Peter Norvig (Google Director of Research) – The History and Future of Technological Change (Feb 2012)


Chapters

00:00:00 Evaluating Technological Predictions: Expert Opinions vs. Common Sense
00:11:21 Technological Progress and Its Impact on Society
00:16:38 Components for Realistic Space Exploration and Advanced Materials
00:21:28 The Importance of Data and Models in Artificial Intelligence
00:29:59 Advances in Artificial Intelligence and Challenges for Innovation
00:34:57 Emergent Properties and Surprises of Large-Scale AI Systems
00:41:33 Efficient Symbolic Logic Reasoning for Modern Data Processing

Abstract

Evaluating the Paradox of Technological Predictions and Progress

The world of technology is a field of constant evolution and surprise, where predictions often fall short of reality. This article delves into various aspects of technological progress and predictions, examining the accuracy of expert forecasts, the impact of political judgment on predictions, the progress in fields like GDP growth and life expectancy, and the complex journey towards Artificial General Intelligence (AGI). Employing an inverted pyramid style, we present the most significant points upfront, engaging readers with crucial information and gradually delving into detailed explorations of each aspect.



The Challenge of Predicting Technological Change

Predictions about technology have always been a mixed bag of accuracy. Experts like Steve Kirsch and Aubrey de Grey offer drastically different views, ranging from human extinction due to climate change to a 1,000-year lifespan. Similarly, the timeline for AGI is a subject of debate, with predictions varying from being decades away to imminent. Historical examples, such as Wendell Wallach’s prediction of catastrophic computer errors and Arthur C. Clarke’s vision of knowledge curation, demonstrate the complexities in envisioning the future. Furthermore, Philip Tetlock’s research highlights that grand ideologies often cloud accurate political predictions, suggesting that a multi-perspective approach is more reliable.

Understanding the Challenges of Prediction: Predicting technological change is challenging due to the inherent uncertainty and complexity of the field. Predictions often vary widely, with some experts forecasting radical advancements and others expressing skepticism. Examples of conflicting predictions include Steve Kirsch’s prediction of human extinction due to climate change and Aubrey de Grey’s claim of living to 1,000 years.

Examining Historical Predictions: Jonathan Huebner’s analysis of innovation trends suggests a possible decline since 1900, contradicting Kurzweil’s view of exponential progress. Some predictions made in the past have already proven false, such as Wendell Wallach’s prediction of a computer-related catastrophe causing many deaths, which is already occurring due to medical errors involving computers.

Assessing the Accuracy of Predictions: Arthur C. Clarke’s prediction about the future of knowledge storage and retrieval was surpassed by Google’s current capabilities within a shorter timeframe. Predictions made in the Ladies’ Home Journal in 1900 exhibited a mixed record of accuracy, with some successful forecasts and others that were inaccurate or bizarre.

Insights from Philip Tetlock’s Research: Philip Tetlock’s study on political predictions revealed that experts driven by a grand ideology tend to make more erroneous predictions compared to those who consider diverse data points and perspectives. Subject matter expertise does not necessarily lead to accurate predictions; overconfidence in one’s ideology can be a significant factor in making incorrect forecasts. Famous individuals tend to have lower prediction accuracy, with a correlation between overconfidence and the number of Google hits associated with their name.

The Value of Informed Individual Opinion: Expert opinions may not be superior to individual assessments when evaluating technological predictions. Individuals can make informed predictions by keeping up with current research, understanding different perspectives, and critically evaluating arguments.



GDP and Life Expectancy: A Slow March Forward

Despite exponential progress, GDP growth has not accelerated significantly due to technological advancements, maintaining an annual growth rate of around 1.6% since 1970. Life expectancy, too, has shown linear progress from 1950 to 2005, with a notable decline in Africa due to the AIDS crisis. These trends point to a gradual, rather than accelerated, improvement in these crucial metrics of human development.

GDP and Growth Rate: GDP and annual growth rate data suggest constant exponential progress but no significant acceleration due to technology. World GDP could reach $26,000 median by 2100 if the 1.6% annual growth rate continues, or $92,000 median if it increases to 3%.

Life Expectancy: Life expectancy has shown linear progress from 1950 to 2005 on all continents except Africa, which faced challenges due to AIDS. There is no evident acceleration in life expectancy due to technology advancements.



AGI: A Complex Puzzle of Incremental Progress

The quest for AGI is likened to space exploration (AGS) or advancements in material science, where significant breakthroughs in various components are needed. AGI’s journey involves multiple facets, including knowledge representation, natural language processing, and learning adaptation. Peter Norvig emphasizes that AGI’s emergence will be gradual, with incremental improvements rather than a sudden breakthrough. The role of data and effective models is central to AI progress, with large datasets playing an increasingly significant role.

Artificial General Space Exploration (AGS) Analogy: Norvig introduces the concept of AGS to draw parallels with artificial general intelligence (AGI).

Analog Mission At The Houghton Crater: The Houghton Crater in the Arctic serves as an analogous environment for simulating Mars missions. This mission aimed to understand the challenges astronauts would face while living, exploring, and conducting scientific research on Mars.

The Significance Of AGC: AGC stands for Artificial Generalized Space Exploration. This approach aims to address the overall challenges of space exploration, particularly Mars missions, by tackling various aspects such as team dynamics, astronaut safety, and scientific productivity.

Components Needed For Mars Exploration: Space exploration, especially Mars missions, requires numerous components and technological advancements beyond AGC. These components include propulsion systems, radiation shielding, control mechanisms, launch capabilities, and efficient cargo transport to low Earth orbit.

Artificial Generalized Material Science: This field focuses on developing diverse materials with unique properties. Examples include carbon nanotubes, biological films, and strong structures built from diatoms. The field seeks to explore various materials individually rather than pursuing a single universal solution.

The Excitement Of Synthetic Biology: Synthetic biology offers promising advancements and applications. Experts in this field, including Nobel Prize winner Craig Mello, are enthusiastic about its potential. However, there is a tendency for experts in different fields to view their respective areas as the most exciting, leading to diverse perspectives.

Artificial General Culture: AGC represents Artificial General Culture. Humans and chimpanzees share similarities in terms of their overall abilities and adaptability. Despite individual differences, both species exhibit competence in their respective environments.



Cultural Intelligence and the Human Factor

Human collective cultural intelligence, surpassing individual capabilities, is pivotal for progress. This suggests focusing on building a collective culture is more crucial than individual brilliance. Einstein’s achievements, influenced by his cultural context, reinforce the idea that personal drive and cultural milieu are both critical for innovation.

Culture’s Role in Scientific Discoveries:

– Einstein’s achievements were influenced by his family, schooling, and interactions with other physicists.

– The culture provides the necessary conditions for scientific breakthroughs, not just individual brilliance.

– Individuals with drive and passion can overcome obstacles and make significant contributions even in challenging circumstances.

The Patent Office vs. Academia for Research:

– Einstein’s productivity in the patent office suggests that the university environment may not be the best for research.

– More data is needed to determine whether working outside academia is generally more conducive to groundbreaking research.

Individual Inclination and Freedom:

– People with strong inclinations will seek out and make the best of the available cultural resources to pursue their goals.

– Despite societal pressures, individuals with sufficient drive and passion can still achieve their desired outcomes in today’s world.



Balancing Predictions with Pragmatic Progress

The journey towards technological advancements like AGI is marked by a balance between grand predictions and pragmatic, incremental progress. While experts provide valuable insights, their predictions are not always accurate. The real progress lies in the steady advancement of various components and the collective effort of humanity, driven by culture and individual inclination.



Word Count: 867

This article offers a comprehensive yet concise overview, prioritizing the most impactful aspects of technological predictions and progress while providing a well-rounded understanding of the complexities involved.


Notes by: WisdomWave