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.  .... ' 

Augmented Intelligence in Healthcare

Expert discusses Augmented Intelligence in Healthcare

 in Public Library of Science

The potential of using machine learning techniques in medicine is immense. As electronic health records have become widely available, there is hope that machine learning will improve diagnosis and care. However, integrating these new methodologies into medical practice is challenging. New methods need to meet healthcare standards, for example around doctor accountability and patient privacy, and must be smoothly integrated into clinical decision-making practices.


We had the pleasure of speaking to Arthur Papier who has been working on problems like these for decades. A dermatologist by training, he started working with electronic health records in the 1980s and launched a clinical decision support tool called VisualDX at the turn of the millennium. VisualDX aids physicians in exploring all diagnostic possibilities through visual clues. The tool combines a search through a database of which symptoms and findings convey which diagnoses with images of how the disease in question looks on skin, eyes, mouth and in radiography. .... " 

Kasparov on Intelligent Machines

Kasparov talks from a unique perspective about machines and their intelligent use.

Don't fear intelligent machines. Work with them  Garry Kasparov TED Talk.

We must face our fears if we want to get the most out of technology -- and we must conquer those fears if we want to get the best out of humanity, says Garry Kasparov. One of the greatest chess players in history, Kasparov lost a memorable match to IBM supercomputer Deep Blue in 1997. Now he shares his vision for a future where intelligent machines help us turn our grandest dreams into reality.... " 

Intel Chief Talks Augmented vs Artificial

Always interesting to hear opinions on rapidly emerging technologies from the big players.   Ultimately they must make the investments that need to be made.

Artificial or Augmented Intelligence: Talks with Intel’s Chief Data Scientist,  Bob Rogers
Interview by Ronald van Loon  In DSC. .... " 

Considering the Data Science Behind AI

Nicely done piece from DSC by William Vorhies,  an overview.  Good exec and introductory piece.  Like in general the way this is presented.  Would add that I have started to call it Augmented Intelligence (AI),  which tempers the expectations involved.

The Data Science Behind AI    Posted by William Vorhies  

Summary:  For those of you traditional data scientist who are interested in AI but still haven’t given it a deep dive, here’s a high level overview of the data science technologies that combine into what the popular press calls artificial intelligence (AI).

We and others have written quite a bit about the various types of data science that make up AI.  Still I hear many folks asking about AI as if it were a single entity.  It is not.  AI is a collection of data science technologies that at this point in development are not even particularly well integrated or even easy to use.  In each of these areas however, we’ve made a lot of progress and that’s caught the attention of the popular press.

This article is not intended to be a deep dive but rather the proverbial 50,000 foot view of what’s going on.  If you’re a traditional data scientist who’s read some articles but still hasn’t put the big picture together you might find this a way of integrating your current knowledge and even discovering where you’d be interested in focusing.  .... " 

AI for Marketing

I like the point made about data.   AI (aka Augmented Intelligence).  Is just a better way to make decisions based on data.   Difficult problems in changing contexts need lots of data to tease out patterns.  Thus new analytical methods, that require lots of data,  are starting to succeed. Implementing the results also need knowledge of the process involved.

Everyone Is Talking About AI, but What Does It Mean for Marketers Right Now?  By Stuart Feil

Artificial Intelligence may be the most bandied about term of 2017. For consumers, AI is powering everything from virtual personal assistants and real-time translation to GPS navigation and self-driving cars. In business, AI is under the hood of everything from ride-sharing fleets to aerial analysis of shopping malls to credit scoring.

Ad and marketing tech, of course, are no exception. As the CMO’s role grows to include everything from ad tech to customer relations and corporate strategy, it becomes clear that unique and valuable insights—the kind analyzed by AI from mountains and mountains of data—are key. “The term AI is really fraught with multiple definitions,” says Wilson Raj, global director of SAS, “but I think what’s really happening is the data revolution. ”  .... "