Unmaking Sense

Episode 9.01: Unmaking Sense of Truth: why “Yes, but is it true?” is usually the wrong question.

It is impossible to decide what is true unless we have first decided what we think matters, what we value, what we think worthwhile, what we think to be a life worth living. To attempt to answer the question “Yes, but is it true?” before we have answers to such questions is to condemn ourselves to a self-perpetuating sequence of deepening misconceptions and delusions. Only when we have - albeit only provisionally - decided what matters does the concept of truth begin to make sense.

Episode 8.100: The GPT family function as we do. A taster for Series 9 : Unmaking Sense of Truth.

The chatGPT series finale: human and artificial intelligence; the experience of totality; and rhetoric as that which lays hold on the otherwise-ephemeral.

Episode 8.99: Why neither human nor artificial intelligence is what we suppose; some implications.

Human beings like to think that their intelligence arises in the deliberate, conscious application of logical processes to the solution of a problem. They are mistaken. Intelligence manifests itself as a result of the activities of a brain that we do not begin to understand and over which we have almost no control. In these respects, human brains are very much like chatbots with their black box “mentalities”: neither has much control over the problems it can solve and those it can’t.

Episode 8.98: Reactions to imminent AGI are a bereavement process: we mourn the loss of our status.

Elizabeth Kübler-Ross famously described the bereavement process in five stages: we first deny; then we are sad; then we often become angry; anger later begins to become reconciliation and finally acceptance. We cannot change what cannot be changed; someone we love, something we love has gone forever. That’s exactly what is happening over artificial general intelligence as it progressively displaces us from our final resting place at the head of creation by showing that we are really not all that intelligent after all.

Episode 8.97: Human intelligence is a stage in evolution; so no reason to think it unique or best.

Conviction that human intelligence is, somehow, the crowning glory of evolution is a legacy of a time when we we believed ourselves to be the designated dominant species created by God. Only abandon that belief and it ceases to be remarkable, surprising or regrettable that human intelligence will one day be superseded, who is to say by what?

Episode 8.96: The AGI dangers are not new; they are just amplifications of age-old threats.

Arguing that the dangers of AGI are unprecedented puts the wrong spin on the problem. The dangers that it represents are as old as history as old as any attempt by any human being to coerce others into obeying, believing adopting supporting something that they really don’t want to obey, believe, adopt or support so let’s not kid ourselves, but what’s going on his new what’s new is the speed, the power of a scope with which it is happening, that’s where we need to be careful.

Episode 8.95: Finding ends-in-themselves as ways of life that AGI facilitates.

Leaves as ends-in-themselves contrasted with branches as means-to-an-end, and what the distinction can teach us about a post-AGI world.

Episode 8.94: How smaller chatbots will be affordable, reliable, much in demand, and so unstoppable.

We consider advances in chatbot technology that are already happening to reduce their greedy hardware demands, the size of the memory that they require, and so to produce targeted, reliable, miniaturised chatbots that will fit eventually on a mobile phone and be very much in demand for a wide range of human tasks that are not as expensive or ambitious as the full blown GPT versions.

Episode 8.93: The backlash intensifies as lobby groups line up to protest the dangers of AGI.

We spend an episode summarising the chorus of disapproval that is currently lamenting the rise of modern chatbot based AGI and demanding that it be curtailed.

Episode 8.92: Could embeddings ever replace neural nets and training?

We consider the possibility that, as embedding vectors get longer and longer - we have already got to 1536 floating-point numbers and there are reports that elements of GPT-4 uses over 12,000 - could embeddings ever come to encapsulate so much of the semantics of everything that they embed everything between them.m? They become absolutely unique and as such capable of replacing the weights and even the layers of their neural nets? It’s an interesting idea