KIN
Winter Workshop 2016, 7th December 2016.
According to Narrative
Science, 62 per cent of organisations will be using Artificial Intelligence
(AI) by 2018.
If you asked most people when they last encountered
something that used artificial intelligence, they’d probably conjure up a
mental image of robots, and might be hard pressed to think of something in everyday
use. Machine intelligence and machine learning –
the new synonyms for “artificial intelligence” – are on the rise and are going
to be pervasive. Anyone using a smartphone is already using some sort of
machine intelligence with Google
Now’s suggestions, Siri’s
voice recognition, or Windows
Cortana personal assistant. We don’t call these “artificial intelligence”,
because it’s a term that alarms some people and has earned some ridicule down
the years. But it doesn’t matter what you call it; the ability to get computers
to infer information that they aren’t directly supplied with, and to act on it,
is already here.
But what does all this mean in a practical sense? Can – or should
we - rely on intelligent machines to do
the heavy (physical and cognitive) lifting for us, and if so, what does the
future hold for knowledge and information professionals?
The rise of the chatbot
It’s taken about 10 years, but social media has finally been
accepted as a business tool, rather than just a means for people to waste time.
If you look at any contemporary enterprise collaboration system, you’ll find
social media features borrowed from Facebook or Twitter embedded into the
functionality. Organisations have (finally) learnt that the goal of social
technology within the workplace is not simply to maximise engagement or to
facilitate collaboration, but rather to support work activities without getting
in the way. Having said that, we still can’t extract ourselves from email as
the primary tool for doing business. Email is dead, long live email!
Some progress then. But technology never stands still, and
there’s more disruption on the way, led as usual by the consumer society. Early
in 2016, we saw the introduction of the first wave of artificial intelligence
technology in the form of chatbots
and virtual assistants. This is being heralded as a new
era in technology that some analysts have referred to as the “conversation interface”.
It’s an interface that won’t require a screen or a mouse to use. There will be
no need to click, swipe or type. This is an era when a screen for a device will
be considered antiquated, and we won’t have to struggle with UX design. This
interface will be completely conversational, and those conversations will be
indistinguishable from the conversations we have with work colleagues, friends
and family.
Virtual
Assistants are personalised cross-platform devices that work with
third-party services to respond instantly to users requests which could include
online searching, purchasing, monitoring and controlling connected devices and
facilitating professional tasks and interactions.
Will it be another 10 years before we see this technology
accepted as a business tool? I think not, because the benefits are so
apparent. For example, given the choice
of convenience and accessibility, would we still use email to get things done,
or would we have a real-time conversation? Rather than force workers to stop
what they’re doing and open a new application, chatbots and virtual assistants inject
themselves into the places where people are already communicating. Instead of
switching from a spreadsheet to bring up a calendar, the worker can schedule a
meeting without disrupting the flow of their current conversations.
Companies like Amazon and Google are already exploring these
technologies in the consumer space, with the Amazon Echo and Google Home products; these are screenless
devices that connect to Wi-Fi and then carry out services. This seamless experience puts services in
reach of the many people who wouldn’t bother to visit an App Store, or would
have difficulty in using a screen and keyboard, such as the visually impaired.
We’ll be looking at some examples of how chatbots and
virtual assistants are being used to streamline business processes and interface
with customers at the KIN
Winter Workshop on the 7th December 2016.
Machine Learning
It is worth clarifying here what we normally mean by
learning in AI: a machine learns when it changes its behaviour based on experience.
It sounds almost human-like, but in reality the process is quite mechanical. Machine learning began
to gain traction when the concept of data mining took off in the 1990’s. Data
mining uses algorithms to look for patterns in a given set of information.
Machine learning does the same thing, but then goes one step further – it
changes its program's behaviour based on what it learns.
One application of machine learning that has become very
popular is image recognition. These applications first must be trained – in
other words, humans have to look at a bunch of pictures and tell the system
what is in the picture. After thousands and thousands of repetitions, the
software learns which patterns of pixels are generally associated with dogs,
cats, flowers, trees, etc., and it can make a pretty good guess about the
content of images.
This approach has delivered language translation,
handwriting recognition, face recognition and more. Contrary to the assumptions
of early research into AI, we don’t need to precisely describe a feature of
intelligence for a machine to simulate it.
Thanks to machine learning and the availability of vast data
sets, AI has finally been able to produce usable vision, speech, translation
and question-answering systems. Integrated into larger systems, those can power
products and services ranging from Siri and Amazon to the Google car.
The interesting – or worrying, dependent on your perspective
– aspect of machine learning, is that we don’t know precisely how the machine
arrives at any particular solution. Can we trust the algorithms that the machine
has developed for itself? There is so much that can affect accuracy, e.g. data
quality, interpretation and biased data.
This is just one facet of a broader discussion we will be exploring at the KIN Winter Workshop, and specifically
those deployments of machine learning for decision making and decision support.
Jobs and Skills
The one issue that gets most people agitated about AI, is
the impact on jobs and skills. A recent survey by Deloitte suggested 35% of UK
jobs would be affected by automation over the next two decades. However, many
counter this by saying the idea is to free up people’s time to take on more
customer-focused, complex roles that cannot be done by machines.
I think this video from McKinsey
puts the arguments into perspective by differentiating between activities and jobs. Machines have a proven track record of being able to
automate repetitive, rule driven or routine tasks. That’s not the same as
replacing jobs, where routine processes are only part of a wider job function. According to McKinsey, taking a cross section
of all jobs, 45% of activities can be automated, and we’re not just talking
about predominantly manual labour. They go on to say that up to a third of a
CEO’s time could be automated.
Other research by the Pew Research
Centre has said 53% of experts think that AI will actually create more
jobs.
The question we need to be asking ourselves is what knowledge
and skills do we need to develop now
in order to make the most of this technology revolution happening around us and
ensure we remain relevant. If organisations don’t find out more about these
technologies and how they can be used to improve efficiency or productivity,
they can be sure their competitors are!
If you haven’t yet registered for the KIN Winter Workshop – “KnowledgeOrganisation in the ‘Machine Intelligence’ era", (KIN Member link) do so soon! If you’re not
currently being affected by AI, you soon will be. Make sure you’re
ready!
Steve Dale
KIN Facilitator
