Within a decade, the form factor for computing will radically change from staring at screens with flat imagery, to participating in embedded virtual worlds with fully navigable, hyper-realistic environments. Those environments will be filled with software agents, some hybrid human and others entirely AI, that are entirely unrecognizable as anything but real to 90% of the population.
This week, we look at a breakthrough artificial intelligence release from OpenAI, called GPT-3. It is powered by a machine learning algorithm called a Transformer Model, and has been trained on 8 years of web-crawled text data across 175 billion parameters. GPT-3 likes to do arithmetic, solve SAT analogy questions, write Harry Potter fan fiction, and code CSS and SQL queries. We anchor the analysis of these development in the changing $8 trillion landscape of our public companies, and the tech cold war with China.
In this conversation, we chat with Kevin Levitt who currently leads global business development for the financial services industry at NVIDIA. He focuses on global trends in accelerated compute and AI for consumer finance – including fintech, retail banking, credit card and insurance. Prior to joining NVIDIA, Kevin served as Vice President of Business Development at Credit Karma, and Vice President of Sales for Roostify.
More specifically, we touch on the role data plays in the financial industry, how the needs of financial institutions have changed, the age of big data, the definitions between artificial intelligence and machine learning, how to train an AI algorithm, the reasoning behind the incredible amount of parameters machine learning solutions consume, the fundamental purpose of AI/ML in financial services, what NVIDIA’s platforms comprise of, and lastly the future of AI/ML.
You work. You get money. You take money and invest it. If you are lucky, it becomes larger. Otherwise, it becomes smaller. If you have a lot of money, you can either start a company or not. If you start a company, you invest in your own ability to influence outcomes and in your own transformation function. There are other, personal utility functions also being satisfied in executing the transformation function. Alternately, you focus on the work of getting capital into other companies. For this allocation and selection work, you are rewarded. To this, you can add the capital of others, until you are doing selection on their behalf.
The Securities and Exchange Comission punted again on allowing a passive Bitcoin ETF to enter the market. It failed to approve the VanEck SolidX Bitcoin Trust, instead opting to open a commentary period to address several questions around Bitcoin price formation and the health of the exchanges. A similar outcome faces the Bitwise Bitcoin ETF. You can tell I am not a fan of this waffling, and there are two core reasons: (1) the years-long delay and uncertainty is responsible for financial damage to both traditional and crypto investors, and (2) the premise of the objections misunderstand the environment of the Internet and the way our world is shaping up in the 21st century.
