Over the last three or four centuries, different mathematicians proposed several competing versions of a calculus formalizing the reasoning about plausibility, but there was a lack of consensus about the axioms that should sit at the foundation of such a plausibility calculus.  This conundrum was finally broken in 1961 by Richard Cox, a physicist at the Johns Hopkins University.  Today, we will discuss his brilliant idea.

How AGI is going to measure the plausibility on uncertain statements in the real-world scenarios of incomplete information so that, among other things, we too can know what AGI is thinking?

A charge is often laid at the door of the large language models (LLMs) that they rely on probabilistic generation, assuming that this is somehow a bad thing, and that a more deterministic behaviour would somehow be a better idea for the future artificial general intelligence (AGI).  Before the advent of the LLMs, almost all practical computer systems followed deterministic logic encoded in their software, but as I will argue in this episode, the future AGI will be neither deterministic nor deductively logical.

This episode is broadly based on a chapter from E.T.Jaynes (2003) Probability theory: The logic of science. Cambridge, UK: Cambridge University Press. 

Will artificial intelligence discover God?

We take objects, classes, and relationships for granted, but they are just conventions we’ve agreed to use, not truths carved into reality. In this episode, we explore why these human-centric conventions matter, and why guiding AGI to adopt them may be the difference between an intelligible world model and one we can’t understand at all.

There is no question that the proliferation of the AI throughout the global economy will result in a massive reformat of the job market. A lot of jobs will be lost. A lot of completely new careers will emerge. What can you do about it to find yourself on the right side of the equation?

A Chinese government sponsored cyberattack leveraged the American AI technology and infrastructure against the American government agencies and corporations. Things are about to get worse.

Before humans act, they imagine outcomes. AI doesn’t. Yann LeCun thinks that must change, and his JEPA architecture could be the missing link between powerful neural networks and real-world intelligence driven by internal models of reality.

The advent of large language models (LLMs) fundamentally changed the behaviour of computer systems that we learned to trust over the last several decades of development of computation.

In this special Wtf? on World Models, I will give you a break-down of what this whole hype is about, where it is misplaced and why it is a good kind of hype to have for those who are interested in the artificial general intelligence (AGI).