Technology Evidence File

Nov18: New Technology Information: Indicators and Surprises
Why Is This Information Valuable?

Using Machine Learning to Replicate Chaotic Attractors”, by Pathak et al

SURPRISE.
Advances in a machine learning area known as “reservoir computing” have led to the creation of a model that reproduced the dynamics of a complex dynamical system. If this initial work can be extended it represents a significant advance. That said, this is not the same thing as AI learning and being able to reproduce and predict the dynamic behavior of a complex adaptive system, such as financial markets, and economies.
The Impact of Bots on Opinions in Social Networks” by Hjouji et al
Using both a model and data from the 2016 US presidential election, the authors conclude that “a small number of highly active bots in a social network can have a disproportionate impact on opinion…due to the fact that bots post one hundred times more frequently than humans.” In theory, this should make it easier for platforms like Twitter and Facebook to identify and close down these bots. The authors also surprisingly found that in 2016 pro-Clinton bots produced opinion shifts that were almost twice as large as the pro-Trump bots, despite the latter being larger in number.
Learning-Adjusted Years of Schooling” by Filmer et al from the World Bank
This valuable new indicator metric combines both the time spent in school and how much is learned during that time. The authors find that LAYS is strongly correlated with GDP growth. They also find wide gaps between countries, with some education systems being much more productive (in terms of learning per unit of time) than others. The good news is that this points to a substantial source of future gains for these economies in total factor productivity, provided their education systems can be improved.
The Condition of College and Career Readiness, 2018” by ACT Inc.
More disappointing results based on a well-known indicator of US K-12 education system performance.

About three-fourths (76%) of 2018 ACT-tested graduates said they aspire to postsecondary education. Most of those students said they aspire to a four-year degree or higher. Only 27% met all four C&C ready benchmarks; 35% met none. Readiness levels in math have steadily declined since 2014. Sample size = 1.9m. “Just 26% of ACT-tested 2018 graduates likely have the foundational work readiness skills needed for more than nine out of 10 jobs recently profiled in the ACT JobPro® database. This has significant (and negative) implications for future productivity and wage growth.
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Oct18: New Technology Information: Indicators and Surprises
Why Is This Information Valuable?
In a new book, “AI Superpowers”, Chinese venture capitalist Kai-Fu Lee makes an important point: There is a critical difference between AI innovation and AI implementation. Success in the latter depends on the ability to collect and analyze large amounts of data – and this is an area where China is outpacing the rest of the world, because of its size, its state capitalism model, low level of concern with privacy, and its data intensive approach to domestic security.
Provides a logical argument for how and why China could gain a significant advantage in key artificial intelligence technologies.
US House of Representatives Subcommittee on Information Technology published a new report titled “Rise of the Machines.” Highlights: “First, AI is an immature technology; its abilities in many areas are still relatively new. Second, the workforce is affected by AI; whether that effect is positive, negative, or neutral remains to be seen. Third, AI requires massive amounts of data, which may invade privacy or perpetuate bias, even when using data for good purposes. Finally, AI has the potential to disrupt every sector of society in both anticipated and unanticipated ways.”
Report’s conclusions are an interesting contrast to Kai-Fu Lee’s. Similar to critiques by Gary Marcus and Judea Pearl, it highlights the limitations of current AI technologies, which suggests we are further away from a critical threshold than many media reports would suggest. That said, it also agrees that once a critical threshold of AI capability is reached, it will have strong disruptive effects.

However, the report agrees with Lee that privacy concerns are a potentially important constraint on AI progress.
China is Overtaking the US in Scientific Research” by Peter Orzag in Bloomberg Opinion. Not just the quantity, but also “the quality of Chinese research is improving, though it currently remains below that of U.S. academics. A recent analysis suggests that, measured not just by numbers of papers but also by citations from other academics, Chinese scholars could become the global leaders in the near future.”
Suggests that the pace of technological improvement in China will accelerate.
Quantum Hegemony: China’s Ambitions and the Challenge to US Innovation Leadership”. Center for a New American Security. “China’s advances in quantum science could impact the future military and strategic balance, perhaps even leapfrogging traditional U.S. military-technological advantages. Although it is difficult to predict the trajectories and timeframes for their realization, these dual-use quantum technologies could “offset” key pillars of U.S. military power, potentially undermining critical technological advantages associated with today’s information-centric ways of war, epitomized by the U.S. model.”
Highlights a key area in which faster Chinese technological progress and breakthroughs could confer substantial military advantage.
A Storm in an IoT Cup: The Emergence of Cyber-Physical Social Machines” by Madaan et al. “The concept of ‘social machines’ is increasingly being used to characterize various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology, which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimension to the relationship between humans and machines making it difficult to understand their evolution at either the systemic or the conceptual level. This article describes these new socio-technical systems, which we term Cyber-Physical Social Machines.”
Increasing complexity creates exponentially more hidden critical thresholds, and ways for a system to generate non-linear effects.
Notes From the Frontier: Modeling the Impact of AI on the World Economy”, McKinsey Global Institute. Adoption of AI could increase annual global GDP growth by 1.2%. Adoption of AI technologies and emergence of their impact is following typical “S-Curve” pattern. At this point, “the absence of evidence is not evidence of absence” of its potential impact.
Excellent analysis of the current state of AI development, rate of adoption, and range of observed effects.
Critical Point: “Because economic gains combine and compound over time…a key challenge is that adoption of AI could widen gaps between countries, companies, and workers.”
Blueprint: How DNA Makes Us Who We Are” by Robert Plomin. Argues that genetic differences cause most variation in human psychological traits. Accumulating evidence for the dominance of nature over nurture has many potentially disruptive implications.

See also, "Top 10 Replicated Findings from Behavioral Genetics" by Plomin et al
Surprise. The implications of the body of research this book compiles and synthesizes has enormous disruptive potential, at the economic, social, and ultimately political level.