Rapid developments in Artificial Intelligence (AI), especially the sub domains of Reinforcement Learning and Machine Learning are high on the agendas of government policy makers in many countries. Last year the US Government* issued comprehensive reports on AI and its possible benefits and impact on society, likewise the European Union and other agencies are also active in reviewing policies on AI, Robotics and associated technology. As recent as one week ago the UK government initiated a new request for comments to its AI subcommittee – What are the implications of Artificial Intelligence?
On the back of the high level of interest from governments and policy makers around the world a new study, Artificial Intelligence and National Security, by researchers at the Harvard Kennedy Center on behalf of the U.S. Intelligence Advanced Research Projects Activity (IARPA) recommends three goals for developing future policy on AI and national security
- Preserving U.S. technological leadership,
- Supporting peaceful and commercial use, and
- Mitigating catastrophic risk
The authors say their goals for developing policy are developed by lessons learned in nuclear, aerospace, cyber, and biotech and that Advances in AI will affect national security by driving change in three areas: military superiority, information superiority, and economic superiority.
Setting out their position the authors make the case that existing AI developments “have significant potential for national security.”
Existing machine learning technology could enable high degrees of automation in labor-intensive activities such as satellite imagery analysis and cyber defense.
They further emphasize that AI has the potential to be as transformative as other major technologies, stating that future progress in AI has the potential to be a transformative national security technology, on a par with nuclear weapons, aircraft, computers, and biotech.
The changes they see in military superiority, information superiority, and economic superiority are outlined below:
For military superiority, they write progress in AI will both enable new capabilities and make existing capabilities affordable to a broader range of actors.
For example, commercially available, AI-enabled technology (such as long-range drone package delivery) may give weak states and non-state actors access to a type of long-range precision strike capability.
In the cyber domain, activities that currently require lots of high-skill labor, such as Advanced Persistent Threat operations, may in the future be largely automated and easily available on the black market.
For information superiority, they say AI will dramatically enhance capabilities for the collection and analysis of data, and also the creation of data.
In intelligence operations, this will mean that there are more sources than ever from which to discern the truth. However, it will also be much easier to lie persuasively.
AI-enhanced forgery of audio and video media is rapidly improving in quality and decreasing in cost. In the future, AI-generated forgeries will challenge the basis of trust across many institutions.
For economic superiority, they find that advances in AI could result in a new industrial revolution.
Former U.S. Treasury Secretary Larry Summers has predicted that advances in AI and related technologies will lead to a dramatic decline in demand for labor such that the United States “may have a third of men between the ages of 25 and 54 not working by the end of this half century.”
Like the first industrial revolution, this will reshape the relationship between capital and labor in economies around the world. Growing levels of labor automation might lead developed countries to experience a scenario similar to the “resource curse.”
Also like the first industrial revolution, population size will become less important for national power. Small countries that develop a significant edge in AI technology will punch far above their weight.
Due to the significant impacts they see from AI they say that Government must formalize goals for technology safety and provide adequate resources, that government should both support and restrain commercial activity of AI and governments should provide more investment and oversight into the long term-focused strategic analyses on AI technology and its implications.
Noting that we are at an inflection point in Artificial Intelligence and autonomy, the researchers outline multiple areas they believe AI driven technologies will disrupt military capabilities – capabilities, which they say, will have far reaching consequences in warfare.
Policy makers around the world would do well to consider carefully the scenarios outlined in the study to ensure that AI technologies are adequately governed to provide assurances to citizens and ultimately to ensure that AI technologies benefit humanity.
*US Government and Agencies recent papers
June 2016—Defense Science Board: “Summer Study on Autonomy”
July 2016—Department of Defense Office of Net Assessment: “Summer Study: (Artificial) Intelligence: What questions should DoD be asking”
October 2016—National Science and Technology Council: “The National Artificial Intelligence Research and Development Strategic Plan”
October 2016—National Science and Technology Council: “Preparing for the Future of Artificial Intelligence”
December 2016—Executive Office of the President: “Artificial Intelligence, Automation, and the Economy”
January 2017—JASON: “Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD
In a wonderfully titled report, Creating Shareholder Value with AI? Not so Elementary, My Dear Watson, the Equity Research company, Jefferies, LLC, take a hard look at IBM’s bet on cognitive computing, or Artificial Intelligence (AI). The 53 page report is well worth reading to understand why the research analysts consider IBM, despite significant investment in to their cognitive computing platform, Watson is losing the opportunity in AI and hence the authors consider IBM stock to under perform.
On a positive note for AI researchers they do acknowledge there is serious business and economic interest in AI, citing Andrew Ng’s Stanford talk on AI as the new Electricity:
AI is the New Electricity….Our checks confirm that a wide range of organizations are exploring incorporating AI in their business, mostly using Machine and Deep Learning for speech and image recognition applications.
And that IBM has an advantage in terms of technology:
IBM’s Watson platform remains one of the most complete cognitive platforms available in the marketplace today.
But IBM fall flat due to hefty service charges and the inability to attract AI talent:
The hefty services component of many AI deployments will be a hindrance to adoption. We also believe IBM appears outgunned in the war for AI talent and will likely see increasing competition.
I’m never a fan of forecasts for market share, forecasts in Robotics have shown how wide off the mark the industrial robotics landscape is from where it was forecast to be, nevertheless the Jefferies numbers are worth looking at, even if much of AI will be in house in organisations such as Google, Facebook, Amazon, etc. Jeffery’s seem to think the value of the market, shown in the chart below, is underestimated “we think these forecasts are unlikely to fully capture the value created by internal use of AI applications such as machine learning. For example, Facebook and Amazon are aggressively using machine learning to improve their offerings, make operations more efficient, and create new embedded services.”
The analysts do note that the singularity is not near and provide an interesting chart depicting the areas they see growth… interestingly they see a large percentage of growth in algorithmic trading strategies, equivalent to 17% of the market! Yet strangely indicate health care spend will be slightly less, and driverless AI even less, despite this being where much of AI is heading today.
Many AI Apps Will Take Time to Emerge; The Singularity Is Not Near While we are big believers in the long term potential of AI and see rapid adoption of machine learning in the near term, our checks convince us that many AI methods and applications will take time to be adopted.
The analysts emphasise how IBM is losing the talent war and also has less access to the rich data of Google, Apple, Facebook and Amazon. Talent will be a major game changer in AI.
The report also does a good job of showing the current flow of investment by major corporations, in terms of acquisitions, and also investment into AI start-ups. Overall the analysis, except the forecasts, gives a fair overview of the AI market, but omits the major $’s flowing into Academic research and the costs of employing and training AI researchers, which is likely already in the early billions… I do however agree that IBM’s Watson risks not capturing the markets share its technology richly deserves – maybe IBM will end up capitalising by its patent’s as it so often has.
Take a look at the report and judge for yourself (PDF).
Robots are everywhere in the media again. In February 2017 The New York Times Magazine published an article titled, “Learning to Love Our Robot Co-workers” (Tingley 2017). An article in The Washington Post in March 2017 warned, “We’re So Unprepared for the Robot Apocalypse” (Guo 2017). And, in The Atlantic Derek Thompson (2015, 2016) paved the way in the summer of 2015 with “A World without Work,” followed in October 2016 with an article asking, “When Will Robots Take All the Jobs?”
The automation narrative told by these articles and other coverage is a story in which the inevitable march of technology is destroying jobs and suppressing wages and essentially making large swaths of workers obsolete.
What is remarkable about the automation narrative is that any research on robots or technology feeds fear, even if the bottom-line findings of the research do not validate any part of it.
There are some good new research papers and essays that seek to dismantle the claim of a world without work. One such paper is highlighted below.
In a June 2017 paper, titled: “Does Productivity Growth Threaten Employment?” together with a talk at the European Central Bank (ECB) – “Robocalypse Now?”, co-researchers, David Autor and Anna Salomons, set out 200 years of fears of mass unemployment driven by automation.
Autor and Salomon sought to test for evidence of employment-reducing technological progress. Harnessing data from 19 countries over 37 years, they characterize how productivity growth — an omnibus measure of technological progress — affects employment across industries and countries and, specifically, whether rising productivity ultimately diminishes employment, numerically or as a share of working-age population. They focus on overall productivity growth rather than specific technological innovations because (a) heterogeneity in innovations defies consistent classification and comprehensive measurement, and (b), because productivity growth arguably provides an inclusive measure of technological progress: The findings:
In brief, over the 35+ years of data that we study, we find that productivity growth has been employment-augmenting rather than employment-reducing; that is, it has not threatened employment.
Another way to consider the robots taking all the jobs, at least in the short term, is summed up by the outgoing Chief Executive of General Electric, Jeff Immelt who did not mince words regarding his feelings about the impending automation take over. Speaking at the Viva Teach conference in Paris, Immelt said:
I think this notion that we are all going to be in a room full of robots in five years … and that everything is going to be automated, it’s just BS. It’s not the way the world is going to work.
With the first ever documented observation of the self-healing phenomena of graphene, researchers hint at future applications for its use in artificial skin.
Graphene, which is, in simple terms, a sheet of pure carbon atoms and currently the world’s strongest material, is one million times thinner than paper; so thin that it is actually considered two dimensional. Notwithstanding its hefty price, graphene has quickly become among the most promising nanomaterials due to its unique properties and versatile prospective applications.
The paper published in Open Physics refers to an extraordinary yet previously undocumented self-healing property of graphene’s, which could lead to the development of flexible sensors that mimic the self-healing properties of human skin.
The largest organ in the human body, skin has been known for its fascinating self-healing ability – but until now, emulating this mechanism proved too much of a challenge as manmade materials lack this aptitude. Due to unprecedented stretching, bending and incidental scratches, artificial skin used in robots is extremely susceptible to ruptures and fissures. The study offers a novel solution where a sub-nano sensor uses graphene to sense a crack as soon as it starts nucleation, and surprisingly, even after the crack has spread a certain distance. According to the authors, this technology could quickly become viable for use in the next generation of electronics.
According to Dr. Swati Ghosh Acharyya, one of the researchers.
We wanted to observe the self-healing behavior of both pristine and defected single layer graphene and its application in sub-nano sensors for crack spotting by using molecular dynamic simulation. We were able to document the self-healing of cracks in graphene without the presence of any external stimulus and at room temperature.
The results revealed that self-healing occurred by spontaneous recombination of the dangling bonds whenever within the limit of critical crack opening displacement.
The researchers subjected single layer graphene containing various defects like pre-existing holes and differently oriented pre-existing cracks to uniaxial tensile loading till fracture. Interestingly enough, once the load was relaxed, the graphene started to heal and the self-healing continued irrespective of the nature of pre-existing defects in the graphene sheet. No matter what length of the crack, the authors say they all healed, provided the critical crack opening distance lied within 0.3 – 0.5 nm for both the pristine sheet as well as for the sheet with pre-existing defects.
Simulating self-healing in artificial skin will open the way to a variety of daily life applications ranging from sensors, through to mobile devices and ultracapacitors. In case of the latter, graphene-based devices would have an advantage of the large surface of graphene to provide increase in the electrical power by storing electrons on graphene sheets. Apparently such supercapacitors would have as much electrical storage capacity as lithium-ion batteries but could be recharged in minutes instead of hours.
The original article is fully open access and available on De Gruyter Online.
In Late December 2016 Rethink Robotics, supplier of Co-Bots secured an additional US$ 18 million investment. The new round, despite being somewhat short of the US$ 33 million sought as indicated by their SEC filing, included funding from the Swiss headquartered private equity investment firm, Adveq, as well as contributions from all previous investors, including Bezos Expeditions, CRV, Highland Capital Partners, Sigma Partners, DFJ, Two Sigma Ventures, GE Ventures and Goldman Sachs.
I think that Rethink’s Baxter and Sawyer robots are setting a new standard in advanced robotics for businesses of all sizes – the only downside is that Rethink sub contract the manufacturing of their robots which gives them less control of delivery scheduling and has possibly considerably hindered their over all growth, cash flow outlays and profitably. This could reflect, in a very hot growth market, the less than enthusiastic take up by new investors and indeed appetite for considerably increasing investment from existing investors. However in the coming months I would expect Rethink would secure the additional US$ 15 million they seek, maybe via Asian manufacturing partners, a region that is becoming increasingly important for Rethink as they endeavor to capture a larger share of the co-bot market.
In addition to Rethink’s new investment – a very interesting, relative, new comer to the industrial robotic manufacturing scene, the Advanced Robotics Manufacturing (ARM) Institute, a U.S. national, public-private partnership, has announced funding of US$ 250 million.
The U.S. Department of Defense awarded the public-private Manufacturing USA institute to American Robotics, a nonprofit venture led by Carnegie Mellon, with more than 230 partners in industry, academia, government and the nonprofit sector across the U.S. The institute will receive $80 million from the DOD, and an additional $173 million from the partner organizations.
Based in Pittsburgh, ARM is led by a newly established national nonprofit called American Robotics, which was founded by Carnegie Mellon University and includes a national network of 231 stakeholders from industry, academia, local governments and nonprofits.
The mission of ARM is essentially four-pronged. To 1) empower American workers to compete with low-wage workers abroad; 2) create and sustain new jobs to secure U.S. national prosperity; 3) lower the technical, operational, and economic barriers for small- and medium- sized enterprises as well as large companies to adopt robotics technologies; and 4) assert U.S. leadership in advanced manufacturing.
ARM’s 10-year goals include increasing worker productivity by 30 percent, creating 510,000 new manufacturing jobs in the U.S., ensuring that 30 percent of SMEs adopt robotics technology, and providing the ecosystem where major industrial robotics manufacturers will emerge.
These investments keep robotics on course to be one of the main investment areas for improving manufacturing productivity and indeed increasing jobs and corporate profitability.
The ARM investment sounds very similar to those of the EU’s public / private initiative announced in June 2014, albeit that is a Euro 2.8 billion initiative and less ambitious, but very worthy, target of adding 240,000 new jobs.
Photo: ARM Institute impact
A new report released by the Whitehouse indicates that accelerating Artificial Intelligence (AI) capabilities will enable automation of some tasks that have long required human labor. The report authors indicate that these transformations will open up new opportunities for individuals, the economy, and society, but they will also disrupt the current livelihoods of millions of Americans. At a minimum, some occupations such as drivers and cashiers are likely to face displacement from or a restructuring of their current jobs.
The challenge for policymakers will be to update, strengthen, and adapt policies to respond to the economic effects of AI.
Although it is difficult to predict these economic effects precisely, the report suggests that policymakers should prepare for five primary economic effects:
- Positive contributions to aggregate productivity growth;
- Changes in the skills demanded by the job market, including greater demand for higher-level technical skills;
- Uneven distribution of impact, across sectors, wage levels, education levels, job types, and locations;
- Churning of the job market as some jobs disappear while others are created; and
- The loss of jobs for some workers in the short-run, and possibly longer depending on policy responses.
More generally, the report suggests three broad strategies for addressing the impacts of AI-driven automation across the whole U.S. economy:
- Invest in and develop AI for its many benefits;
- Educate and train Americans for jobs of the future; and
- Aid workers in the transition and empower workers to ensure broadly shared growth.
Key points from the report
The authors state it is unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will continue to reach and exceed human performance on more and more tasks.
AI should be welcomed for its potential economic benefits. However there will be changes in the skills that workers need to succeed in the economy, and structural changes in the economy. Aggressive policy action will be needed to help Americans who are disadvantaged by these changes and to ensure that the enormous benefits of AI and automation are developed by and available to all.
Today, it may be challenging to predict exactly which jobs will be most immediately affected by AI-driven automation. Because AI is not a single technology, but rather a collection of technologies that are applied to specific tasks, the effects of AI will be felt unevenly through the economy. Some tasks will be more easily automated than others, and some jobs will be affected more than others—both negatively and positively. Some jobs may be automated away, while for others, AI-driven automation will make many workers more productive and increase demand for certain skills. Finally, new jobs are likely to be directly created in areas such as the development and supervision of AI as well as indirectly created in a range of areas throughout the economy as higher incomes lead to expanded demand.
Strategy #1: Invest in and develop AI for its many benefits. If care is taken to responsibly maximize its development, AI will make important, positive contributions to aggregate productivity growth, and advances in AI technology hold incredible potential to help the United States stay on the cutting edge of innovation. Government has an important role to play in advancing the AI field by investing in research and development. Among the areas for advancement in AI are cyberdefense and the detection of fraudulent transactions and messages. In addition, the rapid growth of AI has also dramatically increased the need for people with relevant skills from all backgrounds to support and advance the field. Prioritizing diversity and inclusion in STEM fields and in the AI community specifically, in addition to other possible policy responses, is a key part in addressing potential barriers stemming from algorithmic bias. Competition from new and existing firms, and the development of sound pro-competition policies, will increasingly play an important role in the creation and adoption of new technologies and innovations related to AI.
Strategy #2: Educate and train Americans for jobs of the future. As AI changes the nature of work and the skills demanded by the labor market, American workers will need to be prepared with the education and training that can help them continue to succeed. Delivering this education and training will require significant investments. This starts with providing all children with access to high-quality early education so that all families can prepare their students for continued education, as well as investing in graduating all students from high school college- and career- ready, and ensuring that all Americans have access to affordable post-secondary education. Assisting U.S. workers in successfully navigating job transitions will also become increasingly important; this includes expanding the availability of job-driven training and opportunities for lifelong learning, as well as providing workers with improved guidance to navigate job transitions.
Strategy #3: Aid workers in the transition and empower workers to ensure broadly shared growth. Policymakers should ensure that workers and job seekers are both able to pursue the job opportunities for which they are best qualified and best positioned to ensure they receive an appropriate return for their work in the form of rising wages. This includes steps to modernize the social safety net, including exploring strengthening critical supports such as unemployment insurance, Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance for Needy Families (TANF), and putting in place new programs such as wage insurance and emergency aid for families in crisis. Worker empowerment also includes bolstering critical safeguards for workers and families in need, building a 21st century retirement system, and expanding healthcare access. Increasing wages, competition, and worker bargaining power, as well as modernizing tax policy and pursuing strategies to address differential geographic impact, will be important aspects of supporting workers and addressing concerns related to displacement amid shifts in the labor market.
Finally, if a significant proportion of Americans are affected in the short- and medium-term by AI-driven job displacements, US policymakers will need to consider more robust interventions, such as further strengthening the unemployment insurance system and countervailing job creation strategies, to smooth the transition.
I will add detailed comments and my thoughts as I digest the full report in the coming days.
The document focuses on autonomous vehicles, eldercare, manufacturing and more
The new U.S. Robotics Roadmap calls for better policy frameworks to safely integrate new technologies, such as self-driving cars and commercial drones, into everyday life.
The detailed document also advocates for increased research efforts in the field of human-robot interaction to develop intelligent machines that will empower people to stay in their homes as they age. It calls for increased education efforts in the STEM fields from elementary school to adult learners
The roadmap’s authors, more than 150 researchers from around the nation, also call for research to create more flexible robotics systems to accommodate the need for increased customization in manufacturing, for everything from cars to consumer electronics
The goal of the U.S. Robotics Roadmap is to determine how researchers can make a difference and solve societal problems in the United States. The document provides an overview of robotics in a wide range of areas, from manufacturing to consumer services, healthcare, autonomous vehicles and defense. The roadmap’s authors make recommendation to ensure that the United States will continue to lead in the field of robotics, both in terms of research innovation, technology and policies.
We also want to make sure that research solves real life problems and gets deployed,
said Henrik I. Christensen, a professor computer scientist at the University of California San Diego, and the document’s lead editor.
We need to make sure that we are making an impact on people’s lives.
Unmanned vehicles and policy
The advances in the field of self-driving cars have far outpaced the predictions researchers made in the 2013 edition of the roadmap. But autonomous vehicles still have several obstacles to overcome, the researchers said. “It is important to recognize that human drivers have a performance of100 million miles driven between fatal accidents,” Christensen said. “It is far from trivial to design autonomous systems that have a similar performance.”
Self-driving cars need to become more like industrial robots, which can run autonomously for three years without human intervention, he added. Also, the many methods and technologies used in the field of self-driving vehicles need to be resolved into a single standard. “Systems integration might not get a lot of press, but it is essential,” Christensen said.
Finally, local, state and federal agencies need to formulate policies and regulations that ensure these cars can share the road safely with vehicles driven by people. Regulations and policies also need to be put in place for unmanned aerial vehicles, better known as drones or UAVs. When this is done, UAVs could revolutionize the way we ship goods by air, monitor the environment—and much more. They could help first responders during natural disasters and terrorist attacks.
Researchers also need to get better at controlling swarms of UAVs and robots. “Currently, it takes a small group of people to run complex UAVs. This ratio needs to be inverted so that one person can control a small group of UAVs and other autonomous robots. Human-robot interactions should resemble the relationship between an orchestra conductor and musicians,” Christensen said. “Individual players need to be smart enough to take cues from the conductor and play on their own.”
Health care and home companion robots
A major wave of companion robots is about to enter the market, as the population of developed countries ages. For example, 50 percent of the Japanese population is over 50 years old. “We need to help the elderly stay in their homes,” Christensen said. “And robots can help us get there.”
To reach this goal, robots will need to have a better understanding of their surroundings and become more reliable. Existing systems are equipped with basic navigation methods. But long-term autonomy with little or no human intervention needs to be the goal. In addition, robotic home companions will need to be able to perform a wider range of tasks.
It is also essential that robots be easy enough to control so that they can be used by everyone. That means that home care robots, for example, need user interfaces that are no more complicated than a TV remote.
“This needs to be moon shot for robotics research,” Christensen said.
In recent years, the need to customize products such as cars has increased dramatically. For example, a high-end vehicle can feature millions of different options, from the color of its seats to the configuration of its electronics. As a result, manufacturers have turned to increasingly sophisticated technology to drive assembly lines. This in turn has brought many factories back to the United States. In the past six years, the U.S. manufacturing sector has added 600,000 jobs. “Tremendous growth in robotics doesn’t have to mean job losses,” Christensen said.
But this expansion of robotic systems in industry must overcome two major obstacles, the roadmap states. Researchers need to develop user interfaces that will allow workers to operate robotic systems with little or no training. In other words, user interfaces need to become more like video games, Christensen said.
Also, robots’ manipulation skills need to improve dramatically, to match at least the dexterity of a young child. Right now, the most advanced robots have the grasping abilities of a one-year-old, Christensen said.
An Industrial Internet and the Internet of Things
For all applications, the core challenge is flexible integration of robotic systems with human operators and collaborators. Researchers envision an environment where physical systems are linked wirelessly via smart sensors and smart chips, within an industrial Internet of Things. This will make it easier for robots to navigate their environment and work with people. At the same time it is important to design these systems to be secure so that they cannot be hijacked or used in cyber attacks.
Amazon is at the forefront of this movement and owns 40 percent of application program interfaces, or APIs, related to IoT—which are open source, Christensen said. “This is going to create a whole new economy,” he said.
Robotic systems will dramatically change everyday life both in the home and at work in coming years. As a result, the public and the workforce need to be trained to interact with these systems. Training needs to happen at all levels, from kindergarten to 12th-grade and in trade schools before college. But most education efforts need to be focused on kindergarten through 12th-grade. Too many young people are dropping out of high school and will be left behind by this new economy based on robotics and the Internet of Things, Christensen said.
“We need to empower people to use robots,” he said. “We need to realize that most of the interfaces we design today for robotic systems aren’t easy to use.”
A shared robotics infrastructure
Researchers also are making a call to build a common, shared research infrastructure for robotics in the United States. The research network would expand existing sites, with a focus on testing autonomous driving, medical and health care robotics, micro- and nanorobotics, agriculture robotics, UAVs and underwater robotics. Each site would need about $3 million to be revamped into a shared facility.