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We spent the last 10 years trying to get machines
to understand us better. It looks like the next decade might be more about
innovations that help us understand machines, Deloitte predicts in its
end-of-year Future Trends report.
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Few business leaders doubt AI’s
abilities to contribute to the team, and Deloitte says there’s plenty of
evidence suggesting businesses that use AI pervasively throughout their
operations perform at a higher level than those that don’t. But there’s a trust
issue when implementing AI into the workforce. Specifically, enterprises have a
hard time trusting AI with mission-critical tasks.
In short, if humans don’t trust
machines or think they’re making the right call, it won’t be used.
“With AI tools increasingly
standardized and commoditized, few businesses may realize true competitive
gains from crafting a better algorithm,” the report states. “Instead, what will
likely differentiate the truly AI-fueled enterprise from its competition will
be how robustly it uses AI throughout its processes. The key element here,
which has developed much slower than machine learning technology, is trust.”
Deloitte elaborates the argument.
Computers were once seen as more or less infallible machines whose calculations
were never wrong that simply processed discrete inputs into discrete outputs.
As algorithms increasingly shoulder
probabilistic tasks such as object detection, speech recognition, and image and
text generation, the real impact of AI applications may depend on how much
their human colleagues understand and agree with what they’re doing.
“What may matter in the future is not
who can craft the best algorithm, but rather who can use AI most effectively.”
In that case, developing processes
that leverage AI in transparent and explainable ways will be key to spurring
adoption.
One of the biggest clouds hanging
over AI today is its black-box problem. Because of how certain algorithms
train, it can be very difficult, if not impossible, to understand how they
arrive at a recommendation.
“Asking workers to do something
simply because the great and powerful algorithm behind the curtain says to is
likely to lead to low levels of buy-in.”
How does this lack of trust manifest
itself in the creative industries and its increasing use of generative AI tools
like OpenAI’s DALL-E 2 image generator and GPT-3 text generator.
“In many cases, generative AI is
proving itself in areas that were once thought to be automation-proof,” says
Deloitte. “Even poets, painters, and priests are finding no job will be
untouched by machines.”
That does not mean, however, that
these jobs are going away, the report insists. “Even the most sophisticated AI
applications today can’t match humans when it comes to purely creative tasks
such as conceptualization, and we’re still a long way off from AI tools that
can unseat humans in jobs in these areas.”
The prevailing approach to bringing
in new AI tools is to position them as assistants, not competitors.
“Workers and companies that learn to
team with AI and leverage the unique strengths of both AI and humans may find
that we’re all better together,” says Deloitte. Think about the creative,
connective capabilities of the human mind combined with AI’s talent for
production work. We’re seeing this approach come to life in the emerging role
of the prompt engineer.”
As enterprises consider adopting
these capabilities, they could benefit from thinking about how users will
interact with them and how that will impact trust.
“Think of deploying AI like
onboarding a new team member,” the consultancy advises. “We know generally what
makes for effective teams: openness, rapport, the ability to have honest
discussions, and a willingness to accept feedback to improve performance.
Implementing AI with this framework in mind may help the team view AI as a
trusted copilot critic.”
For some businesses, the
functionality offered by emerging AI tools could be game-changing. But a lack
of trust could ultimately derail these ambitions.
Deloitte also addresses the longer
term future of AI, which it characterizes as “exponential intelligence.”
“Affective AI — empathic emotional
intelligence — will result in machines with personality and charm,” says Mike
Bechtel, Deloitte’s chief futurist. “We’ll eventually be able to train
mechanical minds with uniquely human data — the smile on a face, the twinkle in
an eye, the pause in a voice — and teach them to discern and emulate human
emotions. Or consider generative AI: creative intelligence that can write
poetry, paint a picture, or score a soundtrack.”
After that, we may see the rise of
general-purpose AI: intelligence that has evolved from simple math to polymath.
Today’s AI is capable of single-tasking, good at playing chess or driving cars
but unable to do both. General-purpose AI stands to deliver versatile systems
that can learn and imitate a collection of previously uniquely human traits.
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