2016 has
been the year of Artificial Intelligence and machinelearning. With the year
being almost at an end, let me chime in to the gang of pundits who venture into
prediction land and pronounce what we get out of our glass balls. So here are
my 5 plus 2 bonus ones.
AI gets mainstream in
Consumer Environments
Alexa paved
the way, the Google Assistant is on its heels, Microsoft Cortana wants to get
there, too – and Apple, amazingly, is a late starter in this environment. Amazon
started with a pretty smart strategy by not overselling the capabilities of its
underlying AI, as Apple did with Siri, which caused some grief for Apple and
some laughs for many people around. More and more helpful Alexa skills are
developed and implemented that improve its usefulness. Similarly Google; they
started late but are in the game now, too – following a different strategy of
adding new functionality by just making it available in contrast to Amazon, who
opt to have users individually enable ‘skills’. Identification of what these
systems can do will be an interesting question.
Facebook’s Mark
Zuckerberg created a butler for his house, who he calls Jarvis, like the
one of Tony Stark in the Ironman movies.
Google
recently based its translation engine on machinelearning and AI, seeing vastly
improved translations. Facebook’s translations base on an AI, too – although this
one still seems to have a lot to learn.
Not to
mention all the countless other consumer services Google has, that utilize
machinelearning and AIs in the background.
Two of the
main developments to look at here are platforming/interfaces/protocols and, of
course, security.
AI-driven, intelligent
Business Applications
Microsoft,
Salesforce, and Oracle paved the way. SAP recently chimed in after being silent
about machine learning and artificial intelligence for (too) long. The bottom
line here is that these big vendors, and many other, here unnamed ones, have
understood that AI is not an end in itself, but a means to an end. They all are
strengthening the capabilities of their business applications by supporting
them with processes that base upon machine learning algorithms, thus delivering
solutions that are more helpful for their users and/or customers. Be it
intelligent follow-up, relationship intelligence, proactive (or rather
prescriptive) maintenance, smart target group segregation, chatbots automating
support, intelligent knowledge bases, or smart product recommendations to site
visitors.
Let’s just
look at service and support, also in sales. Automation with the help of bots
can be of incredible help here. Although, earlier in 2016 bots tended to offer
a very limited and poor customer experience, this will be on the rise.
Underlying AIs will learn, implementers of bots will learn to start with
simple, meaningful interactions and to get more complex from there. Overall
there is a massive potential for improved experiences at scale.
AI on the Dark Side of the
Force
As we have
seen, especially with Microsoft’s Tay, training AI is not a trivial thing. As
there is no conscience, there is no ethics – rather a blank slate – so it is
easy to lead an AI to the ‘Sark Side of the Force’. This may happen via
explicit or implicit bias. Tay became malicious because of trolls, other
systems simply suffered from something that one could call the ‘programmers’
bias’. Other AIs, like the Tesla autopilot, sometimes are used beyond their
safe limits, which may cause accidents. Remember the first fatality caused by a
self-driving car, or the incident where allegedly the car crossed a red traffic
light? Techrepublic recently collected their
top 10 fails. We will see more of these AI fails the more we use them and
rely on them, until we manage their training better. It is simple as this: The
more we trust AIs, the higher profile potential incidents will have.
Vendor Lock in by AI
This has
the potential to become an interesting one; might not happen in 2017, though. Every
vendor has an incentive to be sticky,
‘Democratization’ (yuk) of AI
I hate this
term. But it is the one that currently is used. Earlier we called this
commoditization.
Simpler and
less catchy.
But
regardless how we call it – it is happening. My first two points already hinted
at it. More and more functionality will be driven by or at least involve some
measure of machine intelligence. With that prices will drop. The phase of
creaming strategies will end. This may happen in 2017 or a bit later, but it
will happen.
Soon.
Bonus Ones
These two
topics came into my mind while writing down my thoughts on what will mainly go
on. However, we should have a look at these two topics, too – in 2017 and
beyond.
AI and Morals
Which leads
me to the topic of ethics. As such an AI doesn’t have any morals – unless we
train them. This will lead to very interesting questions that need to get
answered.
Starting
with the very simple one: Which morals are right?
There are
the three laws
of robotics, but these are incomplete in themselves. It is very easy to
create moral dilemmas. Here, clearly lots of thought is necessary.
This also
holds true for the simpler topic of AI and law. The question who is responsible
in the case of an accident caused by or involving an automatically driven car
may serve as an example.
I expect
these questions being addressed more and more in the next years.
Criminalization of AI
Where there
are good uses there are also evil uses. As said above an AI in and of itself does
not have a conscience (yet) nor does it have morals – unless we train them to
have.
Now, where there
are benevolent trainers there are also malevolent. It is evident that not only
businesses but also government agencies are building and using AIs (ever heard
of SKYNET?) – and it is a safe bet that criminals are doing, too. And as
criminals tend to have a somewhat stretched sense of morals and ethics we can
expect the same of AIs that they are using. So we will likely see a lot of
crime being conducted with the help of artificially intelligent machines.
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