AI, ML & DL – A Bluffer’s Guide

AI, ML and DL are our attempts to get machines to think and learn in the way that we can. Get that right and you’ll take the power of the human multiplied a million-fold, to have a breathtakingly capable machine. Probably our new robot overlords but we’ll cover that later. Whilst I do not have any issue with these developments, and do believe it is both attainable and useful, we are not there yet. To date we have these incredibly fast calculators that are essentially linear and binary. These are our modern computers. There are boffins in labs developing non-linear and non-binary counting machines but they are not here yet. This means that we are left with the brute force approach to problem solving. Run the right algorithm (at least to start it is provided by a   human) and you can get the giant calculator to supply an answer, often the correct one but f not then it can learn from its mistakes, rewrite the algorithm and try again. (By the way: that is ML/DL in a nutshell) Machine learning and AI.jpg Here is a definition of ML: Machine learning is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. That’s it. It is a computer learning to improve and tweak it’s algorithm, based on trial and error. Just like we learn things. No difference. Here is a definition for AI: Artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. However, AI is where things can really come unstuck. The aim is to get machines to think as we do. In a non-linear way. Human beings deal exceptionally well with ambiguity and we have an ability to match things up like apparently different words and images. Have you ever been transported back in time, in an instant, by a song clip or a smell? That is  human, no one taught you to do that. A computer could conceivably do that but only if it had previously been instructed to do so. It can do it so very fast you would be forgiven for thinking it was natural. It is not though, it is programmed to do it. Sure, it might have learnt to improve its own algorithm (Machine Learning again) to do that based on observations of human behaviour. It is still just mimicking what it sees as the appropriate behaviour, there has never been that spontaneous connection that you experienced that transported you to another time and place, even fleetingly. A recent high-profile example of AI and ML going a little bit awry and showing bias is in this article here. “Amazon Reportedly Killed an AI Recruitment System Because It Couldn’t Stop the Tool from Discriminating Against Women“ Well worth listening to the video and understanding the unconscious bias exhibited by the builders of the algorithms. There are efforts to remove the human biases that the machines learn from and perpetuate. But what is Deep Learning, I hear you cry? It  can simply be differentiated from Machine Learning as when the need for a human being to categorise all the different data inputs is eliminated. Now the machine (still only  the really fast calculator). Think self-driving cars, drones and many more much duller things. Presently, we humans need to be involved in the categorisation. There is even a Data Labelling factory in China to use humans to ‘teach’ machines what it is  that they are seeing. Equitable, Just, Neutral and Fair are components of moral behaviour that reside in the interpretation of the present societal norms, and not everyone agrees with them. Different cultures can have quite different views on a correct moral choice. Remember this when someone is trying to argue about the infallibility of computers. They can only be programmed with lagging data and they will always reflect us and our biases. For better or worse. bias see-saw.jpg
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Data Ethics For Business

We exist in an increasingly data driven world. More and more, we are encouraged or directed to ‘listen to the data’ above all else. After all, the data doesn’t lie. Does it?

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Data Ethics in business is the name of the practice used to ensure that the data being used to make high-value commercial decisions is of the highest quality possible. However, there is a catch. Human beings are the catch. We have  gut-instinct, prejudices, experience, belief systems, conditioning, ego, expectation, deceit, vested interests etc. These behavioural biases all stand to cloud the data story, and usually do.

A high-value commercial decision does not necessarily have immediate financial consequences. Although, in commercial terms, a sub-optimal outcome is invariably linked with financial loss. In the first instance, the immediate effects of a high-value decision can be on organisational morale or have reputational consequences.

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When a high-value decision is to be made there are invariably advocates and detractors. Both camps like to believe that they are acting in the service of a cause greater than themselves. Occasionally, some of the actors cloud the story because their self-interest is what really matters to them, and they try hard to mask that with the veneer of the greater good. Hence the term ‘Data Story’, because behind the bare numbers and pretty graphics  there is an entire story.

The concept of conducting a pre-mortem examination of the entire data story to model what can go wrong is becoming more important for senior decision makers. It is getting increasingly difficult to use the traditional internally appointed devil’s advocate as, due to the inherent complexity of understanding a data story, this function needs to be performed by subject matter experts. Although the responsibility for decision-making always falls on the Senior Management, they want to do it with a full breakdown of the many facets of the data story.

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In order to achieve this, individuals with a unique blend of talents, experience and inquisitiveness must be used. People with absolute objectivity and discretion, who don’t rely on inductive reasoning. Ones who are robust enough to operate independently, diplomatically and discreetly and have executive backing to interrogate all the data sources, ask the difficult questions and highlight any gaps, inconsistencies, irregularities. From this they can provide a report for the Executive Sponsor(s) with questions to ask and inquiries to make so a well-informed decision can be made.

After all, when there is  lots at stake, no one wants to be remembered as the person that screwed-up and tried to blame the data?