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
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 were presented, such as 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.
00:11:21 Technological Progress and Its Impact on Society
Kurzweil’s Analysis: Kurzweil’s theory about exponential progress in technology is inconclusive and subjective, as it relies on lists of important innovations that are open to interpretation.
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.
Artificial General Space Exploration (AGS) Analogy: Norvig introduces the concept of AGS to draw parallels with artificial general intelligence (AGI).
00:16:38 Components for Realistic Space Exploration and Advanced Materials
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.
00:21:28 The Importance of Data and Models in Artificial Intelligence
AGI Components: AI researchers need to focus on creating the individual components that make up an AGI rather than trying to solve the whole problem at once.
Data and Models: Two key components necessary for AGI development are data and models. More data and more models of what the data does are needed to make progress.
Examples of Data and Model Impact: Image resizing without a model of the world can produce good results by removing pixels that are most like their neighbors. Filling in missing parts of an image can be done effectively by searching a large database of similar images. Machine translation can achieve good results with a simple model and a large amount of paired data.
Testing in the Context of AGI: Traditional unit testing is insufficient for testing AGI systems. Testing needs to be probabilistic and online to account for the continuous changes in the state of the web.
AGI Development Progress: Progress is being made on the development of AGI components, but there is still a long way to go.
00:29:59 Advances in Artificial Intelligence and Challenges for Innovation
Probabilistic First-Order Logic: Importance of dealing with uncertainty in real-world interactions. Need for a probabilistic first-order logic that can handle complex quantifications. Difficulty in combining probabilistic and first-order models. Progress being made by research groups at Stanford, Berkeley, and other institutions.
Hierarchical Representation in Problem Solving: Significance of hierarchical representation for complex problem-solving tasks. Ability to solve vision problems by progressively building up representations from pixels to faces. Challenge in constructing hierarchical representations efficiently.
Learning from Data: Necessity of learning from data to avoid manual programming. Importance of online learning for continuous updates and efficiency.
Components for AGI: Integration of probabilistic first-order logic, hierarchical representation, and online learning. Readiness for AGI research once these components are developed.
Innovation and Technological Progress: Discussion of the perceived decline in big innovation versus small technological tweaks. Skepticism towards the notion that all significant scientific discoveries have been made. Belief that individuals have more power now to make meaningful contributions and change the world.
Importance of Training and Education: Question regarding the training system’s impact on innovation. Belief that exceptional individuals will make breakthroughs regardless of training. Focus on identifying and nurturing talent rather than relying solely on formal education.
00:34:57 Emergent Properties and Surprises of Large-Scale AI Systems
Challenges in Recognizing AGI: Peter Norvig believes that the arrival of Artificial General Intelligence (AGI) won’t be a sudden shift but rather a gradual process. He compares it to the Turing test, where people may argue about the exact point at which a machine passes the test.
Emergent Properties from Extensive Networks: Google has observed surprising emergent properties due to the vastness of its network of staff and agents working together. One example is discovering unexpected limitations in bandwidth between computers. The company has encountered scenarios where jobs allocated to different machines fail to achieve optimal performance due to interference and crosstalk.
Co-evolution with the Web: Norvig highlights the dynamic relationship between Google and the web. Google’s actions, such as indexing and ranking, influence the web, and vice versa. This ongoing interaction has resulted in unintended consequences and challenges that were not initially considered.
Norvig’s Perspective on Space Exploration: He advocates for prioritizing robotic missions over human exploration due to cost-effectiveness and practical considerations. According to Norvig, robots are more suitable for long-term missions, as they can withstand harsh conditions and do not require life support systems. He believes that public interest in space exploration can be fulfilled through robotic missions, and that sending humans to Mars should not be the primary focus.
Notable AI Researchers: Norvig mentions several prominent researchers working on AGI, including Daphne Koller at Stanford, Stuart Russell and his group at Berkeley, and the Cognitive Robotics group at Toronto.
00:41:33 Efficient Symbolic Logic Reasoning for Modern Data Processing
Expressivity vs. Efficiency: There’s a tradeoff between the expressivity of logic and the efficiency of inference. Choosing the right subset of logic to capture important information while enabling efficient processing is crucial.
Approximations in Inference: Approximations are often necessary for efficient inference. Approximations should be close enough to provide meaningful results.
Challenges in Citation Parsing: Citations often contain errors such as misspellings, missing information, or slightly incorrect titles. First-order logic is required to quantify over all possible papers, authors, and institutions and reason about identity between them. Solving such problems with first-order logic is now feasible, unlike a few years ago.
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.
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.
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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.
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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.
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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.
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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.
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