Peter Norvig (Google Director of Research) – Rethinking the Software Industry | National Academy of Sciences (May 2017)

Chapters

Abstract

The Evolution of Technology: From Traditional Programming to Ethical Machine Learning

Revolutionizing Programming: Traditional vs. Machine Learning Approaches

The technological landscape is undergoing a dramatic transformation, with machine learning leading the charge. This paradigm shift moves us from traditional programming, where developers meticulously craft instructions, to an era where machines learn from data. This transition is not just about changing methodologies; it’s a leap from mathematical to empirical science. Machine learning enables tasks beyond the scope of traditional programming, like object recognition in images or language translation, by tapping into the implicit knowledge humans possess. However, this shift brings with it complex challenges.

Machine Learning and Programming: Unveiling the Inner Workings

In machine learning, machines write programs instead of humans, essentially becoming their own programmers. Humans assume the role of teachers, providing examples to train the machine. The resulting machine learning models often resemble black boxes, making it difficult to comprehend their inner workings. Deep learning models amplify this complexity, consisting of multiple interconnected black boxes.

Ethical Implications and the Future of Machine Learning

As machine learning reshapes our interaction with technology, ethical considerations take center stage. The reliance on vast data raises serious concerns about privacy, security, and the potential reinforcement of biases. Machine learning systems, devoid of inherent ethical decision-making capabilities, require the involvement of multidisciplinary teams to navigate these moral waters. This calls for a shift from individual programmer responsibility to centralized ethical oversight, ensuring responsible AI development and deployment.

Ethics in Machine Learning

Machine learning systems lack inherent ethical decision-making capabilities, leading to the need for specialized teams and review processes to address ethical considerations in AI development. In contrast, traditional programming allowed for individual programmers to consider ethical implications while writing code. However, with the advent of AI, centralized teams, review boards, and committees have been established to handle ethical issues, such as bias mitigation, fairness, and accountability.

Bridging the Gap: New Science of Programming and Learning

The complexity of modern AI systems necessitates a new science of programming and learning. Traditional analytical tools fall short in understanding these complex models. Although we have the methodology to analyze them, the limitations of current tools make researchers hesitant to apply these methods. Therefore, the development of better tools is crucial for effectively analyzing and optimizing complex models, ensuring they align with our true desires.

Interdisciplinary Collaboration: Key to Navigating AI Complexities

Addressing the intricacies of AI development requires an interdisciplinary approach. The convergence of disciplines such as cognitive science and social science is essential. This collaboration extends to understanding users, their interactions, and the societal implications of AI. There’s a growing need to derive simpler rules from complex models, akin to how children learn, and to separate the understanding of machine learning models from human learning. Additionally, expertise in education and psychology is vital for selecting appropriate learning materials, especially in group teaching scenarios.

The Attention Economy and Market Influence

In the attention economy, the challenge lies in quantifying the value of users’ attention. Market mechanisms often struggle to provide fair compensation for copyrighted material, especially when decision-makers lack long-term incentives. Our actions in marketplaces, like spending 100 hours playing a game, send signals that shape recommendations and choices, often leading to regrettable decisions. This underscores the need for tools that help articulate and pursue our genuine desires, reflecting our collective preferences in the marketplace.

Attention Economy and Copyright Challenges

The “attention economy” refers to the value of someone’s attention, which can be commodified and exploited. There is a need to be mindful of the potential negative impacts on individuals caused by capturing their attention without appropriate compensation. The example of professors using copyrighted material in their lectures illustrates the difficulties in obtaining fair compensation for copyright holders when the decision-makers (the professors) are not the ones directly paying for the use of the material.

Conclusion

Machine learning is not just a technological advancement; it’s a beacon for a new era of programming and ethical considerations. As we venture deeper into this domain, the challenges of model complexity, ethical decision-making, and the need for interdisciplinary collaboration become increasingly apparent. Addressing these challenges is pivotal for harnessing the full potential of machine learning and steering its development towards a responsible and beneficial impact on society.

Notes by: Alkaid