Your Personal Sim: Pt 1 — Your Attention Please

The Brave New World of Smart Agents and their Data

A Multi-Part Series

Part 1 — Your Attention Please (this post)
Part 2 — Why Agents Matter
Part 3 — The Agent Environment
Part 4 — Deep Agents
Part 5 — Deep Training 
More soon…

John Smart is a global futurist, helping people thrive in a world of accelerating change. These posts are from his book, The Foresight Guide, free online at The Guide aims to be the best single intro the field of professional foresight, and also a great Big Picture guide to our 21st century future. Check it out!

Siri, iOS 7, June, 2013

Part 1 — Your Attention Please: A New World Is Almost Upon Us

Summary (tl;dr)

  • This series will explore the five- to twenty-year future of smart agents and the knowledge bases they use and build. These may be the most socially important forms of AI that will emerge in the coming generation.
  • As we’ve seen in the headlines about deep learning since 2012, the AIs are presently awaking all around us, whether we want them to or not. To paraphrase futurist Stewart Brand, “We are gaining superpowers, so we better get good at using them.”
  • A new kind of software agent called a personal sim is the most empowering and intimate form of AI on the horizon. We’ll soon be using sims that model our interests, goals, and values in their knowledge bases, and which act as our assistants and digital interfaces to the world.
  • In their early years we’ll likely think of sims as bright but slightly autistic children, much better at many tasks than we are, but still unschooled and unwise in many ways. At the same time, the knowledge bases our sims use will be full of errors, and won’t be sufficiently open at first.
  • The takeaway from this series will be that we will need to build and raise our sims and their knowledge bases well, with love and care, as they will be central to how billions of us live our lives in the 2020s and beyond.

Series Questions

As futurist who enjoys the Big Picture perspective, I’ll ask you to consider some “Big Questions” in this series. I hope they spur good discussions, and new insights, for each of us:

  1. How big a force for social change do you think AI (smart software and hardware) will be in the next generation? Could anything be bigger?
  2. What are the benefits and risks of smart agents (intelligent software we can talk to, with an always-improving model of the world, itself and us)?
  3. Is the convergence of neuroscience & computer science (deep learning) the best way to keep AI development accelerating? If not, what could be better? This convergence seems obviously the best path, by far.
  4. If we increasingly “grow & garden” our deep learning-based AIs rather than “engineer & program” them, how will we make them safe?
  5. Will we need social reforms like basic income to deal with the social impacts (tech unemployment, growing rich-poor divides, etc.) of accelerating AI, automation, and globalization?
  6. In our world, what are the most effective strategies to make a “Good” Society? Five E’s seem particularly important: Empowerment, Equity, Empathy, Evidence-based Thinking and Behavior, and lastly, that uniquely American priority, Entertainment (fun, beauty, art, escape).
  7. Mind-Stretching Question: If people can preserve much of their life’s memories and identity while they live, simply via interacting with their personal sims, which they may leave behind for their loved ones, and also inexpensively preserve themselves when they die, so their brains circuits, memories, and personalities can one day be scanned and “uploaded” into future computers (see, will these two new ways of living on make for a better society? If so, how?


The biggest advance in computing technology that humanity has yet seen is sneaking up all around us, right now. In just the last five years, our leading IT companies are building the first truly useful intelligent assistive personal software agents, or “smart agents” or “bots” for short. The exponential convergence of speech recognition, natural language understanding, deep machine learning, increasingly deep and valuable knowledge graphs and knowledge bases of big data, and context from our digital devices, including our online habits, email, social networks, purchases, and location data, is allowing smart agents to anticipate our daily actions, wants and needs.

Conversational agents include Google Now, Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, Facebook’s M, the Siri founder’s startup Viv, Baidu’s Deep Speech (technically, the speech rec front end to an agent), IBM’s Watson Analytics (technically, an analytics front end to an agent), and a large number of offerings from smaller companies, such as Soundhound’s Hound, call customer service and center automation agents and agent platforms like IPSoft’s Amelia, Next IT’s Alme (I am an advisor to NextIT), Creative Virtual’s V-Person, Go Moment’s Ivy (for hotels), platform tech support bots like Slack’s Slackbot, scheduling bots like’s Amy/Andrew and Clara Lab’s Clara, email assistant Crystal, chatbots like Microsoft’s Tay, and a host of others around the world, either less well-known or presently in development. Smart agents are increasingly good at understanding the semantic meanings in our text, voice, GPS and other data, inferring our likely next actions, and predicting our needs, based on our current context. When they don’t understand, they are also learning how to ask questions to clarify our intent.

At the same time, Google, Baidu, Microsoft, Amazon, Facebook, IBM, Nvidia, Factual, and other IT platform leaders are building both proprietary and open knowledge graphs and knowledge bases, vast databases that allow automated semantic structuring and semantic search of public and private information, and which use machine learning to do reasoning and inference with this data. In early 2015, Google began ranking websites based on their factual accuracy, not just PageRank. Any site with more than a few factual inaccuracies, according to the base, such as the antivaxxer sites, now gets a lower ranking in the new algorithm, renamed RankBrain, as it is centered around artificial neural networks.

Certainly today’s clickbait and unhelpful comments will eventually follow, a topic called personalized search. Imagine YouTube’s comment wasteland autosorted by relevance, truthfulness, and truenames. We’d all start reading the comments on YouTube videos again. Just like spam was tamed via a knowledge base of known bad actors and spam reporting buttons in our cloud-based email, all data on the web will be semantically rated, ranked, and filtered by a combination of AI, agents, and people. Roll RankBrain forward a few years, and we can see how powerfully and irreversibly tomorrow’s open knowledge bases and AI will change the web, making it much more valuable and relevant to each of us. Because our brains are highly evolved to use language, emotional tone, and visuals, our coming conversational agents, and the dashboards, infographics, and choices they continually offer us, will be the primary means by which the web is customized, to each of us.

All this has some very big implications. As we’ll discuss in a future post, each of us will be able to use agents and their knowledge bases to increasingly see only what we want to see. That power can lead us into an ignorant, biased, filter bubble hell, as Eli Pariser warns in The Filter Bubble (2012), or into a well-crafted and empowering set of digital living and working spaces, each of which measurably transports us to new heights of insight, empathy, and productivity. Which way we use our personal agents will be ours to choose.

Google’s Knowledge Graph for Semantic Search (Launched May 2012)

As these knowledge bases and their brain-like networks grow, we can expect not only knowledge, but also truth, opinion, reputation, probability, goals, values, and other information graphs to become available on the public and private web, in both open and proprietary forms. In 2005, to honor futurist George Gilder’s seminal 20th century thinking on technology, I called that near-future world a valuecosm a time when increasingly granular maps of the values, interests, and goals of participating users become part of the open public web. The valuecosm is a predictable outgrowth of today’s datacosm (cheap and abundant big data), the telecosm (cheap and abundant telecommunications) of the 1990s, and the microcosm (cheap and abundant microprocessors) of the 1980s. It is almost upon us.

Microsoft CEO Satya Natella says “smart agents will supplant the web browser,” and “bots are the new apps.” Such statements are aspirational at present, but will be increasingly true in coming years. Looking back from the view twenty years hence, we will come to see today’s web of social big data (Web 2.0), as the precursor to a “Knowledge-Mapped” and “Agent Web”, a near-future when agents and their knowledge bases become the main way we choose to interface with the world. That seems worthy of the title Web 3.0. It is a world where semantic knowledge bases, brain-like machine learning and smart agents all emerge in one big transition, essentially at the same time.

Each of the coming posts will look at a different piece of this Web 3.0 future. We’ll begin with an introduction (first and second posts), then look at how agents will likely be built (our third to fifth posts), then consider eight societal domains where we can expect big impacts from agents as their smartness accelerates over the next two decades. The eight domains are:

1. Personal Agents — News & Entertainment, Education, & Personal Growth

2. Social Agents — Teams, Relationships, Poverty and Social Justice

3. Political Agents — Lobbying, Repres. & Tax, Basic Income & Tech Unemp.

4. Economic Agents — Shopping, Financial Mgmt, Funding and Startups

5. Builder Agents — Built Environment, Innovation & Productivity, Science

6. Environ. Agents — Population, Pollution, Biodiversity & Sustainability

7. Health Agents — Health, Wellness, Dying and Grieving

8. Security Agents — Security, Privacy & Transpar., War, Crime & Corrections

In each area, we’ll look at two sets of scenarios for how smart agent adoption might play out. The first will typically be a dystopia, a sample of social outcomes we’d like to avoid, and the second a protopia, a measurably better world that a large majority of us would like to reach.

In the long run, I’m optimistic that agents will help us make major progress on these social variables. But in the short run, lots of bad things could easily happen. So let’s look at both sides of the future of agents, and please let me know what I’ve missed or am getting wrong. Together we can create much better foresight than any of us can alone.

At present, there are no general-interest books on the present and future of conversational smart agents and their knowledge bases, to my knowledge.

But here are the three best background books I’d recommend for this series:

  • For an easy intro, read Scoble and Israel’s The Age of Context: Mobile, Sensors, Data, and the Future of Privacy (2013). You’ll learn about current and coming context-aware platforms and technologies feeding the knowledge graph, and some of their social implications.
  • For the next level up, read Eric Siegel’s excellent Predictive Analytics (revised for 2016), a great overview of all the industries and ways people are presently building knowledge and anticipation from big data, with a gentle introduction to AI.
  • For another level up, read Pedro Domingos’ The Master Algorithm (2015), a well-written and layman-accessible intro to the “Five Tribes” of machine learning. Two of these tribes, neural networks and evolutionary computing, are particularly important to the future of AI, in my view. Neural networks for the next decade at least, with evolutionary computing joining in sometime later, as we’ll discuss.

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 an Entertainment bonus read, see Smolan and Erwitt’s gorgeous coffee table book, The Human Face of Big Data (2012). [They made a documentary too, but the voiceovers made it unbeautiful, IMO. Great art and beauty are hard to achieve, like everything valuable in society]. 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 idea, that we and our technologies are understandable, from both structural and functional perspectives, as a kind of emerging global superorganism, is quite old, and is actually increasingly apt, as we’ll argue later.

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.

Know any other great background books or studies I should list here? Let me know by email or in the comments, 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. Sims is a great term for a personalized agent, as we’ll see. Going forward, bots or sims may end up being the terms used most often in culture, simply because they are both the shortest and simplest. 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.

Our sims won’t have to look like us in order to have an accurate internal model of who we are. In academic labs, a sim that acts like a great butler, secretary, humorist, guide, or coach, who is not a visual copy of us, is usually more popular than a sim 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 “sim” is an good term for the mental architecture of a personalized smart agent, whatever its appearance. In the not-too-distant future, I can imagine us saying “my sim said this”, or my sim did that” when we talk about social events in this brave new world.

Charles Carson, Downton Abbey

As their smartness grows, we’ll increasingly use our trusted sims to advise us, and act on our behalf. As our most personal agents, our sims will be continually conversationally trained by us, and each will have private personal data about us that we don’t share with the outside world. They’ll help us make choices that better reflect and protect our interests, goals, and values, and as learning agents, they’ll also increasingly acquire interests, goals, and values of their own.

I’ve been thinking about sims 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. Prior to Sept. 2014 I called personalized agents digital twins”, to signify that they would become like software twins as they acted for us in the world. I now find “sim” an even simpler and better term.

Science fiction authors, futurists, and visionaries usually get to the future first. But they get there by making us slog through a majority of false futures, and only careful critiques can help us distinguish the two in advance. See Wikipedia’s excellent AI in Fiction page for examples of both in Sci-Fi, and see back issues of The Futurist magazine for examples of both from the futurist community. In the commercial arena, Apple was the first big company to bring the vision of the personal sim 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 sim on an iPad-like device. The real iPad debuted in 2010, and Siri was launched on the iPhone in 2011. Pretty good foresight, don’t you think?

Apple’s Knowledge Navigator (1987)

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

  • When you act in the world in coming years, how will you know when to trust your sim’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?
  • How early should children be allowed to use sims? How early should educational sims, via smartphones, be given to emerging nations youth?
  • 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 sims, via what futurist Thomas Frey calls teacherless education?
  • How intimate will you let your sims get with you? How will we best respond when some people start to fall in love with their sims (see Her, 2013, that scenario)?
  • What will be the impact of therapy sims? Correctional sims? Shopping sims? Financial management sims? Voting sims? Activism sims?
  • If your mother dies in 2030, will you find it helpful to talk to the sim 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 sim 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, and the thought-provoking short film, Eternity Hill (see the trailer here). How will this coming “simmortality” change our culture?

These are just a few of the big social questions raised by sims, 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 mid-century our sims 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 powerful sims will increasingly 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 middle of this century. Each of us will experience our own “personal singularities” as our increasingly intelligent sims, and the data and machines they control, start to reach and then exceed us in their understanding and mastery of the world. Sims, I would predict, are 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 sim 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 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, waiting to be seen.

Evo Devo Universe Blog

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 bottom-up approach now looks to be the most probable future, and it lies directly ahead of us.

A world of exponentially more intelligent agents and sims 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.

But this is also 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.”

Stewart Brand, and The Long Now Foundation

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

  • Who will build the most trustable and popular agents? Big corps? Open source? Government? What about sims?
  • What does our future economy look like, in a world of ever-smarter personal sims?
  • What is the future of politics, as our agents and sims 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.

Nvidia’s Pascal Hardware for Deep Learning (Source:

John Smart is CEO of Foresight University and author of The Foresight Guide. You can find him on Twitter, LinkedIn, or YouTube.

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Part 2 —> Why Agents Matter