Towards Machine Learning Systems Design
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Abstract
Machine learning solutions, in particular those based on deep learning methods, form an underpinning for the modern artificial intelligence revolution that has dominated popular press headlines and is having a strong influence on the wider tech agenda. In this talk I will give an overview of where we are now with machine learning solutions, and what challenges we face both in the near and far future. These include practical application of existing algorithms in the face of the need to explain decision making, mechanisms for improving the quality and availability of data, and dealing with large unstructured datasets.
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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 (metadata). 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 blog post on "What is Machine Learning?"
Artificial Intelligence and Data Science
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.
Natural and Artificial Intelligence: Embodiment Factors
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 to^{1}.
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.
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 sbuconsciousness as well as our motor control etc. By analogy I sometimes like to think of us as a Formula One engine. 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.
In contrast, our computers have less computational power, but they can communicate far more fluidly. They are more like a gokart, less well powered, but with tires that allow them to deploy that power.
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 overinterpret 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.
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 overinterpret 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 humancomputer 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 this Guardian article from 2015 on marketing in the internet era.
You can also check my blog post on "System Zero"
What does Machine Learning do?
Any process of automation allows us to scale what we do by codifying a process in some way that makes it efficient and repeatable. Machine learning automates by emulating human (or other actions) found in data. Machine learning codifies in the form of a mathematical function that is learnt by a computer. If we can create these mathematical functions in ways in which they can interconnect, then we can also build systems.
Machine learning works through codifing a prediction of interest into a mathematical function. For example, we can try and predict the probability that a customer wants to by a jersey given knowledge of their age, and the latitude where they live. The technique known as logistic regression estimates the odds that someone will by a jumper as a linear weighted sum of the features of interest.
$$ \text{odds} = \frac{p(\text{bought})}{p(\text{not bought})} $$
logodds = β_{0} + β_{1}age + β_{2}latitude.
Here β_{0}, β_{1} and β_{2} are the parameters of the model. If β_{1} and β_{2} are both positive, then the logodds that someone will buy a jumper increase with increasing latitude and age, so the further north you are and the older you are the more likely you are to buy a jumper. The parameter β_{0} is an offset parameter, and gives the logodds of buying a jumper at zero age and on the equator. It is likely to be negative^{2} indicating that the purchase is oddsagainst. This is actually a classical statistical model, and models like logistic regression are widely used to estimate probabilities from adclick prediction to risk of disease.
This is called a generalized linear model, we can also think of it as estimating the probability of a purchase as a nonlinear function of the features (age, lattitude) and the parameters (the β values). The function is known as the sigmoid or logistic function, thus the name logistic regression.
$$ p(\text{bought}) = \sigmoid{\beta_0 + \beta_1 \text{age} + \beta_2 \text{latitude}}.$$
In the case where we have features to help us predict, we sometimes denote such features as a vector, $\inputVector$, and we then use an inner product between the features and the parameters, $\boldsymbol{\beta}^\top \inputVector = \beta_1 \inputScalar_1 + \beta_2 \inputScalar_2 + \beta_3 \inputScalar_3 ...$, to represent the argument of the sigmoid.
$$ p(\text{bought}) = \sigmoid{\boldsymbol{\beta}^\top \inputVector}.$$
More generally, we aim to predict some aspect of our data, $\dataScalar$, by relating it through a mathematical function, $\mappingFunction(\cdot)$, to the parameters, β and the data, $\inputVector$.
$$ \dataScalar = \mappingFunction\left(\inputVector, \boldsymbol{\beta}\right).$$
We call $\mappingFunction(\cdot)$ the prediction function.
To obtain the fit to data, we use a separate function called the objective function that gives us a mathematical representation of the difference between our predictions and the real data.
$$\errorFunction(\boldsymbol{\beta}, \dataMatrix, \inputMatrix)$$
A commonly used examples (for example in a regression problem) is least squares,
$$\errorFunction(\boldsymbol{\beta}, \dataMatrix, \inputMatrix) = \sum_{i=1}^\numData \left(\dataScalar_i  \mappingFunction(\inputVector_i, \boldsymbol{\beta})\right)^2.$$
If a linear prediction function is combined with the least squares objective function then that gives us a classical linear regression, another classical statistical model. Statistics often focusses on linear models because it makes interpretation of the model easier. Interpretation is key in statistics because the aim is normally to validate questions by analysis of data. Machine learning has typically focussed more on the prediction function itself and worried less about the interpretation of parameters, which are normally denoted by w instead of β. As a result nonlinear functions are explored more often as they tend to improve quality of predictions but at the expense of interpretability.
These are interpretable models: vital for disease etc.
Modern machine learning methods are less interpretable
Example: face recognition
The DeepFace architecture (Taigman et al. 2014) consists of layers that deal with translation and rotational invariances. These layers are followed by three locallyconnected layers and two fullyconnected layers. Color illustrates feature maps produced at each layer. The net includes more than 120 million parameters, where more than 95% come from the local and fully connected layers.
Sometimes deep learning models are described as being like the brain, or too complex to understand, but one analogy I find useful to help the gist of these models is to think of them as being similar to early pin ball machines.
In a deep neural network, we input a number (or numbers), whereas in pinball, we input a ball.
Think of the location of the ball on the leftright axis as a single number. Our simple pinball machine can only take one number at a time. As the ball falls through the machine, each layer of pins can be thought of as a different layer of 'neurons'. Each layer acts to move the ball from left to right.
In a pinball machine, when the ball gets to the bottom it might fall into a hole defining a score, in a neural network, that is equivalent to the decision: a classification of the input object.
An image has more than one number associated with it, so it's like playing pinball in a hyperspace.
import pods
from ipywidgets import IntSlider
pods.notebook.display_plots('pinball{sample:0>3}.svg',
'../slides/diagrams',
sample=IntSlider(1, 1, 2, 1))
Learning involves moving all the pins to be in the correct position, so that the ball ends up in the right place when it's fallen through the machine. But moving all these pins in hyperspace can be difficult.
In a hyper space you have to put a lot of data through the machine for to explore the positions of all the pins. Adversarial examples exploit this idea.
If you have access to the pinball machine, you can use gradient methods to find a position for the ball in the hyper space where the image looks like one thing, but will be classified as another.
Probabilistic methods explore more of the space by considering a range of possible paths for the ball through the machine. This helps to make them more data efficient and gives some robustness to adversarial examples.
Bayesian Inference by Rejection Sampling
One view of Bayesian inference is to assume we are given a mechanism for generating samples, where we assume that mechanism is representing on accurate view on the way we believe the world works.
This mechanism is known as our prior belief.
We combine our prior belief with our observations of the real world by discarding all those samples that are inconsistent with our prior. The likelihood defines mathematically what we mean by inconsistent with the prior. The higher the noise level in the likelihood, the looser the notion of consistent.
The samples that remain are considered to be samples from the posterior.
This approach to Bayesian inference is closely related to two sampling techniques known as rejection sampling and importance sampling. It is realized in practice in an approach known as approximate Bayesian computation (ABC) or likelihoodfree inference.
In practice, the algorithm is often too slow to be practical, because most samples will be inconsistent with the data and as a result the mechanism has to be operated many times to obtain a few posterior samples.
However, in the Gaussian process case, when the likelihood also assumes Gaussian noise, we can operate this mechanims mathematically, and obtain the posterior density analytically. This is the benefit of Gaussian processes.
import pods
from ipywidgets import IntSlider
pods.notebook.display_plots('gp_rejection_sample{sample:0>3}.png',
directory='../slides/diagrams/gp',
sample=IntSlider(1,1,5,1))
Olympic Marathon Data


The first thing we will do is load a standard data set for regression modelling. The data consists of the pace of Olympic Gold Medal Marathon winners for the Olympics from 1896 to present. First we load in the data and plot.
import numpy as np
import pods
data = pods.datasets.olympic_marathon_men()
x = data['X']
y = data['Y']
offset = y.mean()
scale = np.sqrt(y.var())
import matplotlib.pyplot as plt
import teaching_plots as plot
import mlai
xlim = (1875,2030)
ylim = (2.5, 6.5)
yhat = (yoffset)/scale
fig, ax = plt.subplots(figsize=plot.big_wide_figsize)
_ = ax.plot(x, y, 'r.',markersize=10)
ax.set_xlabel('year', fontsize=20)
ax.set_ylabel('pace min/km', fontsize=20)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
mlai.write_figure(figure=fig,
filename='../slides/diagrams/datasets/olympicmarathon.svg',
transparent=True,
frameon=True)
Things to notice about the data include the outlier in 1904, in this year, the olympics was in St Louis, USA. Organizational problems and challenges with dust kicked up by the cars following the race meant that participants got lost, and only very few participants completed.
More recent years see more consistently quick marathons.
Alan Turing


If we had to summarise the objectives of machine learning in one word, a very good candidate for that word would be generalization. What is generalization? From a human perspective it might be summarised as the ability to take lessons learned in one domain and apply them to another domain. If we accept the definition given in the first session for machine learning,
$$
\text{data} + \text{model} \xrightarrow{\text{compute}} \text{prediction}
$$
then we see that without a model we can't generalise: we only have data. Data is fine for answering very specific questions, like "Who won the Olympic Marathon in 2012?", because we have that answer stored, however, we are not given the answer to many other questions. For example, Alan Turing was a formidable marathon runner, in 1946 he ran a time 2 hours 46 minutes (just under four minutes per kilometer, faster than I and most of the other Endcliffe Park Run runners can do 5 km). What is the probability he would have won an Olympics if one had been held in 1946?
To answer this question we need to generalize, but before we formalize the concept of generalization let's introduce some formal representation of what it means to generalize in machine learning.
Our first objective will be to perform a Gaussian process fit to the data, we'll do this using the GPy software.
import GPy
m_full = GPy.models.GPRegression(x,yhat)
_ = m_full.optimize() # Optimize parameters of covariance function
The first command sets up the model, then m_full.optimize()
optimizes the parameters of the covariance function and the noise level of the model. Once the fit is complete, we'll try creating some test points, and computing the output of the GP model in terms of the mean and standard deviation of the posterior functions between 1870 and 2030. We plot the mean function and the standard deviation at 200 locations. We can obtain the predictions using y_mean, y_var = m_full.predict(xt)
xt = np.linspace(1870,2030,200)[:,np.newaxis]
yt_mean, yt_var = m_full.predict(xt)
yt_sd=np.sqrt(yt_var)
Now we plot the results using the helper function in teaching_plots
.
import teaching_plots as plot
fig, ax = plt.subplots(figsize=plot.big_wide_figsize)
plot.model_output(m_full, scale=scale, offset=offset, ax=ax, xlabel='year', ylabel='pace min/km', fontsize=20, portion=0.2)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
mlai.write_figure(figure=fig,
filename='../slides/diagrams/gp/olympicmarathongp.svg',
transparent=True, frameon=True)
Fit Quality
In the fit we see that the error bars (coming mainly from the noise variance) are quite large. This is likely due to the outlier point in 1904, ignoring that point we can see that a tighter fit is obtained. To see this making a version of the model, m_clean
, where that point is removed.
x_clean=np.vstack((x[0:2, :], x[3:, :]))
y_clean=np.vstack((y[0:2, :], y[3:, :]))
m_clean = GPy.models.GPRegression(x_clean,y_clean)
_ = m_clean.optimize()
Deep GP Fit
Let's see if a deep Gaussian process can help here. We will construct a deep Gaussian process with one hidden layer (i.e. one Gaussian process feeding into another).
Build a Deep GP with an additional hidden layer (one dimensional) to fit the model.
import GPy
import deepgp
hidden = 1
m = deepgp.DeepGP([y.shape[1],hidden,x.shape[1]],Y=yhat, X=x, inits=['PCA','PCA'],
kernels=[GPy.kern.RBF(hidden,ARD=True),
GPy.kern.RBF(x.shape[1],ARD=True)], # the kernels for each layer
num_inducing=50, back_constraint=False)
import deepgp
# Call the initalization
m.initialize()
Now optimize the model.
for layer in m.layers:
layer.likelihood.variance.constrain_positive(warning=False)
m.optimize(messages=True,max_iters=10000)
m.staged_optimize(messages=(True,True,True))
Olympic Marathon Data Deep GP
fig, ax = plt.subplots(figsize=plot.big_wide_figsize)
plot.model_sample(m, scale=scale, offset=offset, samps=10, ax=ax,
xlabel='year', ylabel='pace min/km', portion = 0.225)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
mlai.write_figure(figure=fig, filename='../slides/diagrams/deepgp/olympicmarathondeepgpsamples.svg',
transparent=True, frameon=True)
Olympic Marathon Data Deep GP
Fitted GP for each layer
Now we explore the GPs the model has used to fit each layer. First of all, we look at the hidden layer.
m.visualize(scale=scale, offset=offset, xlabel='year',
ylabel='pace min/km',xlim=xlim, ylim=ylim,
dataset='olympicmarathon',
diagrams='../slides/diagrams/deepgp')
import pods
pods.notebook.display_plots('olympicmarathondeepgplayer{sample:0>1}.svg',
'../slides/diagrams/deepgp', sample=(0,1))
fig, ax = plt.subplots(figsize=plot.big_wide_figsize)
m.visualize_pinball(ax=ax, scale=scale, offset=offset, points=30, portion=0.1,
xlabel='year', ylabel='pace km/min', vertical=True)
mlai.write_figure(figure=fig, filename='../slides/diagrams/deepgp/olympicmarathondeepgppinball.svg',
transparent=True, frameon=True)
Olympic Marathon Pinball Plot
The pinball plot shows the flow of any input ball through the deep Gaussian process. In a pinball plot a series of vertical parallel lines would indicate a purely linear function. For the olypmic marathon data we can see the first layer begins to shift from input towards the right. Note it also does so with some uncertainty (indicated by the shaded backgrounds). The second layer has less uncertainty, but bunches the inputs more strongly to the right. This input layer of uncertainty, followed by a layer that pushes inputs to the right is what gives the heteroschedastic noise.
Challenges in deploying AI.
Currently this is in the form of "machine learning systems"
 Fog computing: barrier between cloud and device blurring.
 Computing on the Edge
Complex feedback between algorithm and implementation
Major new challenge for systems designers.
 Internet of Intelligence but currently:
 AI systems are fragile
Machine Learning System Design
Pigeonholing
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 subtask could perhaps be thought of as a particular stage in machining (automating a factory) or a subroutine call in computing. Machine learning allows any complex subtask, 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 subtask 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 subtasks (following the road, identifying pedestrians, etc).
The current 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 subtasks can lead to a single point of failure, where if any subtask 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
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), a founding paper of control theory. In this spirit, 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.
 The centrifugal governor is a physical system and it is an integral part of a wider physical system that it regulates (the engine).
 The parameters of the governor were set by hand, our modern artificial intelligence systems have their parameters set by data.
{One of the first automated decision making systems was Watt's governor, as held by "Science" on Holborns viaduct. Watt's governor was a key component in his 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 Watt's 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 Watt's 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.
Asking Siri "What is a trillion to the power of a thousand minus one?" leads to a 30 minute response 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 turnaround 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 my blog post on "Decision Making and Diversity"
Emukit
 Multifidelity emulation: build surrogate models when data is obtained from multiple information sources that have different fidelity and/or cost;
 Bayesian optimisation: optimise physical experiments and tune parameters of machine learning algorithms;
 Experimental design/Active learning: design the most informative experiments and perform active learning with machine learning models;
 Sensitivity analysis: analyse the influence of inputs on the outputs of a given system;
 Bayesian quadrature: efficiently compute the integrals of functions that are expensive to evaluate.
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
Ananthanarayanan, Rajagopal, Steven K. Esser, Horst D. Simon, and Dharmendra S. Modha. 2009. “The Cat Is Out of the Bag: Cortical Simulations with 10^{9} Neurons, 10^{13} Synapses.” In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis  Sc ’09. doi:10.1145/1654059.1654124.
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the challenge of understanding what information pertains to is known as knowledge representation.↩
The logarithm of a number less than one is negative, for a number greater than one the logarithm is positive. So if odds are greater than evens (oddson) the logodds are positive, if the odds are less than evens (oddsagainst) the logodds will be negative.↩