DebKR

To the Stars

Artificial Intelligence

Focussed vs. Diffuse Thinking

Focussed vs. Diffuse Thinking

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Some reading materials (highlighted = likely useful for TU812 TMA03):

Chapters 1-3 of A Mind for Numbers are especially helpful in providing helpful information and additional exercises related to the materials of Module 1.

Worthwhile Additional Popular Works

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Learning About Learning

Learning About Learning

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A key theme of part three of the systems module TU812 is ‘learning about learning’, and this course on Coursera by Dr Barbara Oakley – which covers that from one angle – came highly recommended: http://www.coursera.org/learn/learning-how-to-learn. It’s subtitled ‘powerful tools to help you master tough subjects’, which is definitely the kind of help I need right now with this systems course. The MOOC course is, I believe, in the top 3 on Coursera, although it’s worth pointing out the focus is on helping you with mathematical or scientific subjects. Yet the ideas and study tips should be extendable across to more abstract and wordy subjects (the ones I struggle most with).

Two Types of Thinking: Focussed and Diffuse

If we understand something about how the brain works, we can develop and deploy strategies to help us learn more optimally. As humans we have two thinking modes – with Dr Oakley refers to as ‘focussed’ and ‘diffuse’. These are mental states where we’re, respectively, concentrating really hard on something (like reading & note-taking from a course text) or allowing our minds to wander and day-dream (which is, of course, my preferred mental state!). Another way to think of the two different states is that focussed is looking at the details (a church within the landscape), while diffuse is looking at the much bigger picture (drinking in the whole scene as far as the eye can see). [Read more…] about Learning About Learning

Thoughts on Learning Systems

Thoughts on Learning Systems

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It seems clear to me (after thinking about & studying both learning and learning systems recently – as well as participating in several, both online & through distance learning) that a proper learning system – as I envisage it & will create it – follows this basic process [stated as description of ALGORITHM(S) – note that this is in very rough note form only!]:

1. READ unstructured textual information & conduct CONCEPT or KEYWORD ANALYSIS > ie HIGHLIGHT key ideas/concepts as you go > this is dependent upon what is deemed RELEVANT at that TIME > this can come from a database of CONCEPTS’ aka keywords & key phrases, RANKED for RELEVANCY (needs to be seeded ie pre-populated by HUMAN but later can be BUILT UPON by AI thru RECURSIVE LEARNING); text is parsed, OCCURENCE of keywords & phrases counted & recorded, CONTEXT of key word/phrase occurrence is noted > basically the web search algo but applied to a specific knowledge domain

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Vector Spaces

Vector Spaces

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Thought on waking this morning:

Vector Spaces*

Had gone to bed asking: ‘OK so what should I do for our AI system? What’s next?’

Assume this answer means I should be studying Vector Spaces – and hence Linear Algebra – more fully for my delve into AI.

Makes sense given LA is such a strong math basis for all ML courses out there, also for AI generally.

But I can’t help wondering if this also relates somehow to my recent Systems Struggles. E.g. the vector space is an abstract mathematical way of representing a realworld physical geometric reality. Can this offer a parallel to Systems Modelling which might [Read more…] about Vector Spaces

Machine Learning Engineering

Machine Learning Engineering

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machine-learning-engineeringDipping a toe in the Udacity pool:
I received an email from the UK/Europe Business Growth Manager of Udacity, looking for people with knowledge of/experiencein Machine Learning to lead and/or support face-to-face training sessions in Machine Learning in London. The email included the link to Udacity’s Nano Degree in Machine Learning.

While I’m not yet skilled or experienced enough at this time to lead a session, I felt really excited that I might have an opportunity to be a session supporter and assistant. That would be cool as I’d be exploring and networking in such an important growth area in tech skills, as well as supporting the growth of MOOCs here in London, which is something I feel really passionately about. [Read more…] about Machine Learning Engineering

Mathematics Monday: Simple linear regression (revision)

Mathematics Monday: Simple linear regression (revision)

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mathematics-compassMathematical compass:
Someone recently suggested I make mathematics my compass (while I was busy diving down rabbit holes reading up on twistors and the like). Maybe they’re correct. I realised that if I was going to do that, I’d need to brush up on my map-reading skills first!

I’ve been so busy engrossed in learning Python I’ve sorely neglected the other streams of the training syllabus I set myself back in late February (is it really that long ago?). So I decided I need to dedicate at least one day a week to bringing my maths back up to speed.

I’m having to start almost back at square one. (Demoralising.) Calculus is a tad rusty to say the least: serious revision required. Did I really quit accountancy for this? Appears to be madness. Surely there’s some hidden logic in there somewhere, it just seems pretty elusive right now. Well, onwards and upwards anyway… [Read more…] about Mathematics Monday: Simple linear regression (revision)