The Reality of the AI Revolution
The Reality of the AI Revolution
Artificial intelligence (AI) is one of the most evocative
and confusing terms in technology. In making sense of it, Talend CTO, Laurent
Bride says AI is an everyday reality today but with plenty more to deliver in
the future…
By Laurent Bride, Talend CTO 28TH FEBRUARY 2017
According to Accenture, artificial intelligence (AI)
could add an additional US$814 billion in 2035 to the UK’s economy—with growth
rates increasing from 2.5 percent to 3.9 percent in 2035.
We have seen a machine master the complex game of Go,
previously thought to be the most difficult challenge of artificial processing.
We have witnessed vehicles operating autonomously, including a caravan of
trucks crossing Europe with only a single operator to monitor systems. We have
seen a proliferation of robotic counterparts and automated means for
accomplishing a variety of tasks. All of this has given rise to a flurry of
people claiming that the AI revolution is already upon us.
However, while there is no doubt that there have been
significant advancements in the field of AI, what we have seen is only a start
on the path to what could be considered full AI.
Understanding the growth in the functional and
technological capability of AI is crucial for understanding the real world
advances we have seen. Full AI, that is to say complete, autonomous sentience,
involves the ability for a machine to mimic a human to the point that it would
be indistinguishable from them (the so-called Turing test). This type of true
AI remains a long way from reality. Some would say the major constraint to the
future development of AI is no longer our ability to develop the necessary
algorithms, but, rather, having the computing power to process the volume of
data necessary to teach a machine to interpret complicated things like
emotional responses. While it may be some time yet before we reach full AI,
there will be many more practical applications of basic AI in the near term
that hold the potential for significantly enhancing our lives.
With basic AI, the processing system, embedded within the
appliance (local) or connected to a network (cloud), learns and interprets
responses based on “experience.” That experience comes in the form of training
through using data sets that simulate the situations we want the system to
learn from. This is the confluence of Machine Learning (ML) and AI. The
capability to teach machines to interpret data is the key underpinning
technology that will enable more complex forms of AI that can be autonomous in
their responses to input. It is this type of AI that is getting the most
attention. In the next ten years, the use of this kind of ML-based AI will
likely fall into two categories:
Improvement and automation of daily life: Managing
household tasks, self-driving cars and trucks and the general automation of
tasks that robots can perform significantly faster and more reliably than
humans;
Exploration and development of new trends and insights:
Artificial intelligence can help accelerate the rate discovery and science
happening worldwide every day. The use of AI to automate science and technology
will drive our ability to discover new cures, technologies, tools, cells,
planets, etc., ultimately pushing artificial intelligence itself to new
heights.
There is no doubt about the commercial prospects for
autonomous robotic systems for applications like online sales conversion,
customer satisfaction, and operational efficiency. We see this application
already being advanced to the point that it will become commercially viable,
which is the first step to it becoming practical and widespread. Simply put, if
revenue can be made from it, it will become self-sustaining and thus continue
to grow. The Amazon Echo, a personal assistant, has succeeded as a solidly
commercial application of autonomous technology in the United States.
Autonomous vehicle technology is one of the most
publicised and one of the most needed applications of AI. There were 1,730
reported road deaths in Great Britain in 2015 and a further 22,144 serious
injuries. Autonomous vehicle technology
has the potential to significantly reduce this figure and greatly improve
availability and efficiency of transportation for everyone.ai revolution
In addition to the automation of transportation and
logistics, a wide variety of additional technologies that utilise autonomous
processing techniques are being built. Currently, the artificial assistant or
“chatbot” concept is one of the most popular. By creating the illusion of a
fully sentient remote participant, it makes interaction with technology more
approachable.
There have been obvious failings of this technology (the
unfiltered Microsoft chatbot, “Tay,” as a prime example), but the application
of properly developed and managed artificial systems for interaction is an
important step along the route to full AI. This is also a hugely important
application of AI as it will bring technology to those who previously could not
engage with technology completely for any number of physical or mental reasons.
By making technology simpler and more human to interact with, you remove some
of the barriers to its use that cause difficulty for people with various
impairments.
The use of AI for development and discovery is just now
beginning to gain traction, but over the next decade, this will become an area
of significant investment and development. There are so many repetitive tasks
involved in any scientific or research project that using robotic intelligence
engines to manage and perfect the more complex and repetitive tasks would
greatly increase the speed at which new breakthroughs could be uncovered.
There is also the tantalizing possibility that as we
increase the capability of our AI systems, they could actually perform research
and discover new avenues to explore. While this is still a long way away, it
could greatly accelerate the discoveries needed for many advancements that
could improve and extend our lives.
The dystopian vision of robots assuming complete control
of society is unlikely; the nuances of perception, intuition, and plain old
“gut-check reactions” still elude machines. Learning from repetition, improving
patterns, and developing new processes is well within reach of current AI
models, and will strengthen in the coming years as advances in AI –
specifically machine learning and neural networking – continue. Rather than
being frightened by the perceived threat of AI, it would be wise to embrace the
possibilities that AI offers.
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