Title Post

The Prism, the Pointing Arm, and What Hinton Got Right (and Wrong).

In a recent talk, Geoffrey Hinton offered a thought experiment he thinks settles a long-running argument about machine consciousness. The setup is simple. You have a multimodal chatbot with a camera, a robot arm, and language. You place an object in front of it and ask it to point. It points. You then sneak a prism in front of the camera lens. You ask again. It points off to one side, because the prism has bent the light. You tell it about the prism. The chatbot replies: “Oh, I see. The prism bent the light. The object is actually straight in front of me, but I had the subjective experience that it was off to one side.”

Hinton’s claim is that the chatbot, in saying this, is using the phrase “subjective experience” exactly the way you and I use it. Therefore the chatbot had a subjective experience. Therefore the line we draw between human and machine experience is, in his words, rubbish.

I had to think about this, because Hinton is that guy, and the example is doing more work than it first appears. But I also want to say where I think the argument is weaker than it is being sold, and where I think it is stronger.

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How I used Claude to build a 5,000-entry index for a 600 page technology book without going crazy. The hard part of indexing is not inserting tags, but you will want to automate the boring mechanical labour.

I have spent the last couple of years writing a book about Embedded AI for No Starch Press (NSP). It has been three times the amount of work that I was expecting. The editing process feels never ending and by the end you will never want to read your book again. It does make for a better book though, and I am now an advocate for having an external editor.

One of the more tedious aspects of putting together a book is index tagging. The publisher can index for you, but it will cost around $4 per page and this comes out of your royalties. Not many people get rich writing a book, but there is no point throwing away hard earned royalties! There are lots of things you have to do manually when creating a book (like writing), but this feels like a task that a Large Language Model (LLM) should be good at.

NSP expects the finished index to weigh in at 5 to 8 percent of the manuscript word count, so for a 100,000-word book you are building something in the range of 5,000 to 8,000 words. This is a substantial document in its own right.

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Up until now we have been using ChatGPT and Gemini to scratch our vibe-coding itch. But the future is agentic and a lot of folks swear by Claude so we thought we would give it a go and use the Anthropic Academy to learn how.

If you want to use Claude Code you are going to need at least a Pro account (i.e., pay Anthropic some money). There is an interesting tweet (what do we call them now?) by Boris Cherny about how he uses Claude Code. As its creator, his advice is credible. Boris runs five Claude’s in parallel in terminal and jumps between them as they spit out a result. He has another 5–10 Claude’s in the browser (http://claude.ai/code). We are going to start with a more modest single Claude and take it from there.

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We recently came across the Joint Strike Fighter Air Vehicle C++ Coding Standards for the F-35 program. This was published by Lockheed Martin many years ago, but it contains some useful guidelines for coding C++ on embedded systems. The number one objective for the document was, the software must not fail.

Although written for military aircraft, the philosophy behind these rules applies directly to modern embedded systems. If you are building firmware for medical devices, robotics, automotive systems, or industrial control, this distilled guide captures the most practical lessons for your own C++ standards.

Our position on guidelines is that they need tailored for the application. Guidelines exist to reduce risk. The level of restriction they impose should match the level of consequence in the system. In life-critical systems the cost of failure is too high to optimise for elegance. If you are going to ignore a guideline, you had better know what you are doing. Coding standards should scale with the risk, and breaking a rule should always be an informed decision rather than an act of convenience.

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Any bio-inspired robot needs hardwired instincts. That means giving PrimalBot the ability to sense its environment, protect itself, and react instantly, without waiting for the processor. To achieve this we had to design a custom shield that provides dedicated reflex circuits. On it, we’ve integrated:

• Four hardware reflex loops that can bypass the controller and activate/stop the motors instantly.
• A Pulse Width Modulation (PWM) generator to enable speed control of the motors.

The shield is designed to act like a robotic brainstem, handling the critical low-level functions that keep the system safe and responsive, while freeing higher-level layers to focus on learning, planning, and decision-making. In this article, we’ll walk through the design and simulation process of the reflex loops.

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If you use a Mac for your development then your circuit simulation options are fewer than for Windows, but there are options.

The genesis of this article was a design for artificial reflexes. Our usual approach is to draw up the schematic and then jump straight to breadboarding. However, there are no off the shelf designs for electronic nerves so we didn’t know exactly how we would approach this problem. We needed to play around with components using different analog conditioning blocks until we got the required output. Simulation is the perfect use case for this scenario.

It seems like anytime you want to do something new, it involves learning another software package. Electronic simulation is no different. Whatever you end up using will probably have the word “spice” in it or be a thin wrapper around one of the SPICE engines.

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