AGI is not here … just yet. [*BLOGPOST IN PROGRESS – CHECK BACK FOR DAILY UPDATES!* (AJM, Tuesday, May 14, 2024; 0200 Hawai’i Time)] But – it’s getting close. Very, VERY close. What’s interesting – and important to all of us – is that there is no “singular” AGI, and it’s important to make this… Continue reading Emerging AGIs: Early 2024 Playing Field
Category: A Resource – Annotated Collection
AI, Climate, and Energy (Resource Collection)
One of the most important things in the emerging AI-world is how we handle the ENERGY NEEDS of AI systems. This actually invokes a much bigger question – how will we handle energy needs overall? How do we mitigate (and potentially reverse) the climate crisis? How do we build sustainability and resilience into our energy… Continue reading AI, Climate, and Energy (Resource Collection)
Evolution of NLP Algorithms through Latent Variables: Future of AI (Part 3 of 3)
AJM Note: ALTHOUGH NEARLY COMPLETE, references and discussion still being added to this blogpost. This note will be removed once the blogpost is completed – anticipated over the Memorial Day weekend, 2023. Note updated 12:30 AM, Hawai’i Time, Tuesday, May 29, 2023. This blogpost accompanies a YouTube vid on the same topic of the Evolution… Continue reading Evolution of NLP Algorithms through Latent Variables: Future of AI (Part 3 of 3)
Variational Free Energy: Getting-Started Guide and Resource Compendium
Many of you who have followed the evolution of this variational inference discussion (over the past ten blogposts), may be wondering where to start. This would be particularly true for readers who are not necessarily familiar with the variational-anything literature, and would like to begin with the easiest, most intuitive-explanatory articles possible, and then gently… Continue reading Variational Free Energy: Getting-Started Guide and Resource Compendium
Kullback-Leibler, Etc. – Part 3 of 3: The Annotated Resources List
I thought it would be (relatively) straightforward to wrap this up. Over the past several posts in this series, we’ve discussed the Kullback-Leibler (K-L) divergence and free energy. In particular, we’ve described free energy as the “universal solvent” for artificial intelligence and machine learning methods. This next (and last) post in this series was intended… Continue reading Kullback-Leibler, Etc. – Part 3 of 3: The Annotated Resources List