The document discusses the future of artificial intelligence, highlighting advancements in analytics, machine learning, and the shift towards more adaptive AI systems. It emphasizes the importance of context and natural language understanding in AI development, as well as the challenges of instilling human values and creativity in AI. Experts predict that AI will augment human thinking and require transparency, ethical standards, and collaboration for its potential to be fully realized.

1. Forward thinking
What’s next for AI
March 2017
bit.ly/ibm-ai-future

2. 2
Thinking beyond today
In the past few years, we have seen significant strides in analytics,
machine learning and artificial intelligence techniques. While we’re
only beginning to scratch the surface of what AI can help us do,
many are already working on what’s next.
We interviewed 30 AI experts to get their take on what advances
in AI technology and applications we can expect in the next decade.
© IBM, 2017

3. Necessity is the mother of innovation
“Faced with a constant onslaught of data, we
needed a new type of system that learns and
adapts, and we now have that with AI. What
was deemed impossible a few years ago is not
only becoming possible, it’s very quickly
becoming necessary and expected.”
— Arvind Krishna, Senior Vice President
of Hybrid Cloud and Director of IBM Research
© IBM, 20173

4. A new innovation
equation
the rise of small data
+ deep reasoning
+ more unsupervised,
efficient deep learning
+ GPUs and new
AI hardware
innovation in artificial
intelligence
4 © IBM, 2017

5. 5
Beyond deep
learning
 From big to small data
- Shift toward models requiring
less data for similar accuracy
 From deep learning to
deep reasoning
- Move from perception
& recognition tasks to decision
making
 From supervised to
unsupervised learning
- Work with less labeled data &
less human guidance
Learn more: http://bit.ly/ibm-ai-innovation
© IBM, 2017

6. Breaking the language barrier
“Language is a very tough nut to crack because
it allows us in a succinct way, without using a
whole lot of symbols, to say an extraordinarily
diverse set of things. It’s a significantly more
complex problem than perception, recognizing
objects or moving from speech to text.”
— Vijay Saraswat, Chief Scientist
for IBM Compliance Solutions
6 © IBM, 2017

7. Human-computer
interaction changes
 Moving from on-screen type to
voice represents
a sea change in computing
 The future of AI will come with
natural language understanding
& context
 This will usher in more robust
virtual assistants
& pervasive AI
7 © IBM, 2017

8. “You really get
me!”
To gain better understanding, AI
must know context
 It will need to know:
- Where you’ve been
- Where you’re going
- What your goals are
 It must remember the entire
conversation, not just the
last query
 It must be connected
to the world around you
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Learn more: http://bit.ly/ibm-ai-conversation
© IBM, 2017

9. Creativity is in the eye of the beholder
“We still have to define what creativity means.
We know some attributes like something that
is novel and unexpected, yet useful. It’s easy
for AI to come up with something novel just
randomly. But it’s very hard for it to come up
with something that is novel and unexpected
and useful.”
— John Smith, Manager of
Multimedia and Vision at IBM Research
9 © IBM, 2017

10. The quest for AI
creativity
 AI does well as a creative
“mimic”
 It can reduce the mundane
execution tasks in creative
work
 AI can also serve as
inspiration for human
creativity.
 But can it — or should it — be
taught to be innately creative?
10 © IBM, 2017

11. Where does AI
fit?
 Teaching computers to be
creative is different from the
way humans learn to create.
 How can computers learn the
subjective idea of beauty?
By studying pixels? Color
palettes?
 In the end, AI will most likely
remain a creative partner.
Learn more: http://bit.ly/ibm-ai-creativity
11 © IBM, 2017

12. Keeping watch
“AI can be used for social good. It can be used
for business purposes. But it can also be used
for other types of social impact in which one
man's good is another man's evil. We must
remain aware of that.”
— James Hendler, Director of the Institute for
Data Exploration and Applications,
Rensselaer Polytechnic Institute
12 © IBM, 2017

13. Building trust in AI
There is a wealth of potential for
artificial intelligence if we foster it
properly. To do this, we need:
 Constant transparency into AI
systems & explanations for
their recommendations
 General education on AI
 Common standards for
interoperability & integration
 High ethical standards for
AI-driven decisions
 Continued collaboration
between scientists, academics,
industry, & government
13 © IBM, 2017

14. Whose values?
 Instilling human values in AI is
challenging, but possible
 First, we must determine
whose values to use
 Should computers be held to a
different set of values than
humans?
 We must vigilantly prevent bias
from entering AI systems
 And accept that computers
might be imperfect ethical
actors because they will not
feel emotion & consequences
like humans do
Learn more: http://bit.ly/ibm-ai-trust
14 © IBM, 2017

15. A final thought
“Birds flap their wings to fly, but to make
humans fly, we had to invent a different type of
flying—one that did not occur in nature. And
so, similarly, through AI, we’re going to invent
many new types of thinking that don't exist
biologically and that are not like human
thinking. Therefore, this intelligence does not
replace human thinking, but augments it.”
— Kevin Kelly, co-founder of Wired and
author of the best-seller The Inevitable
15 © IBM, 2017

16. Thank you to the experts
Arvind Krishna, IBM Honor Sherlock, IBM Michael Karasick, IBM
Aya Soffer, IBM James DiCarlo, MIT Michael Witbrock, IBM
Bowen Zhou, IBM James Hendler, RPI Murray Campbell, IBM
Chieko Asakawa, IBM Jason Toy, Somatic Rachel Bellamy, IBM
Costas Bekas, IBM Jay Turcot, Affectiva Rob High, IBM
David Konopnicki, IBM John Shtok, IBM Satinder Singh, U of Michigan
Dharmendra Modha, IBM John Smith, IBM Shivon Zillis, Bloomberg Beta
Gabi Zijderveld, Affectiva Kevin Kelly, Wired Vijay Saraswat, IBM
Grady Booch, IBM Margaret Boden, U of Sussex William Chamberlin, IBM
Guru Banavar, IBM Mark Sagar, Soul Machines Yoshua Bengio, U of Montreal
16 © IBM, 2017

17. Author: Laura DeLallo,
Senior Editor, IBM
Contact: lauradelallo@us.ibm.com
@ldelall0
Report: bit.ly/ibm-ai-future
IBM Research: research.ibm.com
© Copyright IBM Corporation 2017
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March 2017
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