Jon Strol of Clarivate Analytics explains what we mean when we talk about artificial intelligence and some of the technologies behind it

1. Artificial Intelligence
Techniques & Applications
30 May 2018
Jon Stroll, Director, Research Analytics
jon.stroll@clarivate.com

2. 2
What I’m covering today
• Artificial Intelligence: definition and terms we use
(interchangeably) to describe it
• Techniques explained
• Reframing AI as ‘prediction capability’
• The accelerators that are making this happen,
FAST!

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‘Artificial Intelligence’, the phrase coined by John McCarthy in 1955, helped frame a new wave of
academic research against which defines our experience today. Consider it as a ‘suitcase word’ –
one that offers many meanings depending on who’s reading it
Artificial Intelligence is a discipline that covers computer
science and expands quickly to many disciplines including
engineering, mathematics, robotics & psychology
The majority of all AI (in terms of problems it can solve) is
Narrow or Applied, in that it solves the problem it is
designed to do (a point‐solution)
General AI is the ‘holy grail’, creating a single system that can
learn & solve any problem, we’re some way off this..
Search = Artificial Intelligence
(Web of Science)
https://www2.deloitte.com/se/sv/pages/technology/articles/p
art1‐artificial‐intelligence‐defined.html

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Unpicking what comprises natural intelligence helps you understand the current approach and
terminology that is used today within ‘Narrow AI’ – a focus upon one task within a specific sector.
Artificial
Intelligence
• Giving computers capabilities to mimic humans,
including language and reasoning (cognitive analytics)
Machine Learning
• Enabling computers to classify inputs and/or make
predictions, having been ‘trained’ with historical data
sets
Deep Learning
• Specialization of neural networks for computers to ‘self‐
teach’ or ‘self‐learn’
Data Science
• Application of statistics & visualization that affect
behaviors
Think
Learn
Reason
Apply
Beyond the framework above, you can broaden out further and extend into Robotics (a package
of hardware and software to perform a task) as well as Smart Machines (autonomy to decide &
act within the ‘set of rules’).
Domains include learning, reasoning, planning, adaptability, vision, speech recognition, automation,
mobility..

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There are a number of different and complementing techniques to support Narrow AI. Researchers
and engineers will understand the differences and nuances involved whilst business innovators will
seek to apply these for practical use against industry challenges.
Heuristic
• Domain specific
knowledge
designed to add
speed to solve a
problem but may
not be the best
overall solution
Support Vector
Machines
• A supervised
machine learning
algorithm that
can be used for
both
classification and
regression
(averages)
purposes
• Example: text
classification
(Spam email) and
image
recognition
Neural Networks
• Set of algorithms
to recognize,
learn and
recognize
patterns
• Uses
backpropagation
– a binary choice
yes/no – and
then repeats
• Example: Credit
Card Fraud
Markov Decision
Process
• Mathematical
framework
(choice &
reward) to
support
optimization
• Example:
Robotics, used
for planning and
decision‐making
– to define a
sequence of
actions
Natural Language
Processing
• Interaction
between
computers &
humans
• Speech
recognition,
understanding &
generation
• Example: Wall
Street Journal
chatbot for
headlines & stock
prices
Individually these techniques do not deliver ‘general intelligence’ but they can support
components of intelligence
Algorithm: a procedure for solving a mathematical problem in a finite number of steps that frequently
involves repetition of an operation; broadly : a step‐by‐step procedure for solving a problem or
accomplishing some end especially by a computer (Miriam‐Webster)

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Taking a view on Machine Learning as this is showing the most promise in terms of advancing or
developing ‘AI’ for today’s needs
A dog has
‐ 4 legs?
‐ Fur?
‘Rules’ don’t always work so
we have to ‘train’ the
dataset to make intelligent
decisions
Machine learning delivers a continual feedback loop to update, evaluate and then ‘learn’
from the output to improve the response. These can be highly complex data models.
Taking data and turning it into intelligence – delivering information you didn’t have before
Reliant upon
1. Training data for training the algorithm
2. Input data for predicting
3. Feedback data to improve accuracy

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What machine‐learning or deep learning provides are predictions – so reframe by viewing your
workflow or tasks in the context of how a ‘prediction’ can improve or enhance the ability to
complete it. This can be a single point task or a collection of tasks.
http://theweek.com/speedreads/654463/google‐more‐than‐1000‐artificial‐intelligence‐projects‐
works
‐ Implementation of Machine Learning or Deep Learning is through the development of tools
‐ View a job as a series of tasks or steps, each of which may need a decision (some automated,
others with human intervention)
‐ Machine Learning or Deep Learning provides an opportunity to rethink workflows or ‘how to
get things done’ – current implementation of ‘AI’ is narrow – single task focused – workflows
may need to be reengineered

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And predictions can carry risk, some inherent in the algorithms themselves
Race & Gender bias (including) Google advertising showing CEO jobs more frequently to
men / IBM facial recognition being more effective for white men / feedback loop
creating ‘racial bias’ (crime detection)
https://www.newscientist.com/article/2166207‐discriminating‐algorithms‐5‐times‐ai‐
showed‐prejudice/
Algorithmic discrimination in advertising from Facebook (2017)
‘Facebook said on Wednesday it was temporarily disabling the ability of advertisers on
its social network to exclude racial groups from the intended audience of ads while it
studies how the feature could be used to discriminate’.
http://fortune.com/2017/11/29/facebook‐suspends‐ad‐targeting‐that‐allows‐for‐
discrimination/
The difficulty with machine‐learning is that you can’t necessarily see what the algorithm is doing,
in that you need to fine‐tune through output review in order to verify (this may lead to Input
Data Risks and Training Data Risks)

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Several important recent trends has contributed to the explosion in AI, not only in research but in
terms of wider business impact
• AI techniques are complex, data driven and need huge computing
power to support them. AWS has transformed this model. Cloud Computing
• Huge growth in both structured and unstructured datasets with
technology emerging to capture and store even further Big Data
• ‘AI’ techniques on demand without needing to code/create. Eg, IBM
Watson. Also, APIs can be used in conjunction with OpenSource.APIs
• Being able to rely upon & develop against open & trusted sources,
TensorFlow from Google providing machine learning library &
algorithms and training sets
Opensource
• Mobile devices, sensors, cars being connected to the internet –
generating massive data – with new standardized data exchange
models emerging (JSON / REST)
Internet of Things

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The explosive growth in AI is reflected in how companies think & research these new approaches,
taking a look at Google, for example, the impact of their research output is four times higher then
world average.
‐ Additional areas of research (sector‐wide)
‐ Enterprise‐level: Extending the
matching variables beyond current
techniques
‐ Broader access: extending how users
can access ‘AI’ so it becomes
‘everyday’
‐ Deductive reasoning: Applying logic to
generate new ideas and insights from
very limited data sets
• Highlighting Google’s growth in publication & citation impact beyond it’s peers
(2016)
• Acquisition of Deep Mind (2014), $400million, has contributed to this increase
https://www.technologyreview.com/s/603984/googles‐ai‐
explosion‐in‐one‐chart/

11. Sharing a DARPA perspective on Artificial
Intelligence
What it can do, what it can't do, and where it is
headed.
https://www.youtube.com/watch?v=‐
O01G3tSYpU