On Augmenting Intelligence with AI

 Interesting piece.  How do you fit in  ' AI' components into an intelligent assistant framework?  Do we have the needed pieces?  What else need to be built and enhanced.

AlphaFold, GPT-3 and How to Augment Intelligence with AI

By Niko Grupen  in A16z  Andreeseen

This is the first post in a two-part series. Read Part 2 here. 

Around the same time that Alan Turing was shaping his theories of machine intelligence in Manchester, another future giant of the computing world, Douglas Engelbart, was developing an alternative computing paradigm over 5,000 miles away in the Bay Area. 

Engelbart believed that computers, with their ability to synthesize and manipulate vast quantities of information, should help humans solve problems, rather than remove them from the problem-solving loop. This ideology is now known as augmented intelligence. Engelbart’s contributions to the field (both as a PhD student at UC Berkeley and at SRI in the decades after) were perhaps best exemplified through “The Mother of All Demos” in 1968, where he unveiled for the first time many of the computing features we now take for granted — the mouse, GUIs, hyperlinks, word processing, version control, and even video conferencing — in a single demonstration.

Although it’s enticing to think about artificial intelligence passing human equivalency tests like Turing’s Imitation Game (or maybe something more sophisticated for today’s generalist AI models), we really should be thinking about how Engelbart’s ideas translate to our modern AI era. Put another way, how do we build the next Mother of All Demos?

In reality, the next mother of all demos will be much more than just a demonstration. We already have the ingredients — a whole new set of AI tools — so now we need to think about how these ingredients can help us reimagine and redesign our existing workflows and user experiences. In doing so, we can usher in a new class of AI-native experiences for search, scientific research, game design, and more.

The Mother of All Demos in the age of deep learning

If we’re building a version of the Mother of All Demos in the deep learning era, we could begin with these models and tools. Although research labs release new models seemingly every week, these are a great starting point to begin rethinking how we interact with technology:

GPT-3

The latest in OpenAI’s GPT series, GPT-3 is a 175-billion parameter language model that is trained on practically all of the text that exists on the Internet. Once trained, GPT-3 can generate coherent text for any topic (even in the style of particular writers or authors), summarize passages of text, and translate text into different languages. OpenAI also recently released a follow-up, InstructGPT, that incorporates human feedback to reduce harmful or biased outputs.

Copilot

Github’s Copilot takes “translating text into different languages” to the programming world. Built on top of OpenAI’s Codex — think GPT-3, but trained on words and code — Copilot is an “AI pair programmer” that will generate anything from individual lines to whole functions of code, based on a docstring describing what the code is supposed to do. Recently, DeepMind released a competing code-writing model, AlphaCode, that solves coding challenges from popular programming competitions.  .... '