How Can Artificial Intelligence Make Us More Free, Less Distracted, and More Effective?

Back in 1999, I read a great book by Bruce Sterling called "Distraction"; billed as a fictional view into the status of U.S. public service in the year 2044, the technology and cultural ideas packed into that book still resonate more than 15 years later. The book in part motivated me to strive to make a difference in public service, if only to avoid some of the more dystopian views in the book. Apparently I'm not the only one who found the book packed with ideas, Cory Doctorow also wrote a great review in 2008. 

The book's central premise: that all of us could suffer from "Distraction" from what really matters, especially in a world with 300+ cable channels, 24/7 news, and always-on social media in the United States, is an idea that I'd like to explore more fully given our rapidly changing world of today.

Technology is amoral, it is how we humans choose to use it that determines good vs. bad outcomes.

I'd submit that soon, perhaps very soon -- if we can avoid being distracted from what really matters -- we will need to have some substantial discussions and decisions regarding how we use technology to perform the business of running a nation and co-existing in the world.

Of particular note, the rise of machine learning and artificial intelligence. How will this impact us as individuals, as a nation, and how we do public service?

Where Will Artificial Intelligence Improve Public Service the Most?

In the United States, public service should be about what "We The People" choose to do together that we can't do by ourselves. If we are to be, as President Lincoln aptly described, a "of the people, by the people, for the people" the question then is where do both machine learning and artificial intelligence fit into that vision?

Back in 2015, as an Eisenhower Fellow in Australia and Taiwan, I had several discussions with CEOs and government representatives about the rise of the Internet of Everything and how the devices, sensors, and the data streams associated with the Internet of Everything would begin to interrelate with machine learning. Since that time, we've continued to see impressive gains in what artificial intelligence can do as well as the rise of several open source AI projects that allow individuals to be begin to experiment with AI themselves at scale.

Expertise is only gained by doing experiments -- something startups know quite well. Experiment and expertise both have the same root "ex peria" meaning out of danger. Experiments are risky, they don't always work -- however you are always guaranteed to learn something as a result of the experiment, even if it's what to try differently for the next round.

We need a place, or places, in public service where we can collaborate with citizens and private sector partners on new ways of doing the business of public service.

Given this need, I'd like to suggest that we may need to consider developing a civilian "ARPA for AI" -- referring to what used to be the Advanced Research Projects Agency created in 1958 by President Eisenhower. ARPA invested in the early R&D that gave birth to the ARPAnet, now known as the internet. While ARPA now exists as DARPA with the addition of "Defense" in front of its acronym, where is its civilian counterpart? Especially on the topic of AI?

There are several small civilian organizations doing scattered R&D, though none with the same success or focus as DARPA. Also AI is no longer a topic of just R&D. What we need in an "ARPA for AI" that does experiments in the context of the organization and processes they seek to improve, taking time to listen and collect data on the processes to be improved both before and after implementing an approach.This would help show what works and provide tangible return on investment information to decision makers about the benefits of such improvements.

I'd submit that the benefits to our nation and world with AI are much more in the civilian domain.

We, the United States, should help pioneer and show the world how AI can be used to help make people more free, prosperous, and secure in keeping with the U.S. Constitution. AI is a technology that by itself is amoral, how we choose to use it determines where it is good vs. bad.

The questions worth asking now: how can AI make us more free, less distracted, and more effective as individuals, as a nation, and -- for those who choose to do as such -- participants in public service. By public service, I mean members of the public, private sector partners, and government professionals working together.

The Need for Deeper, Focused Substance vs. Quick Shiny

It probably would be a disadvantageous approach to pursue the benefits of AI in an uncoordinated fashion across many different individual agencies. Agencies and departments are already fragmented (in part by design) as this would miss some of the deeper, substantial gains possible with rethinking how public service is done and delivered completed.

This year, 2016, is 240 years after the events of 1776. If we wanted to, the United States could use the next 10 years to perform a systematic "upgrade" in how we do the business of the people, of the nation, and of public service just in time for the 250th anniversary in 2026. 

In particular, a lot of this would include rethinking beyond how we use technology tools -- we need to rethink how we do the work to improve the stakeholder experience.Most importantly, we need focus on those meaningful elements of public service that need to be done to improve the United States and the world, and also focus on either automating or ending the less meaningful and often rote elements of public service that may no longer be necessary.

While startups often create a place where experiments and new ways of working can be done, several parts of public service cannot fail -- which means we will need to identify a systematic, substantive approach that identifies:

1. What parts of public service absolutely, positively must run-on-time and not fail; i.e., crucial parts of defense, the economy, etc.
2. What parts of public service are most likely to produce significant "returns on investment" if new, better ways of doing the business of public service were found at the local, state, or national levels, and thus might be best for in-situ experiments including AI and new ways of working?
3. What parts of public service are rote or less meaningful in today's rapidly changing world, and thus might be best decreased, completely automated, or stopped?

These three questions will require us to dive into the substance of public service and not just a quick "shiny gleam of let's do artificial intelligence" without taking the time to understand the local, state, and national interactions involved in delivering such stakeholders services.

Taking the time to listen, learning, and understand the context will be crucial.If we did decide to use the next 10 years to perform a systematic "upgrade" in how we do the business of the people, of the nation, and of public service just in time for the 250th anniversary in 2026 -- we will need to resist the temptation of pursuing quick shiny solutions and focus on tackling the really hard, thorny, substantial issues that have been accumulating in the background for decades now.

Joy in Tackling The Really Hard Challenges

Three years ago this month I parachuted into the FCC as its new CIO. There previously had been 9 CIOs in 8 years. One of the disconcerting trends I quickly noticed was a reluctance in the past to tackle some of the really hard, thorny, substantial issues that needed tackling. These thorny issues were risky and complicated, and wouldn't be solved overnight.

There was a huge incentive for folks to avoid them and instead pursue only quick "shiny" activities that did not systematically improve the condition of how the organization delivered its results to its stakeholders. Similarly there was a huge disincentive to take risks and do something new.

Yet by the time I parachuted in, we had to tackle the hard, thorny, substantial because they had continued to grow -- to include an ever increasing amount of our IT budget being spent just to maintain 207 different legacy systems and a general slowdown in how fast we could deliver new prototype solutions. I took professional joy knowing it was going to be hard, risky, and require a focus on substantial long-term investments with measurable improvements along the way. In particular, this meant making a wholesale move to public cloud and commercial services, which back in 2013 was a pioneering effort.

A year ago this month was when we moved any remaining on-premise IT to public cloud and commercial service providers over Labor Day weekend. It took a team of positive #ChangeAgents in the hundreds working days and nights. Once we were done, we achieved:

1. Substantial improvements in how fast we could now prototype new solutions, from 6 months to 48 hours;
2. Sizable decrease from >85% to 50% in terms of how much we were spending to maintain all the different systems; and
3. Unity among the team to #ChangeAgents to be creative problem solvers who continue to proactively search for new ways of delivering results differently and better.

That final and problem most important benefit of what we did last year -- among the team to #ChangeAgents to be creative problem solvers who continue to proactively search for new ways of delivering results differently and better -- is what we need now in public service.

Networks of Creative Problem Solvers Across Organizations

We need creative problem solvers who continue to proactively search for new ways of delivering results differently and better especially when it comes to exploring how machine learning and artificial intelligence can improve public service for us all.

Artificial intelligence, combined with the internet of everything and advances in the distribution of storage, processing, and services -- to include interoperability across Software as a Service and Platforms as a Service cloud solutions and improved peer-to-peer, blockchain-like distributed services -- holds the possibility of truly enabling us to be a nation "of the people, by the people, for the people". We'll need to move all agencies to cloud and commercial services, we'll need to make them more agile so they can then go further and explore machine learning and AI an inter-organizational scale to improve public service.

No longer does the "business of government" have to be done just in D.C., it can be distributed and shared in a way that's open, visible, and participatory for all who want to be involved.

We did a mini-example of this at the FCC back in late 2013, when we provided an open source, downloadable app that allowed you to test your connection speed and, if you wanted, anonymously share that statistic by provider with the FCC to better inform decision-makers at the agency. This crowd-sourcing approach is just the tip of the iceberg as to what's possible if people want to share data to improve the results and decisions of public service in aggregate.

In addition, lots of the hiring and procurement functions of public service are paper-intensive, time-intensive, and human-intensive. This is partly due to the need for them to follow an equitable process. Yet an open source AI could do the same, and probably do it faster. Humans can still be in the loop for the deeper, more creative functions -- and they could also help "teach the machine" in the months and years ahead as well, benefiting both in the process.

The result would mean time for humans to do harder, more challenging tasks and better benefit to stakeholders as less paper, less time, and less work is involved to do a hiring or procurement function. By making it open source, the public could verify the process is both equitable and not unduly biased. 

Closing Thoughts

I'm passionate about making organizations more effective and more adaptive in turbulent environments. The decade ahead will require us to continue to adapt at increasing speed. This is particularly true of the social institutions that help us be a nation "of the people, by the people, for the people".

On the idea of a "civilian ARPA", I'm not alone in such a proposal; the Partnership for Public Service recently released a report echoing the theme of encouraging and sustaining innovation (see page 31).

On the idea Artificial Intelligence to improve both innovation and policymaking, friend and colleague Michael Krigsman of CxOTalk and I have been asked to co-chair an IEEE Subcommittee on these topics (note: I am doing this in my Eisenhower Fellow capacity).

On the idea of positive #ChangeAgents to improve public service, the Executive Leadership Conference that friend and colleague Teresa Bozzelli and I are Chairing (October 23-25) has just released a call for shark tank-like pitches for what the the U.S. Government could do differently and better in 2017 -- if you have ideas and want to give a talk, throw us your best pitch via this link.

Finally, if you have thoughts on how can make us more free, less distracted, and more effective as individuals, as a nation, and in public service -- feel free to share your comments below. 

Onwards and upwards as positive #ChangeAgents,

-d.