The Alignment Problem: Machine Learning and Human Values by Brian Christian.
Brian holds a BA in Computer Science and Philosophy from Brown University. And holds an MFA in Poetry from the University of Washington. He is a visiting scholar at the University of California, Berkeley. His focus is cognitive science and human-compatible AI.
He received the Eric and Wendy Schmidt Award for Excellence in Science Communication for his work on The Alignment Problem. Yes, I fully agree this is a wonderful book to read.
His previous books include The Most Human Human and Algorithms to Live By. Both address AI technology and the human experience. When AI services (ChatGPT, CoPilot, Dalle 3, etc.) attempt to execute tasks that result in errors, ethical and potential risks emerge. So, researchers call this the alignment problem.
At the book’s core, Brian is examining ethical and safety issues as machine learning technologies are both advancing into society more rapidly than projected. Perhaps the key element is the ability for machine learning services to execute tasks on behalf of humans across almost every aspect of our lives. This also markets from education, retail, supply chain, and energy to name just a few.
Is The Alignment Problem a first generation issue ?
To be fair, when ChatGPT started the consumer AI revolution in November 2022, existing AI services were quickly out to market to gain an early foothold. However researchers found early systems were very inaccurate.
As Brian is addressing, resumes from leading companies were ‘trained’ by machine learning services which soon revealed data bias. For example an overwhelming number of applicants for a high tech job are male and as a result of inaccurate algorithms, female candidates with just as equal qualifications were not moved forward.
We now have data revealing US court systems are using machine learning services to determine bail also assess male defendants differently due to a lack of equal data in the models. As a result, white male defendants with prior convictions are out on bail while Hispanic, Asian, and African American males are denied bail even though they held no prior record of arrest.
GANs, RAG, and MLOps are improving data accuracy
At the same time, we also need to acknowledge that AI today has power services which reduce both bias and hallucinations. A Generative Adversarial Network (GAN) is a machine learning framework consisting of two neural networks. They are designed to ‘compete’ against each other to generate synthetic data that resembles real data. GANs comprise two networks what are locked in a continuous competition, where a generator tries to create increasingly convincing fake data, while a discriminator becomes better at detecting fakes. This adversarial process leads to the generation of highly realistic synthetic data. Please consider an interesting book GANs in Action.
In addition, Retrieval Augmented Generation (RAG) sharpens the capabilities of a large language models by accessing and incorporating data from external sources before generating a response. This results in more accurate data outputs. Please consider the book Understanding Large Language Models for additional insights to RAG.
In conclusion, this book is a fascinating read on many levels. Some readers may find concepts difficult. Yet, this should not stop you from learning as critics have praised his efforts. Brian is making a well-researched book available that simply cannot be missed.