Today's corporations and governments are in the business of defining the balance of these aspects of our participation in society and the economy. Beliefs about the immutability of different attributes about what makes a person (or an employee) and how economies are built (cutting the pie, vs. growing the pie) determine the policy decisions you make, top down. As the core example this week, let's take Deutsche Bank. Facing pricing pressure and headwinds in several of its businesses, Deutsche is responding with a plan to fire 18,000 employees by 2022 and an announced investment of €13 Billion in technology and innovation by 2022. They even spun up a hipster-colored neobank as a proof point. Wall Street ain't buying it.
How do the Americans and the Chinese have such different ethical takes on privacy, self-sovereignty, media, and the role of government? We can trace the root cause to the DNA of the macro-organism in which individuals reside, itself built over centuries and millenia from the collective scar tissue of local human experience. But there is more to observe. The technology now being deployed in each jurisdiction -- like social credit, surveillance artificial intelligence, monitored payment rails, and central bank cryptocurrency -- will drive a software architecture into the core of our societies that reflects the current moment. And it will be nearly impossible to change! This is why *how* we democratize access to financial services matters. We must be careful about the form, because we will be stuck with it like Americans are stuck with the core banking systems from the 1970s. But the worry is not inefficiency, it is programmed social strata.
I presented earlier this week at the Ally Invest virtual conference, and the prompt asked for a description of what happens to finance from Fintech to Crypto / Blockchain to Augmented Reality / Virtual Worlds and finally to Artificial Intelligence.
Instead, we are going to tap again into a new development in Art and Neural Networks as a metaphor of where AI progress sits today, and what is feasible in the years to come. For our 2019 “initiation” on this topic with foundational concepts, see here. Today, let’s talk about OpenAI’s CLIP model, connecting natural language inputs with image search navigation, and the generative neural art models like VQ-GAN.
Compared to GPT-3, which is really good at generating language, CLIP is really good at associating language with images through adjacent categories, rather than by training on an entire image data set.
