FutureGrasp Tom Campbell

Identifying, tracking, forecasting and assessing emerging and disruptive technologies

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By The Numbers

Over 20 Years Experience

In Emerging and 
Disruptive Technologies

2 Patents

With Numerous Applications and Invention disclosures

Over a Dozen Conferences Organized on Emerging Technologies

Over 20 Peer-Reviewed Articles

Over 40 Conference Proceedings

Over 70 Keynote and Invited Presentations

Focus


The focus of FutureGrasp, LLC is to identify, track, forecast and assess emerging and disruptive technologies. "Grasp" means both "to seize upon" and "to comprehend." A double entendre, FutureGrasp thus seeks to enable our clients to lead in rapid technology changes.

Based in Evergreen, Colorado (just West of Denver), FutureGrasp is well-positioned to serve both the West and East coasts.

AI

Big Data

Biotech

Internet of Things

NanoTech

Materials

3D & 4D Printing

Semiconductors

About the Founder


Dr. Thomas A. Campbell
Dr. Thomas A. Campbell — recognized analyst and researcher in emerging and disruptive technologies — 
is the Founder & President of FutureGrasp, LLC, which advises organizations on trends and implications of emerging technologies. From February 2015 to August 2017, he was the first National Intelligence Officer for Technology (NIO-TECH) with the National Intelligence Council (NIC) in the Office of the Director of National Intelligence (ODNI). As the first NIO-TECH, he served as the focal point within the ODNI for all activities related to emerging and disruptive civil technologies. In collaboration with other NIOs and government agencies, he drafted, coordinated reviews of, and briefed a broad portfolio of intelligence products for senior policymakers—including senior directors in the National Security Council, senior staff of the Office of Science & Technology Policy (OSTP), Senators, Congressmen, and senior ranking officers within the Department of Defense. Tom established and managed liaison relationships with academia, industry, and others to ensure a comprehensive understanding of technology and its intersection with global military, security, economic, financial and energy issues.

The combination of a unique holistic view of technology and deep experience in multiple genres of research communities enables Tom to successfully identify technology trends. Tom’s insights have informed senior policymakers, enabled millions of dollars of industry and academic funding, broken ground in new research areas, and kept diverse groups abreast of the rapid pace and implications of technology change. His career encompasses national and international experience in government, academia, industry, startups and national laboratories. He was recently appointed Senior Fellow with the Council on Competitiveness (Washington, D.C.), and a Special Adviser to BootstrapLabs Group, LLC (San Francisco, CA).

Prior to his government service, Tom was Research Associate Professor and Associate Director for Outreach with the Institute for Critical Technology and Applied Science (ICTAS) at Virginia Tech for over six years. He led corporate outreach and facilitated large, multi-principal investigator (PI) program and proposal developments in interdisciplinary areas. Tom assisted commercialization of faculty research into spin-offs, joint ventures, and licensing. He organized national and international outreach for ICTAS, including establishing an innovation research center on every continent and organizing and running major conferences. He built a research lab from scratch and performed fundamental research on nanomaterials and 3D- and 4D-printing, culminating in the 2014 Outstanding Paper award in the top journal in the field. He was frequently called upon by journalists from around the world as an expert in 3d and 4d printing, and he gave dozens of keynotes and invited talks to industry and the US Government.

Leveraging his expertise in emerging technologies, Tom was a Senior Fellow (Non-resident) with the Strategic Foresight Initiative of the Atlantic Council in Washington, D.C. for almost two years. He helped guide the Atlantic Council in consideration of emerging technologies for their implications to geopolitics and societal disruption. 

Tom’s industry experience covers both small business and large corporations. He worked three years at ADA Technologies, Inc., where he led proposal concept, writing, and project execution of Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) proposals. He helped spin-off two companies from ADA as separate small businesses. For five years, Tom was a Research Scientist at Saint-Gobain Crystals (part of the French Fortune 100 company Saint-Gobain), where he was PI and R&D Project Leader during a $13 million dollar expansion project for optical microlithography materials processing. 

Tom is a recipient of the prestigious Alexander von Humboldt Research Fellowship, granted to global researchers for post-doctoral research in Germany. Living in Freiburg, Germany for 16 months, he executed all his research in the German language. He developed and implemented novel experimental studies of interfacial kinetics and crystal characterizations of germanium-silicon compounds. Following his return to the United States, he was a Member of the Board of Directors of the American Friends of the Alexander von Humboldt Foundation and Chair of its Strategic Planning Standing Committee.

Tom holds a Ph.D. in Aerospace Engineering Sciences from the University of Colorado at Boulder. His research was funded under a three-year NASA Graduate Student Researcher Program Fellowship. He developed, constructed and implemented an in-situ, real-time visualization capability using non-intrusive X-ray radioscopy to study opaque semiconductor materials and liquid metals; he built the measurement and controls system from scratch with ca. $1.0 million worth of equipment. Tom holds a B.E. in Mechanical Engineering (Magna Cum Laude, Honors in Mechanical Engineering) from Vanderbilt University. At Vanderbilt, he was President of the Pi Tau Sigma Mechanical Engineering Honor Society, a Member of the Tau Beta Pi Engineering Honor Society (comprising the top 10% of engineering students based on GPA), and a Member of the Gamma Beta Phi Honor Society.

ADVISORY ROLES
ENGAGEMENTS
Dr. Campbell is available for speaking engagements and panel participation. Below are upcoming and past events.
UPCOMING:

...

PAST (AS OF SEPTEMBER 15, 2017):

1. "Benefits and Risks of Future Forecasting Methodologies," US Army Futures Forum: Structuring Our Futures Thinking, (December 6, 2017), Washington, DC, panel participant.

2. "Applied AI Workshop: The Hard Things about Deploying and Scaling AI," Bootstrap Labs Applied AI Insiders Workshop, (November 30, 2017), San Francisco, CA; keynote.

3. “Drones are Flying High, but what about their Valuations?,” (November 8, 2017), DC Finance, New York City, NY, panelmoderator, https://thenycmeetings.com/ 

4. “Security and Technology Trends,” (November 7, 2017), Thomson Reuters CP Live Expert Talk, New York City, NY, keynote.

5. “Exploring Legal, Ethical and Policy Implications of Artificial Intelligence,” (September 26, 2017), World Bank Group – Law, Justice and Development, panel participant.

Contact Us

Contact

Thomas A. Campbell, Ph.D.
Address
29851 STAGECOACH BLVD. EVERGREEN, CO 80439
Phone
571-748-8094
Email
tom.campbell@futuregrasp.com

BLOGS
By Tom Campbell 04 Dec, 2017

Beyond “The Good Old Days”

Some people like to reminisce. They wish they could return to a less complex world without the unending stream of news, email and social media. They preferred life when there were fewer people, less traffic, and cheaper groceries. They miss when there was no constant threat of terrorism. They want to return to “the good old days.”

Despite this desire to return to supposedly simpler times, we cannot turn back. Our world is vastly different now compared to even a few decades ago. Because of phenomenal advances in medicine, housing, transportation and many other areas, Earth now supports a population of over seven billion people. It is estimated that by 2030, global population will reach 8.6 billion. [1] Sustaining such numbers demands strong interconnectedness and reliable communications. Unfortunately, more people also create increasing pressures on resources: water, energy, food, rare earth metals – all are more difficult to acquire.

Moreover, we are living in a veritable data explosion. In 2012, the amount of information generated over the internet doubled roughly every 1.25 years; [2] it is assuredly even faster now. Every day now we create 2.5 quintillion bytes of new data. [3] This data tsunami tasks our local and federal governments in gathering data, comprehending it, and crafting sound policies.

Additionally, societal changes will further intensify with development and deployment of new technologies. Fundamentally, we have taken evolution into our own hands. [4] We now live far longer than the average lifespan of roughly 40 years only a century ago. The on-going merger of man and machine (biological and digital) opens new vistas for us in knowledge and entertainment, but it also further amplifies the needs for information recording and processing.

The only way we can survive and thrive in this constantly changing and increasingly complex world is to get assistance from our machine creations. Artificial intelligence (AI) must be a core aspect of that help, as it offers unprecedented capabilities to monitor, control and assess a wide range of situations. AI will not (at least in the near term) remove the human from the loop; instead, it will augment our capacities for better data collection, analysis and decision making.


Increasingly Complex World

“Indeed, developments over the ensuing quarter century have revealed a far more complex reality, one of much less international consensus on what constitutes legitimacy in principles, policies, and process and not much in the way of a balance of power in practice. This more uneven, complex world has been quite disorderly, a conclusion that emerges clearly through an examination of the major historical events of this period, the gap between global challenges and responses, and regional developments.” 

--RICHARD HAASS, A World In Disarray: American Foreign Policy and the Crisis of the Old Order


From early 2015 to late 2017, I had the honor and privilege to serve as the first National Intelligence Officer for Technology (NIO-TECH) within the National Intelligence Council (NIC) of the Office of the Director of National Intelligence (ODNI). As NIO-TECH, I had the opportunity to contribute to the Global Trends: Paradox of Progress [5] publication that was submitted to the incoming Administration on January 2017. In this well-researched report, the NIC laid out how economic, political, social, technological and cultural forces collide and might affect our future out to 2037.  

The core theme to this recent Global Trends is how there exists a paradox in our progress as we have developed our world:

"We are living a paradox: The achievements of the industrial and information ages are shaping a world to come that is both more dangerous and richer with opportunity than ever before. Whether promise or peril prevails will turn on the choices of humankind. The progress of the past decades is historic—connecting people, empowering individuals, groups, and states, and lifting a billion people out of poverty in the process. But this same progress also spawned shocks like the Arab Spring, the 2008 Global Financial Crisis, and the global rise of populist, anti-establishment politics. These shocks reveal how fragile the achievements have been, underscoring deep shifts in the global landscape that portend a dark and difficult near future." [6]

There are no easy solutions for the issues created by such complexity, but there is a growing realization that better data analysis can help. For example, it is estimated that 99% of the data in the Department of Defense is not analyzed but lies ‘dark,’ awaiting the right analyst or algorithm. [7] An initial push within the Pentagon to tackle this issue is to leverage AI to analyze imagery, thus turning “…the countless hours of aerial video surveillance collected by the U.S. military into actionable intelligence.” [8] For all the reasons listed above, AI analysis is no longer a luxury, but an imperative.

Other countries recognize the commercial and military leverages that AI can provide. China has a major push for AI, with some fearing it will overtake the United States in AI leadership in coming years. [9] [10] In a speech to school children, Russia’s Vladimir Putin recently stated, “Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world.” [11] Canada, Switzerland, Germany and others have major AI initiatives planned or already funded.

AI is seeping into politics, also. New Zealand just deployed the world’s first “AI politician.” [12] Named SAM, the AI bot answers a range of questions on policies for housing, education and immigration. Its creator, Nick Gerritsen, has high hopes for its future. “By late 2020, when New Zealand has its next general election, Gerritsen believes SAM will be advanced enough to run as a candidate. However, it is not legal for AI to contest elections.” There is speculation in technology circles about whether an AI could be a good President of the United States (POTUS). [13]

Overall, we recognize that governance and policies can benefit from AI. Part of that realization may stem from an acknowledgement of our own limitations.


Increasingly Complex Humans

“Evolution is like walking on a rolling barrel. The walker isn't so much interested in where the barrel is going as he is in keeping on top of it.”

--ROBERT FROST, The Letters of Robert Frost to Louis Untermeyer


Nature is a poor project manager. In engineering firms, when there is a big goal there is often brought into the team an expert in planning and operational execution to guide the team toward reaching that goal. This project manager develops Gantt charts, milestones, objectives, and budget, R&D, personnel, and resource plans. Plans are presented to management and, if approved, given appropriate support. All-hands meetings are held, and splinter sub-groups are tasked with specific project actions to contribute to the overall team goal. If the goal is met, great! - the company rejoices in new profits and the team throws a party. If not, people re-group and fix the issues, or move on to other projects (and oftentimes point fingers to who is to blame for the failure).

Nature doesn’t work that way. If one assesses the basic physiology and anatomy of a human [14] today, one would want to fire nature’s project manager. We all carry in our bodies numerous anatomical features that can be considered hangers-on from evolution, the so-called human vestigiality. The appendix, the tail bone, tonsils and other body parts are either not fully required or superfluous to our fundamental operations. So why do we have them? It’s because of the chaotic path that evolution has taken over the millennia. Natural evolution is not project planned, it just happens.

Moreover, we suffer in cold, heat, vacuum and any environment that deviates even a smidgen from the narrow homeostasis [15] that our fragile bodies will allow. We contract cancer and sundries of other chronic diseases. If we are fortunate to live to 90 years old, our life expectancy is only a few years more, whether we are male or female. [16] We require glasses, surgeries, roofs over our heads, central heating in the winter and cooling in the summer, and constant food and drink. We must exercise or our muscles atrophy and our bones degrade. It takes us almost two decades to become independent adults, away from the nurturing environment of our parents.

But isn’t that what being human simply is - shouldn’t we just accept our evolutionary fate? Perhaps, but if so, why have we battled for millennia to overcome our ailments, increase our food supplies, purify our water, improve our hygiene to avoid killer germs, change our environment toward one more benign to our mammalian physiology, and ensure our children live better, longer lives than we do? By doing so, we have increased average lifespans from about 30 years to more than 80 years in some countries.

We might argue that this is purely the survival instinct that all animals possess, but Homo sapiens nowadays go way beyond mere survival. We have the tremendous advantage that we are big-brained and can plan. In a way, we don’t really live in the present, we live in the future as we constantly attempt to anticipate and acquire our next meal / degree / spouse / job / car / house / child / vacation / retirement. We manipulate our surroundings, health and very environment to bend all toward our desire of longer, healthier, and more productive and enjoyable lives.

Our capabilities are so far beyond what even the richest people in the world had available mere centuries ago, it boggles the mind. Years ago, I was touring a beautiful French castle in Loire Valley. After viewing a particularly impressive throne room, my then-manager turned to me and commented, “It was good to be king.” I replied that I’d rather be a commoner now, because we have many more opportunities for education, travel and entertainment; better healthcare; and more access to knowledge than even the wealthiest royalty back in the Middle Ages.

However, we have only just begun our manipulation of matter, environment and body. Our next steps will take us to whole new levels, which will require more than just human minds to comprehend and control our world.

For example, the Internet of Things – comprising devices both digital (e.g., smartphones) and biological (e.g., medical devices and biometric systems) - presents a fundamental challenge in data processing. With estimates of 24 billion ( BI Intelligence ) to 50 billion ( Cisco ) [17] devices connected to the internet by 2020, and over a trillion sensors complementing those devices, mankind is awash in data. How to make anything meaningful of that information for governments and companies is a grand challenge…one that will probably only be solved by advanced AI.

It seems every day in technology news, there is some new market sector embracing AI. Industries of medicine, insurance, finance, autonomous systems, home and personal assistants, and others are embedding AI into their fundamental operations. Why? Because we are limited in our biological processing systems – our three pound brains – in massive data memory and processing. AI can help us to absorb and process this tsunami of information.

Some visionary organizations are embracing this convergence of the biological and digital. In 2015, I had the opportunity to visit the J. Craig Venter Institute in San Diego, California. This institute has as a focus to leverage its earlier mapping of the human genome. [18] When they described how they have as a goal to acquire over one million genomes, I asked how they plan on extracting meaning from them. They answered, “We have a specially-coded AI that does that for us.” Thus, they and others are merging biotech with AI and big data analytics to de-obfuscate what to humans would be mountains of confusing data.

Advances in neurosciences will also accelerate the need for better analysis capabilities. In articles in WIRED, [19] Nature [20] and MIT Technology Review, [21] controls into and from our minds by external means are summarized. Electrodes are surgically implanted in the brain and a map of neuronal activity is attempted to be made. Using algorithmic inputs, those electrodes can stimulate the same neuronal activities in the subject’s mind, with the ultimate goal of developing a commercial neuro-prosthetic to enhance memories. Similarly, those maps can be used to play video games with no physical contact by the patient. Although highly experimental now (and requiring volunteers willing to undergo brain surgery!), several companies, including Neuralink , are researching brain caps that don’t require in-vivo surgeries. Of course, this research raises substantial ethical and other questions. A core unknown is how we would complement our abilities and enable ourselves to process these new data sets to / from our limited minds.


Need for AI

Humanity is at an impasse now. Governments and politicians are struggling to remain on top of a constantly growing set of threat vectors, mountains of data, and fundamental economic and governance issues. Moving well beyond nature’s evolution roadmap, we have created incredible powers of information acquisition, we live longer lives, and we have greater capabilities than our forefathers would have ever believed possible. Additional convergences of biological and digital systems will take us to even higher levels of capabilities, but also increase complexities in society and create even more need for analysis capabilities.

AI can assist us here. A vast and growing sector, AI – especially with deep learning - has the ability to process massive amounts of data, whether it be images, text, voice, numbers, etc. Numerous technology websites report on AI developments; for example, TechCrunch , CNET , Futurism , AINewsFeed . Visionary venture capital firms such as Bootstrap Labs focus on AI investments. [22]  

There are daily reports of major advances by AI that go beyond what a human can accomplish. Art forgeries are detected with 99% accuracy from a single brush stroke. [23] Lip reading is done by an AI with greater accuracy than the best humans. [24] Skin cancer is detected by a deep learning trained AI algorithm with greater fidelity than dermatologists. [25] While still considered Narrow AI [26] , potential exists to expand these accomplishments into greater capabilities and thus to handle more complex problem sets. [27] Although progress to mimic a human in General AI [28] is slow presently, some researchers believe we have only begun to tap the full power of AI. [29]

The challenge before us is to develop AI quickly as our society increases in its economic, political, military and psychological challenges. We have a great future ahead of us, but only if we get help in unraveling the complexities of our modern world.


NOTES


[2] Dominic Basulto, “Why Ray Kurzweil’s predictions are right 86% of the time,” Big Think 2012, http://bigthink.com/endless-innovation/why-ray-kurzweils-predictions-are-right-86-of-the-time , accessed 3 DEC 2017.

[4] For an overview of this issue, see “Evolving Ourselves – Redesigning the Future of Humanity—One Gene at a Time,” Juan Enriquez, Steve Gullans, https://www.amazon.com/Evolving-Ourselves-Redesigning-Future-Humanity-One/dp/0143108344/ref=sr_1_1?ie=UTF8&qid=1511886396&sr=8-1&keywords=evolving+ourselves+juan+enriquez , accessed 4 DEC 2017.

[5] Global Trends: Paradox of Progress, January 2017, https://www.dni.gov/files/documents/nic/GT-Full-Report.pdf , accessed 10/17/17.

[6] Global Trends, ibid.

[14] Or really any living being.

[15] Imagine a graph with two horizontal bars delineating the band within which one can function, outside of which one becomes diseased or dies. This homeostatic band is narrow for organic beings, and becomes even more narrow if we are immune compromised.

[18] In which they were declared co-winners of the first genome mapping by President Bill Clinton. The other national effort was by the National Institute of Health (NIH).

[19] “Inside the race to hack the human brain,” https://www.wired.com/story/inside-the-race-to-build-a-brain-machine-interface/ , accessed November 21, 2017.

[20] “AI-controlled brain implants for mood disorders tested in people,” Sara Reardon, 22 NOV 2017, http://www.nature.com/news/ai-controlled-brain-implants-for-mood-disorders-tested-in-people-1.23031 , accessed 4 DEC 2017.

[22] The author is honored to be a special advisor to Bootstrap Labs, https://bootstraplabs.com/ , accessed 4 DEC 2017.

[26] Narrow AI is defined loosely as an AI that attempts to execute a single task better than a human.

[28] General AI is characterized as an AI that has full cognitive capabilities that can mimic a human.

By Tom Campbell 05 Nov, 2017

COSMOLOGICAL CONTEXT

Identification of the Next Big Thing is a constant pursuit among investors, researchers and industry. To catch that next funding wave, emerging technologies are often proclaimed as paradigm shifters (3D-Printing! Artificial Intelligence! The Internet of Things! Genome Editing! Nanotechnology!). Unfortunately, the press has a tendency to hype even minor discoveries or inventions as if they are widely deployed to the public and the apex of a technology is reached already. But all technologies take time to develop, and frequently they can take a long time as major advances are needed between one capability phase to the next.

Perhaps it is warranted to step back and consider the grand challenges and major technological advances needed for a technology to achieve full fruition. As a framework for this exercise, we shall leverage as an analogy the cosmic scale for hypothetical energy control proposed by the Soviet astronomer Nikolai Kardashev in 1964.

As clarified by Wikipedia  [1] (and as sourced from the original publication  [2] ): “The Kardashev scale is a method of measuring a civilization's level of technological advancement, based on the amount of energy a civilization is able to use for communication. The scale has three designated categories:

Type I civilization —also called a planetary civilization—can use and store all of the energy which reaches its planet from its parent star.

Type II civilization —also called a stellar civilization—can harness the total energy of its planet's parent star (the most popular hypothetical concept being the Dyson sphere—a device which would encompass the entire star and transfer its energy to the planet(s)).

Type III civilization —also called a galactic civilization—can control energy on the scale of its entire host galaxy.”

The Kardashev scale is a grand Gedankenexperiment that catalyzes thought about what humanity might be capable of perhaps hundreds of thousands, if not millions, of years from now. While certainly fun to think about galactic civilizations, could we use this model instead as a paradigm for more grounded technology that is now seeing widespread use and rapid advancement? Let’s consider in this light artificial intelligence (AI).

ARTIFICIAL INTELLIGENCE (AI) SCALE

AI is currently one of the hottest technologies in R&D and commercial applications. Numerous experts tout AI as a paradigm-shifting capability that has potential to disrupt multiple industries. There are countless AI blog posts, reports, and conferences. Technology giants, including Baidu and Google, spent between $20B to $30B on AI in 2016 alone.  [3]

Despite all this interest and investment, all may not be so rosy as society rushes to adopt AI. This is all the more reason to come to a better appreciation of how AI might evolve in the near and further future. As clarified in the recent Global Trends: Paradox of Progress  [4] drafted by the National Intelligence Council (NIC), Office of the Director of National Intelligence (ODNI)  [5] :

" Artificial intelligence (or enhanced autonomous systems) and robotics have the potential to increase the pace of technological change beyond any past experience, and some experts worry that the increasing pace of technological displacement may be outpacing the ability of economies, societies, and individuals to adapt. Historically, technological change has initially diminished but then later increased employment and living standards by enabling the emergence of new industries and sectors that create more and better jobs than the ones displaced. However, the increased pace of change is straining regulatory and education systems’ capacity to adapt, leaving societies struggling to find workers with relevant skills and training."

Some may think AI is a ‘new’ technology as it only just in the last few years became widely recognized, but it has been around for decades. In 1950, Alan Turing proposed the capability to encode intelligence into a machine.  [6] Following several decades of excitement and disappointment (including at least two AI ‘winters’ in which funding and researchers dried up due to a failure of AI to live up to its expectations), there was a step-jump in AI capabilities in 2012. During an imaging contest using a large database, the “Imagenet,” one team successfully leveraged large computational power to improve facial recognition accuracy dramatically. As clarified by MIT Technology Review:

"This was the first time that a deep convolutional neural network had won the competition, and it was a clear victory. In 2010, the winning entry had an error rate of 28.2 percent, in 2011 the error rate had dropped to 25.8 percent. But SuperVision won with an error rate of only 16.4 percent in 2012 (the second best entry had an error rate of 26.2 percent). That clear victory ensured that this approach has been widely copied since then."  [7]

Since that early successful deployment of deep learning (DL), AI researchers have rushed to deploy DL in almost every market sector. Financial services, marketing, transportation, education, the medical industry, and others—all are being assessed now to make processes more efficient, faster and more cost effective.  [8] Intense interest is leveled on AI R&D and the competition for talent is fierce.  [9]

While some argue that AI research has actually plateaued (can’t we accomplish more than just another minor tweak on current DL algorithms?), others debate that we’ve only just begun (but what if we successfully map the brain?). Irrespective of who is right and how rapidly AI develops, advancements in AI will continue apace. Many countries and companies have multi-billion dollar AI initiatives.  [10]

Although it is certainly exciting to have broad discussions about autonomous vehicles  [11] and better chatbots to help us choose that optimal shopping item  [12] , where might we see AI developing long-term? To get a sense of what might come in the future with AI, we propose here a set of advancement levels (‘Types’) of AI, following the pattern earlier noted in the Kardashev Scale. In an attempt to remain at least partially contemporary, we add a Type 0 to our hypothetical AI scale.

Type 0 AI—also called Narrow AI: An AI demonstrates near-perfect single-task capabilities – e.g., speech recognition, search engines, machine-composed news reports.

Type I AI—also called Enhanced Narrow AI: An AI demonstrates capabilities overall approaching that of a human - e.g., Level III-IV autonomous vehicles, conversational chatbots essentially unrecognizable from real humans, robots capable of mimicking the mobility of a human or other animals.

Type II AI—also called General AI: An AI is proven to be able to fully mimic all human capabilities - including planning, memory, analysis, and five and more senses.

Type III AI—also called Superintelligence: An AI is proven to far exceed human capabilities and is capable of continuous self-improvement (self-sensing and self-reprogramming); its IQ would be so high as to be immeasurable.

Like the Kardashev Scale for energy, the leaps needed between Types of AI might be considered large. However, the point of this article is not to daunt us into considering such steps impossible, but rather to take a step back from daily AI research to consider where we might be headed. Is achieving Type III AI as here outlined possible? How might the field of AI transition from our current ‘Narrow AI’ to that of Superintelligence?

POSSIBLE EVOLUTION OF AI

To help us in our thought process regarding AI development, we propose further a rough map from Narrow AI to Superintelligence, overlaid with outside influences of other technologies that might accelerate AI R&D and capabilities; see ­­­­figure.

Presently, we are in the age of Narrow AI . DL and Bots are becoming standard in many applications – e.g., Apple’s Siri, Google’s search engine. Several companies are now championing an “AI-first” approach to technology—e.g., Amazon, Google, Baidu. These investments may enable us to move up the curve in the next two years toward UberBots —algorithms, such as those in Amazon’s Alexa or Google’s Home, capable of answering any structured query in a conversational style of a human. Already in China the company Xiaoi uses a conversational chatbot that millions use daily. “Xiaoi [is] a conversational AI giant whose bots — deployed to almost every business sector in China — have engaged 500 million users and processed more than 100 billion conversations. Xiaoi bots take on different roles in finance, automotive, telecommunications, e-commerce, and other industries, serving many Fortune 100 companies.”  [13]

As these capabilities become more advanced, we may see the Type I AI listed above, Enhanced Narrow AI . Using an autonomous vehicle as an example, an Enhanced Narrow AI may be capable of not only driving with minimal human interaction, but also simultaneously navigating, booking a table at your favorite restaurant, helping you answer your email, etc., all while you dodge city traffic with only a wary eye and a readiness to assume controls if the AI fails to anticipate an accident.

Beyond Enhanced Narrow AI, we propose here the concepts of Narrow AI Cluster and Narrow AI Supercluster . Harkening back to the cosmological analogy earlier, an observation in astronomy that can be leveraged is that of a galactic cluster and galactic superclusters, groupings of galaxies that move together through the universe.  [14] Positioning Narrow AIs into more powerful programming clusters may offer more capabilities than distinct Narrow AIs themselves. One can then envision that Narrow AI Clusters and Narrow AI Superclusters could push us into Levels IV and V autonomous vehicles that could take over all driving responsibilities, while providing one’s favorite entertainment, a private work space in the car, and other amenities now found at best in a well-appointed hotel room.

General AI may be the most difficult Type to achieve, and also the most difficult to recognize. A fascinating development in AI research is the debate among ethics experts and philosophers on what characteristics would be needed to have an AI qualify as General AI. Some thinkers take the position that General AI would only be achieved if the AI becomes conscious or self-aware.  [15] We take the tact here that General AI could be achieved with or without actual machine consciousness. ­

As an analogy, one may debate whether animals are truly conscious. Do they recognize the passage of time beyond base instinctual reactions to weather and seasons? Do they recognize themselves as separate organisms with potential for independent actions? However one answers these questions—and the answers may be species-dependent—one would be hard-pressed to argue that animals are not adapted to their particular environment and their survival. Thus, they are core actors within their respective ecologies, whether or not they are self-aware.

Such may be the same with a General AI. Would a General AI with all the trappings of clusters of Narrow AIs, even without consciousness, be indistinguishable from a human? Would it not also dramatically influence our environment? A recent publication in Science explores this point:

"We contend that a machine endowed with C1 [i.e., global availability consciousness, in which there exists a relationship between a cognitive system and a specific object of thought – ‘Information that is conscious in this sense becomes globally available to the organism; for example, we can recall it, act upon it, and speak about it.’] and C2 [i.e., self-monitoring consciousness, ‘…a self-referential relationship in which the cognitive system is able to monitor its own processing and obtain information about itself.’] would behave as though it were conscious; for instance, it would know that it is seeing something; would express confidence in it, would report it to others, could suffer hallucinations when its monitoring mechanisms break down, and may even experience the same perceptual illusions as humans." [16]

Superintelligence may happen soon after General AI is created. Several labs are now working on training an AI to self-program (equivalent to self-healing or exercise for humans). Google just recently announced they have an AI, AutoML, that is “creating more powerful, efficient systems than human engineers can.”  [17] Some philosophers believe that the leap from General AI to Superintelligence will be so fast that humans may not even recognize that it is happening. A General AI could recognize its foibles, correct them, and new AI could then improve itself in a rapid cycle that could culminate in an AI vastly superior in intelligence and capabilities to that of even a genius human. Where superintelligence might lead society is anybody’s guess, but we can be assured that having a superintelligence could dramatically change how we exist as humans.  [18] Intractable problems such as finding the optimal drug may become child’s play for an AI, and even grand challenges such as the UN Millennium Development Goals  [19] may be solved. There is no precedent for what a superintelligence (or cluster of superintelligences) might mean.

Ultimately, it is impossible to predict how quickly this journey might occur—ergo the ??? on the graph’s abscissa. However, we should not look at AI in a silo. Many other technologies could accelerate the development of AI, as is listed in the textbox Convergences Driving Acceleration . The Internet of Things, assuming proper standards are put in place for data transfer and successful cybersecurity, could provide exabytes or more of additional data upon which an AI might learn. Next-generation chips (e.g., graphical processing units, GPUs; quantum computing designed for AI) are already being designed by multiple companies specifically for AI. Nations are in a race for the world’s fastest supercomputers, which themselves may be applied to create new genres of AI. Trillions of sensors, coupled with robotics and the cloud, would also feed an AI exabytes or more of new data upon which it could learn. It is crucial AI researchers keep an eye on all these and other fields to best leverage their capabilities.

SO WHERE ARE WE ON THE AI SCALE?

Clearly, we are low on the Kardashev Scale now in terms of cosmological expansion and energy control; we humans are not even at Type I yet. Unfortunately, we are on a similar low level with our proposed AI Scale, hovering somewhere around Type 0 AI. However, we may not stay at that level for long with the many billions of dollars now invested into AI R&D, intense interest in AI education, and numerous large technology companies betting their future on AI.

The mapping above of a possible path from our current Narrow AI to Superintelligence is just one means by which we may create an intelligence orders of magnitude more capable than ourselves. Regardless of how we get to General AI or Superintelligence, the future will most probably will have much AI in it.

CONCLUSIONS

While we may be millennia away from controlling all the energy in our galaxy, significant advances can still be made in technologies such as AI to lead us into longer, more productive lives. Recognizing the major milestones needed and possible pathways toward achieving them can offer crucial insights to researchers and policymakers.

As with all technology, what may seem improbable today may become common in the future. Ultimately, we should not shirk from research challenges that are hard, but embrace them with our end goals in mind. As the Norwegian explorer and Nobel Peace Prize winner, Fridtjof Nansen, said, “ The difficult is what takes a little time; the impossible is what takes a little longer.”

REFERENCES


[1] “Kardashev Scale,” https://en.wikipedia.org/wiki/Kardashev_scale , access 10/17/17.

[2] Kardashev, Nikolai  (1964). "Transmission of Information by Extraterrestrial Civilizations". Soviet Astronomy. 8: 217.  

[3] “McKinsey's State Of Machine Learning And AI, 2017,” July 9, 2017,

http://www.forbes.com/sites/louiscolumbus/2017/07/09/mckinseys-state-of-machine-learning-and-ai-2017/ , accessed 10/17/17.

[4] Global Trends: Paradox of Progress, January 2017, https://www.dni.gov/files/documents/nic/GT-Full-Report.pdf , accessed 10/17/17.

[5] The author was the first National Intelligence Officer for Technology with the NIC from February 2015 to August 2017.

[6] A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-460.

[7] “The Revolutionary Technique That Quietly Changed Machine Vision Forever,” September 9, 2014, https://www.technologyreview.com/s/530561/the-revolutionary-technique-that-quietly-changed-machine-vision-forever/ , accessed 10/17/17.

[8] For example, see the portfolio investments of Bootstrap Labs, https://bootstraplabs.com/ , accessed 10/17/17.

[9] “Tech Giants are Paying Huge Salaries for Scarce A.I. Talent,” October 22, 2017, https://www.nytimes.com/2017/10/22/technology/artificial-intelligence-experts-salaries.html , accessed 11/1/17.

[10] Leading nations have multi-billion dollar AI initiatives, including China. In the United States, industry invests far more than the federal government.

[11] Autonomous vehicles have five recognized levels of autonomy—Level I would be on the order of existing cruise control, whereas Level V would mean full autonomy with no drive intervention required. https://web.archive.org/web/20170903105244/https://www.sae.org/misc/pdfs/automated_driving.pdf , accessed 10/17/17.

[12] “How AI Will Change Amazon: A Thought Experiment,” October 3, 2017, https://hbr.org/2017/10/how-ai-will-change-strategy-a-thought-experiment?imm_mid=0f734b&cmp=em-data-na-na-newsltr_ai_20171016 , accessed 11/1/17.

[13] “The Most Successful Bot Company You’ve Never Heard Of,” July 13, 2017,

https://www.topbots.com/xiaoi-bot-chatbot-china-ai-technology/ , accessed 10/17/17.

[14] The Milky Way is part of the Local Group galaxy cluster (that contains more than 54 galaxies), which in turn is part of the Laniakea Supercluster. This supercluster spans over 500 million light-years, while the Local Group spans over 10 million light-years. The number of superclusters in the observable universe is estimated to be 10 million. https://en.wikipedia.org/wiki/Supercluster#cite_note-2 , accessed 10/17/17.

[15] Of course, consciousness itself is rather ill-defined and its fundamental characteristics engender fierce debate among philosophers.

[16] S. Dahaene, H. Lau, S. Kouider, “What is consciousness, and could machines have it?,” Science 358, 486-492, 27 October 2017.

[17] “Google’s Machine Learning Software Has Learned to Replicate Itself, October 16, 2017,

https://futurism.com/googles-machine-learning-software-has-learned-to-replicate-itself/ , accessed 10/17/17.

[18] For a full treatment of this issue, see “Superintelligence: Paths, Dangers and Strategies,” Nick Bostrom, Oxford University Press, 2016.

[19] “Millennium Development Goals and Beyond 2015,” http://www.un.org/millenniumgoals/ , accessed 10/17/17.

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