Abstract

Machine learning is fundamental to two important technological domains, artificial intelligence and data science. In this talk we will attempt to make a simple definition to distinguish between the two, then we will focus on the challenges of machine learning in application to artificial intelligence particularly from the perspective of systems design. We expect a particular challenge to be the deployment of such systems in real environment, where unforeseen consequences of interaction with real world environments will produce embarrassing failures. Because these failures are not bugs, in that the system will be performing as designed, but failures of imagination of the designers we introduce a new term for them: ‘peppercorns’.

Introduction

What is Machine Learning? [edit]

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?..

Artificial Intelligence and Data Science [edit]

Machine learning technologies have been the driver of two related, but distinct disciplines. The first is data science. Data science is an emerging field that arises from the fact that we now collect so much data by happenstance, rather than by experimental design. Classical statistics is the science of drawing conclusions from data, and to do so statistical experiments are carefully designed. In the modern era we collect so much data that there’s a desire to draw inferences directly from the data.

As well as machine learning, the field of data science draws from statistics, cloud computing, data storage (e.g. streaming data), visualization and data mining.

In contrast, artificial intelligence technologies typically focus on emulating some form of human behaviour, such as understanding an image, or some speech, or translating text from one form to another. The recent advances in artifcial intelligence have come from machine learning providing the automation. But in contrast to data science, in artifcial intelligence the data is normally collected with the specific task in mind. In this sense it has strong relations to classical statistics.

Classically artificial intelligence worried more about logic and planning and focussed less on data driven decision making. Modern machine learning owes more to the field of Cybernetics (Wiener 1948) than artificial intelligence. Related fields include robotics, speech recognition, language understanding and computer vision.

There are strong overlaps between the fields, the wide availability of data by happenstance makes it easier to collect data for designing AI systems. These relations are coming through wide availability of sensing technologies that are interconnected by celluar networks, WiFi and the internet. This phenomenon is sometimes known as the Internet of Things, but this feels like a dangerous misnomer. We must never forget that we are interconnecting people, not things.

Embodiment Factors [edit]

compute	≈ 100 gigaflops	≈ 16 petaflops
communicate	1 gigbit/s	100 bit/s
(compute/communicate)	104	1014

There is a fundamental limit placed on our intelligence based on our ability to communicate. Claude Shannon founded the field of information theory. The clever part of this theory is it allows us to separate our measurement of information from what the information pertains to¹.

Shannon measured information in bits. One bit of information is the amount of information I pass to you when I give you the result of a coin toss. Shannon was also interested in the amount of information in the English language. He estimated that on average a word in the English language contains 12 bits of information.

Given typical speaking rates, that gives us an estimate of our ability to communicate of around 100 bits per second (Reed and Durlach 1998). Computers on the other hand can communicate much more rapidly. Current wired network speeds are around a billion bits per second, ten million times faster.

When it comes to compute though, our best estimates indicate our computers are slower. A typical modern computer can process make around 100 billion floating point operations per second, each floating point operation involves a 64 bit number. So the computer is processing around 6,400 billion bits per second.

It’s difficult to get similar estimates for humans, but by some estimates the amount of compute we would require to simulate a human brain is equivalent to that in the UK’s fastest computer (Ananthanarayanan et al. 2009), the MET office machine in Exeter, which in 2018 ranks as the 11th fastest computer in the world. That machine simulates the world’s weather each morning, and then simulates the world’s climate in the afternoon. It is a 16 petaflop machine, processing around 1,000 trillion bits per second.

Figure: The Lotus 49, view from the rear. The Lotus 49 was one of the last Formula One cars before the introduction of aerodynamic aids.

So when it comes to our ability to compute we are extraordinary, not compute in our conscious mind, but the underlying neuron firings that underpin both our consciousness, our subconsciousness as well as our motor control etc.

If we think of ourselves as vehicles, then we are massively overpowered. Our ability to generate derived information from raw fuel is extraordinary. Intellectually we have formula one engines.

But in terms of our ability to deploy that computation in actual use, to share the results of what we have inferred, we are very limited. So when you imagine the F1 car that represents a psyche, think of an F1 car with bicycle wheels.

Figure: Marcel Renault races a Renault 40 cv during the Paris-Madrid race, an early Grand Prix, in 1903. Marcel died later in the race after missing a warning flag for a sharp corner at Couhé Vérac, likely due to dust reducing visibility.

Just think of the control a driver would have to have to deploy such power through such a narrow channel of traction. That is the beauty and the skill of the human mind.

In contrast, our computers are more like go-karts. Underpowered, but with well-matched tires. They can communicate far more fluidly. They are more efficient, but somehow less extraordinary, less beautiful.

Figure: Caleb McDuff driving for WIX Silence Racing.

For humans, that means much of our computation should be dedicated to considering what we should compute. To do that efficiently we need to model the world around us. The most complex thing in the world around us is other humans. So it is no surprise that we model them. We second guess what their intentions are, and our communication is only necessary when they are departing from how we model them. Naturally, for this to work well, we need to understand those we work closely with. So it is no surprise that social communication, social bonding, forms so much of a part of our use of our limited bandwidth.

There is a second effect here, our need to anthropomorphise objects around us. Our tendency to model our fellow humans extends to when we interact with other entities in our environment. To our pets as well as inanimate objects around us, such as computers or even our cars. This tendency to over interpret could be a consequence of our limited ability to communicate.

For more details see this paper “Living Together: Mind and Machine Intelligence”, and this TEDx talk.

Deploying Artificial Intelligence [edit]

With the wide availability of new techniques, we are currently creating Artifical Intelligence through combination of machine learning algorithms to form machine learning systems.

This effect is amplified through the growth in sensorics, in particular the movement of cloud computing towards the customer. The barrier between cloud and device is blurring. This phenomenon is sometimes known as fog computing, or computing on the edge.

This presents major new challenges for machine learning systems design. We would like an internet of intelligence but currently our AI systems are fragile. A classical systems approach to design does not handle evolving environments well.

Machine Learning Systems Design [edit]

The challenges of integrating different machine learning components into a whole that acts effectively as a system seem unresolved. In software engineering, separating parts of a system in this way is known as component-based software engineering. The core idea is that the different parts of the system can be independently designed according to a sub-specfication. This is sometimes known as separation of concerns. However, once the components are machine learning based, tighter coupling becomes a side effect of the learned nature of the system. For example if a driverless car’s detection of cyclist is dependent on its detection of the road surface, a change in the road surface detection algorithm will have downstream effects on the cyclist detection. Even if the road detection system has been improved by objective measures, the cyclist detection system may have become sensitive to the foibles of the previous version of road detection and will need to be retrained.

Most of our experience with deployment relies on some approximation to the component based model, this is also important for verification of the system. If the components of the system can be verified then the composed system can also, potentially, be verified.

Pigeonholing [edit]

Figure: Decompartmentalization of the model into parts can be seen as pigeonholing the separate tasks that are required.

To deal with the complexity of systems design, a common approach is to break complex systems down into a series of tasks. An approach we can think of as “pigeonholing”. Classically, a sub-task could be thought of as a particular stage in machining (by analogy to productionlines in factories) or a sub-routine call in computing. Machine learning allows any complex sub-task, that was difficult to decompose by classical methods, to be reconstituted by acquiring data. In particular, when we think of emulating a human, we can ask many humans to perform the sub-task many times and fit machine learning models to reconstruct the performance, or to emulate the human in the performance of the task. For example, the decomposition of a complex process such as driving a car into apparently obvious sub-tasks (following the road, identifying pedestrians, etc).

The practitioner’s approach to deploying artificial intelligence systems is to build up systems of machine learning components. To build a machine learning system, we decompose the task into parts, each of which we can emulate with ML methods. These parts are typically independently constructed and verified. For example, in a driverless car we can decompose the tasks into components such as “pedestrian detection” and “road line detection”. Each of these components can be constructed with, for example, a classification algorithm. Nowadays, people will often deploy a deep neural network, but for many tasks a random forest algorithm may be sufficient. We can then superimpose a logic on top. For example, “Follow the road line unless you detect a pedestrian in the road”.

This allows for verification of car performance, as long as we can verify the individual components. However, it also implies that the AI systems we deploy are fragile.

Our intelligent systems are composed by “pigeonholing” each indvidual task, then substituting with a machine learning model.

But this is not a robust approach to systems design. The definition of sub-tasks can lead to a single point of failure, where if any sub-task fails, the entire system fails.

Rapid Reimplementation

This is also the classical approach to automation, but in traditional automation we also ensure the environment in which the system operates becomes controlled. For example, trains run on railway lines, fast cars run on motorways, goods are manufactured in a controlled factory environment.

The difference with modern automated decision making systems is our intention is to deploy them in the uncontrolled environment that makes up our own world.

This exposes us to either unforseen circumstances or adversarial action. And yet it is unclear our our intelligent systems are capable of adapting to this.

We become exposed to mischief and adversaries. Adversaries intentially may wish to take over the artificial intelligence system, and mischief is the constant practice of many in our society. Simply watching a 10 year old interact with a voice agent such as Alexa or Siri shows that they are delighted when the can make the the “intelligent” agent seem foolish.

The Centrifugal Governor [edit]

Figure: Centrifugal governor as held by “Science” on Holborn Viaduct

Boulton and Watt’s Steam Engine [edit]

Figure: Watt’s Steam Engine which made Steam Power Efficient and Practical.

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.

Figure: The centrifugal governor, an early example of a decision making system. The parameters of the governor include the lengths of the linkages (which effect how far the throttle opens in response to movement in the balls), the weight of the balls (which effects inertia) and the limits of to which the balls can rise.

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.

The centrifugal governor was a key component in the Boulton-Watt steam engine. It senses increases in speed in the engine and closed the steam valve to prevent the engine overspeeding and destroying itself. Until the invention of this device, it was a human job to do this.

The formal study of governors and other feedback control devices was then began by James Clerk Maxwell, the Scottish physicist. This field became the foundation of our modern techniques of artificial intelligence through Norbert Wiener’s book Cybernetics (Wiener 1948). Cybernetics is Greek for governor, a word that in itself simply means helmsman in English.

The recent WannaCry virus that had a wide impact on our health services ecosystem was exploiting a security flaw in Windows systems that was first exploited by a virus called Stuxnet.

Stuxnet was a virus designed to infect the Iranian nuclear program’s Uranium enrichment centrifuges. A centrifuge is prevented from overspeed by a controller, just like the centrifugal governor. Only now it is implemented in control logic, in this case on a Siemens PLC controller.

Stuxnet infected these controllers and took over the response signal in the centrifuge, fooling the system into thinking that no overspeed was occuring. As a result, the centrifuges destroyed themselves through spinning too fast.

This is equivalent to detaching the governor from the steam engine. Such sabotage would be easily recognized by a steam engine operator. The challenge for the operators of the Iranian Uranium centrifuges was that the sabotage was occurring inside the electronics.

That is the effect of an adversary on an intelligent system, but even without adveraries, the mischief of a 10 year old can confuse our AIs.

Peppercorns [edit]

Asking Siri “What is a trillion to the power of a thousand minus one?” leads to a 30 minute response² consisting of only 9s. I found this out because my nine year old grabbed my phone and did it. The only way to stop Siri was to force closure. This is an interesting example of a system feature that’s not a bug, in fact it requires clever processing from Wolfram Alpha. But it’s an unexpected result from the system performing correctly.

This challenge of facing a circumstance that was unenvisaged in design but has consequences in deployment becomes far larger when the environment is uncontrolled. Or in the extreme case, where actions of the intelligent system effect the wider environment and change it.

These unforseen circumstances are likely to lead to need for much more efficient turn-around and update for our intelligent systems. Whether we are correcting for security flaws (which are bugs) or unenvisaged circumstantial challenges: an issue I’m referring to as peppercorns. Rapid deployment of system updates is required. For example, Apple have “fixed” the problem of Siri returning long numbers.

The challenge is particularly acute because of the scale at which we can deploy AI solutions. This means when something does go wrong, it may be going wrong in billions of households simultaneously.

You can also check this blog post on Decision Making and Diversity. and this blog post on Natural vs Artifical Intelligence..

Conclusion

• Difference between Artificial Intelligence and Data Science are fundamentally different.

• In one you are dealing with data collected by happenstance.

• In the other you are trying to build systems in the real world, often by actively collecting data.

• Our approaches to systems design are building powerful machines that will be deployed in evolving environments.

References