at Google Cloud Day, Judge Business School, University of Cambridge on Nov 19, 2019 [Powerpoint]
Neil D. Lawrence, University of Cambridge

#### Abstract

Artificial intelligence promises automated decision making that will alleviate and revolutionise the nature of work. In practice, we know from previous technological solutions, new technologies often take time to percolate through to productivity. Robert Solow’s paradox saw “computers everywhere, except in the productivity statistics”. This session will equip attendees with an understanding of how to establish best practices around automated decision making. In particular, we will focus on the raw material of the AI revolution: the data.

# What is Machine Learning? 

What is machine learning? At its most basic level machine learning is a combination of

$$\text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}$$

where data is our observations. They can be actively or passively acquired (meta-data). The model contains our assumptions, based on previous experience. That experience can be other data, it can come from transfer learning, or it can merely be our beliefs about the regularities of the universe. In humans our models include our inductive biases. The prediction is an action to be taken or a categorization or a quality score. The reason that machine learning has become a mainstay of artificial intelligence is the importance of predictions in artificial intelligence. The data and the model are combined through computation.

In practice we normally perform machine learning using two functions. To combine data with a model we typically make use of:

a prediction function a function which is used to make the predictions. It includes our beliefs about the regularities of the universe, our assumptions about how the world works, e.g. smoothness, spatial similarities, temporal similarities.

an objective function a function which defines the cost of misprediction. Typically it includes knowledge about the world’s generating processes (probabilistic objectives) or the costs we pay for mispredictions (empiricial risk minimization).

The combination of data and model through the prediction function and the objectie function leads to a learning algorithm. The class of prediction functions and objective functions we can make use of is restricted by the algorithms they lead to. If the prediction function or the objective function are too complex, then it can be difficult to find an appropriate learning algorithm. Much of the acdemic field of machine learning is the quest for new learning algorithms that allow us to bring different types of models and data together.

A useful reference for state of the art in machine learning is the UK Royal Society Report, Machine Learning: Power and Promise of Computers that Learn by Example.

You can also check my post blog post on What is Machine Learning?..

## Embodiment Factors 

Let me explain what I mean. Claude Shannon introduced a mathematical concept of information for the purposes of understanding telephone exchanges.

Information has many meanings, but mathematically, Shannon defined a bit of information to be the amount of information you get from tossing a coin.

If I toss a coin, and look at it, I know the answer. You don’t. But if I now tell you the answer I communicate to you 1 bit of information. Shannon defined this as the fundamental unit of information.

If I toss the coin twice, and tell you the result of both tosses, I give you two bits of information. Information is additive.

Shannon also estimated the average information associated with the English language. He estimated that the average information in any word is 12 bits, equivalent to twelve coin tosses.

So every two minutes Bauby was able to communicate 12 bits, or six bits per minute.

This is the information transfer rate he was limited to, the rate at which he could communicate.

Compare this to me, talking now. The average speaker for TEDX speaks around 160 words per minute. That’s 320 times faster than Bauby or around a 2000 bits per minute. 2000 coin tosses per minute.

But, just think how much thought Bauby was putting into every sentence. Imagine how carefully chosen each of his words was. Because he was communication constrained he could put more thought into each of his words. Into thinking about his audience.

So, his intelligence became locked in. He thinks as fast as any of us, but can communicate slower. Like the tree falling in the woods with no one there to hear it, his intelligence is embedded inside him.

Two thousand coin tosses per minute sounds pretty impressive, but this talk is not just about us, it’s about our computers, and the type of intelligence we are creating within them.

So how does two thousand compare to our digital companions? When computers talk to each other, they do so with billions of coin tosses per minute.

Let’s imagine for a moment, that instead of talking about communication of information, we are actually talking about money. Bauby would have 6 dollars. I would have 2000 dollars, and my computer has billions of dollars.

The internet has interconnected computers and equipped them with extremely high transfer rates.

However, by our very best estimates, computers actually think slower than us.

How can that be? You might ask, computers calculate much faster than me. That’s true, but underlying your conscious thoughts there are a lot of calculations going on.

Each thought involves many thousands, millions or billions of calculations. How many exactly, we don’t know yet, because we don’t know how the brain turns calculations into thoughts.

Our best estimates suggest that to simulate your brain a computer would have to be as large as the UK Met Office machine here in Exeter. That’s a 250 million pound machine, the fastest in the UK. It can do 16 billion billon calculations per second.

It simulates the weather across the word every day, that’s how much power we think we need to simulate our brains.

So, in terms of our computational power we are extraordinary, but in terms of our ability to explain ourselves, just like Bauby, we are locked in.

For a typical computer, to communicate everything it computes in one second, it would only take it a couple of minutes. For us to do the same would take 15 billion years.

If intelligence is fundamentally about processing and sharing of information. This gives us a fundamental constraint on human intelligence that dictates its nature.

I call this ratio between the time it takes to compute something, and the time it takes to say it, the embodiment factor (Lawrence 2017a). Because it reflects how embodied our cognition is.

If it takes you two minutes to say the thing you have thought in a second, then you are a computer. If it takes you 15 billion years, then you are a human.

# Evolved Relationship with Information 

The high bandwidth of computers has resulted in a close relationship between the computer and data. Large amounts of information can flow between the two. The degree to which the computer is mediating our relationship with data means that we should consider it an intermediary.

Originaly our low bandwith relationship with data was affected by two characteristics. Firstly, our tendency to over-interpret driven by our need to extract as much knowledge from our low bandwidth information channel as possible. Secondly, by our improved understanding of the domain of mathematical statistics and how our cognitive biases can mislead us.

With this new set up there is a potential for assimilating far more information via the computer, but the computer can present this to us in various ways. If it’s motives are not aligned with ours then it can misrepresent the information. This needn’t be nefarious it can be simply as a result of the computer pursuing a different objective from us. For example, if the computer is aiming to maximize our interaction time that may be a different objective from ours which may be to summarize information in a representative manner in the shortest possible length of time.

For example, for me, it was a common experience to pick up my telephone with the intention of checking when my next appointment was, but to soon find myself distracted by another application on the phone, and end up reading something on the internet. By the time I’d finished reading, I would often have forgotten the reason I picked up my phone in the first place.

There are great benefits to be had from the huge amount of information we can unlock from this evolved relationship between us and data. In biology, large scale data sharing has been driven by a revolution in genomic, transcriptomic and epigenomic measurement. The improved inferences that that can be drawn through summarizing data by computer have fundamentally changed the nature of biological science, now this phenomenon is also infuencing us in our daily lives as data measured by happenstance is increasingly used to characterize us.

Better mediation of this flow actually requires a better understanding of human-computer interaction. This in turn involves understanding our own intelligence better, what its cognitive biases are and how these might mislead us.

For further thoughts see Guardian article on marketing in the internet era from 2015.

You can also check my blog post on System Zero..

## Lies and Damned Lies 

There are three types of lies: lies, damned lies and statistics

Benjamin Disraeli 1804-1881

Benjamin Disraeli said1 that there three types of lies: lies, damned lies and statistics. Disraeli died in 1881, 30 years before the first academic department of applied statistics was founded at UCL. If Disraeli were alive today, it is likely that he’d rephrase his quote:

There are three types of lies, lies damned lies and big data.

Why? Because the challenges of understanding and interpreting big data today are similar to those that Disraeli faced in governing an empire through statistics in the latter part of the 19th century.

The quote lies, damned lies and statistics was credited to Benjamin Disraeli by Mark Twain in his autobiography. It characterizes the idea that statistic can be made to prove anything. But Disraeli died in 1881 and Mark Twain died in 1910. The important breakthrough in overcoming our tendency to overinterpet data came with the formalization of the field through the development of mathematical statistics.

Data has an elusive quality, it promises so much but can deliver little, it can mislead and misrepresent. To harness it, it must be tamed. In Disraeli’s time during the second half of the 19th century, numbers and data were being accumulated, the social sciences were being developed. There was a large scale collection of data for the purposes of government.

The modern ‘big data era’ is on the verge of delivering the same sense of frustration that Disraeli experienced, the early promise of big data as a panacea is evolving to demands for delivery. For me, personally, peak-hype coincided with an email I received inviting collaboration on a project to deploy “Big Data and Internet of Things in an Industry 4.0 environment”. Further questioning revealed that the actual project was optimization of the efficiency of a manufacturing production line, a far more tangible and realizable goal.

The antidote to this verbage is found in increasing awareness. When dealing with data the first trap to avoid is the games of buzzword bingo that we are wont to play. The first goal is to quantify what challenges can be addressed and what techniques are required. Behind the hype fundamentals are changing. The phenomenon is about the increasing access we have to data. The manner in which customers information is recorded and processes are codified and digitized with little overhead. Internet of things is about the increasing number of cheap sensors that can be easily interconnected through our modern network structures. But businesses are about making money, and these phenomena need to be recast in those terms before their value can be realized.

## Mathematical Statistics

Karl Pearson (1857-1936), Ronald Fisher (1890-1962) and others considered the question of what conclusions can truly be drawn from data. Their mathematical studies act as a restraint on our tendency to over-interpret and see patterns where there are none. They introduced concepts such as randomized control trials that form a mainstay of the our decision making today, from government, to clinicians to large scale A/B testing that determines the nature of the web interfaces we interact with on social media and shopping.

Their movement did the most to put statistics to rights, to eradicate the ‘damned lies’. It was known as ‘mathematical statistics’. Today I believe we should look to the emerging field of data science to provide the same role. Data science is an amalgam of statistics, data mining, computer systems, databases, computation, machine learning and artificial intelligence. Spread across these fields are the tools we need to realize data’s potential. For many businesses this might be thought of as the challenge of ‘converting bits into atoms’. Bits: the data stored on computer, atoms: the physical manifestation of what we do; the transfer of goods, the delivery of service. From fungible to tangible. When solving a challenge through data there are a series of obstacles that need to be addressed.

Firstly, data awareness: what data you have and where its stored. Sometimes this includes changing your conception of what data is and how it can be obtained. From automated production lines to apps on employee smart phones. Often data is locked away: manual log books, confidential data, personal data. For increasing awareness an internal audit can help. The website data.gov.uk hosts data made available by the UK government. To create this website the government’s departments went through an audit of what data they each hold and what data they could make available. Similarly, within private buisnesses this type of audit could be useful for understanding their internal digital landscape: after all the key to any successful campaign is a good map.

Secondly, availability. How well are the data sources interconnected? How well curated are they? The curse of Disraeli was associated with unreliable data and unreliable statistics. The misrepresentations this leads to are worse than the absence of data as they give a false sense of confidence to decision making. Understanding how to avoid these pitfalls involves an improved sense of data and its value, one that needs to permeate the organization.

The final challenge is analysis, the accumulation of the necessary expertise to digest what the data tells us. Data requires intepretation, and interpretation requires experience. Analysis is providing a bottleneck due to a skill shortage, a skill shortage made more acute by the fact that, ideally, analysis should be carried out by individuals not only skilled in data science but also equipped with the domain knowledge to understand the implications in a given application, and to see opportunities for improvements in efficiency.

## ‘Mathematical Data Science’

As a term ‘big data’ promises much and delivers little, to get true value from data, it needs to be curated and evaluated. The three stages of awareness, availability and analysis provide a broad framework through which organizations should be assessing the potential in the data they hold. Hand waving about big data solutions will not do, it will only lead to self-deception. The castles we build on our data landscapes must be based on firm foundations, process and scientific analysis. If we do things right, those are the foundations that will be provided by the new field of data science.

Today the statement “There are three types of lies: lies, damned lies and ‘big data’” may be more apt. We are revisiting many of the mistakes made in interpreting data from the 19th century. Big data is laid down by happenstance, rather than actively collected with a particular question in mind. That means it needs to be treated with care when conclusions are being drawn. For data science to succede it needs the same form of rigour that Pearson and Fisher brought to statistics, a “mathematical data science” is needed.

You can also check my blog post onblog post on Lies, Damned Lies and Big Data..

## Artificial Intelligence and Data Science 

Artificial intelligence has the objective of endowing computers with human-like intelligent capabilities. For example, understanding an image (computer vision) or the contents of some speech (speech recognition), the meaning of a sentence (natural language processing) or the translation of a sentence (machine translation).

### Supervised Learning for AI

The machine learning approach to artificial intelligence is to collect and annotate a large data set from humans. The problem is characterized by input data (e.g. a particular image) and a label (e.g. is there a car in the image yes/no). The machine learning algorithm fits a mathematical function (I call this the prediction function) to map from the input image to the label. The parameters of the prediction function are set by minimizing an error between the function’s predictions and the true data. This mathematical function that encapsulates this error is known as the objective function.

This approach to machine learning is known as supervised learning. Various approaches to supervised learning use different prediction functions, objective functions or different optimization algorithms to fit them.

For example, deep learning makes use of neural networks to form the predictions. A neural network is a particular type of mathematical function that allows the algorithm designer to introduce invariances into the function.

An invariance is an important way of including prior understanding in a machine learning model. For example, in an image, a car is still a car regardless of whether it’s in the upper left or lower right corner of the image. This is known as translation invariance. A neural network encodes translation invariance in convolutional layers. Convolutional neural networks are widely used in image recognition tasks.

An alternative structure is known as a recurrent neural network (RNN). RNNs neural networks encode temporal structure. They use auto regressive connections in their hidden layers, they can be seen as time series models which have non-linear auto-regressive basis functions. They are widely used in speech recognition and machine translation.

Machine learning has been deployed in Speech Recognition (e.g. Alexa, deep neural networks, convolutional neural networks for speech recognition), in computer vision (e.g. Amazon Go, convolutional neural networks for person recognition and pose detection).

The field of data science is related to AI, but philosophically different. It arises because we are increasingly creating large amounts of data through happenstance rather than active collection. In the modern era data is laid down by almost all our activities. The objective of data science is to extract insights from this data.

Classically, in the field of statistics, data analysis proceeds by assuming that the question (or scientific hypothesis) comes before the data is created. E.g., if I want to determine the effectiveness of a particular drug, I perform a design for my data collection. I use foundational approaches such as randomization to account for confounders. This made a lot of sense in an era where data had to be actively collected. The reduction in cost of data collection and storage now means that many data sets are available which weren’t collected with a particular question in mind. This is a challenge because bias in the way data was acquired can corrupt the insights we derive. We can perform randomized control trials (or A/B tests) to verify our conclusions, but the opportunity is to use data science techniques to better guide our question selection or even answer a question without the expense of a full randomized control trial (referred to as A/B testing in modern internet parlance).

## Supply Chain 

On Sunday mornings in Sheffield, I often used to run across Packhorse Bridge in Burbage valley. The bridge is part of an ancient network of trails crossing the Pennines that, before Turnpike roads arrived in the 18th century, was the main way in which goods were moved. Given that the moors around Sheffield were home to sand quarries, tin mines, lead mines and the villages in the Derwent valley were known for nail and pin manufacture, this wasn’t simply movement of agricultural goods, but it was the infrastructure for industrial transport.

The profession of leading the horses was known as a Jagger and leading out of the village of Hathersage is Jagger’s Lane, a trail that headed underneath Stanage Edge and into Sheffield.

The movement of goods from regions of supply to areas of demand is fundamental to our society. The physical infrastructure of supply chain has evolved a great deal over the last 300 years.

## Cromford 

Richard Arkwright is known as the father of the modern factory system. In 1771 he set up a Mill for spinning cotton yarn in the village of Cromford, in the Derwent Valley. The Derwent valley is relatively inaccessible. Raw cotton arrived in Liverpool from the US and India. It needed to be transported on packhorse across the bridleways of the Pennines. But Cromford was a good location due to proximity to Nottingham, where weavers where consuming the finished thread, and the availability of water power from small tributaries of the Derwent river for Arkwright’s water frames which automated the production of yarn from raw cotton.

By 1794 the Cromford Canal was opened to bring coal in to Cromford and give better transport to Nottingham. The construction of the canals was driven by the need to improve the transport infrastructure, facilitating the movement of goods across the UK. Canals, roads and railways were initially constructed by the economic need for moving goods. To improve supply chain.

The A6 now does pass through Cromford, but at the time he moved there there was merely a track. The High Peak Railway was opened in 1832, it is now converted to the High Peak Trail, but it remains the highest railway built in Britain.

Cooper (1991)

## Containerization 

Containerization has had a dramatic effect on global economics, placing many people in the developing world at the end of the supply chain.

For example, you can buy Wild Alaskan Cod fished from Alaska, processed in China, sold in North America. This is driven by the low cost of transport for frozen cod vs the higher relative cost of cod processing in the US versus China. Similarly, Scottish prawns are also processed in China for sale in the UK.

This effect on cost of transport vs cost of processing is the main driver of the topology of the modern supply chain and the associated effect of globalization. If transport is much cheaper than processing, then processing will tend to agglomerate in places where processing costs can be minimized.

Large scale global economic change has principally been driven by changes in the technology that drives supply chain.

Supply chain is a large-scale automated decision making network. Our aim is to make decisions not only based on our models of customer behavior (as observed through data), but also by accounting for the structure of our fulfilment center, and delivery network.

Many of the most important questions in supply chain take the form of counterfactuals. E.g. “What would happen if we opened a manufacturing facility in Cambridge?” A counter factual is a question that implies a mechanistic understanding of a system. It goes beyond simple smoothness assumptions or translation invariants. It requires a physical, or mechanistic understanding of the supply chain network. For this reason, the type of models we deploy in supply chain often involve simulations or more mechanistic understanding of the network.

In supply chain Machine Learning alone is not enough, we need to bridge between models that contain real mechanisms and models that are entirely data driven.

This is challenging, because as we introduce more mechanism to the models we use, it becomes harder to develop efficient algorithms to match those models to data.

# Quantifying the Value of Data 

The situation is reminiscent of a thirsty castaway, set adrift. There is a sea of data, but it is not fit to drink. We need some form of data desalination before it can be consumed. But like real desalination, this is a non trivial process, particularly if we want to achieve it at scale.

There’s a sea of data, but most of it is undrinkable.

We require data-desalination before it can be consumed!

I spoke about the challenges in data science at the NIPS 2016 Workshop on Machine Learning for Health. NIPS mainly focuses on machine learning methodologies, and many of the speakers were doing so. But before my talk, I listened to some of the other speakers talk about the challenges they had with data preparation.

• 90% of our time is spent on validation and integration (Leo Anthony Celi)
• “The Dirty Work We Don’t Want to Think About” (Eric Xing)
• “Voodoo to get it decompressed” (Francisco Giminez)

A further challenge in healthcare is that the data is collected by clinicians, often at great inconvenience to both themselves and the patient, but the control of the data is sometimes used to steer the direction of research.

The fact that we put so much effort into processing the data, but so little into allocating credit for this work is a major challenge for realizing the benefit in the data we have.

This type of work is somewhat thankless, with the exception of the clinicians’ control of the data, which probably takes things too far, those that collate and correct data sets gain little credit. In the domain of reinforcement learning the aim is to take a series of actions to achieve a stated goal and gain a reward. The credit assignment problem is the challenge in the learning algorithm of distributing credit to each of the actions which brought about the reward. We also experience this problem in society, we use proxies such as monetary reward to incentivise intermediate steps in our economy. Modern society functions because we agree to make basic expenditure on infrastructure, such as roads, which we all make use of. Our data-society is not sufficiently mature to be correctly crediting and rewarding those that undertake this work.

We need to properly incetivize the sharing and production of clean data sets, we need to correctly quantify the value in the contribution of each actor, otherwise there won’t be enough clean data to satiate the thirst of our decision making processes.

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The value of shared data infrastructures in computational biology was recognized by the 2010 joint statement from the Wellcome Trust and other funders of research at the “Foggy Bottom” meeting. They recognised three key benefits to sharing of health data:

• faster progress in improving health
• better value for money
• higher quality science

But incentivising sharing requires incentivising collection and collation of data, and the associated credit allocation models.

## Data Readiness Levels 

### Data Readiness Levels 

Data Readiness Levels (Lawrence 2017b) are an attempt to develop a language around data quality that can bridge the gap between technical solutions and decision makers such as managers and project planners. The are inspired by Technology Readiness Levels which attempt to quantify the readiness of technologies for deployment.

See this blog onblog post on Data Readiness Levels..

### Three Grades of Data Readiness 

Data-readiness describes, at its coarsest level, three separate stages of data graduation.

• Grade C - accessibility
• Transition: data becomes electronically available
• Grade B - validity
• Transition: pose a question to the data.
• Grade A - usability

The important definitions are at the transition. The move from Grade C data to Grade B data is delimited by the electronic availability of the data. The move from Grade B to Grade A data is delimited by posing a question or task to the data (Lawrence 2017b).

## Accessibility: Grade C

The first grade refers to the accessibility of data. Most data science practitioners will be used to working with data-providers who, perhaps having had little experience of data-science before, state that they “have the data”. More often than not, they have not verified this. A convenient term for this is “Hearsay Data”, someone has heard that they have the data so they say they have it. This is the lowest grade of data readiness.

Progressing through Grade C involves ensuring that this data is accessible. Not just in terms of digital accessiblity, but also for regulatory, ethical and intellectual property reasons.

## Validity: Grade B

Data transits from Grade C to Grade B once we can begin digital analysis on the computer. Once the challenges of access to the data have been resolved, we can make the data available either via API, or for direct loading into analysis software (such as Python, R, Matlab, Mathematica or SPSS). Once this has occured the data is at B4 level. Grade B involves the validity of the data. Does the data really represent what it purports to? There are challenges such as missing values, outliers, record duplication. Each of these needs to be investigated.

Grade B and C are important as if the work done in these grades is documented well, it can be reused in other projects. Reuse of this labour is key to reducing the costs of data-driven automated decision making. There is a strong overlap between the work required in this grade and the statistical field of exploratory data analysis (Tukey 1977).

The need for Grade B emerges due to the fundamental change in the availability of data. Classically, the scientific question came first, and the data came later. This is still the approach in a randomized control trial, e.g. in A/B testing or clinical trials for drugs. Today data is being laid down by happenstance, and the question we wish to ask about the data often comes after the data has been created. The Grade B of data readiness ensures thought can be put into data quality before the question is defined. It is this work that is reusable across multiple teams. It is these processes that the team which is standing up the data must deliver.

## Usability: Grade A

Once the validity of the data is determined, the data set can be considered for use in a particular task. This stage of data readiness is more akin to what machine learning scientists are used to doing in Universities. Bringing an algorithm to bear on a well understood data set.

In Grade A we are concerned about the utility of the data given a particular task. Grade A may involve additional data collection (experimental design in statistics) to ensure that the task is fulfilled.

This is the stage where the data and the model are brought together, so expertise in learning algorithms and their application is key. Further ethical considerations, such as the fairness of the resulting predictions are required at this stage. At the end of this stage a prototype model is ready for deployment.

Deployment and maintenance of machine learning models in production is another important issue which Data Readiness Levels are only a part of the solution for.

## Recursive Effects

To find out more, or to contribute ideas go to http://data-readiness.org

Throughout the data preparation pipeline, it is important to have close interaction between data scientists and application domain experts. Decisions on data preparation taken outside the context of application have dangerous downstream consequences. This provides an additional burden on the data scientist as they are required for each project, but it should also be seen as a learning and familiarization exercise for the domain expert. Long term, just as biologists have found it necessary to assimilate the skills of the bioinformatician to be effective in their science, most domains will also require a familiarity with the nature of data driven decision making and its application. Working closely with data-scientists on data preparation is one way to begin this sharing of best practice.

The processes involved in Grade C and B are often badly taught in courses on data science. Perhaps not due to a lack of interest in the areas, but maybe more due to a lack of access to real world examples where data quality is poor.

These stages of data science are also ridden with ambiguity. In the long term they could do with more formalization, and automation, but best practice needs to be understood by a wider community before that can happen.

# Assessing the Organizations Readiness 

Assessing the readiness of data for analysis is one action that can be taken, but assessing teams that need to assimilate the information in the data is the other side of the coin. With this in mind both Damon Civin and Nick Elprin have independently proposed the idea of a “Data Joel Test”. A “Joel Test” is a short questionaire to establish the ability of a team to handle software engineering tasks. It is designed as a rough and ready capability assessment. A “Data Joel Test” is similar, but for assessing the capability of a team in performing data science.

## Operations Research, Control, Econometrics, Statistics and Machine Learning 

data + model is not new, it dates back to Laplace and Gauss. Gauss fitted the orbit of Ceres using Keplers laws of planetary motion to generate his basis functions, and Laplace’s insights on the error function and uncertainty (Stigler 1999). Different fields such as Operations Research, Control, Econometrics, Statistics, Machine Learning and now Data Science and AI all rely on data + model. Under a Popperian view of science, and equating experiment to data, one could argue that all science has data + model underpinning it.

Different academic fields are born in different eras, driven by different motivations and arrive at different solutions. For example, both Operations Research and Control emerged from the Second World War. Operations Research, the science of decision making, driven by the need for improved logistics and supply chain. Control emerged from cybernetics, a field that was driven in the by researchers who had been involved in radar and decryption (Wiener 1948; Husband, Holland, and Wheeler 2008). The UK artificial intelligence community had similar origins (Copeland 2006).

The separation between these fields has almost become tribal, and from one perspective this can be very helpful. Each tribe can agree on a common language, a common set of goals and a shared understanding of the approach they’ve chose for those goals. This ensures that best practice can be developed and shared and as a result, quality standards can rise.

This is the nature of our professions. Medics, lawyers, engineers and accountants all have a system of shared best practice that they deploy efficiently in the resolution of a roughly standardized set of problems where they deploy (broken leg, defending a libel trial, bridging a river, ensuring finances are correct).

Control, statistics, economics, operations research are all established professions. Techniques are established, often at undergraduate level, and graduation to the profession is regulated by professional bodies. This system works well as long as the problems we are easily categorized and mapped onto the existing set of known problems.

However, at another level our separate professions of OR, statistics and control engineering are just different views on the same problem. Just as any tribe of humans need to eat and sleep, so do these professions depend on data, modelling, optimization and decision-making.

We are doing something that has never been done before, optimizing and evolving very large-scale automated decision making networks. The ambition to scale and automate, in a data driven manner, means that a tribal approach to problem solving can hinder our progress. Any tribe of hunter gatherers would struggle to understand the operation of a modern city. Similarly, supply chain needs to develop cross-functional skill sets to address the modern problems we face, not the problems that were formulated in the past.

Many of the challenges we face are at the interface between our tribal expertise. We have particular cost functions we are trying to minimize (an expertise of OR) but we have large scale feedbacks in our system (an expertise of control). We also want our systems to be adaptive to changing circumstances, to perform the best action given the data available (an expertise of machine learning and statistics).

Taking the tribal analogy further, we could imagine each of our professions as a separate tribe of hunter-gathers, each with particular expertise (e.g. fishing, deer hunting, trapping). Each of these tribes has their own approach to eating to survive, just as each of our localized professions has its own approach to modelling. But in this analogy, the technological landscapes we face are not wildernesses, they are emerging metropolises. Our new task is to feed our population through a budding network of supermarkets. While we may be sourcing our food in the same way, this requires new types of thinking that don’t belong in the pure domain of any of our existing tribes.

For our biggest challenges, focusing on the differences between these fields is unhelpful, we should consider their strengths and how they overlap. Fundamentally all these fields are focused on taking the right action given the information available to us. They need to work in synergy for us to make progress.

While there is some discomfort in talking across field boundaries, it is critical to disconfirming our current beliefs and generating the new techniques we need to address the challenges before us.

Recommendation: We should be aware of the limitations of a single tribal view of any of our problem sets. Where our modelling is dominated by one perspective (e.g. economics, OR, control, ML) we should ensure cross fertilization of ideas occurs through scientific review and team rotation mechanisms that embed our scientists (for a short period) in different teams across our organizations.

## Five AI Myths 

1. AI will be the first wave of automation that adapts to us.
2. Hearsay data has significant value.
3. The big tech companies have the landscape all ‘sewn up’
4. ‘data scientists’ will come and solve all problems.
5. The normal rules of business don’t apply to AI.

The five AI myths are patterns of thinking I’ve identified amoung those that are trying to take advantage of artificial intelligence to deploy new products.

The first myth is the “promise of AI” myth, that AI will be the first wave of machine-based automation that adapts to us, rather than us having to adapt to it. The reality is that we haven’t yet created machines that are as flexible as humans, the automation we are producing is still ‘fragile’, in that if it encounters unforeseen circumstances it breaks. This is a consequence of the way we design systems, flexible natural systems such as ourselves are evolved, not designed. And evolved systems have a first priority to ‘not fail’. What we think of us ‘common sense’ in the human is in reality a set of heuristics that prevent us doing stupid things in the name of achieving a goal. Our AI systems don’t exhibit this.

The second myth is that there is value in ‘hearsay data’. Hearsay data is data that people have heard exists, so they say it exists (See this blog onblog post on Data Readiness Levels. (Lawrence 2017b). The failure to understand the importance of data quality is resulting in unrealistic projects staffed by people with the wrong skill sets. Most decision makers don’t understand that implementation of a machine learning model is relatively trivial. But preparation of the data set and the data ecosystem around the model is extremely difficult. So the wrong investments are made, millions spent on recruiting machine learning PhDs and minimal spend on data infrastructure and systems for data auditing.

The third myth is that platform effects mean that there is no room for knew innovation in AI. Three factors will prevent the platforms dominating in the long term. Firstly, they are not agile, their approach to AI software development is grounded in the world that pre-dates wide availability of machine learning systems. Agile software development needs revisiting in the context of machine learning and this form of cultural change is difficult to achieve. In practice, these companies are larger than they need to be to deliver their services because they can afford to employ people to handle operational load. Newer agile companies will develop a better culture around data and machine learning. One that requires less operational overhead. This doesn’t just reduce costs, but it increases speed of movement and develops better understanding of the underlying systems. See this blog on blog post on The 3Ds of Machine Learning Systems Design..

The fourth myth is that soon there will be a wave of Data Scientists who will be equipped to enter companies and resolve their problems around data and AI. The mistake here is to assume that these graduates will have been trained in the necessary skills to do data science within a company. In fact, Universities will naturally focus on algorithms and models, because that material is teachable. Much more important is systems thinking and data wrangling. Processes to ensure that data is actionable. The weakness of senior decision makers, including CIOs and CTOs is that they don’t have a deep understanding of the technology, so they don’t perform critical thinking in this space. It’s a problem that can be deferred and solved by a mythical set of experts who will soon be arriving. In reality, domain expertise is key to successful data science, and bridging existing expertise with an understanding of the new landscape is far more important to delivering succesful systems.

The final myth is perhaps the most perniscious. It involves a suspension of normal business skepticism where AI is concerned. It may arise from the use of the term AI, which implies intelligence. If these systems were really ‘intelligent’, in the way a human is intelligent, plus if they had the skills of a computer, that really would be revolutionary. However, that’s not what’s happening, and won’t happen in the foreseeable future (i.e. on timelines that matter to business). In reality this is an evolution of existing technology, and it has the ususual challenges of adpotion that existing technologies have. The challenge for decision makers is how to assimilate the implications of this new technology within their business skill set. This means familiarisation, and doing courses etc isn’t good enough. Senior business leaders need to take time out working closely with the technology in their own environments to better calibrate their understanding of its strengths and weaknesses.

Recommendation: How to bust to these myths? The primary recommendation for businesses is that they start pilot projects which have executive sponsorship. They involve the CTO (or CIO or CDO), a technical ‘data science’ expert and a target domain area. Instead of feigning knowledge in this space, each admits their own ignorance of the other domains, and starts from scratch. Egos are left out of the room. The small pilot project is explored and delivered with the real challenges being noted. In this way each of the individuals will learn quickly where the pitfalls are.

One challenge is that for most projects the data will be too poor to even conduct the pilot. However, one data source that is consistently of good quality across companies is financial data. So a further suggestion is to initially focus on collaborating with the CFO and focus on financial forecasting (or similar). If the CFO, CTO and CEO gain a better understanding of the capabilities of data science, then the company can begin to turn around its systems and culture focussing on the important changes, making calibrated changes, rather than reacting to the sensationalism around AI.

Importantly, don’t go all in. Major companies are susceptible to what I call ‘(Grand Old) Duke of York Effect’, march 10,000 people to the top of the hill and march them down again. Command and control is not the right response to an uncertain and environment. Don’t think like regular troops, think like special forces, small groups with specialist expertise that are empowered to think independently and explore the landscape. Find which hill to march up, before committing significant resource.

# References

Cooper, Brian. 1991. Transformation of a Valley: Derbyshire Derwent. Scarthin Books.

Copeland, B. Jack, ed. 2006. Colossus: The Secrets of Bletchley Park’s Code-Breaking Computers. Oxford University Press.

Husband, Phil, Owen Holland, and Michael Wheeler, eds. 2008. The Mechanical Mind in History. mit.

Lawrence, Neil D. 2017a. “Living Together: Mind and Machine Intelligence.” arXiv. https://arxiv.org/abs/1705.07996.

———. 2017b. “Data Readiness Levels.” arXiv.

Stigler, Stephen M. 1999. Statistics on the Table: The History of Statistical Concepts and Methods. Cambridge, MA: harvard.

Tukey, John W. 1977. Exploratory Data Analysis. Addison-Wesley.

Wiener, Norbert. 1948. Cybernetics: Control and Communication in the Animal and the Machine. Cambridge, MA: MIT Press.