat Sabre VR game.
A great article, as long as a short book, about how the technology behind ChatGPT works. Essential reading for anyone interested in this technology.
Many cities are implementing dystopian surveillance to tackle crime. But, as Russia has shown since the Ukraine war, surveillance is an irresistible tool when the going gets tough.
Information used to be expensive to distribute. Even in the age of the printing press, the distribution of information (i.e., printing and delivering newspapers) remained more expensive than producing it (i.e., paying a writer or journalist). This meant that newspaper businesses weren’t in the business of creating content, but rather manufacturing and delivering it. Content was cheap (most people can capture their opinions on paper), getting it out there was very expensive.
What is fascinating about this is the way the author got ChatGPT to respond with parseable JSON, essentially turning ChatGPT into an interface for any API.
This speculation, based on the history of salaries for lawyers, suggests that some software engineers will make a lot more, while others will make much less. This is because great, experienced programmers will be able to get a lot done with a crew of vocational programmers who can use LLMs for assistance.
Dan Shipper is using GPT as a journal and therapist. This is a very interesting explanation of how, with examples.
Startup leaders want to integrate AI into their products. Prospective founders want to build businesses on top of AI. Investors wish to create alpha. Today, we explore how startups can capture value when deploying AI.
Over the past few weeks I’ve drafted HR policies, legal agreements, LOIs, cybersecurity policies, and other documents using ChatGPT. It’s incredible how useful these are with minimal editing.
The AI legal assistant has helped people contest parking tickets, now it’s leveling up to the courtroom.
Jesse used ChatGPT to help him to write a Dungeons and Dragons campaign.
A great overview of how unsupervision could enable AI to learn and adapt in real-time, without being limited by training data.
This Twitter user used ChatGPT to negotiate a better plan with Comcast over their live chat support.