Microsoft and OpenAI get ahead in the LLM competition
Microsoft and OpenAI released a bunch of good new LLM products. Google has released Bard, but it is still lagging behind.
AI21 Labs’ mission to make large language models get their facts...
AI21 Labs chief scientist Yoav Levine explains how retrieval augmented language modeling can solve one of the biggest problems of LLMs.
Democratizing the hardware side of large language models
Cerebras CEO Andrew Feldman discusses the hardware challenges of LLMs and his vision to reduce the costs and complexity of training and running large neural networks.
A gentle introduction to model-free and model-based reinforcement learning
Neuroscientist Daeyeol Lee discusses different modes of reinforcement learning in humans and animals, AI and natural intelligence, and future directions of research.
ChatGPT: It’s only words
In the rising excitement about large language models, particularly ChatGPT, people are taking sides. What is largely missing is any critical analysis of what these failures and successes tell us about the relationship between these models and intelligence.
Can you trust ChatGPT and other LLMs in math?
Large language models like ChatGPT are inconsistent at math. Here's why it matters.
ChatGPT: It can tell but does not know
ChatGPT is Polanyi's paradox in reverse. It can tell, but it doesn't know.
OpenAI’s AGI strategy
A new blog post by Sam Altman explains OpenAI's updated roadmap for artificial general intelligence. He answers some questions and leaves many others unanswered.
Best practices for deploying AI within large organizations
Developing AI products within large organizations is a completely different process than developing stand-alone solutions within start-ups or writing code for your Ph.D. work for that matter.
To understand language models, we must separate “language” from “thought”
To understand the power and limits of large language models (LLM), we must separate “formal” from “functional” linguistic competence.
Best practices for deploying AI within large organizations
Developing AI products within large organizations is a completely different process than developing stand-alone solutions within start-ups or writing code for your Ph.D. work for that matter.
How to balance employee initiative and continuous A/B testing
It can be beneficial to think of nurturing your team’s morale and ambition as an ongoing A/B test for management. Here are a few strategies that can smooth the process.
7 ways to promote creative liberty in the workplace
By Luke Fitzpatrick
Image source: 123RF
Businesses always look for new ways...
