This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
If you want to get results, you need to think about instructional design and learningtheory. And, there is no shortage of learningtheories and research. As someone who has been designing and delivering training for nonprofits over the past twenty years, the most exciting part is apply theory to your practice.
If you’re registered for ICLR 2023, we hope you’ll visit the Google booth to learn more about the exciting work we’re doing across topics spanning representation and reinforcement learning, theory and optimization, social impact, safety and privacy, and applications from generative AI to speech and robotics.
The first of these is the preference structures given by multi-objective RL, where the agent is given multiple reward functions R 1 , R 2 , R 3 , , and has to find a policy that achieves a good trade-off of those rewards according to some specified criterion. Alternatively, see the main paper.
Alternatives to adversarial training : Adversarial training (and the rest of todays best alignment techniques) have failed to create LLM agents that reliably avoid misaligned goals. Alternative approaches to mitigating AI risks These research areas lie outside the scope of the clusters above. Wen et al. , Sheshadri et al.,
And a technical note: it needs to be in some first-order system or alternatively, you need to measure proof checking time as opposed to proof length. Daniel Filan (00:28:50): If people remember my singular learningtheory episodes , theyll get mad at you for saying that quadratics are all there is, but its a decent approximation. (00:28:56):
We organize all of the trending information in your field so you don't have to. Join 12,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content