Background:
In 1945, Vannevar Bush envisioned a future where we rely heavily on tools and software. Early programming involved writing tables and assembly language. Fortran and templates were used for more advanced programming.

Programming as a Mathematical Science:
Programmers were seen as micromanagers, telling the computer exactly what to do step by step. Programming was based on logic, proofs, and assertions.

Modern Programming:
Today, programmers often rely on Stack Overflow and copy code, rather than writing everything from scratch. Programming has shifted from a mathematical science to a more natural science approach. Programmers observe existing code, experiment, and adapt it to their needs.

Machine Learning’s Impact on Programming:
Machine learning is changing the way we program. We endow computers with flexible models and the ability to observe and change themselves. This enables self-driving cars, chess-playing computers, and other AI applications.

AlphaZero vs. Deep Blue:
Deep Blue (1997) used expert chess knowledge, historical data, and algorithmic approaches to beat Kasparov. AlphaZero (2017) discarded all that and learned to play chess solely from the rules and by playing against itself. This demonstrates the power of machine learning to surpass human expertise.

Conclusion:
Machine learning is revolutionizing programming, but it doesn’t eliminate the need for programmers. Programmers are still needed to design algorithms, understand underlying concepts, and solve problems creatively.

Tools and Approach:
The approach to AI has evolved from a logical approach to a natural history approach and now resembles a hard science. The focus is on gathering data, exposing systems to data, and conducting experiments. This approach requires fewer programmers and less emphasis on writing code. Instead, the focus is on showing the machine the right data.

Data Science:
Data science is the intersection of programming, math/statistics, and knowledge of the subject matter. Data scientists must have a basic understanding of all three areas. Thesis advisors often possess these skills but may lack programming expertise.

The Danger Zone:
The danger zone in data science occurs when individuals with programming and domain knowledge but limited statistical expertise make mistakes. However, having sufficient data can mitigate the risk of serious statistical errors.

Democratizing Data Science:
The goal is to simplify data science to make it more accessible to a broader range of individuals. This will enable more people to contribute to the field and accelerate progress.

Automation and Domain Expertise:
Makoto Koki, a cucumber farmer’s son from Japan, sought to automate cucumber sorting using TensorFlow and a camera. Despite lacking domain expertise and professional programming skills, Koki’s engineering background enabled him to successfully train a network to sort cucumbers into nine categories.

Necessary Knowledge for Building Deep Learning Systems:
Basic understanding of the system’s components and their functions, such as the number of convolutional layers, learning rate, and loss function. Ability to provide hints to the system, such as lighting conditions, class imbalance, and desired error distribution.

Challenges in Democratizing Deep Learning:
The need to eliminate the requirement for deep technical knowledge, such as understanding internal system parameters. Automating tasks like data augmentation, correcting mislabeling, and handling distribution skew. Developing systems that are differentiable end-to-end to allow for continuous improvement and error correction throughout the data processing pipeline.

Importance of Data Processing:
Data processing and data wrangling are crucial for successful deep learning applications. Many startups allocate excessive time to model building and overlook the significance of data preparation and production system maintenance.

Differentiable Systems:
Deep learning systems should be differentiable end-to-end, enabling error correction throughout the entire data processing pipeline. Current systems only allow for error correction in the model building step, neglecting the impact of data cleaning and transformation choices. Building differentiable systems will allow for continuous improvement and adaptation to changing data and requirements.

Technical Debt in Machine Learning:
Complex machine learning models can introduce technical debt by eroding boundaries between modules and making it harder to debug. Data dependencies in machine learning systems are more costly than code dependencies, as they can lead to errors that are difficult to detect and correct.

Data Quality and Errors:
Data errors are common in machine learning systems due to the lack of comprehensive tools and methodologies for managing and validating data. These errors can have significant consequences, such as the accidental removal of McGill University’s web pages from Google’s index due to a misunderstanding of regular expressions.

Need for Probabilistic Reasoning:
Many programming languages are based on logical reasoning, with constructs like if statements that evaluate to true or false. Machine learning requires the ability to reason probabilistically, as data is often uncertain and conclusions are based on probabilities rather than absolute truths.

Challenges in Machine Learning:
Machine learning systems can suffer from biases, which can lead to unfair or discriminatory outcomes. Overfitting occurs when a model learns too closely to the training data and fails to generalize well to new data. Underfitting occurs when a model fails to capture the underlying patterns in the data and makes inaccurate predictions.

Opportunities in Machine Learning:
Machine learning has the potential to solve complex problems and achieve breakthroughs in various fields, such as healthcare, finance, and transportation. By addressing the challenges and developing better tools and methodologies, machine learning can be used to create systems that are more reliable, accurate, and ethical.

Machine Learning and AI Use Probabilities and Not Boolean Logic:
Peter Norvig highlights the differences between AI and traditional programming. In AI, we deal with uncertainty and require probabilities, not just true or false values. AI requires reasoning from evidence in both directions and operating in any direction, not just from inputs to outputs.

Probabilistic Programming for Simulating and Predicting Outcomes:
Probabilistic programming allows us to build models of how variables relate to each other and simulate them. By observing one value, we can infer the likely values of other variables that could have led to that observation. This approach is more intuitive and closely aligns with machine learning and AI problems, moving away from Boolean logic and towards random variables.

Democratizing AI through Query Languages:
Norvig emphasizes the need for programming languages that are accessible to less skilled programmers. Query languages can be extended to allow users to perform more sophisticated tasks without writing complex programs.

Examples of Query Language Applications:
A query to send a mailing to customers within a specific demographic can be written without directly asking customers for their age or income. The system can infer these attributes from past purchase patterns, with a margin of error that is acceptable for such applications. In electronic medical records, a query can be used to generate a differential diagnosis for a patient, considering multiple possible diseases and their likelihoods, rather than just returning a single diagnosis.

Conclusion:
Norvig emphasizes the importance of probabilistic programming and query languages in making AI more accessible and useful for various applications, including those involving uncertainty and complex data analysis.

Monte Carlo Simulation in Probabilistic Programming:
Probabilistic programming offers an alternative approach to the SQL model by introducing multiple possible values in each field, resulting in a more complex model. Monte Carlo simulation is used in probabilistic programming to attempt various scenarios and summarize the results effectively.

User Interface Challenges:
The challenge lies in presenting the complex idea of multiple possible values to the user in a clear and simple manner. The goal is to find treatments with no adverse effects that cover all diagnoses while considering the uncertainty in the data.

Mars Climate Orbiter Failure:
The Mars Climate Orbiter mission encountered a failure due to a mix-up between imperial and metric units. The spacecraft came in at 57 kilometers instead of the intended 220 kilometers, resulting in its loss.

Probability and Accuracy:
The failure highlights the importance of explicitly dealing with probability in complex systems. Traditional approaches focused on providing a single answer, while probabilistic methods would have detected anomalies in the data.

Variance and Model Mismatch:
The failure was not primarily due to the position of the spacecraft but rather due to its variance not matching the expected model. Tracking variance explicitly could have prevented the accident.

Distributed Teams and Communication:
The team’s split location between JPL in Pasadena and Lockheed Martin in Colorado may have contributed to the failure. Co-located teams might have communicated more effectively and identified the discrepancies.

Conversation as a Simpler Approach:
Peter Norvig emphasizes the significance of simplifying communication between humans and machines, suggesting the use of natural language processing (NLP) to enable conversational interactions.

Drawbacks of Voice Menus:
Norvig criticizes traditional voice menus, highlighting their frustration and inefficiency due to their structured and inflexible design.

Mismatch Between Programming and Natural Language:
He explains that conventional programming approaches, with their rigid if-then constructs, are not suitable for conversational systems because they don’t align with how people naturally communicate.

Examples of Conversational Success in Fiction:
Norvig points to fictional examples of successful conversational systems, showcasing their ability to engage users and foster emotional connections.

Current State of Conversational Agents:
Norvig acknowledges the progress made by major tech companies in developing conversational agents, citing their effectiveness in performing specific tasks like setting alarms, providing recipes, and delivering weather information.

Challenges in Expanding Conversational Capabilities:
He highlights the limited range of tasks that conversational agents can currently handle and the difficulty in predicting their capabilities.

Need for Improved Understanding and User Experience:
Norvig emphasizes the need for a deeper understanding of user needs and expectations, along with improvements in the user experience, to unlock the full potential of conversational systems.

The Current State of Conversational Agents:
* Conversational agents, such as Siri and Alexa, are not as user-friendly as traditional app interfaces.
* They require users to follow a specific tree-like structure of interactions, which is inflexible and limiting.
* This approach does not provide a natural and intuitive user experience.

Need for a Better Model:
* There is a need for a better model for building conversational agents that are more user-friendly and flexible.
* This model should allow users to interact with the agent in a more natural and conversational manner.
* It should also enable users to share data selectively and securely with different entities.

Potential Drawbacks of Conversational Agents:
* Widespread adoption of conversational agents could lead to a step backwards in usability and user interface design.
* The lack of a single responsible entity for the user experience can make it difficult to create a cohesive and seamless interaction.
* Conversational agents may not always be able to provide the best possible service, as they may be limited by the capabilities of the underlying systems and services.

Benefits of Conversational Agents:
* Conversational agents can provide a more personalized and tailored user experience.
* They can potentially enable users to interact with multiple services and systems in a more seamless manner.
* Conversational agents may offer a more natural and intuitive way for users to interact with technology.

Conclusion:
* The future of conversational agents is uncertain, and it remains to be seen whether they will become the dominant mode of user interaction.
* There are both potential benefits and drawbacks to the use of conversational agents, and it is important to carefully consider these factors when designing and implementing such systems.

Lost Artifacts in Programming:
Peter Norvig shares an example of a situation where a user interface study led to the removal of a scale bar from a map, which was inconvenient for him in a specific scenario.

The Need for Customizable User Interfaces:
Norvig emphasizes that one-size-fits-all user interface decisions may not work for everyone. He suggests that user interfaces should be dynamic and adaptable to individual preferences.

Executable Documentation and Reasoning:
Norvig proposes that all aspects of programming, including documentation, reasoning, and user interface experiments, should be captured in an executable format. This would allow for easy modification and updates based on changing needs.

Benefits of Executable Documentation:
Executable documentation would enable users to make changes to the user interface or other aspects of the program without having to go through complex coding processes.

Conclusion:
Norvig advocates for a programming approach that captures all relevant information in an executable format, ensuring that the program remains adaptable and responsive to changing requirements and user preferences.

Privacy, Security, and Fairness in the Age of AI Assistants:
We have a huge issue with privacy, security, and fairness, as apps often request excessive permissions. We need a better security model that allows us to specify how we want to be safe. We should shift from micromanaging AI systems to providing utility functions and letting them learn through reinforcement learning.

The Challenge of Specifying What We Want:
Moving from telling computers what to do step by step to specifying objectives is challenging. We often don’t know exactly what we want, leading to mistakes like King Midas’ desire for everything to turn to gold.

The Role of Philosophers in AI:
We need to consult philosophers to understand the deep needs and goals that drive our actions. Mick Jagger’s focus on what we want versus what we need is misguided, while Bobby Kennedy’s emphasis on our needs is more accurate.

The Marketplace of Wants vs. Needs:
We’ve built a marketplace that exploits our wants and desires, leading to outcomes we don’t truly want. We need to create a marketplace that aligns our collective goals with our deep needs.

Can You Trust Your AI Assistant?:
AI assistants are not completely on our side as they work for the companies that created them. However, they may be more trustworthy than companies with conflicting interests, such as Uber. The more open these assistants become, the more we can trust them.

Centralization of Power in Cloud Computing:
The investment in data centers by large companies doesn’t necessarily lead to concentrated power. These companies may share their data and allow third-party companies to build assistants on top of it. This could lead to a more diverse marketplace of AI assistants, increasing trust and choice for users.

Challenges in Global Finance:
Lack of reliable individual records for lending decisions in developing countries. Currency volatility and the need for hedging strategies.

Education:
Difficulty in finding teachers with computer science skills. Teaching programming languages may not be as beneficial in the long run. Importance of learning problem-solving and modeling skills.

Understanding the Human Mind:
Crucial for designing user interfaces and meeting people halfway. Need for a better understanding of user needs, language, and physiological cues.

User Interface Design:
Balancing user-friendly interfaces with open-ended capabilities. Importance of understanding user engagement and comprehension.

Data Privacy:
Hidden actions and data collection by apps. Need for more visible and granular control over app permissions. Avoiding the “zero, one, many” approach to data access.

Security Vulnerabilities:
Ease of large-scale data breaches due to excessive access permissions. Importance of defensive system design and limiting access to specific requests.