Artificial intelligence guru Jack Clark has written the longest, most interesting Twitter thread on AI policy that I've ever read. After a brief initial introductory tweet on August 6, Clark went on to post an additional 79 tweets in this thread. It was a real tour de force. Because I'm currently finishing up a new book on AI governance, I decided to respond to some of his thoughts on the future of governance for artificial intelligence (AI) and machine learning (ML). Clark is a leading figure in the field of AI science and AI policy today. He is the co-founder of Anthropic, an AI safety and research company, and he previously served as the Policy Director of OpenAI. So, I take seriously what he has to say on AI governance matters and really learned a lot from his tweetstorm. But I also want to push back on a few things. Specifically, several of the issues that Clark raises about AI governance are not unique to AI per se; they are broadly applicable to many other emerging technology sectors, and even some traditional ones. Below, I will refer to this as my "general critique" of Clark's tweetstorm. On the other hand, Clark correctly points to some issues that are unique to AI/ML and which really do complicate the governance of computational systems.

I'm finishing up my next book, which is tentatively titled, "A Flexible Governance Framework for Artificial Intelligence." I thought I'd offer a brief preview here in the hope of connecting with others who care about innovation in this space and are also interested in helping to address these policy issues going forward. The goal of my book is to highlight the ways in which artificial intelligence (AI) machine learning (ML), robotics, and the power of computational science are set to transform the world -- and the world of public policy -- in profound ways. As with all my previous books and research products, my goal in this book includes both empirical and normative components. The first objective is to highlight the tensions between emerging technologies and the public policies that govern them.

The use of training, validation and test datasets is common but not easily understood. In this post, I attempt to clarify this concept. The post is part of my forthcoming book on learning Artificial Intelligence, Machine Learning and Deep Learning based on high school maths. And then comes up with an important statement: Reference to a "validation dataset" disappears if the practitioner is choosing to tune model hyperparameters using k-fold cross-validation with the training dataset. Model selection: involves selecting optimal parameters or a model.

Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These capabilities are also highly desirable in robots. They are displayed by machines to some extent - yet, as is so often the case, the artificial creatures are lagging behind. The key foundation is an internal representation of the body that the agent - human, animal, or robot - has developed. In the biological realm, evidence has been accumulated by diverse disciplines giving rise to the concepts of body image, body schema, and others. In robotics, a model of the robot is an indispensable component that enables to control the machine. In this article I compare the character of body representations in biology with their robotic counterparts and relate that to the differences in performance that we observe. I put forth a number of axes regarding the nature of such body models: fixed vs. plastic, amodal vs. modal, explicit vs. implicit, serial vs. parallel, modular vs. holistic, and centralized vs. distributed. An interesting trend emerges: on many of the axes, there is a sequence from robot body models, over body image, body schema, to the body representation in lower animals like the octopus. In some sense, robots have a lot in common with Ian Waterman - "the man who lost his body" - in that they rely on an explicit, veridical body model (body image taken to the extreme) and lack any implicit, multimodal representation (like the body schema) of their bodies. I will then detail how robots can inform the biological sciences dealing with body representations and finally, I will study which of the features of the "body in the brain" should be transferred to robots, giving rise to more adaptive and resilient, self-calibrating machines.

I had this conversation some time ago with an Engineer who came from a traditional background. By that I mean, he had been in the same industry (heavy engineering) for 30 years. Of these, he had been in the same company for 25 years (and this was his second job). In his world, to understand the behaviour of a phenomenon, you needed to carry out a physical experiment to model that phenomenon and only then could you predict it. So, as someone who teaches AI / machine learning at #universityofoxford – it made me think that this approach has some advantages to introducing people to AI/ML.

When Gulzira Aeulkhan finally fled China for Kazakhstan early last year, she still suffered debilitating headaches and nausea. She didn't know if this was a result of the guards at an internment camp hitting her in the head with an electric baton for spending more than two minutes on the toilet, or from the enforced starvation diet. Maybe it was simply the horror she had witnessed – the sounds of women screaming when they were beaten, their silence when they returned to the cell. Like an estimated 1.5 million other Turkic Muslims, Gulzira had been interned in a "re-education camp" in north-west China. After discovering that she had watched a Turkish TV show in which some of the actors wore hijabs, Chinese police had accused her of "extremism" and said she was "infected by the virus" of Islamism.

Karim Lakhani and Marco Iansiti's forthcoming book is a great place to catch up with the new rules. I say this because their research was a source for my book #CLEVER. How will you compete in the Age of AI? Nice article in the HBS Digital Initiative online site on our forthcoming book with Marco Iansiti - look out for AI-driven collisions in every sector.

The explanation of Logistic Regression as a Generalized Linear Model and use as a classifier is often confusing. In this article, I try to explain this idea from first principles. This blog is part of my forthcoming book on the Mathematical foundations of Data Science. Machine learning involves creating a model of a process. To create a model of a process, we need to identify patterns in data.

The use of training, validation and test datasets is common but not easily understood. In this post, I attempt to clarify this concept. The post is part of my forthcoming book on learning Artificial Intelligence, Machine Learning and Deep Learning based on high school maths. And then comes up with an important statement: Reference to a "validation dataset" disappears if the practitioner is choosing to tune model hyperparameters using k-fold cross-validation with the training dataset. Model selection: involves selecting optimal parameters or a model.

Sometimes, you see a diagram and it gives you an'aha ha' moment I saw it on Frederick kratzert's blog Using the input variables x and y, The forwardpass (left half of the figure) calculates output z as a function of x and y i.e. f(x,y) The right side of the figures shows the backwardpass. Receiving dL/dz (the derivative of the total loss with respect to the output z), we can calculate the individual gradients of x and y on the loss function by applying the chain rule, as shown in the figure. This post is a part of my forthcoming book on Mathematical foundations of Data Science. The goal of the neural network is to minimise the loss function for the whole network of neurons. Hence, the problem of solving equations represented by the neural network also becomes a problem of minimising the loss function for the entire network.