Expectations, Game Theory, AI, Capitalism & Moloch

I concur with Liv's introduction, the pace of acceleration is staggering. If we, primarily as consumers and often unwitting contributors to AI development there is an insurmountable amount of information to sort, process and come to conclusions on - yet still contribute to the debate. The unfortunate feeling is that none of our contributions will be listened too, never mind being valued.

Schmachtenberger equates FIAT money to 'units of power' where compound interest makes money by itself. "There is now a maximum incentive to turn as much of the world as possible into capital in my holding, and because other people are and could use that against me there is now an arms race to do it faster than that guy.' An apt description of one of the primary drivers of AI.

Comments

The AI Dilemma and "Gollem-Class" AIs

From the Center for Humane Technology Tristan Harris and Aza Raskin discuss how existing A.I. capabilities already pose catastrophic risks to a functional society, how A.I. companies are caught in a race to deploy as quickly as possible without adequate safety measures, and what it would mean to upgrade our institutions to a post-A.I. world. This presentation is from a private gathering in San Francisco on March 9th with leading technologists and decision-makers with the ability to influence the future of large-language model A.I.s. This presentation was given before the launch of GPT-4. One of the more astute critics of the tech industry, Tristan Harris, who has recently given stark evidence to Congress. It is worth watching both of these videos, as the Congress address gives a context of PR industry and it's regular abuses. "If we understand the mechanisms and motives of the group mind, it is now possible to control and regiment the masses according to our will without their...

Enhancing LLM Performance: Buffer of Thought and Mixture of Agents

As Large Language Models (LLMs) continue to advance, researchers are exploring innovative techniques to further enhance their accuracy and usefulness. Two promising approaches in this domain are Buffer of Thought and Mixture of Agents. Buffer of Thoughts The Buffer of Thoughts technique aims to improve the reasoning capabilities of LLMs by introducing an intermediate step in the generation process. Instead of directly producing the final output, the model first generates a "buffer" or a series of intermediate thoughts, which serve as a scratchpad for the model to reason and plan its response. This buffer allows the model to break down complex tasks into smaller steps, perform multi-step reasoning, and maintain a coherent line of thought throughout the generation process. By externalizing its thought process, the model can better organise its knowledge and arrive at more logical and consistent outputs. The BoT approach has shown promising results in tasks that require multi...

Claude 3.5 Sonnet, literary analysis capabilities, an experiment

There are those in the AI observer community that have been suggesting of late that 'AI has plateaued' which reveals, to me, a lack of understanding of how models develop. It's not iterative design but step changes we are witnessing. The differences between Claude 3 Sonnet and 3.5 Sonnet are stark. One test I often carry out to asses the current capabilities of LLMs is to request a simple prompt to analyse an unpublished poem, commentating on the style and literary devices employed. The outputs have improved significantly over the 18 months I have employed this approach. This is my recent attempt. The poem is self written, self published (so not widely available) to ensure that it's unlikely to have found its way into the training data. For added context, implied in the text, this was written during a residency at a Museum and Art Gallery and came from a conversation with a member of staff about his late father. Calm is museum archival software. PROMPT. "analyse...

Charting the emergence of AGI?

Search This Blog