MaCro Philosophy Blog

KL-Divergence Part 2: An oddly satsifying visualisation of 6-variable gradient descent

A followup to the non-techincal interactive KL-Divergence and Gradient Descent tutorials showing gradient descent on the final 6 variable example from the initial post.

Modelling Consciousness Part 1: The Axioms of IIT

Integrated Information Theory (IIT) starts from five axioms, aimed to capture the essential aspects of every possible experience. These five axioms are then used to construct an information-theoretic theory of consciousness based on the physical cause-effect properties of a system. I propose to explore what happens when the axioms are translated to a level much closer to the original starting point, the representational level. This first installment looks at the axioms of IIT that we will take as the starting point for our theory, and suggests some modifications.

KL-Divergence Part 2: Gradient Descent

A followup to the non-techincal interactive KL-Divergence tutorial. This time introducing Gradient Descent.

KL-Divergence a Zero-Maths Interactive Tutorial

This post provides interactive tools for understanding KL-Divergence without having to delve into any of the maths. You can attempt to match hidden distributions by adjusting parameters using KL-Divergence as a guide.

AI Suffering

The future of AI brings the possibility for creating entities that we have moral responsibility for. I explore some issues surrounding this topic and argue that perhaps all general intelligence entails the capacity for suffering.

MiCro Post

Intelligence: Artificial vs Biological

Visualisations of the difference between biological and artificial work on intelligence show a focus on learning to be the most salient difference.

Representative Reasoning: A short story

Something very different this week. A short story about the mind, the future of intelligence, politics, meditation, and the idea that we can only create things in our image.

The Kinds of Intelligence Symposium

Videos are now available for the Kinds of Intelligence Symposium. I collect them here, with a simple visualisaton of the topics covered.

Why am I Conscious? Cats on Mats and Zombie Hats

'Why am I conscious?' is both the greatest and worst question of all time. In this post I discuss how to make sure we're answer the good version.

This Week in Machine Learning and AI Podcast

No post this week. Instead, here's a link to a podcast I did for TWiML - This week in machine learning and AI

Revisiting Introspection: The Reverse Playing Card Experiment and Dennettian Predictive Processing

In previous posts I tried to 'save' our everday intuitions about experience. In this post I introduce the reverse playing card experiment, and my direct experience sampling results, both of which show my previous conclusions to be wrong. I then introduce my current attempt to think about the problem, through Dennettian Predictive Processing.

Interaction Tests

Some informal prototypes for testing the hypothesis that interaction makes a noticeable difference when trying to decode information in the world.

MiCro Post

The Unreliability of Introspection

I present a classic experiment and show a nice example of the lack of precision in our peripheral vision. I argue that this doesn't show that we are commonly mistaken about the contents of consciousness, but just that the contents contain generally include high-level predictions of the world.

The Refrigerator Light Illusion and a Mindfulness Tool

Is your refrigerator light on when the door is closed? How can you ever know? Perhaps consciousness works in the same way. Are you conscious when you're not specifically noticing it? How can you ever know? This post includes a tool for discussing this question that also works well as a mindfulness reminder.

MiCro Post

The Power of Prediction (1)

I show a visual example of one of the powers of decoding information through prediction. A network built to predict its inputs automatically filters out noise.

Strange Loops Part 3: How to Build One

In the last two posts I explored strange loops and argued that humans are perfect examples of them. In this post I discuss how to build them.

Strange Loops Part 2: You are a Strange Loop!

In the last post I broke down the components of strange loops. In this instalment go through the components one by one to see whether or not you are a strange loop.

Strange Loops Part 1: What is a Strange Loop?

Douglas Hofstadter thinks you are a strange loop. I think he's right. Unfortunately, the two best examples of strange loops are Gödel's incompleteness theorem and the human brain, neither of which are particularly easy to understand. In this post I break down the key components of strange loops (without too much logic). In the following weeks I will cover the ways in which we are strange loops, and how we can use modern AI to build them.

Human vs Machine Intelligence

AI improves along the dimension that we use to measure it. If we use a human-inspired definition of intelligence to determine our measures of success, we should expect more human-like AI. If we use a machine-oriented definition of intelligence, we should expect less human-like AI. I analyse the two different definitions of intelligence and conclud that, whether wise or foolish, we are currently walking the path towards human-like AI.

Investigating the BAAN Scenario

In the Benevolent Artificial Anti-Natalism scenario it is imagined that a superintelligence, being not susceptible to existence bias, might realise that human suffering is inevitable and use its powers to compassionately prevent the human race from continued existence.

Matthew Crosby

A blog primarily about philosophy of mind, consciousness, AI, and the future of intelligence.

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