Gratitude to Academia:

Larry Page begins his presentation by expressing gratitude to the community of computer science researchers who trained him and gave him the tools to build his company. He emphasizes that the work they do is extremely important and adds that his own father was a professor.

The Power of Small Teams in Tech:

Page describes how small teams can wield enormous influence in the technological landscape. He illustrates this with an anecdote about how his then-small company managed to attract millions of customers, highlighting the transformative power of technology to scale impact.

Technological Anxiety:

Page addresses societal fears about job loss due to technological advancements. He argues that this fear is unwarranted, citing how automation has already changed industries, such as farming, without leading to societal collapse. Instead of fear, he suggests people should feel excitement about the possibilities.

Underappreciated Importance of Computer Science:

Page laments that computer science is not considered “sexy,” leading to a decrease in enrollments. He underscores this as a problem since he believes the field can tackle globally beneficial projects.

Potential in Automated Transportation:

Page is especially excited about the potential for automated cars to revolutionize transport, save lives, and improve work-life balance. He indicates that the technology can make infrastructure more effective and improve the quality of life by expanding job choices for people living in metro areas.

Identifying Key Problems:

Page emphasizes the need for a disciplined approach to identifying the world’s biggest problems that technology can solve. He questions whether the community is focusing enough on what really matters.

The Untapped Potential in Robotics:

Finally, he delves into robotics, indicating there are still a multitude of problems waiting to be solved in the field. He touches upon advancements in sensors and safety measures in industrial robotics that pave the way for a new era of useful and safe robots.

Conclusion:

Page ends by encouraging the community to focus on the vast untapped potential in various fields, advocating for a shift from anxiety to optimism about what technology can achieve.

Risk Aversion in Innovation:

Larry Page argues that people often focus on projects guaranteed to succeed. This safety-first approach may involve making minor improvements to existing systems. While this may be comfortable and measurable, Page contends that it is not impactful in the long run.

The Lure of Community Approval:

People tend to gravitate toward projects that are easily publishable or measurable, in part because there’s already a community that can assess and validate the work. This offers an easier path but according to Page, often leads to work that doesn’t truly matter.

Embracing Novel Ideas:

Page emphasizes the importance of doing something new or different, even when it’s challenging to gain community support. He cites the example of the book search initiative that many thought was impossible or unrelated to their funded research. Despite this, Page pushed for the project because he saw its potential and practicality.

Personal Involvement and Demonstration:

To overcome resistance and inertia, Page often took matters into his own hands. He provided examples like Street View and book scanning where he had to personally demonstrate their feasibility by setting up initial prototypes. He had a camera mounted in his car for Street View and scanned books to measure the time and issues involved, eventually gaining traction for these projects.

Importance of Calculated Risks:

Page criticizes the tendency to declare projects as ‘impossible’ without actually performing any calculations or feasibility studies. He notes that many projects can be achieved with reasonable resources if one takes the time to analyze their practicality.

Time Lag in Acceptance:

Page shares that it took around six years to get people to work on book scanning, even though the practicality and importance were clear early on. This delay was not due to any significant technological advancements but rather a slow shift in people’s willingness to take on challenging projects.

Conclusion:

Page’s overarching message is that innovation and meaningful work often require stepping out of one’s comfort zone, taking calculated risks, and persevering despite a lack of immediate community support or measurable success.

Specialization vs. Generalization:

Larry Page criticizes the overly specialized nature of traditional education in fields like computer science. He argues that this type of narrow focus is not healthy for innovation. Instead, Page suggests that there should be individuals who work on more general, interdisciplinary problems.

Advocacy for Broad Engineering Education:

Page suggests that a broad engineering education would be beneficial for tackling complex projects. He observes that many successful initiatives involve interdisciplinary skills—ranging from sensing and the physics of light to mechanical engineering and robotics.

Practicality Breeds Research Opportunities:

Page reflects on resistance he faced for projects deemed too practical by academia, such as ‘photographing the world.’ He argues that what may seem too practical at first can lead to novel research problems, offering chances for groundbreaking work in fields like computer graphics.

Embracing Failure and Its Side Effects:

A common objection to undertaking ambitious projects is the fear of failure. However, Page points out that even ‘failed’ ambitious projects often yield beneficial side effects. For example, a team at Google that was working on core AI ended up developing the ads targeting system, which became a major revenue stream for the company.

Ambition Attracts Talent and Generates Spin-offs:

Page emphasizes that ambitious projects tend to attract smart, hard-working individuals. Even if the project doesn’t achieve its primary goal, it usually results in some other significant advancement. He cites the Apollo projects as an example, noting that while they may not have led to a lasting space industry, they did result in various technological spin-offs.

Conclusion:

Larry Page’s key message revolves around the need for a more interdisciplinary approach to education and project development. He encourages embracing ambitious projects, arguing that even if they fail to achieve their primary objectives, they often lead to unexpected yet valuable advancements.

AI’s Simplicity and Complexity:

Larry Page posits that the eventual development of AI will likely be simpler than many anticipate. He dismisses the notion that it will take decades of intricate algorithms to achieve. Page suggests that the achievement of AI will involve less complexity and more scalability.

Human Genome as an Analogy:

Page uses the size of the compressed human genome as an argument to support his viewpoint on the simplicity of AI. He points out that the human genome is smaller than modern operating systems, suggesting that the ‘software’ that operates our brains must be relatively simple, not requiring highly complex algorithms.

Technological Advancements:

Page highlights the technological leaps since early attempts at AI, pointing out that past failures occurred on computers far less powerful than those available today. He suggests that it’s now more plausible to make significant progress in AI and finds it perplexing that not many are ambitiously pursuing it.

Lack of Competition in AI:

Page indicates that there is surprisingly little competition in the AI research field. He notes his own efforts to encourage more people to venture into this domain but acknowledges that many are hesitant, often offering various reasons for their reluctance.

Singularity University:

Page briefly mentions Singularity University, an initiative focused on exploring technological discontinuities like AI. This model of education engages young people and seasoned professionals to ponder over transformative technologies. Page thinks that this might be a good blueprint for future higher education programs.

Conclusion:

Larry Page emphasizes the overlooked potential of AI and urges for more ambitious efforts in this field. He suggests that the computational barriers are less daunting than before and promotes a rethinking of education models to address this and other transformative technologies.

Observation Leading to Chrome OS:

Larry Page shares insights into the development of Chrome OS, revealing that the idea was partially sparked by a simple observation: many people in meetings were only using a browser on their laptops. This led to questioning the complexity of entire operating systems when the primary usage was so singular.

Unix’s Legacy and Starting Fresh:

Page mentions Unix’s 40th birthday to highlight that most modern operating systems are derivatives of Unix. He advocates for the need to occasionally start fresh and simplify, aiming to make software really work for users.

Software Bloat and Efficiency:

He expresses concerns over software bloat, describing it as a layering of bad software development techniques. He emphasizes the gap between the theoretical minimum time a task should take on a computer and the actual time it takes, citing how services like Gmail aim for efficiency, as they are used globally.

Theoretical Minimums:

Page introduces the concept of “theoretical minimums” as benchmarks for what computational tasks should ideally take in terms of time. He notes that while large-scale services like Gmail operate above their theoretical minimums, they are not egregiously so. He implies that most desktop applications are far less efficient, being many times above their theoretical minimums.

Case Study on Software Efficiency:

Page points to a study comparing Microsoft Word on different hardware setups to illustrate how software bloat has led to inefficiencies. He states that older machines were faster at executing common tasks than newer setups, emphasizing that while newer versions of software have more features, they often sacrifice efficiency for the most commonly used functionalities.

In summary, Larry Page discusses the thinking behind Chrome OS, emphasizing the need for software to be efficient and purpose-built for user needs. He advocates for the reconsideration of software development practices to counter the prevalent issue of software bloat.

Cheap Yet Powerful Laptops:

Larry Page identifies a massive opportunity in the prevalence of low-cost laptops, stating that they could efficiently run most basic tasks. He sees this as a way to make computing more accessible, aiming to optimize systems to fully utilize these inexpensive but capable machines.

Computer Maintenance Issues:

Page discusses the maintenance overhead of owning multiple computers, indicating that the industry hasn’t addressed this concern adequately. He suggests that better, simpler operating systems could make it feasible for individuals to own multiple computers without the burden of constant management.

Application Installation:

He briefly touches on the complications surrounding software and application installations, suggesting that web models, which don’t require installations, are advantageous. Native Client is hinted to address this issue further.

Open Systems and Collaboration:

Page recalls growing up in an era where Microsoft OSs dominated the computing landscape, criticizing it as a “closed system” that hindered innovation and adaptability. He appreciates open source alternatives like Linux that allow for modification, study, and improvement, fostering a collaborative environment.

The Push for Open Source:

In closing, he expresses enthusiasm for promoting open systems, advocating for a computing world where the core software and systems are open, modifiable, and can be improved by a community.

Larry Page presents his thoughts on the untapped potential of low-cost computers, the necessity for easier system maintenance, and the advantages of open-source computing. He champions a shift toward making both hardware and software more accessible, user-friendly, and open for collaborative development.

Open for Collaboration:

Larry Page emphasizes his desire to collaborate with the audience, suggesting openness to external ideas and expertise. This marks an approach that is not insular but collaborative.

Privacy in Cloud Storage:

Page responds to a query about keeping book searches private. He shifts the focus to the bigger issue of data protection laws. He argues that data stored in the cloud should have the same legal protections as data stored on individual machines, highlighting an oversight in current laws.

AI, Data, and Scale:

Answering a question about artificial intelligence, Page says the field requires scale to make significant advancements. He favors “relatively dumb algorithms” combined with massive data sets for learning. Page mentions Google’s machine translation as an example, saying that good algorithms alone aren’t enough; they need substantial parallel text for training.

Time and Resource Commitment in AI:

Page challenges the conventional wisdom in AI development. He believes the focus should be on practicality and scale, rather than trying to “be smart on the whiteboard.” He suggests that even if one had computational resources similar to a human brain, it would take many years to see if an AI system is successful, emphasizing the need for long-term commitment and large-scale resources.

Android vs iPhone:

Larry Page addresses the competition between Android and iPhone, emphasizing Android’s rapid development and forthcoming updates. He suggests that Android started a version behind iPhone but is catching up. Page anticipates that improvements in hardware, along with a large app environment distinct from iPhone’s, will offer a competitive edge.

Android User Experience:

Page shares his personal experience with Android, mentioning it’s his only phone and works well for him. He hints at new hardware already released in Europe that he believes will make the Android experience better, including thinner devices without keyboards, which he considers advantageous for all purposes except typing.

Hardware Evolution:

Page is optimistic about the cost-effectiveness and abundance of displays, which leads him to question the need for dedicated devices like alarm clocks. He foresees a future where people will have multiple hardware devices, contradicting the notion of a single universal device for all needs.

Google’s Role in Future Devices:

When asked about Google’s place in a future where a single device might manage multiple functions, Page disagrees with the premise. He argues that people will continue to want multiple hardware devices, driven by declining costs and increasing functionality. He mentions his interest in consumer electronics and the wide array of devices seen at electronics shows as evidence for this trend.

Special Purpose Devices vs Computers:

Larry Page opines that specialized gadgets should evolve into “real computers” to offer more general utility. While phones have recently become powerful computing devices, many other gadgets with LCDs and input-output mechanisms have not.

Future of Devices:

Page envisions a future where we’ll have numerous devices offering rich Human-Computer Interaction (HCI) opportunities. These might even include technology that projects information directly into our eyes, eliminating the need for physical displays.

Input-Output Limitations:

Page identifies input-output systems as a significant bottleneck in current human-computer interaction. He cites the limitations of typing on a laptop, using a touchscreen phone, and voice inputs. These systems are either too slow, require focused attention, or are impractical in noisy or private settings.

User Interfaces and Research:

Expressing dissatisfaction with current user interfaces, Page calls for more research in the HCI field. He criticizes existing designs, like car interfaces, that have moved from simple, memorizable buttons to display systems requiring visual attention, stating such designs could be dangerous. He considers HCI a significantly under-invested area, offering immense potential for impactful research.

Gap in Ambitious Energy Research:

Larry Page discusses that there’s a lack of focus on ambitious projects capable of world-changing impacts in the energy sector. While many startups are working in areas like solar thermal energy, they are not aiming to compete with conventional energy sources like coal on cost.

Startup Motivation vs Global Needs:

Page points out that startups are content serving smaller, local demands for renewable energy, often at higher prices. For example, California buys solar energy at three times the price of coal. These startups are not interested in solving global energy issues; their focus is on business success, often leading to Initial Public Offerings (IPOs).

Scaling the Solution:

According to Page, the scale of the problem requires solutions from companies worth hundreds of billions of dollars, not just startups aiming for billion-dollar valuations. He highlights a “scale problem,” indicating that the current pace of innovation is not fast enough to meet the demands of a global energy crisis.

Google’s Approach:

Google aims to accelerate this pace by making small investments in projects that focus on ambitious, scalable solutions, such as cost-competitive solar thermal systems and geothermal energy. The goal is to foster innovation that could otherwise take years to come to fruition.

Investment in Underexplored Areas:

Google has looked into areas like geothermal energy, where drilling deep into the earth can produce significant heat. The challenges in drilling for heat differ from drilling for oil, and Google has begun funding these ambitious projects to expedite progress.

In summary, Larry Page emphasizes the need for more ambitious and scalable research in the energy sector. He suggests that both startups and larger entities need to realign their focus to solve broader global energy challenges, not just localized or less impactful problems.

Scalability in AI and Semantic Web:

The questioner, Dejun Do, expresses concerns about the scalability problems in traditional AI and semantic web technologies. He notes that these systems struggle to handle large-scale reasoning and suggests that adding semantic capabilities could enrich search results.

Importance of Tackling Large Problems:

Larry Page argues that in order to solve scalability issues, one needs to tackle big and impactful problems from the start. He believes that many existing algorithms don’t scale because they were not designed with large-scale applications in mind.

Simple yet Scalable Solutions for AI:

Page anticipates that when scalable AI eventually emerges, its underlying principles will likely be straightforward but executed on a large scale. He argues that success in this field will result less from innovative leaps and more from sustained hard work to scale existing algorithms.

Practical Issues and Hard Work:

Page emphasizes that getting things to actually work on a large scale involves overcoming a host of practical issues, which he considers to be 99% hard work. He believes there is a bias against this kind of hard work in the research community, which is detrimental to progress.

In summary, Larry Page stresses the need for focusing on scalability and tackling large, impactful problems to advance in AI and semantic technologies. He believes that most challenges in making systems work at scale are not insurmountable technical problems, but rather require concerted effort and practical problem-solving.