![INTRODUCTION
“As leaders, it is incumbent on all of us to
make sure we are building a world in which
every individual has an opportunity to thrive.
Understanding what AI can do and how it fits
into your strategy is the beginning, not the
end, of that process.”1
- Andrew Ng, former VP & Chief Scientist of Baidu, Co-
Chairman and Co-Founder of Coursera, the former
founder and lead of Google Brain, and an Adjunct
Professor at Stanford University.
2
1 Ng, A. (2019). “Andrew Ng: What AI Can and Can’t Do.” [online] Harvard Business Review.
Available at: https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now MM.DD.20XX
CONTENTS
I.
Introduction
II.
What is AI?
III.
What is ML?
IV.
Why now?
V.
Applications & Limitations](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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AI & ML Overview
- 1. AI & ML OVERVIEW Charlotte Isabella Aspinall INTRO TO AI
- 2. INTRODUCTION “As leaders, it is incumbent on all of us to make sure we are building a world in which every individual has an opportunity to thrive. Understanding what AI can do and how it fits into your strategy is the beginning, not the end, of that process.”1 - Andrew Ng, former VP & Chief Scientist of Baidu, Co- Chairman and Co-Founder of Coursera, the former founder and lead of Google Brain, and an Adjunct Professor at Stanford University. 2 1 Ng, A. (2019). “Andrew Ng: What AI Can and Can’t Do.” [online] Harvard Business Review. Available at: https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now MM.DD.20XX CONTENTS I. Introduction II. What is AI? III. What is ML? IV. Why now? V. Applications & Limitations
- 4. INTRODUCTION MM.DD.20XX4 1. AI & ML Overview 2. Machine Learning: Part One 3. Machine Learning: Part Deux 4. AI: Political Economy 5. AI: Safety & Ethics 6. AI: Who’s Who and What’s Next?
- 5. WHAT IS AI? “We define [AI] as the study of agents that receive percepts from the environment and perform actions. Each such agent implements a function that maps percept sequences to actions …”2 - Stuart J. Russell, Professor of Computer Science University of California, Berkeley; and Peter Norvig, Director of Research, Google Inc.; 1995 5 MM.DD.20XX2 Russell, S. J., Norvig, P., (1995) “Artificial intelligence: a modern approach.” CONTENTS I. Introduction II. What is AI? III. What is ML? IV. Why now? V. Applications & Limitations
- 6. WHAT IS AI? MM.DD.20XX6 AI Machine Learning Neural Networks Deep Learning “Software that can make decisions and act autonomously”3 4 Koch, 2016. “How the Computer Beat the Go Master,” 20. “The methods underlying AlphaGo … have huge implications for the future of machine intelligence”4 3 Professor Nello Cristianini, speaking at Instant Expert AI, Dec 2019 Computer Vision Robotics NLP
- 7. WHAT IS ML? “Alpha Go isn’t just an expert system built with handcrafted rules … instead it uses general machine learning techniques to figure out for itself how to win at Go”5 - Demis Hassabis, Founder & CEO of Google DeepMind; January 2016 7 5 https://ai.googleblog.com/2016/01/alphago-mastering-ancient-game-of-go.html MM.DD.20XX CONTENTS I. Introduction II. What is AI? III. What is ML? IV. Why now? V. Applications & Limitations
- 8. WHAT IS ML? MM.DD.20XX8 Data Algorithm Output Validation Check of how accurate the result is Use by user Additional data point recalibrates the algorithm Output data is fed back into data source
- 9. WHAT IS ML? MM.DD.20XX9 SUPERVISED LEARNING UNSUPERVISED LEARNING £350k REINFORCEMENT LEARNING
- 10. WHAT IS ML? MM.DD.20XX10 K-Means ClusteringDecision Trees & Random Forests Principal Component Analysis (PCA) Linear & Logistic Regression Isolation Forests Sentiment Analysis Reinforcement Learning Artificial Neural Networks Convolutional Neural Networks
- 11. WHY NOW? “[…] in no part of the field have discoveries made so far produced the major impact that was then promised”6 - Sir James Lighthill, in an evaluation of AI research compiled for the British Science Research Council in 1973. It is held that this paper led to the “AI Winter” when funding for research in this area dried up. 11 6 James Lighthill (1973): "Artificial Intelligence: A General Survey" in Artificial Intelligence: a paper symposium, Science Research Council MM.DD.20XX CONTENTS I. Introduction II. What is AI? III. What is ML? IV. Why now? V. Applications & Limitations
- 12. WHY NOW? MM.DD.20XX12 7 Milton Lim: https://www.actuaries.digital/2018/09/05/history-of-ai-winters/
- 13. WHY NOW? MM.DD.20XX13 MOORE’S LAW MORAVEC’S PARADOX AMARA’S LAW “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run”8 Computing performance doubles every 18 months “It is comparatively easy to make computers exhibit adult level performance in solving problems on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one year- old when it comes to perception and mobility.” 8 C. Frey, 2019, The Technology Tray: Capital, Labor, and Power in the Age of Automation (Princeton, NJ: Princeton University Press), 323. 9 H. Moravec, 1988, Mind Children: The Future of Robot and Human Intelligence (Cambridge, MA: Harvard University Press), 15.
- 14. APPLICATIONS & LIMITATIONS “The danger is that if we invest too much in developing AI and too little in developing human consciousness, the very sophisticated artificial intelligence of computers might only serve to empower the natural stupidity of humans.”10 - Yuval Noah Harari, 21 Lessons for the 21st Century 14 10 Yuval Noah Harari, 21 Lessons for the 21st Century MM.DD.20XX CONTENTS I. Introduction II. What is AI? III. What is ML? IV. Why now? V. Applications & Limitations
- 15. APPLICATIONS & LIMITATIONS MM.DD.20XX15 Fraud Detection Sentiment Analysis Facial Recognition Forecasting & Prediction Object Recognition Chat bots Pattern Recognition Anomaly Detection Recommendation Systems Cyber Security Personalisation NLP Gamification
- 17. THANK YOU Charlotte Isabella Aspinall INTRO TO AI
Notas do Editor
- AI is being described as the General Purpose Technology of our time, set to displace up to 47% of jobs in the next 15 years. But how much do you really know about AI and how it works? My name’s Charlotte and I’ve just returned to Accenture having spent a year on Leave of Absence to learn about Big Data, Machine Learning and AI. Today I want to introduce the first episode of a 6 part series to share with you some of what I’ve learned.
- I want to kick off this presentation with an important quote from Andrew Ng. I believe that we are all in a position to help build a world where every individual has an opportunity to thrive. I hope that today I can help you to understand what AI can do, and what it cannot, to help you drive positive change.
- So by way of introduction I want to do 2 things. Firstly I want to introduce myself, and secondly introduce why I’m giving this talk. My name is Charlotte and I’ve been with Accenture for 4 years. I joined straight after university having studied PPE (Politics, Philosophy & Economics) with Mandarin. I was part of H&PS for 3 years before taking a sabbatical. In my last 18 months with the company I was a solution design architect on a Student Lifecycle Project, which touched on analytics and Big Data. I was fascinated by the seemingly unlimited possibilities of these technologies. However, having no background in computer science or statistics, I couldn’t get a foot in the door to learn more. And as good as MyLearning is, learning an entirely new discipline requires more in-depth resources, as well as time and focus. So I found a “Master’s in Big Data and Management” taught at a university in Rome – if I’m going to study something difficult I want to study somewhere warm with good wine. I’m very grateful to everyone who supported and enabled me to take a leave of absence. As well as learning a huge amount, I can honestly say that it was the best year of my life, so thank you. During my studies I became very interested in AI on both a technical and philosophical level. This presentation series is a summation of everything I’ve learnt – from lectures, books, conferences, blog posts, Netflix documentaries – you name it. And I want to share it with you. I want to share it with you because I have had the luxury of time and freedom to explore this topic; and I know that there are many of you who are interested in knowing more about this topic but perhaps don’t know where to start.
- This is the first episode in a 6 part series. This series is designed to be a good overview to help you understand what AI is and what it isn’t. To dispel some myths, help you speak with more confidence (both to clients and to your friends) about AI, and help you understand and work better alongside data scientists and AI/ML engineers. A quick note on what this series isn’t. This isn’t going make you a data scientist. This isn’t comprehensive. And whilst some code will be introduced, I do not expect you to know how to code (or even read code). I will explain it line-by-line as and when it comes up; it’s just important that you see what’s actually happening and that ML isn’t ‘magic’. Unfortunately, it is still just computers computing.
- The best place to start with when explaining a topic is to find the right definition. There are many definitions of Artificial Intelligence, but the one quoted here stands up best to scrutiny. AI is the study of agents that receive percepts (that is, an input ‘perceived’ by the agent) from the environment and perform actions. Each such agent implements a function that maps percept sequences (that is, multiple observed inputs) to actions. This definition reflects what we understands computers to do – receive inputs, perform computations, and produce outputs. The computations used in AI relate to ‘statistical inference’ – just in case you were interested.
- But this definition makes AI sound so . . . simple. ‘Input, output’ doesn’t sound so complicated. So what is it that makes AI seem so special? At a recent AI focused event hosted by New Scientist, Professor Nello Cristianini described AI as “software that can make decisions and act autonomously”. This addition of the word “autonomous” makes a difference, this is where AI starts to get complicated, where, unlike rule-based programming, we can’t see exactly how it works. Alongside this addition of acting autonomously, for a tool to be considered artificially intelligent, it must also be able to learn. Machine learning theories have been around for many years; however it’s only been in the last decade that we’ve had the computing resources to enable such approaches to perform well. This move away from rule-based programming, towards machine learning in the 21st century, enabled machines to emulate human intelligence, as reflected in AlphaGo’s defeat of the world’s best Go player in 2016. I recommend watching the related Netflix documentary, where you watch as AlphaGo plays some moves no human would make, that ultimately lead to victory. What is important to know is that AI is no one single technology or methodology, it is instead a collection of technologies. When you get most introductions of AI you usually see a diagram that looks something like this. This diagram shows you how ML, DL and NN interplay. But I’d like to add more to this. The field of AI also comprises areas of research into computer vision, robotics and neural linguistic programming. These all utilise different elements of machine learning, but exist as stand-alone technologies and areas of research. The collection of all of these fields fall under AI.
- So I’ve talked about the importance of machine learning as part of AI, but what actually is ML? In essence, ML is the ‘secret sauce’ which has pushed AI back into the forefront of computing research and development. Previously, so-called ‘intelligent’ algorithms, such as DeepBlue which beat the world chess champion Garry Kasparov in 1996, relied on rule-based programming. This means that the rules are known, and every move can be coded. Therefore, when faced with a particular chess board, the program can run every conceivable move from that point, and play the move which has the highest chance of leading to a win. However, the program used for DeepMind is completely unusable for any other application. It has been told how to play chess and that is what it can do. It cannot ever understand how to play a different game, not without re-programming the program from the ground up. Google’s DeepMind however tried a different approach when creating an AI which could learn any game. Instead of programming each step of a game, it told the algorithm the rules of the game and had it play against itself hundreds of thousands of times in order for it to learn the best game strategies. Each time the machine played, it took the strategy and outcome of the last game and used it as an input to devise its next strategy. This is machine learning.
- So how does a machine learn? It is, after all, not a human being, and it does not have the same inner workings as the human mind, as much as researchers are trying to get computers to mimic the human brain. Before we dive into the different types of machine learning and algorithms, let’s start at a super high level. All machine learning starts with data. This could be spreadsheets, text files, photos, videos . . . Anything stored in a digital format. For now, let’s stick with the idea of spreadsheets. The data points on these spreadsheets are fed into an algorithm, which has been coded by a data scientist (and we’ll get into those later on). This algorithm runs its computations against all of the data points in the spreadsheet and spits out an output. This output can again be in many different formats, and there’s super interesting research into text-generation, photo and video manipulation in this area. More often than not the output will be a number or a decision code, essentially a label. Once you have the output, or labels, this can then be used by the end-user, or it can be fed into further automations to trigger various actions. The output is then fed back into the algorithm and this is crucially where the learning happens. By validating the output generated by the algorithm against the actual result (prediction versus reality), the algorithm can be adjusted to be more accurate in future. The output data is then fed back into the data source to be an additional data point for the algorithm in future. It is this cyclical nature of machine learning which separates it from previous rule-based programming.
- Machine learning approaches can generally be split into two categories. The first is ‘supervised learning’. This is where we have a data set in which the “right answers” are already given – or ‘labelled data’. For example we may have a data set which gives us information about houses – size, number of bathrooms, garden yes/no and the sale price of that house. We can use that data set to help train an algorithm where we can learn which variables (size, number of bathrooms etc.) have the most impact on our independent variable – house prices. Once the algorithm is trained, we can then use our program to help us predict future house prices. In unsupervised learning however we do not have labels for our data. In this scenario the machine will look for patterns and will try to group similar data points based on similar factors. We can tell our algorithm how many groups we’re looking for, or we can let the machine decide for itself how many distinct groups there are. For example we could present a data set describing various fruits: colour, weight, size; and we ask our algorithm to tell us how many different fruits there are, or how many of each type there are. There is a third way and this is reinforcement learning, which will be covered more in the second deep dive on Machine Learning. In business settings, supervised learning is the most commonly used approach, with some uses of unsupervised learning being applied. Reinforcement learning can be used, but this is still being explored in academic fields, and you won’t see it as much being used by businesses.
- There are many different types of Machine Learning methods, each suited to different tasks. This is by no means an exhaustive list, but just to give you a sense of the variety and breadth out there. It is the job of a data scientist to know which algorithm is best to build an accurate model. In fact, when building a model, data scientists may try many different approaches to find the one with the highest accuracy. In blue we have our supervised learning algorithms: linear & logistic regression, decision trees and random forests and sentiment analysis. These are best used in cases when have a regression or a classification problem. A regression problem is when you want to know numbers, such as, given details about a house on the market, how much would you predict it is sold for? A classification problem is when you want to classify items, such as, given details of someone applying for a loan, will they or won’t they default on the repayments? Or, as in the case of sentiment analysis, is this tweet positive or negative? In deep purple we’ve got our unsupervised learning algorithms. These help us achieve a number of different goals. Firstly we can use unsupervised learning to help us cluster the data, such as with K-Means clustering. This is different from categorisation because rather than feeding in the pre-defined groups, we ask our algorithm to find groups on its own, which might reveal categories we hadn’t previously been aware of. We can also look to reduce the dimensions, or complexity, of our data using Principal Component Analysis. And finally we can look for anomalies in our data using the Isolation Forest algorithm. Neural networks are advanced enough to be used with labelled or unlabelled datasets, hence the rather hideous combination of colours there. Reinforcement Learning is out on its own as, as mentioned before, it is its own beast and plays to a different (or given) set of rules. All of these approaches are different ways for a machine to learn from data and produce statistical inferences, which can then be used to drive decisions. I’ll be covering the basics of how these algorithms work in the two sessions on Machine Learning in this series.
- AI is a field of research which has been around for the last 70 years, so it begs the question – why are we starting to hear so much about it now? I wanted to start this section with this quote from Sir James Lighthill. In 1973 he was commissioned by the British Science Research Council (which has since morphed into the UK Research and Innovation organisation), to evaluate the state of AI research in the UK. And this is what he came back with – “in no part of the field have discoveries made so far produced the major impact that was then promised”. Almost overnight funding for AI research was pulled in all but two universities, and it is widely held that this led to the first ‘AI winter’ where there was a dearth of research or progress in the field.
- We can see this AI Winter in this excellent diagram of the history of AI I found during my research – I can’t claim credit for it unfortunately. There have been two periods of excitement, followed by ‘winters’ of limited progress. As we can see we’re currently in an era of explosive growth, and whilst there are those who would warn you that we are about the dip into another AI winter, there are others who believe we have hit the ‘eternal spring’. (I’ll be covering this in the episode on AI & Political Economy), If the current trend is one of growth and adoption, what’s changed?
- I want to introduce you to three key concepts, all of which are at play in regards to AI. These can be attributed to other areas of research and technology, so they’re good to know, and at the very least might help you at a pub quiz one day. You’ve most likely heard of the first law, if not by name but by nature. Amara’s Law states that: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run”. Amara’s law draws out the hurdles to adoption of new technologies. Implementing a new technology requires many changes to take place prior to adoption. In the case of AI, organisations need to move from paper-based, manual processes to digital technologies before they can create the data pipeline necessary to support AI. And all of that takes time, money and resources. We are now starting to see organisations that are reaching the end of their digital transformation who are now in a position to take advantage of AI/ML technologies. The second law will again be a familiar one to many of you – Moore’s law is an empirical observation that computing performance doubles every 18 months. Computing power is incredibly important in executing complicated algorithms against huge datasets. It has taken time for such computing power to be available. Now with more compute power, and the ability to execute code across multiple processors in the cloud, churning through the massive or real-time datasets required for AI is quicker and cheaper, reducing the limitations of time and cost to implementing such technologies. The final element in this triangle is Moravec’s Paradox – perhaps lesser known than the other two. Moravec’s Paradox said that “It is comparatively easy to make computers exhibit adult level performance in solving problems on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one year-old when it comes to perception and mobility.” This paradox still holds true. There are things which computers are simply better at doing – computing. But many activities are still very limited to the realm of human capabilities – walking upstairs, dexterity, empathy, creativity. For anyone needing reassurance that The Terminator isn’t going to be knocking on their door anytime soon, look up ‘robot fails’ on YouTube and have a good laugh. Moravec’s Paradox was written in 1988, and it alludes to the challenge in capturing tacit knowledge. For example, can you describe, in minute detail, how to drive? There is a huge amount of information that has to be learnt in order to know how to drive. Without being to articulate that knowledge, rule-based programming was unable to create programs which could successfully drive in complex situations. However, with the move towards machine learning algorithms that imitate how humans learn, machines can accumulate that tacit knowledge for themselves. This has vastly increased the variety of situations in which AI technologies can be applied. Therefore, as more organisations reach digital maturity, computing power increases and machines are able to learn tacit knowledge, we are starting to see AI technologies become increasingly prevalent.
- So let’s look at the areas where AI & ML is most applicable, and importantly, where it is not.
- There are so many applications of AI & Machine Learning. This technology is being applied across so many industries, from the finance sector taking advantage of forecasting models, to logistics companies using machine learning to direct its deliveries. Autonomous driving will take some years to hit to mainstream, but self-driving vehicles are already in operation across Amazon warehouses to stack shelves and pick products. I could take you through a list of examples where companies are using these technologies in innovative new ways, but what I want to do is give you a general idea of what machine learning can do. I hope this will help you to recognise opportunities where this could be applied with your clients. Firstly anomaly detection is useful for understanding fraudulent activities, or cyber security risks. NLP is brilliant for translating documents, chatbots, sentiment analysis and engaging with customers. NLP can be used to make sense of masses of complex reports, generating simple summaries, or even creating original documents. Moving onto pattern recognition, this is really well applied in facial recognition, object recognition and forecasting and prediction. Forecasting works not only for financial data, but also for customer footfall, call centre calls, the chance of getting a space in the shopping centre car park at 4:45pm on Christmas Eve . . . Gamification also uses pattern recognition. Be it an educational platform or a performance management scheme, being able to recognise where players are struggling and need extra assistance is a job for pattern recognition. I know I said I wouldn’t give you a list of examples, but just the one - Duolingo uses this approach to understand which particular words language learners struggle with, and how frequently words should come up in their games in order for linguists to remember them. Gamification does also fall partially under the theme of recommendation systems, which can be used to drive personalisation. Recommendation systems do exactly what they say on the tin and recommend products or services to customers, but they can also be used to recommend next best actions to staff, or used in personalised healthcare. This is a very quick whip through of the general themes where AI and machine learning can be applied. I really hope that a couple of these have connected with you and you can think of a couple of cases with your clients where these could be applied.
- So now you’ve got your million dollar idea, let me tell you why it won’t work. No, just kidding. But in all seriousness, there are some things you need to think about before you put this in front of your client. Firstly, in designing your solution look at the technology. I started this presentation by saying that I thought that Data Analytics was this seemingly unlimited world. Now, in reality, there are limits to what the tools can do. Creative, empathetic AI is confined to research labs at the moment, as are back-flipping robots. Start by looking for cases where similar solutions have been used. Or talk to a data scientist, they’ll be well placed to tell you whether the technology for what you want to achieve exists. Secondly, whilst your solution may work in principal, building a sustainable data pipeline is an uphill battle. Firstly, ensuring the data exists in a useable format is paramount. Perhaps the data you intend to use is in a legacy system that’s hard to extract from, or perhaps its even on paper, or in team member’s heads. How will you get access to this data? You will need to ensure that the data infrastructure required by your tool is available. Okay, say the data exists, and in a useable format that you’ve got access to, but do you have the right skills to be able to build the data pipeline? Data collection, cleaning, analysis, modelling and tool deployment all require their own skills, pretty much all of which are in short supply at the moment. Does your client have people with these skills? Do they need to upskill their workforce or make new hires? How quickly can they do this? These are all important factors defining the success your solution will have. Fourthly, you must check that you are adhering to the law in your AI solution. The key law you absolutely need to be aware of GDPR. There are some really useful primers out there that take all of ten minutes to read and are really helpful. I would encourage you to familiarise yourself with these. Finally, everything is underpinned by trust. If you do not have the trust of your customers, your staff, the public, you are risking the reputation of your client. There is a lot of fear around AI technologies. Some of it is fear of change. But there is a lot of very reasonable fear around how unexplainable the technology is, the ‘black boxes’ which are increasingly dictating our lives. There is fear around how little is known about it and how unregulated it is. And there is fear around how it will change the landscape of the labour market, and how many are jobs are predicted to be lost to machines. Addressing these fears is paramount to the success of AI, and we must make careful decisions about how we chose to use AI. I will be covering these issues and more in the sessions on AI & Political Economy, and AI: Safety & Ethics. We are all in a position to influence the decisions we make in regards to AI so I really encourage you to inform yourselves. Luciano Floridi, Oxford University Professor of Philosophy recently said, the decisions we make in the short run dictate the long run. Don’t you wish we’d made better decisions about energy policy fifty years ago?
- I didn’t mean to end on a note of doom and gloom, but I think it’s important to be aware that just because you have a hammer, it doesn’t make everything a nail. AI and ML are already changing the world we live in, and I truly believe in its power to change the world for good. It’s why I have spent the last year learning about these topics and why I am so excited to share what I’ve learnt with you. I’m happy to take questions now, or do feel free to reach out to me afterwards. Thank you, and good luck on your own AI journeys!