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55 posts
In the last blog we looked at the phenomenon of “jagged intelligence” — the paradox that AIs are simultaneously superhuman and stupid in adjacent skills. We identified a lack of continual, fast learning and embodiment as possible causes of this weirdness, and noted that embodiment is also a “learning problem”, because current ML models can’t learn from a single thread of…
Executive Control is core to what most people recognise as true intelligence. For example, the ability to attend to relevant cues and maintain task dependent information whilst ignoring distracting details and taking appropriate actions. Working Memory (WM) is a core component of Executive Control. “Working memory is a short-term repository for task-relevant information that is…
Check out this fascinating research meeting with Numenta and guest speaker Max Bennett. Max describes this thalamocortical microcircuit theory. More detail can be found in his paper, published here: https://www.frontiersin.org/articles/10.3389/fncir.2020.00040/full#h1
We’re very happy to report that we recently published a preprint on AHA, an ‘Artificial Hippocampal Algorithm’ for Episodic Machine Learning. It’s the culmination of a multi-year research project and is a starting point for the next wave of developments. This article describes the motivation for developing AHA and a brief description of the research.…
The standard definitions of Episodic and Semantic Memory hide some important subtleties that impact the development of Machine Learning algorithms for AI (particularly those building Episodic Learning capability). This article explores this issue and provides a context for upcoming articles work to take us a step closer to ‘animal-like’ machine learning
Artificial neural networks (ANNs) – are conceptually simple; the combination of inputs and weights in a classical ANN can be represented as a single matrix product operation followed by an elementwise nonlinearity. However, as the number of learned parameters increases, it becomes very difficult to train these networks effectively. Most of the tricks that enable…
Originally published in March 2019 in an electronic journal in Japanese Introduction The purpose of this essay is to survey the relationship between decision making and large-scale cerebral networks with regard to conscious access, a purported neural correlate of consciousness, and to provide clues for computational modelling and general understanding of the issue. The author…
We are exploring the nature of equivariance, a concept that is now closely associated with the capsules network architecture (see key papers Sabour et al, and Hinton et al). Machine learning representations that capture equivariance must learn the way that patterns in the input vary together, in addition to statistical clusters in the input (that a typical…
Competitive learning is a branch of unsupervised learning that was popular a long, long time ago in the 1990s. Older readers may remember – the days before widespread use of GSM mobile phones and before Google won the search engine wars! Although competitive learning is now rarely used, it is worth understanding the broader range of…
ML Today Today’s Machine Learning has demonstrated unprecedented performance in what seems like every application thrown at it. Almost all the success has been based on advanced memory systems that can learn to recognise an input based on a large number of training examples. This is the equivalent to memory for facts. The biological terminology…
Reproduced left to right from [8,10,1] Within a 5 day span in October, 4 papers came out that take a significantly different approach to AI hierarchical networks. They are all inspired by biological principles to varying degrees. It’s exciting to see different ways of thinking. Particularly at a time when there is growing skepticism…
Today’s blog post is about Reinforcement Learning (RL), a concept that is very relevant to Artificial General Intelligence. The goal of RL is to create an agent that can learn to behave optimally in an environment by observing the consequences – rewards – of its own actions. By incorporating deep neural networks into RL agents,…
This article assesses the research paper, ‘A Distributional Perspective on Reinforcement Learning’ by the authors, Marc G. Bellemare, Will Dabney and Remi Munos, published in the proceedings of the 34th International Conference on Machine Learning (ICML) in 2017. Bellemare et al.’s paper will be assessed on several criteria. Firstly, content is assessed through providing some…
One of the features of our brain is its modularity. It is characterised by distinct but interacting subsystems that underlie key functions such as memory, language, perceptions, etc. Understanding the complex interplay between these modules requires decomposing them into a set of components. Indeed, this modular approach to modeling the brain is one of the…
The standard machine learning approach is to learn to accomplish a specific task with an associated dataset. A model is trained using the dataset and is only able to perform that one task. This is in stark contrast to animals which continue to learn throughout life and accumulate and re-purpose knowledge and skills. The…
Our brain is a source of great inspiration for the development of Artificial General Intelligence. In fact, one of the common views is that any effort in developing human-level AI is almost destined to fail without an intimate understanding of how the brain works. However, we do not understand our brain that well yet. But…
Today’s post tries to fit the theoretical concept of Predictive Coding with the unusual structure and connectivity of Pyramidal cells in the Neocortex. Pyramidal neurons Pyramidal neurons are interesting because they are one of the most common neuron types in the computational layers of the neocortex. This almost certainly means they are critical to many…
Figure 1: The Region-Layer component. The upper surface in the figure is the Region-Layer, which consists of Cells (small rectangles) grouped into Columns. Within each Column, only a few cells are active at any time. The output of the Region-Layer is the activity of the Cells. Columns in the Region-Layer have similar – overlapping –…
What’s AlphaGo? AlphaGo is a system that can play Go at least as well as the best humans. Go was widely cited as the hardest (and only remaining) game at which humans could beat machines, so this is a big deal. AlphaGo has just defeated a top-ranked human expert. AlphaGo Nature paper (Silver et al 2016)…
This week we have a couple of interesting links to share. From our experiments with generative hierarchical models, we claimed that the model produced by feed-forward processing should not have loops. Now we have discovered a paper by Bengio et al titled “Towards biologically plausible deep learning” [1] that supports this claim. The paper looks for…
By Gideon Kowadlo I’m reading a fascinating book called The Righteous Mind, by Jonathan Haidt. It’s one of those reads that can fundamentally shift the way that you see the world. In this case, the human world, everyone around you, and yourself. A central idea of the book is that our behaviour is mainly dictated…
Figure 1: Our interpretation of the Thalamocortical system as 3 interacting sub-systems (objective, subjective and executive). The structure of the diagram indicates the dominant direction of information flow in each system. The objective system is primarily concerned with feed-forward data flow, for the purpose of building a representation of the actual agent-world system. The executive…
Figure 1: Our headline image is from the Cognitive Consilience: An atlas of key pathways cross-referenced to supporting literature articles. The complexity and variety of routing within the brain can be appreciated with this beautiful illustration. Note in particular the specialisation of cortical cells and the way this affects their interactions with other cells in…
Figure 1: The physical architecture of general intelligence in the brain, namely the Thalamo-Cortical system. The system comprises a central hub (the Thalamus) surrounded by a surface (the Cortex, shown in blue). The Cortex is made up of a number of functionally-equivalent units called Columns. Each Column is a stack of cell layers. Columns share…
The artificial neuron model used by Jeff Hawkins and Subutai Ahmad in their new paper (image reproduced from their paper, and cropped). Their neuron model is inspired by the pyramidal cells found in neocortex layers 2/3 and 5. It has been several years since Jeff Hawkins and Numenta published the Cortical Learning Algorithm (CLA) whitepaper.…
Authors: D Rawlinson and G Kowadlo This is the first of three articles detailing our latest thinking on general intelligence: A one-size-fits-all algorithm that, like people, is able to learn how to function effectively in almost any environment. This differs from most Artificial Intelligence (AI), which is designed by people for a specific purpose. This…
We have found a fantastic resource, part of the IBM Blue Brain Project, that clearly and interactively maps out interactions between neocortical neurons. The data comes from their attempts to simulate a piece of cortex down to the level of biologically-realistic neurons. Interactive neocortex browser tool here: https://bbpnmc.epfl.ch/nmc-portal/web/guest/microcircuit The paper containing the original research…
Erik Laukien is back with a demo of Sparse, Distributed Representation with Reinforcement Learning. This topic is of intense interest to us, although the problem is quite a simple one. SDRs are a natural fit with Reinforcement Learning because bits jointly represent a state. If you associate each bit-pattern with a reward value, it is…
An interesting article by Gerard Rinkus comparing the qualities of sparse distributed representation and quantum computing. In effect, he argues that because distributed representations can simultaneously represent multiple states, you get the same effect as a quantum superposition. The article was originally titled “sparse distributed coding via quantum computing” but I think that gets the…
By David Rawlinson and Gideon Kowadlo The aim of this post is to take a short departure from more technical issues and deal with some of the philosophical questions about Artificial General Intelligence (AGI). The Elephant in the room This metaphor implies willful ignorance of something blindingly obvious. Something that’s difficult to ignore. It requires effort…
By Gideon Kowadlo and David Rawlinson In our last blog post, we discussed the repeating functional columnar structure of the neocortex, and the inconsistent terminology used to discuss it throughout the literature. As mentioned in that post, the function of the column is an important concept for understanding the function of the neocortex, and as a consequence,…
Cell-type-specific 3D reconstruction of five neighboring barrel columns in rat vibrissal cortex (credit: Marcel Oberlaender et al.) It is well established that there is a functional columnar structure repeated throughout the cortex across species (the concept received significant attention after a study by Mountcastle in 1957 [1]; see [2] and [3] for reviews). This suggests…
by David Rawlinson and Gideon Kowadlo Why the retina is worth a deeper look Recently, we have been looking at the retina – light sensitive cell layers inside the eyeball, that detect wavelength and intensity, and compute some initial encoding of this data that is transmitted to the brain proper via the optic nerve. The…
This might be our new test suite for the algorithms! http://www.arcadelearningenvironment.org/ “The Arcade Learning Environment (ALE) is a simple object-oriented framework that allows researchers and hobbyists to develop AI agents for Atari 2600 games. It is built on top of the Atari 2600 emulator Stella and separates the details of emulation from agent design.” Why…
Just a few days ago, Jay posted here about the recent public attention to AI and the potential future dangers that it presents. Discussion on the topic continues, but it’s not all talk. There are at least two institutes focussed on practical steps to mitigate the risks. There is an institute called MIRI that exists…
By Punarjay Chakravarty In the past year or so, there has been a spate of high-profile pronouncements by respected scientists and engineers, cautioning the world about the potential ill-effects of AI. Stephen Hawking, one of the foremost experts on theoretical and astro physics told the BBC that “The development of full artificial intelligence could spell…
I recently published a post on the question of classifying ‘no input’ or missing data. There was a comprehensive reply by Fergal Byrne in a thread on the Nupic Theory mailing list. I am re-posting it here (below) as it provides a valuable perspective of the discussion in the context of human cortical function. Jan 24th 2015 – by Fergal Byrne…
If you’re looking for a formal and precise description of the current HTM/CLA algorithm, then this blog [1, 2] by Fergal Byrne may be of interest. Our experience is that while pseudocode is a good way of describing an algorithm, equations can often be more succinct and specific. The CLA whitepaper [3] describes the algorithm…
By Gideon Kowadlo, David Rawlinson and Alan Zhang Can you hear silence or see pitch black? Should we classify no input as a valid state or ignore it? To my knowledge, the machine learning and statistics literature mainly regards an absence of input as missing data. There are several ways that it’s handled. It can…
DeepMind, based on a deep learning architecture made a big impact in the tech industry. Deep learning continues to gain traction in the machine learning community and in the mainstream, both in terms of public awareness of its capabilities as well as through commercialisation. This article by Simon Winder provides a very accessible introduction to…
TL;DR: An SDR is a Sparse Distributed Representation, described below SDRs are biologically plausible data structures SDRs have powerful properties SDRs have received a lot of attention recently There are a few really great new resources on the topic: Presentation by Subutai Ahmad of Numenta Older introductory presentation by Jeff Hawkins Excellent summary of their characteristics in…
This post asks some questions about the agency of hierarchical action selection. We assume various pieces of HTM / MPF canon, such as a cortical hierarchy. Agency The concept of agency has various meanings in psychology, neuroscience, artificial intelligence and philosophy. The common element is having control over a system, with varying qualifiers regarding the entities…
This post is about a fantastic new paper by Michael R. Ferrier, titled: Toward a Universal Cortical Algorithm: Examining Hierarchical Temporal Memory in Light of Frontal Cortical Function The paper was posted to the NUPIC mailing list and can be found via: http://numenta.org/community-content.html The paper itself is currently hosted at:…
This is a quick post to link a poster paper by Ryan McCall, who has experimented with a Predictive-Coding / Cortical Learning Algorithm (PC-CLA) hybrid approach. We found the paper via Ryan writing to the NUPIC theory mailing list. What’s great about the paper is it links to some of the PC papers we mentioned…
Browsing the NUPIC Theory mailing list, I came across a post by Fergal Byrne on the differences and similarities between Deep Learning and MPF/HTM. It’s a great background into some of the pros and cons of each. Given the popularity and demonstrated success of Deep Learning methods it’s good to understand how they work and how…
Introduction Predictive Coding (PC) is a popular theory of cortical function within the neuroscience community. There is considerable biological evidence to support the essential concepts (see e.g. “Canonical microcircuits for predictive coding” by Bastos et al). PC describes a method of encoding messages passed between processing units. Specifically, PC states that messages encode prediction failures; when…
A quick post here to give readers an idea of HTM/CLA performance on visual object recognition tasks. Although a little out of date now, Craig’s blog gives a good idea of what NUPIC can and can’t do, and how data issues (e.g. training set, variations in object appearance) affect the results. This is a handy…
Hierarchy Viewer? Ongoing research to map out the patterns of connectivity in the human brain continues to produce stunning visuals. Are we looking at the hierarchy as proposed in the Memory-Prediction framework? In any case, it’s fun to explore, which you can do here: http://www.humanconnectomeproject.org/data/relationship-viewer/
by David Rawlinson and Gideon Kowadlo A Temporal Pooler ProposalWe will describe a Temporal Pooler design below. We have tested this design and it gives satisfactory output given the criteria and expectations described below and in the previous articles in this series (post #1, post #2). It is also relatively simple. You could imagine this…
By David Rawlinson and Gideon Kowadlo Jeff’s new Temporal Pooler This is article 2 in a 3 part series about Temporal Pooling (TP) in MPF/CLA-like algorithms. You can read part 1 here. For the rest of this article we will assume you’ve read part 1. This article is about the new TP proposed by Jeff Hawkins. The…
by Gideon Kowadlo and David Rawlinson Introduction One of the keys to understanding the neocortex as a whole, and the emergence of intelligence, is to understand how the cortical hierarchical levels interconnect. This includes: the physical connections, the meaning of the signals being transmitted, and possibly also the way the signal is encoded. Physical connections: Physical…
by Gideon Kowadlo and David Rawlinson Introduction This article contains some musings on the factors that limit the increase of intelligence as a species. We speculate that ultimately, our level of intelligence is limited by at least two factors, and possibly a third: our own cultural development, physical constraints, and an intelligence threshold. We’ll now…
by Gideon Kowadlo and David Rawlinson In a previous post (section ‘Regions’), we discussed the distinction between cortical layers and levels. The literature often describes connections between cortical layers in one level to cortical layers in another level. We are not aware of a written convention for this purpose and propose one here. The basic form describes…
by David Rawlinson and Gideon Kowadlo Article Series This is the first of 3 articles about temporal pooler design for Numenta’s Cortical Learning Algorithm (CLA) and related methods (e.g. HTM/MPF). It has limited relevance to Deep Belief Networks. This first part will describe some of the considerations, objectives and constraints on temporal pooler design. We…
by David Rawlinson and Gideon Kowadlo Introduction The Memory Prediction Framework (MPF) is a general description of a class of algorithms. Numenta’s Cortical Learning Algorithm (CLA) is a specific instance of the framework. Numenta’s Hierarchical Temporal Memory (HTM) was an earlier instance of the framework. HTM and CLA adopt different internal representations so it is…