AI Explained In Plain English

Mastering Artificial Intelligence is an upskilling programme that will take your understanding of AI up to the level of a ‘strategic expert’, who is someone that commands the respect of peers and can help conceptualize, frame and direct AI projects.

Commencing in July 2018, the programme will consist of daily Podcasts (free) that will be supported by specially-written eBooks (premium).

Have fun learning while you listen – while knowing you’ve got a soft copy of the important bits.

Sign up below to access the Podcasts and eBooks, and receive emails when  new material is published:

Why Mastering Artificial Intelligence?

Mastering Artificial Intelligence is the result of my personal quest to deeply understand AI, which I see as the defining  technology of our age:

Human advancement has always relied on the of invention of tools. Millennia ago the great tool inventions included fire, the wheel and the irrigation pump. Today, the great inventions include the transistor, fiber optics, software and artificial intelligence.

And as the economy moves inexorably from one focused on the manufacture and distribution of physical goods to one that increasingly involves the harvesting and analysis of information then we will need new tools that can be used to extract value from information.

Artificial intelligence is the most promising of these information-domain tools and while still at a very early stage AI holds tremendous promise for the future: the concept of replicating and commoditising certain aspects of human intellect is utterly compelling and will truly change the world in a profound way.

Before starting Mastering Artificial Intelligence, I was using a range of online sources in an attempt to identify and understand the core ideas that underpin the AI field.

Examples of questions that interested me were:

  • How state of the art AI systems actually work
  • What sort of real-world problems can be uniquely solved with AI
  • The underlying scientific foundation of AI
  • Whether this is a limit to how powerful AI systems can become

I was also interested in a number of metaphysical questions, such as what is intelligence and is AI real intelligence?

My problem was that I was having trouble finding resources that were light on jargon, math and code and heavy on insight.

I knew that the answer to every question I had was out there somewhere on the web – and available for free – but finding those answers was increasingly feeling like looking for a needle in a haystack.

It became clear that there was no single place online that provided the sort of understanding I wanted, and I think that remains the case today:

  • Media and trade outlets : These do a good job of covering the latest developments in AI, but they cannot be expected to explain those developments at a detailed level. I often read articles in elite publications like Wired, The Economist, New Scientist or MIT Technology Review – which are all excellent – but feel at the end that I now know more, but understand less.
  • Courses: There are plenty of excellent, formal education courses focused on the technical aspects of AI, but they understandably ignore the non-technical aspects and do not dig into the theoretical foundations of the topic, which are understandably taken as fact. And so these courses don’t completely work for me either.
  • Celebrity commentators: Certain people seem adept at throwing out very bold statements about AI, which are lapped up by the media, but I usually find that such statements are made with a distinct lack of supporting evidence, at least not that I find satisfactory.
  • Books: There are a number of good books on AI but I’ve not so far found one that takes a truly holistic perspective  and whose mission is to convey to the reader a deep understanding of AI as a whole.

Maybe you’ve come to the same conclusion?

Why is AI so hard to explain clearly?

I think that the main reason is that AI spans  so many disciplines: computer science, mathematics, physics, electronics, information theory, philosophy, psychology, neuroscience, business and economics.

You really need to understand what each of these fields has to say about AI before a complete picture begins to emerge.

But most people just don’t have the time to invest the 1000s of hours needed to cover this amount of intellectual real estate.

Another reason is that many  of the contentious questions in AI do not actually have clear answers.

This is very unusual in engineering: normally, practicing engineers are dealing with ideas and quantities that are precisely defined using the language of mathematics and whose existence and behaviour can be experimentally verified. But more than that, they know that their field that has a solid scientific foundation.

But AI isn’t like this: we’ll see later that the behaviour of deep neural networks is not understood mathematically.

This means that we have not yet discovered the mathematical structure that connects the semantic content of an image (e.g. the set of ideas that define a ‘gorilla’ with the condition of the artificial neurons that make up that network (e.g. the set of bias and weight values that are programmatically determined when the network is trained).

We can build a neural network that can reliably recognize a ‘gorilla’ but we do not understand why it works. The best we can do is offer an ‘arm waving’ type of explanation for what might be going on, but we really don’t understand it.

As an analogy, Faraday’s Law of Induction and Newton’s Laws Of  Motion are intellectual masterpieces that defined whole fields of physics and engineering.

But we just don’t have anything like this for AI – yet.

One of the several underlying reasons for this is that “intelligence” is not actually a scientific idea. Another is that, so far, nobody knows how to accurately codify abstract ideas like ‘analyse’ or ‘think’ in a way that properly reflects how such behaviors are realised in the brain. We will discuss these and related points in great detail in Module 3.

So what does this mean?

For one thing, it means that the question as to whether or not we will be able to build a computational structure that is “intelligent” is a question for philosophers, sci-fi writers and the mainstream media – but not serious science.

The bottom line here is that if you want to really get to grips with AI you need to develop your own personal view of the field – because there is currently no overall ‘general theory’ of AI.

I’m sure that someday we will have a General Theory of Intelligence – but it will take an massive intellectual leap and require a mind in the class of Albert Einstein, Issac Newton or Michael Faraday to provide AI with a proper scientific foundation.

I very much look forward to that day and hope it happens in my lifetime!

But the best we can do right now is to:

  • Think broadly, but not too broadly – while being prepared to go really deep when and where you need to.
  • Think clearly and objectively – while being prepared to explore where the thinking leads, even if that means a place you’d rather not go.

But this is not how thinking in AI is developing: most people who work in AI conceptualise the subject using a narrow, well-conditioned mindset that has been defined by years or decades of intellectual pursuit in one discipline.

This is true of journalists, bloggers, AI researchers, software developers, economists and business people who are involved with AI.

For example, credentialed AI researchers will have very specific, deep knowledge about one aspect of AI but they might have minimal or no awareness of the latest developments in other areas of AI – which might seem to an outsider to be closely related.

As another example, most engineers who are hard at work building practical neural networks do not know how real biological neurons work (be sure to check out Module 2 which gets into the neuroscience aspects of AI).

Another example would be where people who work in computer vision might not have thought carefully about the connection between the software they are creating and what this means for how images are represented in the brain.

But some really brilliant people do have that special sort of measured, 360-degree understanding, but they tend to have a rather low priority and tend not have ready access to large media platforms. If you want a few examples, then take a look at:

You’ll see that these people are thinking in a direction that is somewhat different to, or even orthogonal to the prevailing consensus. When trying to understand a complex field it is as important to look at the established theory as it is to probe the edges where you will find interesting people who are not seduced by buzz and hype.

I have a sense that there is too much vertically-focused thinking going on in AI right now, and far too little of the horizontal, or ‘joined-up’ thinking that the field’s breadth demands.

The purpose of Mastering Artificial Intelligence is to deliver the particular type of insights that can only come from thinking within and across multiple disciplines.

AI needs more ‘joined up’ thinking

The great Richard Feynman once said that an idea that cannot be explained in plain English is not properly understood.

I suspect that if he were alive today Feynman would take a dim view of how well understood AI is: you do not have to look for long to clearly see that too many AI practitioners cannot provide plain English answers to simple questions.

Too many word salads.

Of course, sometimes we just don’t know the answer and that’s OK: it’s better just to come clean and say you don’t know or aren’t sure, than it is to try to convey the impression  you understand something that you don’t.

Worse, the field of AI seems to be resolving itself into rival camps with the ‘AI will kill us all’ people and the ‘rainbows and lollipops’ people digging themselves in on either side of an intellectual chasm.

This sort of polarization always ends badly, with high-profile people like Elon Musk and Mark Zuckerberg resorting to taunting each other on Twitter.

I guess the same thing happened in the 1950s when Niels Bohr and Albert Einstein faced off over the idea of quantum uncertainty (Bohn won that debate, by the way, but I wonder whether that was more because Einstein found his sheer force of personality overwhelming).

But the media loves a public slanging match between two high-profile figures – and actively works to whip things up. All in the name of journalistic objectivity attracting readers.

The feeling I have is that in spite of the truly remarkable experimental results we’re seeing with the latest AI systems like IBM Project Debater and Google / DeepMind AlphaGo, there is still a lot of intellectual wheel-spinning going on with many – but by no means all – people who work in AI not truly understanding their own field.

Now I realise those words will wind some people up, and I’m sorry for that.

But I’d ask you to try to think objectively as you may at least find an element of truth in what I’m saying?

And I hope my words are not taken as disrespecting academics who are working on AI.

By necessity, mainstream academia is divided up into a multitude of very narrow fields of study and the reason for that is that the deeper you go the more specialised you have to become. It’s just the laws of information theory applied to academic research.

This is totally OK: we need our academics and researchers to be focused!

But, again, have a think about it: are you certain that your own research project would not benefit if you had a broader perspective on AI? And are you sure that your assumptions about aspects of AI that fall outside of your specific domain of expertise are correct?

Have you checked to find out, or are you trusting others?

There’s another problem here, which occurs when someone who has a narrow perspective on AI, but strong opinions about it – gains access to a large media platform that is on the look-out for controversial one-liners which can be used to scare people, or make them angry in order to attract traffic – which is sadly how much of the ad-supported web works these days.

The result is that ordinary people – people like us – then form opinions about hard questions in AI on the basis of one-liners which have been thrown out by people who themselves don’t fully understand their subject or, or if they do, then they have done a bad job of explaining it.

So not only is AI a very complex field that requires a multi-disciplinary approach, I think it is accurate to say that many major media outlets are actively creating and distributing a lot of information noise which serves only to further complicate the topic and polarise opinion.

So I decided to teach myself – and you can join me, if you like

Around the middle of 2017, I came to the realization that the only way I was going to obtain the sort of understanding I sought – that is, ‘deep understanding’ – would be for me to figure things out for myself.

I am a firm believer in Richard Feynman’s idea that even the most complex ideas can be explained in plain English without using technical jargon.

Given that my particular sort of multi-disciplinary experience seemed pretty well-suited to tackling a multi-faceted area like AI, and I seem to be pretty good at writing and explaining complex ideas to others, I thought it should be possible for me to understand the difficult ideas in AI and then explain those in plain English.

In order to provide some focus I decided to structure my own personal learning journey and summarise it in the form of a series of Podcasts that others can listen to when they have time, which has led to Mastering Artificial Intelligence.

Our focus will be the axiomatic foundation of AI, not the math built on top

AI is a very technical field and it is clearly impossible to understand how the low level detail works without the requisite academic training, which means that you really do need to be comfortable with mathematical ideas and have a good feel for electronics, computer science and software.

Here’s an example: you can explain the idea of matrix multiplication to someone in English,  but if you want to actually multiply two matrices together and get an actual result then you have to do that using the language of mathematics.

But you do not need an M.Sc. computer science or a Ph.D. in machine learning to understand the idea of a matrix, and it is this deep understanding that Mastering Artificial Intelligence is all about.

Mastering Artificial Intelligence is focused on the ideas that AI is based on, rather than the mathematical language that connects those ideas together.

Make no mistake, we will be getting into the technical nitty-gritty – for examples how deep neural networks, how convolutional neural networks (CNNs) work and how neuromorphic chips work but we’ll be doing that in a way that focuses on the underlying ideas, not the math and code.

Remember that the math is not why something works. Math is simply a language humans have invented to describe a certain phenomena.

In the interest of objectivity, I should say that some scientists, like Max Tegmark, believe that reality is ultimately just math which means that the parts of our reality that we perceive to be physical are really just an illusion.

He might be right about this, but if he is I would say that the math that human minds have discovered is just a small and incomplete part of a far wider and richer math that our minds may never be able to fully comprehend. So I’d still say that “human math” is not the “why”, but simply the “what.”

Just as the ideas embodied in a novel are conveyed using the English language, so the ideas embodied in a neural network are conveyed using the language of mathematics.

What I’ve found is that if you spend the time getting a firm grip on the ideas that underpin AI systems you can quickly see ways to improve on the state of the art: in spite of its power, AI technology is still very crude and there are many ways to make it even more powerful and we’re going to get into some of that in this programme.

In summary, if you seek a deeper understanding of AI but don’t want AI explained superficially in terms of math, code or specialist jargon then you could be in the right place.

I am confident that Mastering Artificial Intelligence will equip you with the deep insights needed to contribute positively to any AI-related project or conversation – without feeling out of your depth or lacking the confidence to challenge experts.


Programme Content

Podcasts supported by eBooks

Mastering Artificial Intelligence is a daily podcast show supported by eBooks which contain the key ideas covered in the podcasts.

The objective is to provide a complete, joined-up picture of the entire AI space while delivering the deep insights needed to really understand the field.

Overall structure

Mastering Artificial Intelligence is structured as 11 Modules each of which focuses a specific aspect of AI:

  1. The big questions – new thinking on AI that takes us closer to the truth
  2. Neuroscience – most of what you know is probably wrong
  3. Theoretical foundation – key insights that many programmers lack
  4. Technical building blocks – 10 key ideas that define how AI works
  5. AI in action – 200 ways AI is being used to solve real problems
  6. Creating the AI market – the main actors driving the market
  7. The leading edge – how state-of-the-art AI systems actually work
  8. Building stuff – how to use AI in completely new projects
  9. The economic impact of AI – markets, the economy and people
  10. AI in the future – the plausible, the implausible and the plain eccentric
  11. Fact vs. Fiction – taking on the Silicon Valley glitterati

Content format

Each of the above 11 Modules will consist of a set of around 20 podcasts of between 30 and 60 min each. Each Module comes with a specially written PDF eBook (premium content) of between 100 and 200 pages which you can take away and consult later.

The eBooks are not verbatim transcripts of the podcasts, but are specially written in an informal, conversational style while being structured in the form of a management report, with an Executive Summary, Introduction and Conclusion etc. Although the podcasts and eBook for a given Module contain the same ideas and basic content, they are significantly different.

Each podcast also comes with its own PDF eBooklet (premium content) of between 5 and 10 pages which contains the key ideas conveyed in the podcast. The eBooklets are written in the style of a white paper or extended blog article and typically contain images, graphics and other content that is not in the podcast.

Volume of material

In total Mastering Artificial Intelligence will include around:

  • 250 podcasts, each  between 30 and 60 mins
  • 250 eBooklets, each between 5 and 10 pages
  • 11 eBooks, each between 100 and 200 pages


Later on, when the base content of Mastering Artificial Intelligence is complete – which I think will take until the end of 2019 – I envisage creating a new Module that will include a range of long-form discussions with people who are expert in their fields.

I envisage that the style of these sessions will be to deeply discuss a particular controversial idea that arose while researching the programme – such as whether human intelligence might be quantised down to the level of individual neurons, whether biological software exists in the same way as computer software exists or whether the Tree of Life idea – which is the center piece of evolution by natural selection – might actually contradict Gödel’s  first Incompleteness Theorem.

The focus of these discussions will be to build upon some of the ideas that have come out of creating Mastering Artificial Intelligence and will take the listener off the beaten track into new intellectual territory.


The eBooklets and eBooks contain links to books, research papers and online learning resources that were used in the research phase.

In addition, I sometime reach out to people by email to check thinking on a given topic or to float my own ideas to see what the reaction is. Mostly, the people I’m interacting with are senior-level academics who work in AI, mathematics, neuroscience and other related fields.

I should say that the approach taken with Mastering Artificial Intelligence is to try to create something that adds to what already exists – in a complex field where there are a lot of conflicting viewpoints.

If you resolved the AI field into a set of key ideas – drawn from right across the space – then that set of ideas would contain many contradictions. In other words, it would not be self-consistent.

To be blunt, some of the ideas in the AI space make sense, some do not and others are simply wrong. The challenge is to figure out which is which.

If the goal is to distill from this alphabet soup of ideas a subset that is self consistent, and then to further develop that into a rich narrative – which is the purpose of Mastering Artificial Intelligence – then you are going to have to make hard choices.

You cannot just write down what an expert says and accept it as fact. And you cannot sugar coat things or hedge your position with words ‘possibly’, ‘might’ and ‘could’. There is a place for content that is safely hedged like this does, but not here.

Instead, you need to calibrate what a given person is saying using your own deepening understanding of the field and, if necessary, reject their idea as being inconsistent with the facts.

But at the same time you also have to be prepared to accept that a carefully-constructed argument, which you formerly felt sure about, might be wrong.

You have to be willing to adapt and admit that you were wrong. But you also need to have a sufficient level of self confidence to push back and call something out if it doesn’t make sense.

So I do rely on external sources partly as a source of new facts and ideas and partly  as a way to calibrate and enrich a gathering narrative.

Launch date

Daily podcasts and eBooklets will begin to be published in July 2018.

Considering that I originally had the idea for Mastering Artificial Intelligence in Jan-18, you might be wondering why there’s still no content?

Part of the reason is that I need to think about production consistency: the sheer workload involved in creating a daily podcast and the associated eBooklet 5 days a week is such that the content you will be publishing on a given day  must have been already researched.

I’m not going to be just talking randomly about AI, but offering a very carefully-considered position that is part of an overall, rich narrative which will frequently contain new ideas, new ways to look at familiar questions and many intricately constructed logical arguments.

Creating content like this takes literally 1000s of hours of work, or at least it does for me.

Given this, if you think about the reality of maintaining a consistent publication schedule you actually need about 3 months of content pretty much all complete and ready to go – before you actually start publishing anything.

As of June 2018, I’ve got Module 1 complete, Module 2 is in a very advanced state and Module 3 is coming along.

The plan is to continue researching the content for future Modules while almost ‘turning the handle’ while I publish content that has already been researched.

The other part of the reason is that as I’ve tried to explain before, AI is a very hard topic to wrap your head around: you really do need to look at a given question from several very different perspectives (e.g. metaphysical, software, math and neuroscience) before you can arrive at a position that you can support with a strong argument based on fact and logic.

This takes time, and that’s coming from someone who spent years running a technology industry research company, has a science and technology background and is used to the mental labor needed to understand complex topics.

In hindsight, I’m very pleased that I decided to wait until my position on certain contentious topics stabilised. For instance, the question of whether AI is real and whether AI as it is presently defined can ever be real are not questions you can answer by reading a page on Wikipedia.  I’m pleased that I waited because I’ve changed my mind on both of these topics – after having looked carefully at the facts from all sides.


Become a strategic expert in AI

My goal is to take your understanding of AI up to that of a “strategic expert.”

What does that mean?

It means that by the end of this programme others will respect you because they will clearly see that you know what you’re talking about.

It means that by the end of this programme you will be able to engage in a meaningful way with leading practitioners who work in the field of AI.

And it means that by the end of this programme you will be able to help conceptualize, frame and direct AI projects.

But Mastering Artificial Intelligence is not going to explain how to code AI algorithms – there are plenty of excellent online programmes that already do that. For example, you could take Andrew Ng’s excellent course on deep learning which comes with a lot of free video material here.

Instead, Mastering Artificial Intelligence is pitched at a higher level and will equip you with a type understanding that many AI programmers lack.

In the coming years, the most important skill-set in AI will shift from ‘math/code’ to ‘strategic expertise’ – which means being able to clearly understand how AI works and how it can be improved, having the imagination to see how AI can be applied to solve real problems and having the intellectual horsepower to shape, define and direct AI projects.

A ‘strategic expert’ can also, when needed, challenge thinking that doesn’t make sense or confidently admit to not being able to follow vague thinking (in which case, others will thank you for speaking up because they will likely feel the same).

Mastering Artificial Intelligence is intended to deliver the following specific benefits:

  • Strong conceptual understanding of how advanced AI systems work
  • Completely new and improved perspective on the ‘big’ questions in AI, such as what intelligence ultimately is, where intelligence ends and consciousness begins  and the difference between human intelligence and artificial intelligence
  • A good intuitive feel for what AI can and cannot do. In other words, a good sense for the boundary that separates the ‘doable’ from the ‘undoable’
  • The breadth of knowledge and confidence needed to understand or even conceptualise AI projects, help direct AI project teams and understand the practical implications of major AI research announcements
  • The knowledge to  critically analyse media reports about AI, review AI-related business proposals and evaluate AI projects in order to identify the strengths and weaknesses, and ways to improve
  • For those already equipped with the requisite gravitas and business experience, be able to confidently perform in the C-suite, engage meaningfully with the CEOs of AI startups

This might seem like a tall order – but the above describes where I need to get to in AI, and you could get there as well if you join me on Mastering Artificial Intelligence.


People who want to be thought leaders in AI

Mastering Artificial Intelligence is aimed at people who want to take their understanding of AI to the next level.

The content on this website will take you beyond the throwaway  buzzwords and repetitive narratives that typify most popular coverage, past the mathematics, computer code and jargon found at the coal face – and into the realm of real understanding.

If you can relate to one of the following situations then Mastering Artificial Intelligence could be what you need:

  • Have you ever read a newspaper article about AI and been suspicious about some of the claims – but you can’t quite see where the thinking has gone wrong?
  • Do you have a sense that some high-profile commentators might not understand AI quite as well as they should, given the size of their platform – but you’re not confident that you’d do any better?
  • Maybe you’re wondering about the sort of problems that are best solved using AI – but are not sure how to actually identify those problems in a real business situation?
  • Perhaps you run a data science team which is using AI more and more, but deep down you’re not totally confident that you could explain how AI software differs from data science software. What’s special about AI, and why?
  • If you avidly consume media reports on AI then maybe you’re looking for something that will provide a deeper understanding – but you don’t have the time to deal with complex math, code and lots of meaningless acronyms?
  • You might be involved with coding AI systems but although you’re right into the detail you are always on the lookout for fresh insights that will sharpen your perspective and provide you with an intellectual edge over peers.
  • Or maybe you write about AI but feel that the constant pressure of deadlines prevents you from taking the time  to really understand your subject.

Mastering Artificial Intelligence contains compelling answers to all of these questions, and 100s more.

You do not need a computer science degree or coding ability to be able fully benefit from Mastering Artificial Intelligence.

The only prerequisites are a genuine interest in the subject, a willingness to be open minded and think critically and a basic level of scientific literacy – which means that you ideally took some combination of physics, math, chemistry or biology at school.



I have the breadth and depth to clearly explain AI

My name is Andrew Sheehy and I will be your host on Mastering Artificial Intelligence.

I have a strong engineering background which is based on 15 years in electronics and telecoms engineering where I held a range of design, development and management positions at Marconi Instruments, Standard Telephones and Cables (STC), Northern Telecom, Telstra New Zealand and PipingHot Networks (now Cambium Networks).

I then spent 12 years, managing an technology industry research company, Nakono (formerly Generator Research) which focused on digital disruption and provided premium content to many of the world’s most prestigious technology and media brands. During this time I led a team of 10 and personally authored over 10,000 pages of research content on topics right across the digital economy – including artificial intelligence.

More recently I have been reconnecting with my engineering roots by conceiving and architecting software solutions, developing novel IP in the field of video watermarking, directing software development projects and understanding the mathematical foundations and philosophical context of artificial intelligence.

I have always been driven to deeply understand complex topics and enjoy that rush of excitement when  a new insight changes your perception of a subject or allows you to see how to advance the state of the art.

This passion for understanding has always driven me, initially as I sought to deeply understand the ‘back art’ of microwave electronics. And it drove me during the years 12 years I was a professional technology industry analyst as I sought to understand how complex technology markets worked, as well as the overall macroeconomic context.

And my quest to see reality as as it is will give me with the energy I need while tacking my greatest intellectual challenge to date, which is to deeply understand artificial intelligence, share that understanding with others using this website and, hopefully, to play a role in advancing the state of the art.

I hold a B.Sc. (Hons) degree in electronics from Salford University and an MBA degree from London Business School.