Download Preparing for the future of artificial intelligence

Preparing for the future of
artificial intelligence
IBM response to the White House Office of
Science and Technology Policy’s
Request for information (Summer 2016)
On June 27, 2016, the White House Office of Science
and Technology Policy (OSTP) published a Notice
of Request for Information (RFI) soliciting public
input on the subject of preparing for the future of
artificial intelligence. The complete text of the notice
can be found in the Federal Register (White House
OSTP 2016a). Previously, on May 3, 2016, OSTP had
announced a number of new actions related to AI.
As a part of this initiative, the Federal Government
is working to ensure the development of AI for
public good and to aid in promoting more effective
government. Furthermore, OSTP has expressed
interest in developing a view of AI across all sectors
for the purpose of recommending directions
for research and determining challenges and
opportunities in this field. The purpose of this RFI
was to solicit feedback on overarching questions in
AI, including AI research and the tools, technologies,
and training needed to answer these questions.
OSTP believes that the views of the American people,
including stakeholders such as consumers, academic
and industry researchers, private companies, and
charitable foundations, are important to inform an
understanding of current and future needs for AI in
diverse fields. The following 2,000 words essay was
submitted to OSTP by IBM Research.
Introduction
IBM has been researching, developing and investing
in AI technology for more than 50 years. The public
became aware of a major advance in 2011, when
IBM Watson won the historic Jeopardy! exhibition on
prime time television. Since that time, the company
has advanced and scaled the Watson platform, and
applied it to various industries, including healthcare,
finance, commerce, education, security, and the
Internet of Things. We are deeply committed to this
technology, and believe strongly in its potential to
2
benefit society, as well as transform our personal and
professional lives.
To this end, we have engaged thousands of scientists
and engineers from IBM Research and Development,
and partnered with our clients, academics, external
experts, and even our competitors to explore all
topics around AI. And we have developed a unique
point-of-view, informed by decades of research and
commercial application of AI.
At IBM, we are guided by the term “augmented
intelligence” rather than “artificial intelligence”. It is
the critical difference between systems that enhance
and scale human expertise rather than those that
attempt to replicate all of human intelligence. We focus
on building practical AI applications that assist people
with well-defined tasks, and in the process, expose
a range of generalized AI services on a platform to
support a wide range of new applications.
We call our particular approach to augmented
intelligence “cognitive computing.” Cognitive
computing is a comprehensive set of capabilities
based on technologies such as machine learning,
reasoning and decision technologies; language,
speech and vision technologies; human interface
technologies; distributed and high-performance
computing; and new computing architectures
and devices. When purposefully integrated, these
capabilities are designed to solve a wide range of
practical problems, boost productivity, and foster
new discoveries across many industries. This is what
we bring to market today in the form of IBM Watson.
The following are brief responses to the questions in
the RFI (re-ordered and slightly re-factored).
The use of AI
for public good
For decades, we have been stockpiling digital
information. We have digitized the history of the
world’s literature and all of its medical journals. We
track and store the movements of automobiles, trains,
planes and mobile phones. And we are privy to the
real-time sentiments of billions of people through
social media. It is not unreasonable to expect that
within this rapidly growing body of digital information
lies the secrets to defeating cancer, reversing climate
change, or managing the complexity of the global
economy. We believe that many of the ambiguities
and inefficiencies of the critical systems that
facilitate life on this planet can be eliminated. And we
believe that AI systems are the tools that will help us
accomplish these ambitious goals.
We are already doing much of this work:
­­­– For healthcare, AI systems can advance precision
medicine by ingesting patients’ electronic medical
history and relevant medical literature, performing
cohort analysis, identifying micro-segments of
similar patients, evaluating standard-of-care
practices and available treatment options, ranking
by relevance, risk and preference, and ultimately
recommending the most effective treatments for
their patients.
­­­– For social services, AI systems can provide timely
and relevant answers to citizens in need, assist
citizens with insurance, tax, and social programs,
predict the needs of individuals and population
groups, and develop plans for efficient deployment
of resources.
individuals or groups of students, assist students
using a range of learning styles and methods,
and develop effective early education, primary,
secondary, and higher education programs.
­­­– For financial services, AI systems can expand
financial inclusion by qualifying applicants, assist in
providing the best insurance coverage at the right
cost, ensure compliance with federal, state and
local regulations, and reduce fraud and waste in tax
and other financial programs.
­­­– For transportation, AI systems can improve
the efficiency of public transportation systems,
support public vehicles with driver assistance
using semi-automated features, manage incidents,
optimize the use of fuel and support maintenance
of infrastructure and rolling stock.
­­­– For public safety, AI systems can support safety
personnel with anomaly detection using machine
vision, build predictive models for crime, and help
investigators find associations in massive amounts
of information.
­­­– For the environment, AI systems can understand
complex relationships and help construct
environmental models for accurate prediction and
management of pollutants and carbon footprints.
­­­– For infrastructure, AI systems can assist
with prediction of demand, supply, and use of
infrastructure, planning and execution of projects,
and maintenance of built infrastructure.
– For education, AI systems can assist teachers in
developing personalized educational programs for
3
Social and economic
implications of AI
AI systems are already changing the way work gets
done. But history suggests that new technologies like
AI result in higher productivity, higher earnings, and
overall job growth. In particular, we believe that new
companies, new jobs, and entirely new markets will
be built on the shoulders of this technology. And we
believe that AI systems will improve access to critical
services for underserved populations. Overall, we
anticipate widespread improvements in quality of life.
In order to be fully accepted into society, AI systems
need to have significant social capabilities, because
their presence in our lives has a profound impact on
our emotions and on our decision making capabilities
(e.g., elder care). AI systems also need to understand
how to learn and comply with specific behavioral
principles for aligning with human values.
Education for harnessing
AI technologies
The potential for AI solutions for public and private
uses has created a fast growing demand for AI skills.
To meet this demand, top universities are crafting new
AI curricula. Leading firms offer faculty and students
access to cloud platforms with AI-based services,
from image recognition to machine learning. However,
most courses and platforms require programming
skills and advanced mathematics as prerequisites.
Government agencies, research institutions,
universities, and foundations can work together to
make learning to build, understand, and work with
AI systems more accessible to a broader range of
students and professionals retooling their careers.
4
Fundamental questions
in AI research, and
the most important
research gaps
In order for AI systems to enhance quality of life, both
personally and professionally, they must acquire
broad and deep knowledge from multiple domains,
learn continuously from interactions with people
and environments, and support reasoned decisions.
Broadly, the AI fields’ long-term progress depend
upon many advances:
­­­Machine learning and reasoning:
Most current AI systems use supervised learning,
using massive amounts of labeled data for training.
Fundamental research is needed for AI systems that
learn as humans do: through instruction, interaction
(by discussing, debating, watching other people learn),
by doing things (utilizing motor skills), generalizing
from very little data, and by transferring skills across
many tasks.
Decision techniques:
For AI-based systems to succeed broadly, new
techniques must be developed for modeling systemic
risks, analyzing tradeoffs, detecting anomalies in
context, analyzing data while preserving privacy, and
making decisions under uncertainty.
­­­Domain-specific AI systems:
Deeply understanding the domains of human
expertise, such as medicine, engineering, law and
thousands more, poses particularly difficult issues of
knowledge acquisition, representation, and reasoning.
AI systems must ultimately perform professional-level
tasks, such as managing contradictions, designing
experiments, and negotiating.
­­­Data assurance and trust:
Training and test data can be biased, incomplete, or
maliciously compromised. Significant effort should
be devoted to techniques for measuring entropy of
datasets, validating the quality and integrity of data,
and for making AI systems more objective, resilient,
and accurate. People will trust AI systems when
systems know users’ intents and priorities, explain
their reasoning, learn from mistakes, and can be
independently certified.
Radically efficient computing infrastructure:
When deployed at scale, AI systems will need to
handle unprecedented workloads that will require
the development of high-performance distributed
cloud systems, new computing architectures such
as neuromorphic and approximate computing, and
new devices such as quantum and new types of
memory devices.
Data sets that can
accelerate AI research
A major bottleneck in developing and validating
AI systems is public access to sufficiently large,
openly curated, public training data sets. Machine
learning, supervised and unsupervised, requires
large, unbiased data sets to train accurate models.
Deep learning is advancing speech transcription,
language translation, image captioning, and question
and answering capabilities. Each new AI advance,
e.g., video comprehension, requires the creation
of new data sets. Deep domain tasks, such as
cancer radiology, or insurance adjustment, requires
specialized and often hard-to-get datasets. Incentives
must be created for greater sharing of both input
datasets and trained models through mechanisms
like model zoos.
Multi-disciplinary research
Most of the research gaps previously identified
cannot be achieved by AI researchers alone.
Collaboration with experts in multiple disciplines
— such as psychology, philosophy, sociology, art,
regulation, and law — will be crucial. In addition, there
is an important role for professional associations
with industry-specific knowledge to play in informing
AI applications. To this end, IBM is in the process of
creating a network of several academic centers to
jumpstart the scientific ecosystem.
Role of incentives
and prizes
As the fundamental building blocks of AI improve, so
too should the incentives that inspire next-generation,
people-centered systems design. As an example, IBM
established a $5 million AI XPrize for the best use of
AI system to empower teams of people to tackle the
world’s grand challenges. IBM is developing additional
scientific challenges for the AI research community.
5
Safety and control
issues for AI
Other issues:
Business models
To reap the societal benefits of AI, we will first need to
trust AI. That trust will be earned through experience,
of course, in the same way we learn to trust that an
ATM will register a deposit, or that an automobile will
stop when the brake is applied. Put simply, we trust
things that behave as we expect them to.
In market-driven economies, progress also crucially
depends upon the creation of new business models
that reward more effective outcomes and overall
benefits to society.
But trust will also require a system of best practices
that can guide the safe and ethical management of
AI; a system that includes alignment with social norms
and values; algorithmic accountability; compliance
with existing legislation and policy; and protection
of privacy and personal information. IBM is in the
process of developing this system in collaboration
with our partners, university researchers, and
competitors.
In Summary
AI systems are augmenting human intelligence and
will ultimately transform our personal and professional
lives. Its benefits far outweigh its risks. And with the
right policies and support, those benefits can be
realized sooner.
Policy makers should focus on:
Legal and governance
implications of AI
Responsibility must be the foundation for AI
policymaking. Inclusive dialogues can explore
relevant topics, going beyond the headlines and
hype, promoting deeper understanding and a new
skills focus. Every transformative tool that people
have created — from the steam engine to the
microprocessor — augment human capabilities and
enable people to dream bigger and do more. People
with these tools will solve whole new classes of big
data problems. Our responsibility as members of
the global community is to ensure, to the best of our
ability, that AI is developed the right way and for the
right reasons.
6
­­­– Facilitating a fact-based dialogue on the
capabilities and limitations of AI technologies
­­­– Developing progressive social and economic
policies to deploy AI systems for broad public good
­­­– Developing progressive education and workforce
programs for future generations
­­­– Investing in a long-range interdisciplinary research
program for advancing the science and design of
AI systems
© Copyright IBM Corporation 2016
IBM Global Services
Route 100
Somers, NY 10589
USA.
Produced in the United States of America
September 2016
All Rights Reserved
IBM, the IBM logo and ibm.com are trademarks or registered
trademarks of International Business Machines Corporation in
the United States, other countries, or both. If these and other IBM
trademarked terms are marked on their first occurrence in this
information with a trademark symbol (® or ™), these symbols
indicate US registered or common law trademarks owned by IBM
at the time this information was published. Such trademarks may
also be registered or common law trademarks in other countries.
A current list of IBM trademarks is available on the Web at
“Copyright and trademark information” at ibm.com/legal/
copytrade.shtml Other company, product and service names
may be trademarks or service marks of others.
References in this publication to IBM products and services do
not imply that IBM intends to make them available in all countries
in which IBM operates.
Please Recycle