For bonus reading, Chris Steiner’s Automate This: How Algorithms Took Over Our Markets, Our Jobs, and the World (2013) considers some of the markets and jobs that our presently mild forms of automation have been disrupting. Steiner’s book says little about machine learning however, which are the algorithms that will increasingly change things going forward.

For a bonus read, see Smolan and Erwitt’s gorgeous coffee table book, The Human Face of Big Data (2012). Their lovely book offers a high-level tour of the knowledge web, and the ways our lives are changing as big data on all our public and private activities is becoming accessible to more and more of us. At one point, Smolan makes the metaphor that humans and our digital devices are now acting as agents in an emerging global nervous system. This metaphor, that we and our technologies are understandable, from both structural and functional perspectives, as a kind of emerging global superorganism, is quite old. If we are very careful with how we caveat it, it also seems increasingly apt.

Every good idea is usually a lot older than we realize. For those who like history, a great early overview of agents was Caglayan and Harrison’s Agent Sourcebook (1997), written to help businesses implement agents on their computers and the web. An early and very breezy look at agents was Andrew Leonard’s Bots: The Origin of a New Species (1997), focusing mainly on chatbots and spambots on the early web.

Most recently, Chris Brauer of UCL led a study on smart agents in 2015 that is a great resource for a commercial look at their near-future prospects.

Do you know of any other great background books or studies I should list here? Let me know by email or in the comments, and I’ll add them, thanks.

Bots are now in a second renaissance of sorts, with Microsoft, Facebook, Slack, Kik, and others launching bot-building frameworks or stores. Bots can help today with basic and common online problems, like password recovery, booking flights, and many other highly structured activities. They need good language understanding in their domains before we will use them, and the best are well integrated with human agents when the bots aren’t being useful. Companies in agent-assisted call center automation have been doing narrow integrations for a few years. Facebook is attempting to massively grow this market with its bot-building framework inside Messenger (announced Apr 12, the week after this post). That should help train up thousands of new bot builders, and move the ball forward. They’re also building a general purpose messenger bot, M, now in private beta. Given the small number of developers as far as I can tell, and the technical challenges, I’d guess we are still three or more years away from M hitting mass use. But they have users in a learning cycle, and they’re aggressively using deep learning. So kudos to them.

The name used most often to describe smart agents today is virtual assistants (“VAs”). But the term “virtual assistants” is clunky, and it gets confused with living virtual assistants, people that work online for others. Computer scientists have been calling these intelligent agents for years, so like Natella, “smart agents” is the term I’d recommend specifically for software with a statistical understanding of human conversation, emotion, and behavior and with some agency capacity (able to perform tasks for you), whether that software talks to you or not (and most of the time, it won’t). Bots is a great term for any automated agent, smart or not. PAI is a particularly great term for a personalized smart agent, as it is a single syllable, and it is relatively self-explanatory to any who learn what PAI stands for. Let’s find good terms soon, as we’ll talking about these for the rest of the century, in my view.

In a recent Slate article, Will Oremus predicts smart agents will increasingly be “the prisms through which we interact with the online world.” Consider next what happens when we add wearable audio and video augmented reality to our agents, and our knowledge bases get a bit deeper and smarter, aided greatly by the internet of things. In that future, it’s obvious our agents will become the main software interfaces we use to interact with the world, period. Oremus’s article is titled “Terrifyingly Convenient,” a phrase that is a great way to highlight both the disturbing and the enticing aspects of agent technology.

As humans, our minds naturally go to the dystopian aspects of agents first, for deep evolutionary reasons. Only secondly, and warily, do we contemplate their progress-related, or protopian aspects. But both negative and positive outcomes are likely, and we’ll do our best to cover both futures in this series. I discuss the importance of keeping a good balance between these two key ways of thinking in Keeping Intelligent Optimism in The Foresight Guide.

Our most intimate agents will be highly personalized to us, building accurate internal models of our current context, preferences and values. That makes them different enough from unpersonalized agents that I think they deserve their own unique name as well. In 2014 I began calling highly personalized agents “personal sims”, or simply, “sims”. Think of a simulation, or The Sims, a game played with graphical representations or “avatars” of people, one or more of which might look like the player. That is a good name, but I now find PAI, which I began using in late 2017, to be an even better term.

Our PAIs won’t have to look like us in order to have an accurate internal model of who we are. In academic labs, an agentthat acts like a great butler, secretary, humorist, guide, or coach, who is not a visual copy of us, is usually more popular than an agent that looks like the user, which is often seen as narcissistic or creepy by users, at least today. But any highly personalized smart agent, though it may have its own appearance and personality, perhaps like Carson in Downton Abbey, has a good portion of its mental architecture dedicated to being a software simulation of us. Thus both PAI and “sim” is are good terms for such software helpers. In the not-too-distant future, I can imagine us saying “my PAI said this”, or my PAI did that” when we talk about our lives in this brave new world.

Charles Carson, Downton Abbey

As their smartness grows, we’ll increasingly use our trusted PAIs to advise us, and act on our behalf. PAIs will be continually conversationally trained by us, and each will have a growing collection of encrypted private data (emails, social network posts, photos, browsing histories, conversations) about us, much of which is not shared with the outside world. Our PAIs will be able to use that data, and their growing neural network intelligence, to help us make choices that better reflect and protect our interests, goals, and values. As learning agents, they’ll also increasingly acquire interests, goals, and values of their own.

I’ve been thinking about PAIs and their knowledge bases for about fifteen years, since the start of my career in strategic foresight. I gave my first tentative talks on them at a Foresight Institute gathering in 2001. In 2003, I published an extended interview and a popular web article on them, and the conversational interface they would need to build good semantic models of us. In the early 2000’s I called personalized agents “digital twins” and “cybertwins” to signify that they would become like software twins as they acted for us in the world. I used the simpler “sim” from 2014 to 2017, and now I find PAI the simplest and most useful term, and recommend that term to you as we discuss this emerging product and service in coming years.

Science fiction authors, futurists, and visionaries usually get to the future first. But at the same time they also make us slog through a majority of false futures, and only careful critiques allow us to distinguish the two in advance. See Wikipedia’s AI in Fiction page for examples of both in Sci-Fi. In the commercial arena, Apple was the first big company to bring the PAI vision to the general public, in their Knowledge Navigator concept video in 1987. In that video, which was set in 2011, a user talks to a bow-tie wearing personal AI on an iPad-like device. The real iPad debuted in 2010, and Siri was launched on the iPhone in 2011. Pretty good foresight, in my view!

At this point in our intro, a host of PAI-related questions may spring to mind:

When you act in the world in coming years, how will you know when to trust your PAI’s recommendations for who to date, what to read, buy, invest in, or how to vote?

your PAI’s recommendations for who to date, what to read, buy, invest in, or how to vote? How will you judge when its intelligence exceeds its wisdom (common sense), and when it is serving your interests, rather than the company that created it?

when its intelligence exceeds its wisdom (common sense), and when it is serving your interests, rather than the company that created it? How early should children be allowed to use PAIs? How early should educational PAIs, via smartphones, be given to emerging nations youth ?

be allowed to use PAIs? How early should educational PAIs, via smartphones, be given to ? How many “virtual immigrants,” working online in tomorrow’s startups, can we expect when global youth learn English, other leading languages, and technical skills, from birth from their wearable PAIs, via what futurist Thomas Frey calls teacherless education?

working online in tomorrow’s startups, can we expect when global youth learn English, other leading languages, and technical skills, from birth from their wearable PAIs, via what futurist Thomas Frey calls teacherless education? How intimate will you let your PAIs get with you? How will we best respond when some people start to fall in love with their PAIs? See Her, 2013, one take on that scenario.

will you let your PAIs get with you? How will we best respond when some people start to fall in love with their PAIs? See Her, 2013, one take on that scenario. What will be the impact of therapy PAIs? Correctional PAIs? Shopping PAIs? Financial management PAIs? Voting PAIs? Activism PAIs?

PAIs? PAIs? PAIs? PAIs? PAIs? PAIs? If your mother dies in 2030, will you find it helpful to talk to the PAI she talked to for the last ten years of her life? Will you let Google, Facebook, Microsoft, or whoever continue to improve her AI after her passing, and interact with surviving friends and family, so her PAI can become an ever-better interface to all the data of her life? Strange as this sounds, a few startups are already working on that idea today. See Eterni.me. Also the short film, Eternity Hill (see the trailer here). How will this coming PAI “immortality” (or more accurately, just a much longer PAI lifespan than biological lifespan) change our culture?

Eterni.me

These are just a few of the big social questions raised by PAIs, and we’ll try to take a good early look at many of them in this series.

Surprisingly, if accelerating computer hardware and software trends continue, sometime between now and the latter half of this century, our PAIs will begin to seem generally intelligent, to their users, both intelligent in the human sense and in a number of senses wholly new. At the same time, our most advanced PAIs will come to be seen, by their users, as digital versions, and indistinguishable extensions, of us.

In fact, I think that’s what the long-discussed technological singularity will primarily look like, to the typical person, some time in the second half of this century. Each of us will experience our own “personal singularities” as our increasingly intelligent PAIs, and the data and machines they control, start to reach and then exceed us in their understanding and mastery of the world. PAIs are thus the “human face of the coming singularity,” to riff on the title of Smolan and Erwitt’s The Human Face of Big Data (2012).

In this view, we are heading for a primarily bottom-up, diverse, and massively parallel world of distributed PAI intelligence, with a small amount of ideally well-intentioned but ultimately secondary top-down efforts at control of the gathering intelligence storm by various authorities. In my opinion, a very open, distributed, and highly bottom-up approach to machine intelligence is also the only way we’ll actually create all the experiments, data, and training necessary for human-surpassing machine intelligence (also called “general AI”) to emerge, both quickly and (for the most part) safely in coming years.

At the same time, to balance all this new personal empowerment and collaboration capacity, individuals, teams, and nations will need ever better security, privacy, and adaptive political systems. I think those better rules and systems will also emerge by primarily bottom-up means, again with a small fraction of top-down strategies as well.

Understanding all complex adaptive systems, whether they are organisms, organizations, societies, technologies, or even universes, as primarily bottom-up, experimental, and selective, and only very secondarily top-down, rational, and planned, is a way of systems thinking that has a name. It is called evolutionary development, or “evo devo”, and it comes from the field of evo-devo biology, which I believe is the best current framework to understand adaptation and change in living systems. In 2008, philosopher Clement Vidal and I formed a small research community, Evo Devo Universe, to study this particular approach to complexity and change.

A great early book on evo devo thinking, applied to societies and technologies, is futurist Kevin Kelly’s Out of Control (1994). If we live in an evo devo universe, then most processes and events will always be evolutionary, unpredictable, and “out of control,” while a special few things will be developmental, top-down, and predictable. Both unpredictable and predictable futures lie in front of us, each waiting to be seen.

Unfortunately, every popular book I’ve read on the future of artificial intelligence either ignores or discounts the likelihood of a mostly bottom-up, divergent, creative, unpredictable, and “evolutionary” agent-driven future of AI, in combination with a much smaller amount of top-down, convergent, conservative, predictable, and “developmental” set of architectures, priorities, and controls. Yet as I will argue in this series, given the impressive advances we’ve seen in deep learning since 2012, that primarily bottom-up approach now looks to be the most probable future for AI.

A world of exponentially more intelligent agents and PAIs acting as proxies for us, in deep harmony with our robots and machines, will be a tremendously empowering but also a disruptive and potentially dangerous future. Deciding who controls their construction and training, and the sensors and data they have access to, will be among the most important social, commercial, and political choices of the coming generation.

This is a future I don’t think we can avoid. It seems a developmental inevitability, so we better get better at thinking and talking about it. Let’s end this post with a version of a prescription from one of my favorite futurists, Stewart Brand, editor of the Whole Earth Catalog and co-founder of the Long Now Foundation: “We gain new superpowers every month now, whether we want them or not. So let’s get good at using them, to help each other thrive, as best we can.”

As you think about agents and PAIs in coming weeks, I like to suggest three more questions for conversation:

Who will build the most trusted and popular smart agents? Big corps? Open source? Govt? Who will build our most trusted and popular PAIs?

the most trusted and popular smart agents? Big corps? Open source? Govt? Who will build our most trusted and popular PAIs? What does our future economy look like, in a world of ever-smarter personal AIs?

look like, in a world of ever-smarter personal AIs? What is the future of politics, as our agents and PAIs increasingly understand, assist, and advise us?

Lastly, if you are near San Jose this week and have the means, consider taking a day at Nvidia’s 2016 GPU Technology Conference. With 5,000 academics, technologists, and entrepreneurs in attendance, GTC is presently the builder’s deep learning event of the year. It’s got the excitement of Macworld in the 1980’s. A whole new frontier of human-machine partnership is emerging, right now.

A highly recommended skim, for all the tech curious, is Monday’s keynote from CEO Jen-Hsun Huang. If that doesn’t blow your mind and give you a severe case of future shock, I don’t know what will.