at Advances and Challenges in Machine Learning Languages, Centre for Mathematical Sciences, Cambridge on May 21, 2019 [jupyter][reveal]
Neil D. Lawrence, Amazon Cambridge and University of Sheffield

#### Abstract

There has been a great deal of interest in probabilistic programs: placing modeling at the heart of programming language. In this talk we set the scene for data oriented programming. Data is a fundamental component of machine learning, yet the availability, quality and discoverability of data are often ignored in formal computer science.
While languages for data manipulation exist (for example SQL), they are not suitable for the modern world of machine learning data. Modern data oriented languages should place data at the center of modern digital systems design and provide an infrastructure in which monitoring of data quality and model decision making are automaticaly available. We provide the context for Modern Data Oriented Programming, and give some insight into our initial ideas in this space.

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## Boulton and Watt’s Steam Engine 

James Watt’s steam engine contained an early machine learning device. In the same way that modern systems are component based, his engine was composed of components. One of which is a speed regulator sometimes known as Watt’s governor. The two balls in the center of the image, when spun fast, rise, and through a linkage mechanism.

The centrifugal governor was made famous by Boulton and Watt when it was deployed in the steam engine. Studying stability in the governor is the main subject of James Clerk Maxwell’s paper on the theoretical analysis of governors (Maxwell 1867). This paper is a founding paper of control theory. In an acknowledgment of its influence, Wiener used the name cybernetics to describe the field of control and communication in animals and the machine (Wiener 1948). Cybernetics is the Greek word for governor, which comes from the latin for helmsman.

A governor is one of the simplest artificial intelligence systems. It senses the speed of an engine, and acts to change the position of the valve on the engine to slow it down.

Although it’s a mechanical system a governor can be seen as automating a role that a human would have traditionally played. It is an early example of artificial intelligence.

The centrifugal governor has several parameters, the weight of the balls used, the length of the linkages and the limits on the balls movement.

Two principle differences exist between the centrifugal governor and artificial intelligence systems of today.

1. The centrifugal governor is a physical system and it is an integral part of a wider physical system that it regulates (the engine).
2. The parameters of the governor were set by hand, our modern artificial intelligence systems have their parameters set by data.

This has the basic components of sense and act that we expect in an intelligent system, and this system saved the need for a human operator to manually adjust the system in the case of overspeed. Overspeed has the potential to destroy an engine, so the governor operates as a safety device.

The first wave of automation did bring about sabotoage as a worker’s response. But if machinery was sabotaged, for example, if the linkage between sensor (the spinning balls) and action (the valve closure) was broken, this would be obvious to the engine operator at start up time. The machine could be repaired before operation.

## What is Machine Learning? 

Machine learning allows us to extract knowledge from data to form a prediction.

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

A machine learning prediction is made by combining a model with data to form the prediction. The manner in which this is done gives us the machine learning algorithm.

Machine learning models are mathematical models which make weak assumptions about data, e.g. smoothness assumptions. By combining these assumptions with the data we observe we can interpolate between data points or, occasionally, extrapolate into the future.

Machine learning is a technology which strongly overlaps with the methodology of statistics. From a historical/philosophical view point, machine learning differs from statistics in that the focus in the machine learning community has been primarily on accuracy of prediction, whereas the focus in statistics is typically on the interpretability of a model and/or validating a hypothesis through data collection.

The rapid increase in the availability of compute and data has led to the increased prominence of machine learning. This prominence is surfacing in two different, but overlapping domains: data science and artificial intelligence.

The real challenge, however, is end-to-end decision making. Taking information from the enviroment and using it to drive decision making to achieve goals.

## 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).

## 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.

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.

Solve Supply Chain, then solve everything else.

## The Three Ds of Machine Learning Systems Design 

We don’t know what science we’ll want to do in 5 years time, but we won’t want slower experiments, we won’t want more expensive experiments and we won’t want a narrower selection of experiments.

• Faster, cheaper and more diverse experiments.
• Better ecosystems for experimentation.
• Data oriented architectures.

We can characterize the challenges for integrating machine learning within our systems as the three Ds. Decomposition, Data and Deployment.

The first two components decomposition and data are interlinked, but we will first outline the decomposition challenge. Below we will mainly focus on supervised learning because this is arguably the technology that is best understood within machine learning.

## Data 

It is difficult to overstate the importance of data. It is half of the equation for machine learning, but is often utterly neglected. We can speculate that there are two reasons for this. Firstly, data cleaning is perceived as tedious. It doesn’t seem to consist of the same intellectual challenges that are inherent in constructing complex mathematical models and implementing them in code. Secondly, data cleaning is highly complex, it requires a deep understanding of how machine learning systems operate and good intuitions about the data itself, the domain from which data is drawn (e.g. Supply Chain) and what downstream problems might be caused by poor data quality.

A consequence of these two reasons, data cleaning seems difficult to formulate into a readily teachable set of principles. As a result it is heavily neglected in courses on machine learning and data science. Despite data being half the equation, most University courses spend little to no time on its challenges.

Anecdotally, talking to data modelling scientists. Most say they spend 80% of their time acquiring and cleaning data. This is precipitating what I refer to as the “data crisis”. This is an analogy with software. The “software crisis” was the phenomenon of inability to deliver software solutions due to increasing complexity of implementation. There was no single shot solution for the software crisis, it involved better practice (scrum, test orientated development, sprints, code review), improved programming paradigms (object orientated, functional) and better tools (CVS, then SVN, then git).

## The Data Crisis 

Anecdotally, talking to data modelling scientists. Most say they spend 80% of their time acquiring and cleaning data. This is precipitating what I refer to as the “data crisis”. This is an analogy with software. The “software crisis” was the phenomenon of inability to deliver software solutions due to increasing complexity of implementation. There was no single shot solution for the software crisis, it involved better practice (scrum, test orientated development, sprints, code review), improved programming paradigms (object orientated, functional) and better tools (CVS, then SVN, then git).

However, these challenges aren’t new, they are merely taking a different form. From the computer’s perspective software is data. The first wave of the data crisis was known as the software crisis.

### The Software Crisis

In the late sixties early software programmers made note of the increasing costs of software development and termed the challenges associated with it as the “Software Crisis”. Edsger Dijkstra referred to the crisis in his 1972 Turing Award winner’s address.

The major cause of the software crisis is that the machines have become several orders of magnitude more powerful! To put it quite bluntly: as long as there were no machines, programming was no problem at all; when we had a few weak computers, programming became a mild problem, and now we have gigantic computers, programming has become an equally gigantic problem.

Edsger Dijkstra (1930-2002), The Humble Programmer

The major cause of the data crisis is that machines have become more interconnected than ever before. Data access is therefore cheap, but data quality is often poor. What we need is cheap high quality data. That implies that we develop processes for improving and verifying data quality that are efficient.

There would seem to be two ways for improving efficiency. Firstly, we should not duplicate work. Secondly, where possible we should automate work.

What I term “The Data Crisis” is the modern equivalent of this problem. The quantity of modern data, and the lack of attention paid to data as it is initially “laid down” and the costs of data cleaning are bringing about a crisis in data-driven decision making. This crisis is at the core of the challenge of technical debt in machine learning (Sculley et al. 2015).

Just as with software, the crisis is most correctly addressed by ‘scaling’ the manner in which we process our data. Duplication of work occurs because the value of data cleaning is not correctly recognised in management decision making processes. Automation of work is increasingly possible through techniques in “artificial intelligence”, but this will also require better management of the data science pipeline so that data about data science (meta-data science) can be correctly assimilated and processed. The Alan Turing institute has a program focussed on this area, AI for Data Analytics.

Data is the new software, and the data crisis is already upon us. It is driven by the cost of cleaning data, the paucity of tools for monitoring and maintaining our deployments, the provenance of our models (e.g. with respect to the data they’re trained on).

Three principal changes need to occur in response. They are cultural and infrastructural.

First of all, to excel in data driven decision making we need to move from a software first paradigm to a data first paradigm. That means refocusing on data as the product. Software is the intermediary to producing the data, and its quality standards must be maintained, but not at the expense of the data we are producing. Data cleaning and maintenance need to be prized as highly as software debugging and maintenance. Instead of software as a service, we should refocus around data as a service. This first change is a cultural change in which our teams think about their outputs in terms of data. Instead of decomposing our systems around the software components, we need to decompose them around the data generating and consuming components.1 Software first is only an intermediate step on the way to be coming data first. It is a necessary, but not a sufficient condition for efficient machine learning systems design and deployment. We must move from software orientated architecture to a data orientated architecture.

### Data Quality

Secondly, we need to improve our language around data quality. We cannot assess the costs of improving data quality unless we generate a language around what data quality means. Data Readiness Levels2 are an assessment of data quality that is based on the usage to which data is put.

Data Readiness Levels (Lawrence 2017) 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.b

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

• Transition: data becomes electronically available
• Transition: pose a question to the data.

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 2017).

Recommendation: Build a shared understanding of the language of data readiness levels for use in planning documents and costing of data cleaning and the benefits of reusing cleaned data.

### Move Beyond Software Engineering to Data Engineering

Thirdly, we need to improve our mental model of the separation of data science from applied science. A common trap in our thinking around data is to see data science (and data engineering, data preparation) as a sub-set of the software engineer’s or applied scientist’s skill set. As a result we recruit and deploy the wrong type of resource. Data preparation and question formulation is superficially similar to both because of the need for programming skills, but the day to day problems faced are very different.

## Data Science as Debugging 

One challenge for existing information technology professionals is realizing the extent to which a software ecosystem based on data differs from a classical ecosystem. In particular, by ingesting data we bring unknowns/uncontrollables into our decision making system. This presents opportunity for adversarial exploitation and unforeseen operation.

You can also check my blog post on “Data Science as Debugging”

Starting with the analysis of a data set, the nature of data science is somewhat difference from classical software engineering.

One analogy I find helpful for understanding the depth of change we need is the following. Imagine as a software engineer, you find a USB stick on the ground. And for some reason you know that on that USB stick is a particular API call that will enable you to make a significant positive difference on a business problem. You don’t know which of the many library functions on the USB stick are the ones that will help. And it could be that some of those library functions will hinder, perhaps because they are just inappropriate or perhaps because they have been placed there malisciously. The most secure thing to do would be to not introduce this code into your production system at all. But what if your manager told you to do so, how would you go about incorporating this code base?

The answer is very carefully. You would have to engage in a process more akin to debugging than regular software engineering. As you understood the code base, for your work to be reproducible, you should be documenting it, not just what you discovered, but how you discovered it. In the end, you typically find a single API call that is the one that most benefits your system. But more thought has been placed into this line of code than any line of code you have written before.

An enormous amount of debugging would be required. As the nature of the code base is understood, software tests to verify it also need to be constructed. At the end of all your work, the lines of software you write to actually interact with the software on the USB stick are likely to be minimal. But more thought would be put into those lines than perhaps any other lines of code in the system.

Even then, when your API code is introduced into your production system, it needs to be deployed in an environment that monitors it. We cannot rely on an individual’s decision making to ensure the quality of all our systems. We need to create an environment that includes quality controls, checks and bounds, tests, all designed to ensure that assumptions made about this foreign code base are remaining valid.

This situation is akin to what we are doing when we incorporate data in our production systems. When we are consuming data from others, we cannot assume that it has been produced in alignment with our goals for our own systems. Worst case, it may have been adversarialy produced. A further challenge is that data is dynamic. So, in effect, the code on the USB stick is evolving over time.

It might see that this process is easy to formalize now, we simply need to check what the formal software engineering process is for debugging, because that is the current software engineering activity that data science is closest to. But when we look for a formalization of debugging we find that there is none. Indeed, modern software engineering mainly focusses on ensuring that code is written without bugs in the first place.

Recommendation: Anecdotally, resolving a machine learning challenge requires 80% of the resource to be focused on the data and perhaps 20% to be focused on the model. But many companies are too keen to employ machine learning engineers who focus on the models, not the data. We should change our hiring priorities and training. Universities cannot provide the understanding of how to data-wrangle. Companies must fill this gap.

Recommendation: We need to share best practice around data deployment across our teams. We should make best use of our processes where applicable, but we need to develop them to become data first organizations. Data needs to be cleaned at output not at input.

## Outlook for Machine Learning

Machine learning has risen to prominence as an approach to scaling our activities. For us to continue to automate in the manner we have over the last two decades, we need to make more use of computer-based automation. Machine learning is allowing us to automate processes that were out of reach before.

## Data Oriented Architectures 

• Convert data to a first class citizen.
• View system as operations on data streams.
• Expose data operations in a programmatic way.

## Streaming System

• Operate on rows rather than columns.
• Lead to stateless logic: persistence handled by system.
• Example Apache Kafka + Apache Flink
• Streams and transformations
• a stream is a (potentially never-ending) flow of data records
• a transformation is an operation that takes one or more streams as input, and produces one or more output streams as a result.
stream.join(otherStream)
.where(<KeySelector>)
.equalTo(<KeySelector>)
.window(<WindowAssigner>)
.apply(<JoinFunction>)

Apache Flink allows operations on streams. For example the join operation above. In a traditional data base management system, this join operation may be written in SQL and called on demand. In a streaming ecosystem, computations occur as and when the streams update.

The join is handled by the ecosystem surrounding the business logic.

import pandas as pd
import numpy as np
import os
days=pd.date_range(start='21/5/2017', end='21/05/2020')
z = np.random.randn(len(days), 1)
x=z.cumsum()+400
prices = pd.Series(x, index=days)
hypothetical = prices.loc['21/5/2019':]
real = prices.copy()
real['21/5/2019':] = np.NaN

Anne wishes to build a share trading system. She has access to a high frequency trading system which provides prices and allows trades at millisecond intervals. She wishes to build an automated trading system.

Let’s assume that price trading data is available as a data stream. But the price now is not the only information that Anne needs, she needs an estimate of the price in the future.

## Hypothetical Streams

We’ll call the future price a hypothetical stream.

A hypothetical stream is a desired stream of information which cannot be directly accessed. The lack of direct access may be because the events happen in the future, or there may be some latency between the event and the availability of the data.

Any hypothetical stream will only be provided as a prediction, ideally with an error bar.

The nature of the hypothetical Anne needs is dependent on her decision making process. In Anne’s case it will depend over what period she is expecting her returns. In MDOP Anne specifies a hypothetical that is derived from the pricing stream.

It is not the price stream directly, but Anne looks for future predictions from the price stream, perhaps for price in T days’ time.

At this stage, this stream is merely typed as a hypothetical.

There are constraints on the hypothetical, they include: the input information, the upper limit of latency between input and prediction, and the decision Anne needs to make (how far ahead, what her upside, downside risks are). These three constraints mean that we can only recover an approximation to the hypothetical.

What is the advantage to defining things in this way? By defining clearly the two streams as real and hypothetical variants of each other, we now enable automation of the deployment and any redeployment process. The hypothetical can be instantiated against the real, and design criteria can be constantly evaluated triggering retraining when necessary.

## Ride Sharing System

As a second example, we’ll consider a ride sharing app.

Anne is on her way home now, she wishes to hail a car using a ride sharing app.

The app is designed in the following way. On opening her app Anne is notified about driverss in the nearby neighborhood, and given an estimate of the time a ride may take to come.

Given this information about driver availability, Anne may feel encouraged to enter a destination. Given this destination, a price estimate can be given. This price is conditioned on other riders that may wish to go in the same direction, but the price estimate needs to be made before the user agrees to the ride.

Business customer service constraints dictate that this price may not change after Anne’s order is confirmed.

In this simple system, several decisions are being made, each of them on the basis of a hypothetical.

When Anne calls for a ride, she is provided with an estimate based on the expected time a ride can be with her. But this estimate is made without knowing where Anne wants to go. There are constraints on drivers imposed by regional boundaries, reaching the end of their shift, or their current passengers mean that this estimate can only be a best guess.

This best guess may well be driven by previous data.

For the ride sharing system, we start to see a common issue with a more complex algorithmic decision making system. Several decisions are being made multilple times. Let’s look at the decisions we need along with some design criteria.

1. Car Availability: Estimate time to arrival for Anne’s ride using Anne’s location and local available car locations. Latency 50 milliseconds
2. Cost Estimate: Estimate cost for journey using Anne’s destination, location and local available car current destinations and availability. Latency 50 milliseconds
3. Driver Allocation: Allocate car to minimize transport cost to destination. Latency 2 seconds.

So we need:

1. a hypothetical to estimate availability. It is constrained by lacking destination information and a low latency requirement.
2. a hypothetical to estimate cost. It is constrained by low latency requirement and

Simultaneously, drivers in this data ecosystem have an app which notifies them about new jobs and recommends them where to go.

Further advantages. Strategies for data retention (when to snapshot) can be set globally.

A few decisions need to be made in this system. First of all, when the user opens the app, the estimate of the time to the nearest ride may need to be computed quickly, to avoid latency in the service.

This may require a quick estimate of the ride availability.

## Information Dynamics

With all the second guessing within a complex automated decision making system, there are potential problems with information dynamics, the ‘closed loop’ problem, where the sub-systems are being approximated (second guessing) and predictions downstream are being affected.

This leads to the need for a closed loop analysis, for example, see the “Closed Loop Data Science” project led by Rod Murray-Smith at Glasgow.

Our aim is to release our first version of a data-oriented programming environment by end of June 2019 (pending internal approval).

## Conclusion

We operate in a technologically evolving environment. Machine learning is becoming a key coponent in our decision making capabilities, our intelligence and strategic command. However, technology drove changes in battlefield strategy. From the stalemate of the first world war to the tank-dominated Blitzkrieg of the second, to the asymmetric warfare of the present. Our technology, tactics and strategies are also constantly evolving. Machine learning is part of that evolution solution, but the main challenge is not to become so fixated on the tactics of today that we miss the evolution of strategy that the technology is suggesting.

Data oriented programming offers a set of development methodologies which ensure that the system designer considers what decisions are required, how they will be made, and critically, declares this within the system architecture.

This allows for monitoring of data quality, fairness, model accuracy and opens the door to a more sophisticated form of auto ML where full redployments of models are considered while analyzing the information dynamics of a complex automated decision making system.

# References

Lawrence, Neil D. 2017. “Data Readiness Levels.” arXiv.

Maxwell, James Clerk. 1867. “On Governors.” Proceedings of the Royal Society of London 16. The Royal Society: 270–83. http://www.jstor.org/stable/112510.

Sculley, D., Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, and Dan Dennison. 2015. “Hidden Technical Debt in Machine Learning Systems.” In Advances in Neural Information Processing Systems 28, edited by Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, 2503–11. Curran Associates, Inc. http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf.

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

1. This is related to challenges of machine learning and technical debt (Sculley et al. 2015), although we are trying to frame the solution here rather than the problem.

2. Data Readiness Levels (Lawrence 2017) 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.