Early Life:
Rodney Brooks was born in Adelaide, South Australia, in December 1954. He grew up in Australia until he was 22 and a half years old and then came to Palo Alto to attend Stanford University. His parents were a hairdresser mother and a telephone technician father. He had three siblings: an older brother, a younger brother, and a sister.

Interests and Education:
From a young age, Rodney Brooks was fascinated by arithmetic and mathematics. By age eight, he was trying to build computers using whatever he could get his hands on, such as switches, relays, light bulbs, and batteries. He was self-taught and learned from books and whatever he could read. He built a machine that could play tic-tac-toe and not be beaten by 1967. He read science fiction and anything he could find that talked about computers. He was particularly influenced by the movie 2001: A Space Odyssey, which sparked his interest in artificial intelligence. In high school, he had two math teachers who took a special interest in him and helped him get more materials. He did his undergraduate degree in pure mathematics at Flinders University of South Australia, taking 39 courses in mathematics and only one physics and one chemistry course. He was influenced by a faculty member, Bill Cornish, who took him under his wing in abstract algebra. He also had access to the university’s mainframe computer, where he spent 12 hours every Sunday teaching himself computer science. He wrote his first program in Fortran, which simulated a neuron talking to another neuron. He also built a physical neural network that could learn by using electrodes in copper sulfate in an ice cube tray.

Contributions to AI:
Rodney Brooks’ contributions to the field of artificial intelligence include his work on: Behavior-based robotics: Developing robots that use simple behaviors to interact with their environment, rather than relying on complex planning and reasoning. Subsumption architecture: A control architecture for robots that allows them to exhibit multiple behaviors simultaneously, with higher-level behaviors subsuming lower-level behaviors when necessary. Situated cognition: The idea that intelligence is situated in the body and the environment, and that robots should be able to learn and adapt to their environment through interaction. His work has had a significant impact on the field of robotics and has inspired many researchers to develop new approaches to building intelligent robots.

Inspiration from Insects:
Rodney Brooks observed the remarkable capabilities of insects despite their limited computational power. He recognized the potential for organizing computation in a simpler, more efficient manner.

Development of the Subsumption Architecture:
Brooks introduced the subsumption architecture, allowing sensory data to be directly linked to actuators in real-time. This approach enabled rapid responses and overall behavior from minimal computation.

Practical Applications and Influence:
Brooks’ behavior-based systems led to the creation of behavior trees, widely used in programming video games. His emphasis on direct interaction between the world and robots has been influential in robotics research.

Childhood Passion for Building Things:
Brooks’ childhood interest in creating moving objects inspired his pursuit of robotics. He derived immense satisfaction from seeing his creations operate in the real world.

Challenges and Success:
In the late 1980s, Brooks proposed the idea of “Fast, Cheap and Out of Control” robotic missions to explore other planets. He recognized the potential for sending multiple small rovers instead of a single expensive one. Despite initial setbacks, Brooks and his team persevered and eventually partnered with David Scott to develop small rovers for space exploration.

Background:
Rodney Brooks and his team collaborated with the Ballistic Missile Defense Organization (BMDO) after securing the support of an astronaut who had been to the moon. They developed a small rover prototype designed to land near Apollo 15’s lunar module and analyze materials exposed to lunar conditions for 20 years.

Challenges:
NASA’s initial reluctance to support the rover project posed a significant obstacle. Brooks and his team had to explore alternative launch partners, including companies and other nations with launch capabilities.

Collaboration and Innovation:
The partnership with BMDO, a non-NASA entity, proved crucial in overcoming NASA’s hesitation. The prospect of BMDO’s involvement prompted NASA to reconsider and ultimately include the rover project in the 1996 launch, leading to the historic landing of Sojourner, the first rover on Mars, on July 4th, 1997.

Overcoming Obstacles:
Brooks and his team demonstrated perseverance and resourcefulness in navigating the challenges posed by NASA’s initial lack of support. Their ability to secure alternative launch partners, particularly BMDO, played a pivotal role in the project’s success.

Impact and Legacy:
Sojourner’s landing on Mars marked a significant milestone in space exploration. It paved the way for subsequent rover missions to Mars, revolutionizing our understanding of the Red Planet and contributing to our knowledge of its geological and environmental characteristics.

Conclusion:
Rodney Brooks’ determination and innovative approach, coupled with strategic collaborations, ultimately led to the realization of the first rover landing on Mars. This achievement exemplifies the power of perseverance, adaptability, and the importance of forging partnerships to overcome obstacles and achieve scientific advancements.

What Surprised Rodney Brooks the Most in the Current State of Robotics?:
The proliferation of Roomba vacuum cleaners, with tens of millions sold, surprised Brooks. He expressed his transition from pure mathematician to vacuum cleaner salesman as a bittersweet experience.

The Biggest Misunderstanding about Robotics:
Brooks emphasized that more intelligence or learning alone will not enable robots to do everything. He highlighted the slow progress in developing complex robot hands and the challenges in creating humanoid robots that can perform a wide range of tasks.

Rodney Brooks’ Journey to Stanford for Graduate School:
His interest in artificial intelligence led him to pursue graduate studies at Stanford. He chose Stanford over MIT and Carnegie Mellon due to its proximity to Australia and the timing of his application.

Rodney Brooks’ Advisor and Early Work at Stanford AI Lab:
Tom Binford, a vision researcher, was Brooks’ advisor at Stanford. Brooks initially worked on computer vision and was part of the DARPA Image Understanding Program. He analyzed images of airplanes at San Francisco Airport to extract geometric models and identify aircraft types. The processing of each image took several weeks on the DEC-10 mainframe computer.

Rodney Brooks’ Career Path After Completing His PhD:
He worked with Guy Steele at CMU to develop a LISP system for the S1 Mark IIa supercomputer. He spent two years as a postdoc in robotics at MIT before returning to Stanford as a faculty member. Brooks then moved back to MIT in 1984, where he spent the rest of his academic career.

Rodney Brooks’ Inspiration from HAL in 2001:
Brooks, along with many other AI scientists, was influenced by the film 2001: A Space Odyssey and the portrayal of HAL. He attributed the impact to the imaginative vision of people like Marvin Minsky and the magic of the film’s production.

Origins of the Behavioral-Based Robot Approach:
In the mid-80s, the dominant approach in AI and robotics was sense, model, plan, and act. Brooks argued that this approach was too slow and inflexible for dynamic environments. Instead, he proposed a reactive, behavior-based approach that directly linked sensors to actuators, bypassing the need for a detailed world model.

Inspiration from Insects:
Brooks drew inspiration from insects, which can exhibit complex behaviors despite having relatively simple nervous systems. He observed insects in rural Thailand and wondered how they could produce such sophisticated behaviors with limited computational resources.

Embodied and Situated Robots:
Brooks emphasized the importance of building robots that were embodied and situated in the environment. He argued that simulations are doomed to succeed because they allow researchers to simplify the problem and hide the fake stuff. Real-world robots must deal with the complexities of physics and the unpredictability of the environment.

Building from the Bottom Up:
Brooks advocated for a bottom-up approach to building robots, starting with simple systems and gradually adding complexity. This approach allowed him to explore how complex behaviors could emerge from simple building blocks.

Subsumption Architecture:
Brooks developed the subsumption architecture, a control architecture for robots that is based on a cartoon version of evolution. The architecture consists of a hierarchy of layers, with each layer responsible for a specific behavior. Higher layers can subsume the behaviors of lower layers, allowing for more complex behaviors to emerge.

Influences from Cybernetics:
Brooks was influenced by the work of Ray Walters, who built simple robots using vacuum tubes. These robots exhibited complex behaviors despite their limited computational resources. Brooks’ early experiments with building robots as a teenager also influenced his approach to robotics.

Philosophical Influences:
Rodney Brooks was influenced by philosophers who criticized traditional symbolic AI, such as Hubert Dreyfus. Brooks’ approach to robotics is not based on German philosophy or phenomenology, but he did have students who were engaged with that work.

Interdisciplinary Influences:
Brooks was aware of the work of Lucy Suchman and others who studied human psychology and behavior. He participated in intellectual groupings during the 1990s, such as the simulation of adaptive behavior groups and the artificial life groups, which brought together researchers from different fields to explore new ideas in robotics.

Cross-Cultural Influences:
Brooks’ work was influenced by Japanese researchers, who were actively involved in the simulation of adaptive behavior and artificial life groups. He also mentioned a Japanese philosopher who was critical of traditional symbolic AI, but he could not recall the philosopher’s name.

Interdisciplinary Collaboration:
Brooks had students who were engaged with the work of philosophers like Hubert Dreyfus, which allowed him to incorporate insights from philosophy into his approach to robotics. Brooks’ work benefited from the cross-cultural exchange of ideas with researchers from different countries, particularly Japan.

Rodney Brooks’ Exposure to Phenomenology:
As a graduate student, Rodney Brooks studied German philosophy, including Heidegger and Husserl, through a class taught by Rodney Flores. Brooks found the philosophy challenging but gained an understanding of phenomenology and its ideas.

Phenomenology’s Influence on Brooks’ Work:
Brooks was inspired by the phenomenological concept of “ready-at-hand,” which aligned with J.J. Gibson’s idea of affordances, emphasizing how objects communicate their intended use through their design. This concept resonated with Brooks’ approach of direct coupling between perception and action, guiding behavior based on perceived affordances.

Critique of Searle’s Argument:
Brooks disagreed with Searle’s philosophical argument that machines could not possess true intelligence or understanding. Brooks maintained that biological entities are also machines, subject to the same physical forces and principles as non-biological entities. He believed Searle’s argument relied on invoking a higher order of existence that modern science does not support.

Perception and Action Coupling:
Brooks viewed phenomenology as a framework for understanding how perception and action could exist in the world without relying on a central world model or a simulation of reality. This perspective influenced his approach to robotics, emphasizing the importance of direct coupling between perception and action, allowing robots to respond effectively to their environment.

Influence on Students:
Brooks’ students, David Chapman and Phil Agre, embraced phenomenology more strongly, incorporating these ideas into their PhD theses. Chapman and Agre actively engaged in the simulation of adaptive behavior group and artificial life groups, contributing to intellectual discussions across continents.

Engagement with Artificial Life and Simulation of Adaptive Behavior:
Brooks engaged with artificial life and the simulation of adaptive behavior, although the extent of his involvement is not specified in this excerpt. He acknowledged the contributions of his students, Chapman and Agre, who were actively involved in these fields and contributed to intellectual discussions and research.

Background on Rodney Brooks’ Involvement in Artificial Life and Adaptive Behavior:
Rodney Brooks was deeply involved in the Artificial Life and Simulation of Adaptive Behavior communities. He attended conferences and co-edited proceedings, contributing to the field’s development.

Defining Intelligence:
Brooks highlights the lack of a precise definition for intelligence. He cites Patrick Winston’s perspective that intelligence is recognized when observed in machines, similar to human behavior. Brooks acknowledges the difficulties in defining intelligence, leading to the European Simulation of Adaptive Behavior group’s focus on behavior adaptation.

Goals of Artificial Intelligence:
Early AI pioneers aimed to create human-level intelligent systems. The introduction of terms like “Artificial General Intelligence” (AGI) and “Artificial Superintelligence” (ASI) reflects the pursuit of this goal. Brooks views these terms as marketing strategies, emphasizing the importance of understanding how to replicate human capabilities.

Naming Robots:
Brooks’ early robots were named after famous AI researchers. The six-legged robot, Genghis, was named for its ability to traverse challenging terrain like Genghis Khan. Subsequent robots were named after marauding conquerors, symbolizing their ability to navigate diverse environments. Cog, the humanoid robot, was named to represent its cognitive capabilities.

Impact of Rodney Brooks’ Work:
Brooks’ controversial early papers challenged conventional AI approaches. His article “Intelligence without Representation” questioned the reliance on representation in AI, generating significant debate. The paper faced initial rejection but was eventually published and gained widespread recognition, becoming required reading in various AI and psychology courses. Brooks received the Computers and Thought Award in 1991 for his contributions. His paper “Intelligence without Reason” further challenged traditional AI concepts, exploring the relationship between cybernetics, AI history, and embodied and situated cognition.

Rodney Brooks’ Provocative Titles and the Argument of “Planning is Just a Way of Avoiding Figuring Out What to Do Next”:
Brooks enjoyed inflaming the world with provocative paper titles like “Elephants Don’t Play Chess” and “Planning is Just a Way of Avoiding Figuring Out What to Do Next.” “Planning is Just a Way of Avoiding Figuring Out What to Do Next” argues that planning attempts to take a long-term perspective but ends up dividing it into smaller pieces, missing the crucial next step. Brooks’ subsumption architecture aimed to find the next necessary action without overly focusing on later steps.

Moravec’s Paradox and the Focus on Reason in Western Philosophy:
Hanson Hsu points out Brooks’ argument that survival in a changing physical environment is harder for evolution to solve than reasoning or language, contradicting centuries of Western philosophy. This cultural focus on reason as the defining characteristic of human consciousness posed a challenge to Brooks’ approach.

The Counterintuitive Nature of Brooks’ Approach and Its Implications for Self-Worth:
Brooks’ approach to AI was counterintuitive to many, making it difficult to accept. It challenged people’s views of themselves and self-worth, as it focused on the essence of intelligence as how we exist in the world rather than on intellectual pursuits. Early AI research emphasized tasks that smart individuals found difficult or competitive, such as chess and geometric reasoning, defining intelligence in a limited way. In contrast, Brooks saw the essence of intelligence in how organisms interact with their environment, akin to how animals like dogs and ants navigate the world.

Emergence as a Theme in AI:
-Rodney Brooks challenges the computational metaphors used to understand neuroscience, artificial intelligence, and artificial life. He argues that computation is not a fundamental property of intelligence.

Behavior-Based AI and Central Reasoning:
Brooks emphasizes the importance of emergence in AI, suggesting that behavior-based AI’s ability to act without central reasoning was unsettling.
-He draws parallels between this and the unsettling nature of large language models like ChatGPT, which can generate coherent text without reasoning.

The Paradox of Non-Centralized Reasoning:
-Brooks highlights the paradox of non-centralized reasoning in AI systems like subsumption and ChatGPT. These systems can act and produce convincing language without a central reasoning mechanism.

The Limitations of Computational Metaphors:
-Brooks criticizes the computational metaphors used to understand intelligence, arguing that they are based on a narrow view of computation derived from Turing’s work on paper arithmetic.

The Challenge to Computational Metaphors:
-Brooks believes that the computational metaphors for intelligence will not stand the test of time and that a new understanding of intelligence is needed. He suggests that this new understanding may take centuries to develop.

The Nature of Emergence:
Rodney Brooks emphasizes the concept of emergence in systems, particularly complex natural systems like the brain. He explains that emergent properties arise from the interactions of multiple subsystems, not from any single component or “magic” element. Brooks highlights the example of a gasoline-driven car, where the ability to drive on the freeway is an emergent property of the various components working together, not any specific part.

Reductionist Engineering and Emergence:
Brooks discusses the challenges of engineering complex systems using reductionist methods, which involve decomposing the system into smaller parts. He argues that reductionist approaches often struggle to capture the emergent properties that arise from the interactions of these parts. Brooks emphasizes the need to encode the emergence somehow in reductionist analysis and acknowledges that this can be a challenging task.

Decomposition and Understanding:
Brooks raises concerns about how the decomposition of natural systems into parts can bias our understanding of their emergent properties. He mentions the example of neuroscience, where the focus on neurons led to a neglect of the role played by other cells in the brain, such as those that hold the brain together. Brooks stresses the importance of considering the totality and wholeness of a system when studying its emergence.

Changing Landscape of AI Research:
Brooks reflects on the changing approaches and emphases in AI research between 1990 and 2010, before the rise of deep learning. He highlights the popularity of various learning mechanisms during this period, including neural networks, support vector machines, and reinforcement learning. Brooks acknowledges the surprising dominance of deep learning in recent years, but cautions against assuming that it is the ultimate answer or the greatest idea.

Reinforcement Learning and DeepMind:
Brooks discusses the emergence and success of reinforcement learning in the 1990s and 2000s, particularly DeepMind’s notable achievements with its alpha programs. He emphasizes the long history of reinforcement learning, with foundational papers dating back to the 1960s and Christopher Watkins’ Q-learning approach in the 1980s. Brooks points out that different learning approaches have experienced periods of popularity over time, reflecting the dynamic nature of AI research.

Bias Towards the Newest Ideas:
Brooks observes a tendency in the AI community to favor the latest ideas and approaches, often leading to an echo chamber effect. He suggests that this bias can be attributed to the increased number of voices and attention in the field, leading to the amplification of certain ideas. Brooks emphasizes the need to recognize that the latest idea is not necessarily the greatest or the ultimate answer, and that a balanced perspective is important in evaluating different approaches.

Genesis of Humanoid Robots:
Rodney Brooks’s inspiration for developing humanoid robots stemmed from a desire to accelerate the research process by skipping intermediate stages of animal-like robots. The 1992 film 2001: A Space Odyssey, particularly the birth of Hal, further motivated him to explore human-level intelligence.

Cynthia Brazil’s Contributions:
Cynthia Brazil, a student of Rodney Brooks, initially focused on six-legged walking robots, studying how they could adapt to broken parts and operate in a compromised state. She later joined the COG team and eventually shifted her interests toward human interaction aspects of robotics. Brazil created Kismet, a robot head that lacked physical capabilities but excelled in social interaction with humans.

Role of Emotion in Robotics:
Rodney Brooks believes that emotions play a role in attracting people to robots, but their practical value in robotics systems is still debatable. He emphasizes the importance of providing cues to humans about a robot’s behavior, such as the eyes of Baxter, an industrial robot, glancing at its intended movement direction. Brooks argues that the appearance of a robot carries an implied promise about its behavior, and displaying emotions without substance can lead to disappointment.

Emotions in Robots:
Showing genuine emotions in robots is challenging and requires consistency with the model of how emotions work, their past experiences, and their environment.

Multi-Agent Robots:
Maja Matarec and Lynn Parker worked on multi-agent robots during the early 1990s. Their research focused on group dynamics and interactions among robots, published in simulation of adaptive behavior journals.

Errol Morris’s Film:
Errol Morris’s wife saw a profile of Rodney Brooks in Gentleman’s Quarterly and suggested him for the film “Fast, Cheap, and Out of Control.” Brooks agreed to participate and filming took place in 1991 or 1992. The movie was released in late 1997 and Brooks was surprised to realize that his ideas hadn’t evolved significantly in the five years since the filming.

Leadership at MIT:
Brooks was responsible for merging computer science and artificial intelligence into the Computer Science and Artificial Intelligence Laboratory (CSAIL). The driving need for this merger was to foster interdisciplinary research and collaboration between these fields. The consequences have been positive, resulting in a vibrant research environment and significant contributions to both fields.

Project MAC and the AI Lab:
Project MAC was a research lab that combined computer science and artificial intelligence, promoting collaboration among researchers.

Separation of the AI Lab:
Within a few years, the AI lab separated from the computer science lab due to differences and turf wars.

Rodney Brooks’ Involvement:
Rodney Brooks joined the AI lab in 1981 and became involved in the planning committee for a new building on campus.

Collaboration and Fundraising:
Patrick Winston stepped down as the head of the AI lab, and Rodney Brooks took over in 1997. Rodney Brooks and Michael Detouzos, head of LCS, worked together successfully on raising funds from industry in Europe and Asia.

Unexpected Loss and Change:
In 2001, Michael Detouzos passed away unexpectedly. As the AI lab and LCS prepared to move into a new building, discussions began about merging the two labs.

Unified Leadership:
Rodney Brooks was chosen to lead the merged lab, which aimed to foster collaboration and joint research initiatives.

Name of the Lab:
The name “Computer Science and Artificial Intelligence Lab” (CSAIL) was chosen after a lengthy debate, with some people wanting to call it simply the “Lab for Computer Science” and others wanting to emphasize the importance of AI.

Rodney Brooks’ Leadership:
Rodney Brooks became the head of CSAIL, which was a merger of the Lab for Computer Science and the AI Lab. Initially, it was uncomfortable for both the AI and computer science researchers to have a former “crazy young guy” as their leader, but Brooks was able to calm down and help everyone work together productively.

Industry Collaborations:
CSAIL fostered many industry collaborations, which helped to internationalize research rather than having the US dominate it. This was a result of skepticism about academia among some advisors to the new president in the early 2000s, which led to a pullback in military funding for research.

Internationalization of Science and Engineering:
The ups and downs of the political and popular spectrum continue to affect who people in the US work with in terms of international collaboration. The internationalization of science and engineering can be scary for many people, especially in the context of perceived AI cold wars with other countries like China.

Founding Lucid:
In 1984, Rodney Brooks co-founded Lucid, a startup in Silicon Valley. The company initially focused on developing a Lisp compiler. Brooks secured venture funding and used an advance from a book sale as a down payment on a house in Lexington, Massachusetts. He continued working on the compiler while teaching at MIT and pursuing other research.

Challenges and Successes:
The compiler project spanned eight years, with updates sent via cartridge tape across the country. Lucid targeted multiple computer architectures due to the fragmented market at the time. Despite initial success, investors expressed concerns about the Lisp business.

Cadillac and Pricing Lessons:
Lucid shifted its focus to developing an interactive debugging environment called Cadillac. The system aimed to be comprehensive, leading to a high price point of $20,000 per seat. The company faced competition from Borland, which offered a more affordable and streamlined system. Ultimately, Lucid went out of business due to pricing and customer-related challenges.

Moving on to iRobot:
Brooks’ entrepreneurial journey continued with the founding of iRobot, a company known for its innovative robots. The lessons learned from Lucid’s failure influenced his approach to pricing and customer needs in subsequent ventures.

iRobot’s Origin and Initial Space Exploration Focus:
iRobot was initially known as IS Robotics, a company founded with the purpose of developing and sending small rovers to explore other planets. Collaborations with Ballistic Missile Defense Organization and NASA led to the placement of an older project on Mars.

Transition to Various Business Models and Research Projects:
iRobot experimented with numerous business models without venture capital involvement. The company sold research robots to universities, partnered with the Japanese government for nuclear power plant inspection robots, and collaborated with Hasbro to build robot toys in China.

Defense and Civilian Success:
iRobot gained significant revenue from the defense department, leading to the development of PackBot robots. The Roomba, a robotic vacuum cleaner, was introduced to the market in 2002 and achieved great success.

PackBot’s Role in Critical Situations:
PackBot robots were deployed at the 9-11 site, assisting in search and rescue operations. They were also used in Afghanistan and Iraq to detect weapons caches and defuse roadside bombs, protecting bomb technicians. PackBot’s contribution at the Fukushima nuclear plant was notable, where it aided in the decommissioning process and provided vital information in highly radioactive areas.

Stepping Away from iRobot and Founding Heartland/Rethink:
Rodney Brooks departed from iRobot in 2011 after 21 years, seeking a hands-on approach in a smaller organization. He recognized the changing labor landscape in China and the need for collaborative robotics to supplement human workers. Heartland/Rethink was established with the aim of integrating robots and people in manufacturing facilities, improving productivity, and enabling local production.

The Safety Feature of Baxter and Sawyer Robots:
Baxter and Sawyer robots were designed to be safe for human interaction, allowing them to operate in close proximity to humans without the need for safety cages or barriers. This innovative approach aimed to transform factory environments by integrating robots and humans in collaborative workspaces.

Teach by Demonstration Programming:
Brooks emphasized the importance of simplifying robot programming. Baxter and Sawyer robots were programmed through “teach by demonstration,” where users could physically guide the robot through desired tasks, making it accessible to individuals without programming expertise. This user-friendly approach aimed to democratize robot programming and expand their use beyond traditional industrial settings.

Financial Failure Despite Artistic Success:
Despite their technological advancements and innovative features, Baxter and Sawyer robots failed commercially. Brooks attributed this failure to cost overruns and a shift in target markets. The original vision of affordable robots for non-traditional manufacturing settings was compromised, leading to higher costs and a focus on conventional manufacturing.

Impact of the Trade War:
Rethink Robotics’ business model relied heavily on manufacturing robots in the United States and selling them in China. The trade war between the United States and China resulted in retaliatory tariffs that increased the price of Rethink Robotics’ robots in China. This made it economically unviable to continue the business, leading to the shutdown of Rethink Robotics in 2018.

Reasons for Leaving Academia:
Rodney Brooks retired from MIT in 2010 after a successful career as a professor and administrator. He felt a strong sense of duty to give back to the academic community that had supported him throughout his career. After fulfilling this obligation, he desired to return to working in a small team environment.

Involvement with Heartland Rethink Robotics:
Brooks worked at Heartland Rethink Robotics for a few years after leaving MIT. He did not teach at MIT during this time and initially had an arrangement where he received no pay. Eventually, he had to make a decision between returning to MIT or continuing at Heartland Rethink Robotics.

Joining the Board of Toyota Research Institute:
Brooks joined the board of Toyota Research Institute in 2016 when it was first getting started. He helped connect the institute with MIT and Stanford and influenced their approach to collaboration. As the institute became more mainstream and integrated within Toyota, the need for an external board overseeing it diminished.

Latest Venture:
Brooks’ latest venture started in 2019 when he co-founded a company. The venture initially focused on developing robots using infrared technology to disinfect surfaces and kill bacteria. Due to the changing understanding of COVID-19 transmission, the company pivoted to building human-centered robots for use in warehouses. These robots are designed to assist human workers, improve ergonomics, and address the labor shortage in the fulfillment industry.

AI and Robotics:
Rodney Brooks’ company, Robust AI, focuses on developing robots called Carter that can autonomously cart stuff around and follow people. Brooks’ connection to Mark Rayburn, founder of Boston Dynamics, goes back to their friendship and shared interest in robotics. Subsumption architecture, a concept developed by Brooks, is related to behavior trees used in many robotics projects today.

Self-Driving Cars:
Brooks acknowledges the use of subsumption architecture-related concepts in self-driving car projects, particularly in the form of behavior trees. He emphasizes the combination of various techniques in self-driving car systems, including search algorithms, classical planning, optimization techniques, and neural models for object recognition.

AI Realism and Hype:
Brooks identifies himself as an AI realist who aims to counter the exaggerated hype surrounding current AI trends. He expresses skepticism about the capabilities of GPTs and deep learning models, arguing that they lack common sense and the ability to generalize to new situations.

Hype Cycle and Autonomous Vehicles:
Rodney Brooks critiques the over-optimistic predictions and hype surrounding technological advancements. He cites the example of autonomous vehicles, where major car companies promised widespread deployment by 2020-2022, but none of these predictions materialized. Brooks emphasizes the lengthy timeline required for technological developments to transition from research to real-world applications.

Autonomous Driving Milestones:
Brooks highlights a significant milestone in autonomous driving achieved in 1987 by Ernst Dickmans in Germany. Dickmans’ autonomous vehicle drove on a public freeway at speeds exceeding 55 miles per hour for over 10 miles without human intervention, amidst other cars. Despite this achievement, Brooks notes that widespread deployment of autonomous vehicles took much longer than initially anticipated.

Overhype and Misguided Expectations:
Brooks criticizes the tendency to overhype research results and create unrealistic expectations for immediate deployment. He emphasizes that technological advancements, such as autonomous driving, require extensive time and effort to transition from research to real-world applications. Brooks cautions against relying on over-optimistic predictions and urges a more realistic assessment of the timeframe for technological progress.

LLMs: Magic or Reality?:
Rodney Brooks highlights the current hype surrounding LLMs like ChatGPT, emphasizing the tendency to overestimate their capabilities due to their impressive performance. He cautions against attributing magical abilities to these models without understanding the underlying technology and the challenges they face.

Distinguishing Competence from Performance:
Brooks emphasizes the gap between human and machine generalization. Humans can observe someone’s performance and infer their competence, but this generalization does not apply to machines. Machines may perform tasks in a narrow manner, and their capabilities may not extend beyond specific contexts.

Language Generation and Its Limitations:
Brooks shares his experience using ChatGPT for personal programming problems, acknowledging its usefulness for generating smooth English. However, he emphasizes that LLMs can be misleading due to their definitive and positive responses, which may not always be accurate or relevant.

The Need for Context and Human Interaction:
Brooks stresses the importance of contextualizing language generation and understanding capabilities to make LLMs useful. He believes that LLMs will improve human-machine language interactions but cautions against equating them with general intelligence.

The Possibility of AGI:
Brooks does not provide his perspective on the possibility and timeframe of AGI in this segment of the transcript.

AGI and the Singularity:
Rodney Brooks believes that building machines as intelligent as humans is possible in principle but uncertain about a timeframe. He sees the singularity as a desire for eternal life without religious belief.

Consciousness:
Brooks acknowledges the lack of understanding and clear language regarding consciousness.

Concerns about Current AI Systems:
Chat GPT and similar AI systems can facilitate the generation of misleading and bogus content. There is a risk of overwhelming the usefulness of channels with excessive data and pollution. Bad actors may abuse AI systems, leading to scams and diminished quality of information.

Regulation and Hype:
Brooks emphasizes the need for regulation to prevent hypesters from promoting harmful or misleading AI products. He calls for a balanced approach, avoiding both excessive hype and unjustified fears.

Potential Benefits of Generative AI:
With proper safeguards, generative AI can enhance search functionality by reducing hallucinations. Natural language interfaces will become more refined, leading to easier and more intuitive interactions with devices.

Lessons from Computing History:
Brooks views computing as a social construct rather than a natural kind. Studying the history of computing can reveal assumptions, missed opportunities, and alternative paths of development. The uniqueness of computers lies in Moore’s Law, which has enabled exponential growth in computational power.

Moore’s Law and the Future of Computer Architecture:
Rodney Brooks highlights the remarkable run of Moore’s Law, which has sustained an exponential growth in computation for an unprecedented duration. He emphasizes that the physics of Moore’s Law is reaching its limits, and the industry must explore alternative approaches to achieve further advancements. Brooks sees the current era as a golden age for computer architecture, as it demands innovation and creativity beyond traditional Moore’s Law scaling.

Studying History to Find New Opportunities:
Brooks believes that studying the history of computing can help identify unexplored adjacencies and potential breakthroughs. He suggests that revisiting past ideas, even those initially considered unsuccessful, may lead to valuable insights and innovations. This historical perspective can guide researchers and engineers toward untapped opportunities in computer architecture.

Honored to be a CHM Fellow:
Brooks expresses his gratitude and surprise at being selected as a fellow of the Computer History Museum. He acknowledges the prestigious company he joins, including Grace Hopper as the first fellow. Brooks views this honor as a recognition of his lifelong passion for understanding computers and their capabilities.

Legacy and Perspective:
Brooks reflects on his early days as a pioneer in the field and his enjoyment of building real organizations. He now aims to contribute a perspective gained from his extensive experience to current discussions on technology and AI. Brooks emphasizes the importance of bringing realism and historical context to these discussions, challenging assumptions and offering insights based on past lessons.

Understanding Computation and AI:
Brooks expresses his desire to delve deeper into the origins and evolution of computation, AI, neuroscience, and artificial life. He seeks to uncover the motivations and intentions behind early work in these fields. Brooks believes that examining the context and perspective of past research can lead to a better understanding of current approaches and potential pitfalls.

Optimism for Positive Change:
Brooks acknowledges the challenges facing humanity and the planet, including climate change and a growing population. He expresses optimism about the potential of new technology innovations to make a positive difference. Brooks encourages rapid progress and innovation, recognizing the urgency of addressing these challenges.

Hopes for the Future:
Brooks hopes for a future where technology benefits humanity by addressing climate change, providing access to clean energy, and improving healthcare. He envisions a future where technology enables more sustainable and equitable societies. Brooks is excited about the potential of technologies such as nuclear fusion, energy storage, and personalized medicine to contribute to a better future.

New Farming Methods:
Utilizing technology, indoor farming, genetic engineering, and biological mechanisms can reduce pollution and water usage in food production.

Energy Production:
Transitioning to renewable energy sources, such as solar, wind, and tidal, is crucial to reduce pollution.

Aging Population and Technology:
As society ages, technology must focus on preserving dignity, independence, and happiness for the elderly.

Advice for High School Students:
Working with others, being part of a team, and seeing technology in action are essential for personal and professional growth.

Advice for Young Innovators and Entrepreneurs:
Be fearless in approaching challenges and don’t let fear prevent exploration and analysis of new opportunities.