Chapter 3. Evo Devo Foresight: Unpredictable and Predictable Futures

Empowerment Policy: S&T, IT, and Collective Intelligence

Once we know the history of accelerating change, giving us exponential foresight, we can anticipate that there are some stunning advances in our collective intelligence lying just ahead of us. We discussed several of those advances in Chapter 2. Once we suspect that social and technical change are not only evolutionary but also developmental processes, we can use evo devo foresight to ask which of our current activities are most likely to make progress on the Eight Goals and continue growing adaptive intelligence, both of people and our machines. A number of policy priorities seem particularly obvious, once we are exponential- and evo devo-aware.

First, alleviating avoidable human and animal suffering clearly belongs at the very top of our political priorities. There are a number of easy wins that don’t require new science or much new technology, including feeding the currently hungry and malnourished, working to prevent further population growth, and making clothing, shelter, basic health and sanitation, and basic security more available to those who need them, locally and globally. These seem top moral priorities for us as individuals, and for our governments.

Second, to make faster inroads on these and all our other problems, we must focus much more seriously than we have to date on advancing science and technology (S&T) in ethical ways. We have many strategies available to us. Education, community building, entrepreneurship, and targeted political and social activism, to grow S&T literacy and capability in our political processes, and improve  how we talk about and reward S&T advances in our culture, are all great strategic approaches. ScienceDebate.org is a great nonprofit trying to get our political candidates to debate how to better support the advancement of science and technology. But in a world without personal sims, and more and more insulation from crisis (compare the 2008 GFC to the Great Depression), they are up against serious public apathy, and we are also trapped in a plutocratic political system that will need fundamental, multidecade reform before minority voices can be better heard. In the meantime, we do what we can. We are starting Foresight U, and our conferences and personal achievement groups, to take the education and community building approach to improving humanity’s S&T focus. Each of us can do a little bit to make S&T advancement a top priority, for our children and our organizations. We will leave further discussion of that to another time.

Third, of all our sciences and technologies, we’ve seen that information technology is by far the most rapidly improving, and the greatest lever we can use to affect all of our other problems, as it is the fastest way to grow our collective intelligence, and to further maximize the Eight Goals. We’ve given many examples in this Guide of how evolutionary goals, like intelligence (mind), individuation, innovation, and freedom, and developmental goals like intensification, interdependence (heart, morality), immunity, and truth can each be most rapidly accelerated via IT-based products, services, and platforms. We’ve discussed that we don’t have to fear the machines, or worry about them taking over, and that they are rapidly becoming deeply biologically-inspired, and an extension of us. We can and will guide them to empower and humanize our societies. We’ve also given a number of reasons why IT-driven acceleration won’t be slowing down in the forseeable future.

IT is the space where most of our efforts, be they education, community building, entrepreneurship, or other avenues of social and political activism are most likely to have the greatest return. It is the fastest moving dragon of change that we most need to learn how to ride. As it grows in intelligence, our relationship to it also changes as well.

We’ve proposed that many aspects of information technology have recently become so valuable that they deserve to be treated as public goods, infrastructure that we all deserve, as planetary citizens. Again, the Nordic democracies lead the way, when Finland made fast internet access a legal right of all its citizens in 2010. That kind of guarantee needs to happen for wireless as well, so groupnets can take off, and people can be connected to whomever they want, whenever they want, as intimately as they want, everywhere in the world. The US nationalized its private road systems in the 19th century, and turned them into public interstates in the mid-20th century. We may need to do something similar, with competitive public-private partnerships, to get connectivity to everyone. IT’s growth will keep surprising us for the rest of our lives, though many of its bounties will come far later than they should, because of political, economic, or social blocks.

Fourth, we have argued that two areas of information technology, neuroscience, and in particular the neuroscience of learning and memory, and computer science, and in particular biologically-inspired approaches like deep learning and evo devo approaches to robotics and automation (embodied intelligence) are going to increasingly be converging, and will increasingly become the most important ways to advance IT. Our funding priorities for these are quite unaware of their importance today.

Source: Code.org

Source: Code.org

Quora estimates there are 30,000 neuroscientists in the US today. Only a small fraction of these study learning and memory. Brian Vastag, in U.S. pushes for more scientists, but the jobs aren’t there, Washington Post 7.7.2012, shows the risk to becoming a PhD neuroscientist in today’s terrible funding climate, where our administration asks for more Americans to get jobs in these fields, but provides atrocious levels of funding for them, ensuring brutal competition for a handful of well-paying jobs. Few people understand how low our priorities are, or that federal research spending on all agencies within the NSF has stagnated relative to inflation since 2004. Postdoctoral jobs in the US start at $39,000 according to the National Postdoc Association. Meanwhile funding for PhDs is so scant that many PhD recipients graduate in major debt. Postdoc positions now average five years for many positions. The job prospects are so poor that several folks in various branches of chemical and biological sciences now don’t encourage their children to go into science careers.

Job prospects are better in the computer sciences, it is also a greatly neglected social policy priority. As Igor Markov notes, undergraduate supply may be starting to overshoot current commercial demand, but the quality of training is not up to the challenge, and the number of doctoral students and academic jobs available to graduates remains quite small.

We need far better and earlier training of computer scientists, and much more interdisciplinary training, so that students can fully understand where this field is going, and understand its many research and entrepreneurial opportunities.

Presently, only 5% of US high schools offer AP computer science, which is shameful. There are few integrations of computer science with business and entrepreneurship training, or funds for internships, which is how students learn, while in training, to apply what they learn. The number of people working in currently emerging areas like neural networks and deep learning is ridiculously small, for all its recent press coverage.

In 2015, Google had roughly 70 people working at it’s Google Brain deep learning research project, led by Jeff Dean. Co-founder Andrew Ng leads a similar-sized group at Baidu’s Institute of Deep Learning. As Will Knight reported in Technology Review, 12.16.2015, Ng’s new implementation of deep learning, Deep Speech 2, recently made the news for being the first-ever speech recognition software system that beat humans, and the first that was entirely trained, like a child, rather than being largely human built, like all previous systems. But the world will need a heck of a lot more experimentation, and hardware-assisted approaches, in order to turn these early and limited successes into a vast range of new industries, products and services.

As we’ll argue in the next section, a very rough look at history argues that it may take on the order of 10,000 people, working for at least ten years on the right problems, to achieve many of our most important advances in science and technology. Even thinking globally, we are a long way from having 10,000 people working on the “right problems” in either neuroscience or computer science and robotics. So we have a lot of good work, and good foresight ahead of us before we get to that exciting and very promising place.

History offers us many lessons in activism, good and bad. There was a social movement, Technocracy, that emerged during the social strife of the Great Depression. It was led by a salesman and former IWW (global organized labor) research director, Howard Scott. Technocracy proposed, rightly in my mind, that science and technology should be our top political priority, that our democracy needed to be more meritocratic, and that we needed more scientists and engineers making political decisions.

But Scott’s approach was also highly top-down and utopian. If implemented, it would have been yet another autocratic, communist hell. He made big claims about the untapped power of the powerful new machines of that era, saying that if the “thousand million horsepower” of existing US machinery were run “at full capacity,” we’d immediately have “five times more output” available. He also argued we should switch to “energy units” as our currency. He said if we’d just take a more technocratic approach to governance, everyone could have a $20,000 guaranteed basic income. Does any of this sound familiar?

Fortunately people are smart, and they quickly recognized the limitations of his vision. The Technocracy movement grew rapidly in the early years of the Depression, until Scott made a rambling, incoherent speech in 1933, broadcast nationwide on radio, that convinced many that the movement was yet another half-baked solution, with little chance of causing any real change. Around that time Scott was also exposed for previously misrepresenting his credentials as a “distinguished engineer,” and Technocracy’s brief window of opportunity to influence US social change was gone.

Technocracy had the right idea about the fundamental importance of science and technology in creating abundance, and the value of a more meritocratic approach in our democracy. Scott was even roughly right, if we are generous, in his forecast about our need to revise our currency. The US did in fact make energy the new foundation of our currency in the 1970s, with petrodollar policy, and that policy continues today.

But Technocracy fundamentally misunderstood the value of free and fair bottom-up competition and capitalism as engines of innovation, at all levels of human activity. It also failed to realize that adaptive collective intelligence, not energy, is the most fundamental source of economic value.

Today, all the leading information-enabling companies, the Amazons, Googles, and Microsofts and Facebooks, are becoming more valuable than our energy companies in our capital markets. Our economy is shifting from energy to intelligence.

D&D-centric companies that most rapidly unleash new bottom-up STEM efficiencies, companies like Uber and AirBnB, are setting stunning new records in their rates of growth, and with the new scale of the mobile web, we are seeing scores of unicorn companies (zero to over 1 billion in valuation in under five, ten, or fifteen years) and even a few “decacorns” (over 10 billion in the same time frame) emerge. Some of these will crash with the next financial downturn, but many will not.

Once we enter the era of distributed intelligence, bio-inspired computing and robotics, sims, and the internet of things, we can imagine the D&D story will be more obvious than ever. Ten years from now, the next major wave of value creation may be the subset of these companies that are best using mostly bottom-up approaches to adding machine intelligence to their products and services.

Evo devo foresight makes it an easy prediction that intelligence- and information-enabling our economies, in ways that scale the fastest via primarily bottom-up means, will continue to be the top creators of value. Those strategies will keep growing in proportional influence, relative to all others, right up to the technological singularity and beyond. Individuals, companies and societies that use them best will continue to attract the most capital, because that’s what the universe apparently wants.

We’ll eventually have a public that understands this, via their personal agents. With luck, we may then see political leaders who are not only global humanitarians, but who are S&T and IT-prioritized, adeptly mixing mostly bottom-up approaches to our problems with a small amount of well-chosen, top-down institutions, rules, and approaches.

In the interim, some of our current IT leaders might even be able to mount successful political campaigns (Musk for Mayor? Page for President?), and demonstrate what aggressive and prioritized IT development can do for a state, a nation, and the world. In the meantime, which could be a very long meantime, we move forward in smaller steps, doing what we can.

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