Report: Artificial Intelligence - The Future is Cognitive

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A Global Affairs Media Network NOV E MBE R 2 0 1 8 I S PE CIA L REPORT ON TH E 2018 U.S.-CH IN A AI TECH SUM M IT

Published in collaboration with The Future Society | AI Initiative



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Nicolas Economou Director, The Future Society CEO, H5

Nicolas Miailhe Co-Founder & President The Future Society

Cyrus Hodes Senior Advisor, The Future Society Advisor, UAE AI Minister

Welcome From The Future Society Governments around the world are coming to terms with the fact that AI will profoundly transform their economies and societies. When we founded The Future Society in 2014 at the Harvard Kennedy School of Government, we did so with the profound belief that the world is undergoing a radical transformation, across industry, cities, regions and nations; and that this necessitates innovative interdisciplinary thinking and dialogue between governments, industry, techno-science and civil society. When Helen Liang approached The Future Society with the idea of brokering a US-China dialogue between business leaders, we saw an opportunity for impact. No doubt, as with any international dialogue, there would be sensitivities to navigate. But anyone who has thought hard about how the world could arrive at an international framework to govern AI knows that no such effort could be successful without buy-in from the first, and second, largest investors in AI technology in the world. The fact is, AI offers tremendous potential for humanity, but also very serious downsides and risks. Governing its rise means striking the right balance as the technology deploys and scales. This is critical in an ever more competitive and interdependent world where markets, industry value-chains and knowledge creation processes are deeply integrated. With the velocity and magnitude of the AI Revolution, we are locked, as it were, together on a rocketship, hurtling towards what we hope will be an exciting new world. It’s up to us to work together to make sure we avoid the many obstacles along the way to build the future society that we want. This is a quest that demands pragmatism and cooperation. That is why The Future Society believes this table-setting dialogue we convened is so important. There are many more who must come to the table, but those of us who gathered in Half Moon Bay this past June are part of a proof-of-concept for how leaders in technology, industry and civil society can build bridges of understanding. We’d like to thank all the speakers, partners, and participants for being a part of this seminal gathering, and very much hope you’ll stay engaged as we work to build more bridges in the coming months. ●

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Helen Liang Managing Partner FoundersX Ventures Co-President, AI Alliance

Welcome From The AI Alliance Throughout the past six months, we have worked hard to make the inaugural US-China AI Tech summit a reality, driven by a deep belief that the pace and nature of innovation in artificial intelligence demands an open exchange of ideas—both for how to capture the promise, and for how to avoid the pitfalls of this powerful new technology. The gathering in Half Moon Bay demonstrated that we are not alone in this important mission. I believe efforts like this one are essential to the effective and safe use of AI for good. The United States and China are the two leading AI powerhouses. As we march forward, I believe we can ensure a brighter future for all by fostering more understanding and building bridges wherever we can find them. Ultimately, no single meeting can address the gravity and scope of the challenge we face, but I hope that this summit can serve as a blueprint for future endeavors. To build upon this dialogue, we’ll be reaching out for your suggestions to build the AI Alliance of Silicon Valley into a think tank which can have lasting impact. Thank you for attending. ●



A Global Civic Debate on Governing the Rise of Artificial Intelligence

You can find our latest report and information on the Global Civic Debate at

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10 I AI In Healthcare

06 I The Future of Mobility

14 I Robotics and Drones

18 I AI For Good

22 I AI In FinTech

26 I AI In the Law

30 I AI In Enterprise

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THE FUTURE OF MOBILITY Presenters: Vance Wagner Director of Strategic Partnerships, China at Energy Foundation Nan Zhou Investment Director, Baidu Changcheng Fund Tao Wang Investment Director, SAIC Capital Dr. Maarten Sierhuis Chief Technology Director, Nissan Research Bert Kaufman Head of Corporate and Regulatory Affairs, Zoox Jason Costa Venture Partner, GGV Capital


utonomous vehicles (AVs) are at the forefront of technological advancements and emblematic of inextricably linked upside potential and downside risks in artificial intelligence technologies. Vance Wagner, the panel’s moderator and Energy Foundation’s director of strategic partnerships in China, linked this to a universal visceral reaction to AI applications and a widespread recognition that self-driving cars are revolutionary. As automotive and tech companies continue to apply artificial intelligence into manufacturing driverless cars, many question what this technology means for the future of mobility. The summit’s opening panel, “Future Mobility” discussed the role of AI in transportation and what a fully autonomous mobility infrastructure will look like in China and the United States moving forward. Recognizing that AV technology and its implications bleed into all industries, the speakers applied their different backgrounds in areas of investment, industry, technology and regulation to examine measures that policymakers and executives must pursue to safely adopt fully autonomous vehicles into society in a positive manner. NOVEMBER 2018 06

“Autonomous vehicles is one of the first AI applications that all people immediately and instinctively understand to be revolutionary.” Vance Wagner KEY TAKEAWAYS The future of AI-powered mobility requires a paradigm shift. While an AI-powered mobility infrastructure will improve transportation, selfdriving cars have accompanying regulatory, societal, cultural and technological implications. The question is not whether the technology behind driverless cars exists or if the finished product can be achieved. Instead, it is how the technology should be implemented in society to guarantee standards for safety and ethics are maintained while gaining efficiency. To move forward in achieving a future of fully autonomous mobility, regulators, investors and engineers must recognize AVs’ disruptions and find ways to alleviate them.


AV infrastructure requires a sharing environment. While the status quo of transportation is privatized car ownership, autonomous vehicles transition away from privatization towards ride sharing and public ownership. The practice is important for implementing AVs into everyday life and achieving technological accessibility. Following a UC Davis “Three Revolutions in Urban Transportation” report, widespread ride sharing is essential to future mobility, as are electric and autonomous vehicles. By conserving energy, cutting emissions, decongesting highways, lowering transportation costs, freeing up parking spaces and improving urban livability, ride sharing will revolutionize urban transportation. Vehicles are more than transportation tools. Cars are traditionally a means of getting from point A to B. Moving forward however, autonomous vehicles will adopt more entertainment or pet-like features, according to Tao Wang, Investment Director at SAIC Capital. This is similar to how AI technology has diversified and expanded communicative practices with the advent of smartphones:

telephones are no longer simply communication tools. With AIpowered mobility, humans will maximize their commute with new activities as time is no longer consumed by the driving practice. Thus, AVs’ implementation should be associated with increases in human productivity.

“in autonomous driving, nobody can win if they don’t work together.” Nan Zhou AI-focused transportation needs to have goals and meet them. Zoox, a U.S.-based self-driving car company, sets goals related to human safety and environmental protection as the company builds fully autonomous vehicles from the ground up. Bert Kaufman, Head of Corporate and Regulatory Affairs at Zoox, prioritized AV goals when discussing what an AI-powered mobility future should look like. In addition to prioritizing AI technology in automobiles, The Nissan Research Center in Silicon Valley focuses on AVs’ impact on humans with regards to safety. As AVs inspire a societal


paradigm shift, standards of zero fatalities and zero emissions should be underscored when testing and implementing the technology. US-China AI cooperation is necessary. Many perceive that China and the United States are in a constant rivalry, especially in terms of technological innovation. Cooperation in tech and business spheres dissolves that misconception as those in the tech and finance industries recognize that collaboration can cut costs, increase efficiency and achieve results faster. However, it is important to recognize that regulatory differences between the nations affect bilateral interactions. Tech sharing and partnerships should occur across borders. As the race to AV realization is not a one-player game and requires a wide ecosystem, cross-border and cross-company tech cooperation will make an AI-powered mobility future come faster, creating a circular economy for mobility. Baidu’s Apollo project, an open source platform, unites OEMs, Tier 1’s and sensors manufacturers from both China and the United States in creating an environment of tech collaboration.


Nan Zhou, Baidu Changcheng Fund’s Investment Director, equated this approach to Google’s Android Open Source Project since both create a standardized operating system that is available to manufacturers. Sharing road data and codes, Zhou maintained, will contribute to the AV effort by making data more abundant and accessible. Business models can capitalize on both Chinese and American markets. The future of mobility and its accompanying paradigm shift will disrupt automated vehicle business models. GGV Capital venture partner, Jason Costa maintained that neither China nor the United States has figured out proper AV business models for the future, but predicted that there will not be competition between the two nations in that regard. Companies should recognize that they can capitalize on both the United States and China’s large markets for AI-powered mobility. Chinese tech and road deregulations provide first-mover advantages in electric vehicles. Efforts pursued by the Chinese government in terms of environmental mandates and deregulations have fostered an AI-friendly environment within the country. As the Chinese government has eased road regulations, data can be quickly and efficiently gathered to the benefit of Chinese tech companies. The mandate to completely phase out the combustion

“I don’t think anyone has totally figured out how the business model will work here yet, but my sense is, over the long term, it’s not really going to be a competition, be it on the business or the tech front.” Jason Costa

complicates AV infrastructure and adoption. Wang said that comparing the United States to China in terms of governmental projects, programs and regulation is like comparing apples to oranges: though China tends to finish projects or implement programs more quickly due to less red tape, American infrastructure lasts longer. The future of mobility is as much or more a regulatory issue than a technological challenge.

engine in China by about 2040 (although no deadline was specified) has helped position China as the largest market for electric vehicles (EVs). Zhou believes that China will be the first to adapt to driverless cars because of its first-mover advantages in electric vehicles and its regulatory environment, advising that the United States should adopt similar methods in its regulation for the future mobility.

Cultural and geographical driving differences complicate AV implementation. Differences in geographical driving practices make universal AV implementation difficult. As self-driving cars capture road data to understand driving habits, cities’ unique road systems and individuals’ adaptive driving habits serve to localize AV technology.

The US and China have varying nation-state regulations. Regulation is a big challenge to implementing a fully autonomous vehicle infrastructure. China has fewere AI regulations than the United States. The speakers viewed this as a Chinese asset, forecasting that though the United States leads in technological capabilities right now, China’s deregulatory environment will close the tech gap. America’s federal structure and its accompanying municipalstate-federal legal framework

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Driving practices are provincial. Driving in Beijing is drastically different than driving in Palo Alto. Not only is the infrastructure unique in both locations and different in terms of urbanization, but Chinese and American drivers drive differently. Though many assert that if you can drive in China, you can drive in the United States, Tao Wang noticed that is not the case, providing his friend—who failed his U.S. driver’s test five times even though he is considered a good driver in China—as an example.


Different driving habits makes cross-border road data sharing difficult. Locational driving dissimilarities caused both Tao Wang and Dr. Maarten Sierhuis, Chief Technology Director of Nissan Research in Silicon Valley, to state that AV technology is also provincial. They recommended that China and the United States test and research AV technology in their respective locations with Dr. Sierhuis wary that road data and deep learning will not account for geographical driving differences, calling for AI techniques outside of deep learning. Kaufman maintained that competition and collaboration should occur between cities and states in this regard before setting out to optimize AV technology internationally. These sentiments contrast with Zhou who asserted that sharing road data between the U.S. and China is important to AV development and will help both countries get closer to AV implementation.

voiced his concerns over complete autonomy, instead calling for a concerted relationship between humans and autonomous systems. Stating that fully autonomous cars are dangerous and human intelligence is still the status quo, he advised coupling AVs with human supervision, with humans in a control setting with the ability to step in during unpredictable driving conditions such as road construction. Rather than having level 5 AVs, he advised striving for level 4 vehicles with the intent to incrementally test from there.

Humans are the centerpiece of AI. Many associate artificial intelligence with human substitutability. Instead, AI technology should focus on bettering humans and improving the state of humankind to make the world more livable. Though the extent of human involvement and which self-driving car level to pursue in the future of mobility is somewhat contested, there is wide consensus that humans are AI’s main focus of attention.

“The things that we agree on are around reducing fatalities and injuries on our roads, making the air that we breathe cleaner and increasing productivity.” Bert Kaufman

Artificial Intelligence is a tool to improve mankind. AI technology intends to make humans happier, healthier, better, more efficient and ultimately smarter. Technological advancements enable humans to learn and problem-solve faster and faster. Wang argued that time and technology will evolve the human brain and grow its creative capacities, using the rising generation’s superior intellect as an example. Not only will AI technology improve human intellect, but also the surrounding world by tackling future and current safety, environmental and urbanization challenges. AI-powered mobility requires human involvement. Dr. Sierhuis

AVs are safer than human drivers. Wang argued, however, that fully autonomous, level 5 vehicles are safer than transportation with human involvement. This is due to humans driving unpredictably. As humans cause 94 percent of crashes leading to road fatalities, Kaufman noted that AI and its application to vehicles can improve that statistic and work towards safer transportation.

AI needs to solve more problems than it creates. An AI-powered mobility future should benefit the world and its inhabitants—whether in terms of safety, productivity or environmentalism. Artificial intelligence is a tool for good as it fosters cross-border cooperation, evolves the human mind, creates safer roads and helps protect the environment. In the paradigm shift towards implementing fully autonomous vehicles, AI should have positive impacts. The future of mobility should create win-win scenarios. Society will undergo the three revolutions to urban transportation (EVs, AVs and ride sharing) more effectively if multiple parties benefit from it. Uber and Lyft serve as key examples.


“It is always about humans and autonomous systems together and we have to figure out what that means.” Dr. Maarten Sierhuis Both companies use market-driven and efficient solutions to overcome setbacks and obstructions to carpooling that traditionally prevent individuals from ride sharing. Specifically, by deciding to carpool, customers pay less, companies profit from driving more riders and cities are less congested by freeing up parking space and decreasing the number of drivers on the road. There needs to be zero fatalities and zero emissions in transportation. The collective goal to achieve zero emissions and zero fatalities in the future frame AV actions and discussions. In order to achieve the goal, tech companies, regulators and investors must prioritize the safety, mobility and sustainability trifecta, which assesses the effectiveness of connected and automated vehicle systems and their application. China and its combustion engine mandate shows that the country is taking the time, gaining the support and investing in needed public and private research to meet safety and emission goals. The timeline towards fully autonomous vehicles is uncertain. A fully autonomous vehicle infrastructure is a work in progress. Dr. Sierhuis forecasts that 95 percent of AV usage will be achieved in the next five to 10 years (depending on definitions of safety and autonomy) but the additional five percent will likely take two to four times that period due to hurdles in technological perceptions and safety implementations. Costa viewed implementation of level 5 autonomous vehicles to be at least three to four decades out. However, Zhou predicted autonomous driving will come in a few years. There has been significant progress in pursuit of AV implementation, strengthened by cross-border cooperation. ●


AI IN HEALTH CARE Presenters: Antonio Regalado Senior Editor for Biomed, MIT Tech Review Othman Laraki Co-Founder & CEO, Color Genomics Katherine Chou Head of Product, AI in Health, Google Derek Zu Strategy & Products, Baidu USA Qirong Ho Chief Technology Officer, Petuum Nikjil Jain Founder & CEO, ObEN


he American healthcare industry is paradoxical. Though approximately 20 percent of GDP is spent on healthcare, the quality of care has not improved despite spending increases and technological advancements. As healthcare spending doubles roughly every decade with individuals’ longevity remaining constant, Antonio Regalado, the panel’s moderator, asserted that the industry is experiencing Eroom’s law. Because of healthcare’s large impact both in economic and human wellness terms, artificial intelligence has the potential to overcome healthcare’s paradox to better the healthcare industry and mankind. The “AI in Healthcare” panel focused on ways to apply AI to improve the quality of healthcare while lowering its costs. Recognizing that healthcare has become more monopolistic, Othman Laraki keyed in on the structural challenges that the U.S. healthcare industry confronts. To address a growing need for effective and efficient care in the face of structural problems, the speakers underscored AI and its role in unlocking new medicinal innovations and treatment options. The panel assembled speakers involved with AI applications into healthcare. As engineers, the speakers NOVEMBER 2018 10

took a tech approach to discussing the healthcare industry—each applying their expertise and company involvement to discuss how AI will supplement human intellect to overcome stagnation in quality of care.

KEY TAKEAWAYS Healthcare is an entrenched industry, but AI can disrupt it. Healthcare is ripe for disruption. As an industry that has not experienced significant growth, healthcare can benefit from information technologies and AI to capitalize on government spending and technological advancements. By identifying causes of entrenchment and anticipating ways that AI will disrupt the current system, healthcare can benefit from technology and improve the industry as a whole.

“We have this paradox in healthcare that despite spending more, we’re not really getting that much bang for our buck.” Antonio Regalado Healthcare is entrenched because of structural challenges. Healthcare’s


value chain has monopolistic layers. The small number of insurance companies with high market caps, Laraki argues, restrict market forces from letting the best healthcare product win in terms of beating out competitors and lowering product costs within the confines of supply and demand. Other structural challenges relate to medical records and accompanying ownership issues. Many Americans, according to Nikjil Jain, do not have access to their own medical records and do not know who does have ownership. These ownership and monopoly challenges have restricted the healthcare industry from improving quality of care and lowering costs. AI can cause regulatory and payment disruption. When people think of AI disruption, they associate it with job displacement. Derek Zu, however, argues that applying more artificial intelligence into healthcare will disrupt the industry’s regulatory and pay structures by transitioning into a value-based structure, rather than causing unemployment. Value-based care is more feasible today due to machine learning’s ability to take in large amounts of surrounding data on an individual basis. This technology then predicts an individual’s type of health

risk, intervenes early on and prevents some of those risks for better patient outcomes and lower overall costs. Artificial intelligence needs to be trusted. People must trust artificial intelligence and its capabilities in order for AI to positively impact the healthcare industry. If the technology is distrusted, it cannot become operational and its abilities will remain unutilized. In order to foster trust, technology needs to perform well and meet the desired outcomes. Humans must also change their mindsets to recognize that AI is a tool rather than a replacement for humans and can improve healthcare and the overall human experience. Katherine Chou predicts that, through human-machine complementarity, AI will become more trusted if applied to preventive and incremental medicine under a valuebased system. Change will take time. The term “disruption” implies that artificial intelligence will sharply alter the status quo. Qirong Ho refutes that notion, instead predicting that AI will gradually change the current healthcare system. Hospital regulations and existing processes prevent overnight change from occurring and require incremental


“The processes become faster for the doctors without necessarily disrupting or changing the underlying system.” Qirong Ho change to alter healthcare’s current proceedings. AI can improve clinical practices. Artificial intelligence increases efficiency and solves problems in a faster, more effective manner. This enables humans to leave data processing, detailed-oriented and habitual work to technology in order to specialize in more nuanced, interaction-based and holistic work that humans do best. Additionally, technological advancements can expand medical capabilities to sophisticate procedures, improving the lives of both physicians and patients. Artificial intelligence can streamline back office hospital processes. Hospitals are consumed by processes rather rather than the provision of care. Regalado notes that hospital staffs have grown, but the amount of doctor


jobs have not—rather, jobs go to administrators, clerks and managers of hospital processes instead of towards people delivering the actual medical care. Ho linked this to hospitals’ regulatory report requirements and their backlogging since the current electronic databases are inadequate. Additional slow hospital procedures relate to electronic record systems and hospital interactions with insurance companies. Artificial intelligence can step in to structure unstructured data and interface with electronic record systems and insurance companies to streamline hospital processes. Not only will this improve the system, but it will provide staff with more patient interaction time. Technology can advance and improve medical procedures. Artificial intelligence is being used to make medical procedures less invasive and more effective. AI has helped realize diabetic retinopathy screening and liquid biopsy procedures, allowing doctors to do more groundbreaking surgeries while providing more options to patients. Zu and Chou highlight how AI can classify images, specifically using algorithms to identify disease areas and tumor regions in pathology slides. This gives doctors more information and greater proficiency to assess and treat patients. Artificial intelligence advantages and its use of genomic data, deep learning applications

and next generation sequencing play a big role in preventative care and personalized medicine. AI use enables doctors to spend more time with patients. Artificial intelligence intends for doctors to spend less time with systems and more time with people by decreasing interface time with electronic record systems and insurance companies. Ultimately, artificial intelligence is about improving doctors’ work lives, whether by improving procedural capabilities, supplementing their own knowledge with AI capabilities or transitioning their time away from systems and towards patient interaction. Healthcare needs to become more available. In order for healthcare to become better, it has to become more available to individuals. As AI intends to simultaneously lower healthcare costs while improving its quality, patient wellness and health serve as primary metrics in determining progress. By using technologies to lengthen individuals’ life spans and make doctor care less expensive, healthcare will benefit more people. Availability implies more accessibility and affordability. Availability does not simply equate to accessibility. If healthcare is accessible but overly expensive, hospitals will deter individuals from receiving medical care. Thus, affordability is necessary as well. Recognizing a tradeoff

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between quality, cost and access within the healthcare system, Chou argues that AI can overcome this dilemma. Technologies should not only drive down costs, but also should additionally improve the quality of care and its availability since AI streamlines procedures and allows doctors to make an efficient use of their time. When medical staff is unavailable, AI should be used. Many locations in the developing world do not have access to the specialized medical staff found in the United States. However, in remote locations, artificial intelligence can serve as a potential substitute to certain types of doctors such as radiologists, pathologists, dermatologists and ophthalmologists since their line of work has an aspect of imaging classification and recognition—things that AI does well. Google, for instance, applied AI in India (where there is a lack of care access) when doing a diabetic retinopathy screening. AI’s ultimate goal is to make technology and care more available. Technology has a trickle-down effect. As groundbreaking technology is used initially in larger enterprises, overtime, its technology reaches households. The goal is to apply AI into healthcare so that its technologies pervade homes; providing individuals with availability to AI and more practical medical care. This is most important in cancer care as early prevention is


key and cancer patients’ frequent hospitals to receive needed care. AI will change doctor roles in the future. The future of medicine and its interaction with artificial intelligence will alter traditional physician roles. As technology has improved, it can process abundant raw and unstructured data and use this information to detect risks and ailments early on, while moving forward to prevent such risks. That is not to say, however, that artificial intelligence can and will replace human doctors. Rather, technologies and humans will specialize in their separate comparative advantages to improve the healthcare system. AI should cover responsibilities that technology is better at than doctors. Physician responsibilities need to be redefined in the artificial intelligence era. This “peeling off” and “decomposition” of doctor tasks will allow technology to assume roles that are inherent to doctors and that technology can better perform. AI is best at imaging classifications related to medical imaging and processing granular and continuous data. Doctors, meanwhile, specialize in physicianpatient interactions, interpreting models and applying processed data to analyze patients holistically. AI augments human intellect. AI can solve problems that the human mind cannot. However, it is not a competition between physicians and technology: both are needed in concert to lower healthcare costs and improve quality of care. Rather than being a rival, AI is an asset for humans to use in overcoming human shortcomings and pushing past barriers mainly related to processing continuous and unstructured data that previously obstructed human advancement. Human-technology interactions have different stages. Similar to how there are five levels to autonomous vehicles, Jain argues there are different levels of AI-care interaction. The care levels differ from AV levels since AI is less involved in the higher levels—the opposite of AV levels. The first level of care is a call center, in which artificial intelligence can completely commandeer humans in that role. As human interaction is not vital in

“AI can augment what the clinicians can do in that space, since they actually no longer have to be necessarily focused just on the specifics of the images— now they can be thinking of the patient holistically.” Katherine Chou that space, AI can help overcome bias, with Google’s “Duplex” serving as an example. The second stage relates to triage nurses that assign patients to the next level of care. Artificial intelligence can also play a direct role in this level, but with less interaction than the previous stage. Upon reaching the third level of senior nurses, care is more clinical and more analysis is required. At the third stage, there is a transition and less AI is applied. As healthcare continues to determine the level of AI involvement in hospitals, recognizing these levels of care will enable doctors and hospital staff to benefit most from AI involvement. Existing technologies should be repurposed into healthcare applications. AI is not confined to one industry or platform. As companies like Google have applied their technologies into healthcare, quality of care has improved without needing to reinvent the wheel and drive costs in developing new technology. Google has applied its capabilities to medical procedures. Specifically, when seeking to apply AI to improve fundus imaging, Google used the same convolutional neural network technology as a Google image search for ophthalmological procedures. Additionally, Google Translate technology and its accompanying sequencing models are used to predict a patient’s future health risks. By repurposing Google technology into healthcare, the company has shown that AI can effectively and practically be implemented into the industry. ObEN.AI repurposed its technologies to assist with mental illness. ObEN, a company that makes virtual copies of humans that “look like you, talk with


you, act like you” transitioned into the healthcare industry by applying its “Avatar PAI” (Personal Artificial Intelligence) technology in providing care work. Recognizing that the older generation struggles to take their pills on time (or at all) without pressure from their grandchildren or primary doctors, the company set out to create avatars of busy primary care doctors and grandchildren to help overcome mental health challenges that seniors face. As there is a strong correlation to seniors not taking medication and increases in healthcare costs over time, the company’s application of AI focuses on lowering costs while improving care. AI needs guardrails to overcome liability issues. In the event that AI inaccurately diagnoses a patient, it is important that measures already exist that recognize the technology will not always be perfect. As similar liability dilemmas confront automated vehicles, we must consider how it will overcome such challenges, constructing guardrails to ensure that technology can endure setbacks. Liability guardrails begin at the product level. When transitioning to greater AI involvement in healthcare, doctors need to play a greater role in reviewing and signing off on AI results. Also, it is important that the technology has a built-in sorter that can identify false negatives so that doctors do not have to review all of the machine’s findings, defeating the point of using technology in the first place. Thus, guardrails should ensure that technology does not derail and patients are not worse off in the event that AI makes a mistake. Guardrails follow a concentric circle path from the model itself to society at large. The first guardrail, Chou notes, focuses on the model itself in ensuring that its technology is accurate and has been signed off by experts. The next level relates to the individual physician and making sure that they are qualified in interpreting the data provided. Moving outwards, the third circle corresponds to the clinical environment and whether the hospital has properly defined how it intends to use AI’s technology. The final guard rail adopts a historical and societal approach to assess the effect of AI application to society as a whole. ●


ROBOTICS AND DRONES Presenters: Ali Zaidi Of Counsel, Morrison & Foerster LLP Victor Wang Managing Partner, CEG Ventures Chris Edvemon Partner, Sinovation Ventures Ali Kashani Head of Postmates X Arnaud Thiercelin Head of US R&D, DJI Mihail Pivtoraiko Founder & CEO, Aptonomy Arye Barnehama CEO, Elementary Robotics


obotics and drones are permeating human societies and work settings. Though many benefits accompany these innovations, they also bring alongside multiple challenges. Identifying robotics’ short and long-term risks and opportunities will not only allow investors and engineers to profit, but will improve the technology itself in order to assimilate into human society. By analyzing drones and robots in marketplace and societal contexts, investors can recognize whether humans will interface with particular technologies and adopt them into everyday life. The “Robotics and Drones” panel assembled tech investors and heads of robotics companies to highlight technologies to be particularly attentive to—whether the advancements are right around the corner or more than a decade out. Recognizing specific effects that robotics and drones will have on infrastructure, security and the workplace, the panelists voiced concerns that investors and developers must acknowledge to ensure technology captures its ambitions and aspirations for the future. Ultimately, if technology NOVEMBER 2018 14

“Focus on the pain points in the economy and do it with assistive rather than replicative solutions.” Ali Zaidi emphasizes human-robotic interactions and we actively seek solutions to overcome its largest considerations and barriers, mankind will gain access to its impressive potential.

KEY TAKEAWAYS Investors should key in on opportune robotic technologies. In the short-term, a three to sevenyear time horizon, there are many opportunities awaiting investors and robotics companies. Certain technologies should receive more attention in order for investors to capitalize and humans to benefit. Not only should investors recognize specific technologies as being opportune in the short-term, but companies need to strategize human-to-robot ratios within their business also. To do so, Arye Barnehama asserts that companies


should keep their return on investment (ROI) calculations in mind when deciding how to deploy, implement, operate and adopt robotics into their operations to best apply their technologies to the workforce. Investors should prioritize technologies that overcome labor shortages. Robotics and drones seek to overcome “labor shortage induced pain points.” Though scaremongering about job loss from technology is widely established, Chris Edvemon asserts that such sentiments are a falsehood. Rather, various industries house many unwanted tasks and occupations that need filling—with restaurant and agricultural industries serving as prime examples. One robotic company creates strawberry picking robots to assist Californian farmers harvest as there is an insufficient number of needed harvesters that threaten farms with bankruptcy. The harvesting robot applies existing technologies to combine computer vision’s confluence with multiple robotic arms and custom-made grasping technology to pick the fruit. Additionally, robotics that wash dishes are opportune

“We’re looking for robotic companies that supplement or outright replace blue collar jobs that nobody wants to do at the moment.” Chris Edvemon investments since restaurants need this task completed but restaurant managers cannot find personnel to fill the blue-collared job. As the dishwashing robot is a leasing model, it becomes more economically viable as well. Sidewalk robots are overlooked technologies. While automated vehicles garner the most attention, Dr. Ali Kashani argues they are overly heavy, fast, large and dangerous. Instead, sidewalk robots can transport necessary items (though not people) in a smaller, lighter and more cautious manner— making them easier to build and commercialize. Instead of requiring a human to travel in a car to obtain an item at the store, sidewalk robots take the human out of the transporting picture by providing the delivered item with its own


mode of transportation. Some investors may be deterred by sidewalks’ regulation being at both the municipal and state levels rather than a single federal level. However, this helps prevent a regulatory monopoly dictating whether the technology will be implemented. Ultimately, sidewalk robots hope to establish a new kind of urban infrastructure while working with different communities to do so. Robots that can maintain infrastructure are important investments. Vertically integrated and fully autonomous enterprise solutions, according to Arnaud Thiercelin, are the next big thing within the three to seven-year time horizon. As renewable energy takes root, it creates a challenge of how to manage various wind turbines, solar farms and dams. Drones provide a solution. Starting from a remote center located anywhere in the world, drones will be able to travel to the energy source, diagnose its infrastructure and later produce direct, follow-up or repair orders. Though this is not an immediate issue in China due to the country’s newer infrastructure, it is becoming a more pressing problem in the


United States with its aging infrastructure. Security is an important robotic application. Self-flying smart drone-based security systems are critical investments for the future. As robotic applications prioritize the “3 D’s” of using drones and robots to assume dirty, dull and dangerous tasks, the security realm relates to each category, particularly the dangerous category. This is because being a security guard is the second most dangerous profession in the United States and China faces security dilemmas from guards often abandoning their posts, according to Dr. Wang. How robotic security applications will interact with humans, especially during its transition period, is an important question. Disruptions caused by emerging technologies will improve the quality of life and the economy. Applying a longer, 16-year time frame to anticipating future technologies is more speculative, but allows for forward thinking in how robots and drones will disrupt the existing order. Automation in particular, whether in manufacturing or transportation, has the potential to overcome traffic management or worker safety issues that humans face. In order for the future to realize these technological hopes, there needs to be a shift away from today’s scarcity paradigm that dictates human decisions. As drones, driverless cars and unmanned aviation alter human preferences and the way we think about obtaining things, technology will further disrupt society in economic and wellness terms. Technology will streamline transportation and traffic. Mihail Pivtoraiko speculates that unmanned aviation used for personal transportation will be especially groundbreaking in the next 16 years. Companies that endeavor to introduce AVs on the road and autonomous, passenger-carrying aerial vehicles in the sky will disrupt ground and aerial transportation. To ensure that automated vehicles do not congest the skies and ground, but rather streamline transportation and traffic, engineers and investors

“As a society we’ve come to accept that a two-pound burrito is delivered to you by a two-ton car, which doesn’t make a lot of sense.” Dr. Ali Kashani must work to solve unmanned traffic management (UTM) issues. Technology platforms can pragmatically help overcome such UTM challenges by uniting different traveling robots on the platform to prevent “swarming” issues. As technologies improve transportation in the future, every industry will be bettered since transportation touches all industries. Robotics will better worker safety and longevity. Many jobs are extremely dangerous and manually intensive. Robotics is intended to assume those more habitual, manual and repetitive tasks—in addition to risky jobs related to maintaining infrastructure—to allow individuals to pursue other jobs perceived as more fulfilling and safer to human health. Highly automated and flexible industrial robots will help fulfill this goal in warehouses and drones that improve security and maintain infrastructure will make humans safer. Ultimately, industrial, infrastructural and security-focused robotics will take humans out of more threatening and risky jobs, which will translate into less workrelated stress and longer lifespans. Technology will enter into more unstructured environments. In the longer time frame, industrial warehouses will witness highly automated and flexible technologies take root. As manufacturing production operations are robotized, warehouses and similarly structured environments will see technological implications first, asserts Barnehama. However, the larger disruption will occur as robotic and drone technologies advance so that they seep out of structured environments like industrial warehouses and into unstructured environments like the home. As more and more people can access and interact with groundbreaking technologies, NOVEMBER 2018 16

robotics and drones developers must consider more than their product design and technology. To benefit most from its technology in the future, robots developers need to consider its human impacts. Product design and investment decisions need to consider the human-robot interface. As artificial intelligence and machine learning have progressed, creations and products that seemed unreachable in the past are more achievable in the present. But often times when diving down into the viability and societal applications of technologies, as Dr. Wang voices, such analyses reveal that certain robots and drones are unfit for assimilation into human life. Thus, investors and engineers must zoom out and think beyond product design, but contemplate personal interactions and social implications. Robots need improved programmability and dexterity. Applying a robotic view to concerns facing technology and its implementation, lack of dexterity and programmability afflicts robots’ assimilation into the workplace and society most. Robots are currently hard to program, especially on the industrial and commercial side due to the legacy of computer science and its usage of archaic programming languages from 20 to 30 years ago. Programming should become more flexible in terms of doing a variety of tasks and learning from human observation. Robots also struggle to simulate human dexterity. Rather than being constrained by AI or computer vision, electromechanical movements and executing fine detailed-typed tasks afflict robots. Drones have privacy concerns. As drones will not be leaving anytime soon, their privacy concerns must be addressed. To do so, technologies should prioritize and subscribe to drone privacy by building the product from the ground up with that priority in mind. Additionally, engineers and investors must look beyond the immediate product to the business side and ways to overcome concerns. By analyzing existing solutions to related


technologies, such as fixed cameras and their accompanying privacy dilemmas, drone companies can take past lessons learned and apply them to the future. Labels also play an important role in addressing privacy challenges. Specifically, when tech companies have a history of compliance and maintain principles of safety, they should apply labels to ensure that others know the company and its products are credible.

“How do we slide this lever between what a human does and what a Robot does?” Mihail Pivtoraiko Products need to be socially accepted. The biggest threat to technology is whether it will be accepted by society and used by consumers. In order to anticipate product failures, companies should perform a project pre-mortem and break down issues in terms of technology and design, and also societal implications. In terms of design, robotics should not be invasive and have an admirable design that blends in. Socially, the product should better lives, whether or not an individual is the direct product user. Taking the sidewalk robot as an example, its technology should have a universally beneficial purpose—such as overcoming food wastage and serving as an alternative to paramedics. Technology should prioritize human-robot interaction. There are various levels of human-product interaction, each of which should prioritize safety. Robots at the Tesla headquarters, according to Edvemon, estrange humans and robots from one another due to safety concerns. Rather, robotics should prioritize its separate levels of human interaction: the robot’s actual user, those interacting with the user (such as customers) and society at large. Since robotics and drones are intended for human use, technology—regardless of how groundbreaking it is—should focus on the human factor. ●



AI FOR GOOD Presenters: David Monsma Executive Director, The Aspen Institute Anil Gupta Digman Chair in Strategy, Globalization, & Entrepreneurship, University of Maryland Charlotte Stanton Silicon Valley Director, Carnegie Endowment for International Peace Tess Posner CEO, AI4ALL Dekai Wu Professor, Hong Kong University of Science and Technology Michl Binderbauer Co-founder & CTO, TAE Technologies Alpesh Shah Senior Director, IEEE Standards Association


rtificial intelligence has the immense potential to better mankind. As people perceive humanity’s inherent goodness differently—ranging from Aristotelian, Hobbesian and Rousseauian perceptions—one questions what role government and social contracts play in the new technological era and whether artificial intelligence’s involvement will exacerbate or alleviate human shortcomings. But how can artificial intelligence be deployed in a favorable manner to better mankind? Since “better” and “good” are ambiguous terms, they require more concrete definitions and practical measures to achieve “AI for good.” The “AI for Good” panel, with David Monsma as its moderator, set out to define good AI applications and prescribe ways to achieve positive AI assimilation in society. The speakers recognized technology’s quickening pace as the world currently experiences a three-part evolution between humans, hybrids and machines. There is now more pressure placed on individuals, communities, enterprises and governments to avoid AI spillovers and misuses. To ensure that artificial intelligence NOVEMBER 2018 18

“Principles are soft governance, high standards to abide by, but it’s up to [stakeholders] individually or as organizations to adopt or to employ them or to enforce them somehow.” David Monsma is positively deployed in human society, technology must be human-centric, diverse, empathetic and environmentally sustainable.

KEY TAKEAWAYS AI development must prioritize inclusivity and diversity. Inclusive artificial intelligence technologies and applications ensure that AI reaches its maximum potential in a responsible and equitable manner. To achieve inclusivity, the tech community and its partners must prioritize diversity. Educating the next generation of AI technologists will effectively ensure that artificial intelligence’s benefits apply to all facets of society. By focusing on educating underrepresented groups and the


rising generation of thought leaders in AI, society overcomes technological exclusivity and inequality. Good AI avoids divisiveness and inequality. Artificial intelligence for good endeavors to dissolve divisive and unequal applications of technology. Tess Posner notes that AI technology should apply to all groups within society to pursue more equal and diverse methods to problem-solving. As AI enables machines to have opinions that can change our own, it carries a lot of clout—AI is not merely a mechanical tool or a passive servant. Cambridge Analytica, Charlotte Stanton notes, serves as an example of divisive AI misuse and reveals that there is a price to pay when AI goes wrong. Dekai Wu views it is artificial intelligence’s duty to counteract such divisive technological deployments by increasing empathy. AI education programs will overcome tech inequalities. AI4ALL takes the mission of fostering AI diversity and inclusivity to heart when educating the next generation of technologists. The organization’s education programs

“Some of the greatest good that can come out of AI requires crossborder data flows.” Anil Gupta and summer camps provide AI development training to underrepresented students. By applying their different backgrounds and perspectives to building technologies and overcoming barriers they see in the world, young AI developers create an inclusive technological environment. Whether it is ensuring privacy, improving how programming is explained, or mitigating bias risks, AI4ALL programs highlight challenges and considerations that the rising generation should recognize when building technology from the ground up. Community engagement plays a role in making AI equitable. Communities help foster beneficial AI environments. When providing students with an artificial intelligence education, AI4ALL focuses on not only improving particular communities through its programs,


but also having the students seek to apply their developed technologies in their own communities to solve issues they personally experience. Posner underscored one high school girl’s AI project. As the daughter of rural farmers, the student’s project related to providing clean water in her community which faced water sanitation issues. Furthermore, a community approach to AI can serve as a social safety net when the market does not exist for certain AI principles or technologies. Specifically, Wu espouses the idea that beneficial technologies cannot always be monetized and that communities should play a role in AI work since often, incentives won’t come from individual investment firms. Ethics courses are necessary but insufficient. To achieve AI for good, technologists need to focus on ethics. Ethically-aligned design work will ensure that artificial intelligence developers prioritize positive applications from the beginning. As the tech culture hyper accelerates from an exponential increase in the technology of “artificial children,” this puts more pressure on engineers to ensure they are morally-focused throughout all


stages of AI implementation. To guarantee that technology prioritizes ethics, education and the market play a significant role. Engineers should receive a core ethics training. Similar to how business students must take core ethic classes during their undergraduate and graduate degrees, Anil Gupta proposes the same should apply to engineers and computer scientists. This provides a bottom-up approach to improving AI ethics and focuses on the rising generations role in AI development. Ethics in technology needs to become a part of the rhetoric in achieving positive AI and such an institutional approach can help that. Ethics need to become opportunistic in the market. Though ethics training will not detract from improving AI’s morality and positive uses, an economic focus supplements academic approach to practically promote ethics in artificial intelligence. Alpesh Shah notes that tech companies and startups are more likely to adopt a humancentered and ethical approach if incentivized by tax cuts and a smart tax structure. Though AI ethics and market incentives are currently misaligned, the economic and tech communities must work together to coordinate market forces with improved AI ethics. So while there is skepticism regarding the efficacy of ethics classes and its translation into practice, adding market incentives can help promote AI ethics. And as Wu and Posner recognize, communities should step in when market forces fall short of promoting technological ethics. AI needs parenting. The manner in which we interact with technology and artificial intelligence will have a lasting effect as AI heavily impacts everyone’s lives, including the lives of children. As AI for good focuses on making AI technologies and applications ethical, focusing on morally interacting with technologies and devices when parenting the rising generation will not only ensure they benefit fully from AI’s potential, but will alter the way that AI interacts with humanity.

“If we’re inclusive and diverse in our approach, we’re going to maximize the potential that AI is both developed responsibly and used towards improving outcome for people, planet and society.” Tess Posner Parents need to raise children to properly interface with AI. Ethical behavior starts in the home. Children observe their parents and adopt their parents’ attitudes, mannerisms and practices. Thus, it is the parents’ responsibility to instill a culture, values and ethics in a child. To ensure that AI is morally applied for beneficial purposes in the future—when AI is at the forefront of society—parents should be conscientious when interacting with their AI devices. Artificial intelligence matures throughout its life. Similar to how children absorb practices and habits from their parents, machine learning collects data from its users and surrounding environment; using that information to adjust its own behavior. As machine learning matures and becomes more autonomous, it eventually reaches a point where its intelligence is no longer artificial, but as Shah recognizes, becomes organic. The “artificial children” have moved out of the house. Thus, in AI’s early stages—the design process and early user interactions—humans need to recognize that the technology will likely make mistakes. But it is important that AI is trained to learn from past aberrations to alert when undesirable patterns and dangers emerge. Individuals need to determine technological ground rules. In an effort to rear technology, AI developers must implement ground rules. Michl Binderbauer notes that identifying what humans want out of AI and the technology’s end purpose will help advance human knowledge generation and achieve TAE Technologies’ mission to accelerate learning and AI processes. This places pressure on individuals NOVEMBER 2018 20

and societies to identify key technological ground rules, requiring both a mindset shift to thinking about AI holistically and collaboratively. Rearing technology takes a village of various partners across sectors, industries and borders in deciding what rules to apply to AI. Collaboration across borders, sectors and industries will improve AI. AI is collaborative. If a user cannot collaborate with AI, then the technology and device are not good. As this litmus test applies to the technology itself, it also applies to collaborative efforts across various industries, sectors and nations. AI will overcome more challenges facing the world in a positive manner as more individuals play a role in its implementation and design going forward. Data needs to be unlocked. A lot of data is locked, adversely affecting the entire ecosystem as corporations reduce their R&D budgets to use startup companies as external incubators. This data lockup is also prevalent between countries as data nationalism pervades digital globalization. Rather, the expansive and high-paced nature of AI calls for a change in governance and a heightened sense of multilateralism via soft laws and treaties, as well as cross-border data flows. Unlocking data both between companies and countries will allow society to keep up with AI’s changing environment and global nature. China and the US have different approaches to developing AI. Both countries and their governments approach AI separately, with China applying top-down policies and the United States proponing market forces. China’s more conservative methods and the United States’ reactive approach to AI policy will play out differently within each country, according to Wu. Since the question of AI is a question about humanity itself, though the technology will evolve differently depending on cultural norms, collaboration despite these differences will enrich both nations as well as AI for good moving forward. ●



AI IN FINTECH Presenters: Paolo Sironi FinTech Thought Leader, IBM Watson Financial Service Sameer Gulati COO, Lending Club JinA Bae Head of Corporate Venture, Hanwha Asset Management Luchang Zheng Founding Partner, Blockchain Hero Gunnar Carlsson Co-Founder and President, Ayasdi Bill Reichert Partner, FoundersX Ventures, Managing Partner, Garage Technology Ventures


inTech has revolutionized the way that banks and insurance companies function. Rather than prioritizing themselves and their services as in the past, banks must emphasize client needs in today’s new technological era. This focus on personalized financial services manifests itself in FinTech—a financial infrastructure for consumer enablement. As FinTech applies data and technology to financial services in an effort to address industry challenges, artificial intelligence is essential to FinTech’s existence and usage. The “AI in FinTech” panelists focused on how artificial intelligence has transformed financial services. Recognizing that financial technology startups must operate as tech companies while respecting financial regulations, the speakers underscored FinTech’s complexity. FinTech forsakes traditional banking to obsess over customers’ financial inclusion and credit accessibility and customization. This, in conjunction with FinTech’s five pillars of social media, analytics, artificial intelligence, blockchain and digitization, make FinTech companies face not only existing financial challenges, but security NOVEMBER 2018 22

“Deep personalization should allow you to anticipate the needs and prompt and fulfill the needs of the consumer without the consumer necessarily needing to trigger an action themselves.” Sameer Gulati and regulatory dilemmas associated with artificial intelligence. KEY TAKEAWAYS Artificial intelligence enables FinTech to occur in real time. FinTech prioritizes financial inclusivity. To achieve this, real time plays an important role in FinTech’s ease of adoption as individuals with a smartphone gain access to quick, personalized and customized financial services. As AI steps in to disrupt the who, what, when and how of finance, as Bae notes, instantaneous decision-making and credit scoring will improve the availability of services in a real time basis.


AI creates deep personalization. Deep personalization in financial services allows FinTech to anticipate customer needs without the consumer having to act themselves. As artificial intelligence and machine learning generate and process individuals’ financial and nonfinancial data, Sameer Gulati notes that AI connects end users and FinTech companies to create continuous interaction. Artificial intelligence also helps evaluate lenders and debtors to speed up financial service processes and improve the customer experience. Because of the new type of relationship fostered with consumers at scale, AI redefines the concept of real time and applies it to finance. FinTech prioritizes financial service speed. The benefit of FinTech is that it improves the customer’s financial services experience. As consumers prioritize speed and ease in their daily lives, so does FinTech. Technological applications such as mobile pay, Luchang Zheng recognizes, improve the efficiency and accessibility of financial transactions while quickening the pace of financial services. As individuals call for faster financial

“Fintech is really the infrastructure for consumer enablement and also small business enablement.” JinA Bae activities, FinTech is pressured to meet time demands by prioritizing that financial services are conducted on a real time basis. FinTech changes financial services’ business model. FinTech is a business model innovation. As financial technologies prioritize information technology to innovate financial services, the financial industry must also become more innovative to keep apace with an increasingly more technological sector. As FinTech’s business model prioritizes peer-to-peer lending and aims to overcome legacy leadership—in which company leaders do not understand nor are motivated by technology and automation—entrepreneurship and technology play important roles. Technology enables FinTech’s business model. FinTech’s fifth


pillar, digitization, is central to its business model. Digitization has enabled innovators like Lending Club to capitalize on a technological approach to financial services and ultimately enable FinTech to be a category within the venture capital world. Technology has helped FinTech companies establish new business models as payment transactions occur by mobile phones and venture capitalists invest in financial technology companies. Banks and FinTech companies do not pursue simple business collaboration. Noting that FinTech is much messier than “collaboration and kumbaya,” Bill Reichert underscored the role of Silicon Valley’s enterprising spirit and the financial industry’s ignorance in creating the current business model. The nature of financial services and their accompanying regulations tend to deter individuals that are familiar with financial proceedings from innovating within the industry. Reichert notes that many entrepreneurs set out to do “insane things” and through their more haphazard experiences with trial and error, the companies that survive either are adopted by large financial


institutions that serve as mentors, or have disrupted the regulatory environment and business models enough to instigate progress within the industry. AI regulatory applications can help manage financial risks. Financial crises are the greatest cause of job losses. Though many assign automation or regulation as the largest instigators of worker displacement, global financial crises have led to more unemployment over the past century than any other cause. As the financial industry endeavors to mitigate the risk of another recession, one questions whether AI can be applied at the highest regulatory level to manage financial risks while still innovating in financial services. AI assists regulation to mitigate risks. Artificial intelligence and machine learning strive to minimize errors. As such, Reichert asserts that AI should play a role in the financial system’s overall governance. Currently, artificial intelligence only identifies strong signals. But to give AI a greater regulatory role in anticipating risks, systems need to capture weak signals as well. Carlsson notes that AI systems should identify smaller, underlying signals that are not primary drivers today, but could become strong signals and risks tomorrow. By doing so, artificial intelligence will apply its

“finance giants in China are trying to improve the efficiency and accessibility of every financial payment and transaction.” Luchang Zheng predictive powers to financial regulation. China has benefited from its tech regulations. Although AI technology was born in the United States, Paolo Sironi notes that China has made great advancements in developing the technology, having the potential to become the largest tech owner in the future. As China has experienced continuous economic growth for the past 40 years and avoided financial recessions, the speakers link this to the nation’s regulatory environment. When compared to South Korea, JinA Bae highlights that China permits insurance companies and banks to pursue most financial activities unless explicitly stated. The opposite is true in South Korea where, unless defined as acceptable, most financial activities are restricted by the Korean government. Overregulation can restrict innovation. Though regulation can help manage financial risks, it can adversely affect insurance companies, banks and

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conglomerates by stunting their innovative growth. Bae notes the challenges facing Hanwha Asset Management under South Korean regulation. After investing in the largest peer-to-peer lender in China and hoping to establish a joint venture together, the South Korean government essentially killed the P-to-P lending business within the country. However, Asian conglomerates are quite influential in terms of dealing with the government, providing opportunities to work with market leaders in advanced markets. Korean conglomerates help companies scale in Asia by convincing the government to open up its market over time. AI and blockchain can overcome security dilemmas. Security dilemmas often accompany digitization. As AI continues to grow, so can its threat to data privacy and security. But while artificial intelligence can often contribute to security dilemmas, AI can also solve them. To ensure that technology overcomes security issues, machine learning and AI should be human-centric and recognize that humans often create security challenges and should thus be a part of their solution. Financial services can be both digital and secure through AI usage. Machine intelligence software


companies, such as Ayasdi, that build predictive models and automated applications using AI can overcome financial problems related to security. By using machine learning to segment populations and transactions based on levels of riskiness, artificial intelligence alerts investigators of fraudulent transactions, money laundering and threats to data protection. Gunnar Carlsson notes that alerting is not enough and that AI should enable investigators to predict when transactions are secure and when they are not, while recognizing that systems will not perfectly predict fraudulences, but can help in filtering cases. Blockchain improves financial data transparency and traceability. As data in blocks cannot be unwritten and consequently cannot be tampered with, blockchain technology secures data. Blockchain also helps protect information and end user privacy by applying its zero-knowledge proof technology. Rather than extracting all information in the banking system when only some information is needed, Luchang Zheng notes that blockchain technologies extract only relevant information in an anonymous manner. Not only does blockchain shorten the data extraction process, but it secures the banking system as a whole. â—?



AI IN THE LAW Presenters: Nicolas Economou Director, The Future Society, CEO, H5, Co-chair, Law Committee, IEEE Global Initiative Jiyu Zhang Associate Professor, Renmin University Lee Tiedrich AI Initiative Co-Chair, Covington & Burling Jia Gu Researcher, Legal Intelligence in China Robert Silvers Partner, Paul Hastings, Former Assistant Secretary for Cybersecurity at U.S. Department of Homeland Security Mike Philips Associate General Counsel, Microsoft Deepa Krishna Director of Business Development, ClearAccessIP


I has unrelentingly ascended into American and Chinese legal systems. As citizens’ liberty, wellbeing, privacy and rights depend on the strength of a nation’s judiciary, many apprehend AI’s increasingly prominent role and its implications for due process. Today, the rule of law by humans and for humans, which Nicolas Economou characterized as the foundation of civilized society, is increasingly challenged, as decisions that affect humans are progressively surrendered to machines in the world’s legal systems. While AI has significant potential to better society and the administration of justice, its deployment must follow norms that will ensure lawyers, courts, other institutions of state, and civil society at large can trust it. The “AI in the Law” panelists shared perspectives on how AI can benefit American and Chinese justice systems while mitigating its risks. Noting that technology has the potential to improve access to and the quality of justice, the speakers’ collective perspectives highlighted the need for artificial intelligence systems, in order to be trusted in the legal system, to be appropriately transparent, effective at meeting NOVEMBER 2018 26

“If AI is central to your business strategy, then you should assume AI will be central to your liability and regulatory exposure.” Robert Silvers the specific purpose for which they are intended, competently operated, and accountable. It is important that AI systems (and their operators) deployed in support of the administration of justice and enforcement of the law remain under the ultimate supervision and control of legal practitioners and courts. KEY TAKEAWAYS China’s judiciary system applies AI to improve efficiency and fairness. China faces both efficiency and fairness challenges. The recent round of legal reforms lowered the number of practicing judges in China by a third in the face of a 30 percent increases in the amount of legal cases sent to Chinese courts annually. At the same time, recent studies show that judges in different geographical regions within China


make dissimilar judgements for similar cases. As there is a significant mismatch between the number of judges and litigations and also uneven judgements made across the nation, the Supreme People’s Court (SPC) has taken a proactive role in introducing AI into China’s judicial system. To overcome these mismatches, the Supreme People’s Procuratorate and SPC are developing intelligent courts and intelligent procuratorates. Technology can streamline tedious court processes. Chinese judges are overwhelmed by the large amount of litigations they confront, with Beijing appellate court judges concluding only a third of their presented cases each year. To improve this statistic, Jiyu Zhang notes that the SPC is introducing automated technologies to complete cumbersome tasks. Specifically, dictating technologies intended to transform court speeches into text will improve efficiency. Additionally, automated technologies that not only correct errors in judicial documents, but also generate parts of the documents themselves will allow judges to spend their time doing more meaningful work.

“As one witnesses the unrelenting ascent of AI in every aspect of the legal system, but also in all institutions of society, we all see a potentially inspiring future.” Nicolas Economou Artificial intelligence can provide judges with more information. Specifically, the SPC hopes to apply AI’s predictive and research capabilities to intelligent courts. By predicting the number of different kinds of cases, judges and court staff can better allocate resources. Also, improving court search capabilities and implementing an AI-fueled profiling system will not only make judicial processes more efficient, but also provide judges in different regions with similar information to make more consistent judgements. Legal profiling systems, Jia Gu notes, reduce information gaps and help judges find similar cases with similar arguments. This technology then assists legal practitioners to sift through large quantities of data. Though these types of technologies are highly contested, they allow


judges to search similar cases electronically, provide sentencing guidelines and help complete risk evaluations. Attention to data can overcome biases. Freedom from bias is essential to justice systems. The U.S. in particular questions to what extent AI applications need to reduce bias in the outputs they produce. Lee Tiedrich notes that as technology continues to advance, communities must comprehensively analyze AI’s impacts on legal bias issues. Since bias is not only morally reprehensible, as Robert Silvers states, but illegal under fair employment, housing and lending laws, robust protections need to be implemented to ensure that AI does not replicate or exacerbate existing biases. As AI becomes more prominent in legal systems, data is vital to containing and detecting bias. Legal data and information need to be accessible. To enable citizens to participate in increasingly AI-enabled legal systems and to be fully aware of what AI systems are doing, as Mike Philips highlighted, individuals need access to adequate data and information


within their legal systems. China’s SPC published all of its judgements since 2013 (approximately 48 million cases) online, giving inclusive access to case conclusions. The SPC also opened a trial network with access to court proceeding videos. As data barriers are torn down and individuals play more involved roles in the legal system, knowing AI’s data inputs will help determine whether outputs are biased. Algorithms are only as good as their data. As AI enters the courtroom, defendants are increasingly assessed by algorithms (for example for bail decisions). In the event that data is undesirably biased, the accompanying algorithm is likely to be adversely affected, producing erroneous outcomes. In addition, as raw data must often be labeled by subject matter experts, undesirable human bias can be introduced. This risk underscores the need for great caution in determining the norms under which courts and judges should feel confident in relying on the assessments made by artificial intelligence.

“The beauty of AI in China is that the SPC, the court system, is the main push behind it.” Jia Gu Qualified experts should operate AI and interpret the data. AI can be used in the service of the law,

but it is a scientific discipline distinct from the law, that requires specific expertise in order to be competently operated. It is important that AI operators, as well as data labelers, have the right domain expertise. As noted, AI requires data labeling, which should be entrusted to those qualified to do so. Additionally, algorithms involved in legal decision-making require expert human operation and interpretation to make AI measurably effective and its findings understandable. Thus, experts with the appropriate scientific, technical, and subject matter competencies play a key role in ensuring the effective and safe operation of artificial intelligence systems in the law. Artificial intelligence systems complicate liability risks. Companies developing AI systems must account for the technology’s accompanying risks and liabilities. Silvers links liability and regulatory exposure to companies’ AI-centered business strategies. As AI-driven products can adversely affect people and cause individuals to come forward saying artificial intelligence injured or disadvantaged them, with State v. Loomis serving as an example, companies need to fortify themselves against such risks via contracts and procedures. AI shifts liability to companies. Currently, more than 90 percent of car accidents are caused by human NOVEMBER 2018 28

“People think and hope that using AI technologies in court will help increase the efficiency as well as how to allocate the resources better just as the AI technologies do in other areas.” Jiyu Zhang error. In this context, individuals are often held legally accountable. But as automated vehicles surface and societies come to rely on AI systems, humans adopt more passive roles, raising questions of how responsibility for accidents should be apportioned. In the context of driverless cars and other entirely automated technologies, liability may be shifted from humans to technology. However, there are underlying convolutions and complexities. While liability shift to companies, Silvers questions “which companies?” As AI technologies have extensive value chains, comprised of OEMs, sensor manufacturers, infotainment companies, operating system manufacturers and others—a single product such as an autonomous vehicle may produce a vastly distributed network of liability. Companies need to develop protection and containment architecture. In order for companies to enjoy the benefits


of AI while monetizing its technologies, companies should build an “architecture of protection and containment.” Simultaneously, contracts play an important role in shifting liability on to other companies in the AI ecosystem. As artificial intelligence grows, not only is the legal system more automated, but judiciaries struggle with more difficult decisions in courts. The lack of different domains to account for AI in the judicial system causes uncertainty, but also enables creativity since there are not many mandating rules. Societal values of transparency in decision-making and of protection of intellectual property must be balanced. A salient challenge that the increased reliance on artificial intelligence produces for the law is the balancing of transparency with intellectual property rights. To illustrate this challenge, panelists cited The Loomis matter, a case in Wisconsin where a man received a lengthy sentence, in part on account of an algorithm that assessed him to be at high risk of recidivism. The court denied the defendant’s request to examine the underlying data and algorithms, in part on intellectual property grounds, a decision that was affirmed on appeal by the Wisconsin Supreme Court. Whereas the balancing of many complex societal values and case-specific information led to these decisions,

they nonetheless leave open the fundamental question associated with the deployment of AI in the legal system: on what grounds should society trust that such systems are effective for the purpose for which they are used? In common parlance: “Does it work? And how do we know?” Providing sufficient information to the institutions of state and to the general public in order to enable adequately informed action is an important principle. At the same time, society must protect entrepreneurship and innovation. Balancing these two considerations with complementary conceptual instruments, including ensuring evidence of effectiveness of AI and accountability for its operators is an important topic for further exploration.

“The more we dig in and the more work we do in the research organization, the more we’re aware of the critical importance of really understanding the technology as well as we possibly can.” Mike Philips The legal community must conscientiously harness AI’s benefits in the future. Law practitioners need to do their research to ensure that AI is deployed in a manner that improves efficiency and is consistent with their profession’s ideals and obligations. Gu highlights China’s research to overcome transparency, efficiency, privacy and fairness issues in its legal system by analyzing the Loomis case, ProPublica’s machine bias article and IEEE’s Global Initiative reports on automated and intelligent systems, amongst others. Understanding changing privacy frameworks in California and Europe, as well as past AI legal cases and the technologies themselves, will better equip legal systems to benefit most from AI. AI usage should align with lawyers’ professional ethics obligations. Lawyers are subject to ethical


“The key in terms of managing the use is to have it be an assistant to the lawyer, but not necessarily replace the lawyer.” Lee Tiedrich obligations. In the United States in particular, attorneys have duties of competence, confidentiality and a duty to honestly represent clients under the rule of law. When deciding how to use AI tools in their legal practice, lawyers need to do their due diligence in understanding the AI tools available and their accompanying strengths and limitations, as well as the specific competence needed to operate such systems safely and effectively. In addition to choosing the correct AI technologies, legal practitioners must make informed decisions when integrating such systems into practice. The ultimate responsibility for the incorporation and effective operation of AI systems used in the provision of legal services rests with lawyers, not AI systems. Businesses and lawyers must adapt to new privacy and security frameworks. As privacy laws proliferate globally, the large supply of data that fuels AI brings potentially serious privacy and security considerations. Though many fear that these laws will prevent innovative data usage, companies can still monetize data and use it to fuel machine learning and other AI applications. However, they must be more conscientious in how they operate within the legal framework. Specifically, companies need to adopt a set of consumer notices, obtain consent to use customer data, have certain opt-ins and -outs rights and give individuals the right to erasure. In regards to data security, companies need to prevent “AI poisoning” related to cyber-attacks that impair data pools’ integrity. As data privacy and security are at the forefront of legal AI discussions, critical information systems need strong cyber protections and companies must learn to operate in a growing legal framework that prioritizes privacy. ●


AI IN ENTERPRISE Presenters: Howie Xu Founder & CEO, TrustPath Joy Tang Founder & CEO, Markable.AI Terry Song Managing Director, JD Capital Julie Choi Head of AI Marketing, Intel Shaun Paga VP of Sales at Soul Machines Anis Uzzaman General Partner, Fenox Venture Capital


nterprises have increasingly adopted AI. This has led to a diverse artificial intelligence ecosystem around the world, involving not only large businesses, but AI startups and venture capitalists as well. Although the different AI players often face dissimilar challenges and opportunities, they have increasingly incorporated artificial intelligence into their operations to capitalize on its many benefits. The “AI in Enterprise” panel gathered speakers from Fenox Venture Capital, Markable.AI, TrustPath, Soul Machines, Intel and JD. The speakers applied their professional expertise and international perspectives to discuss AI’s current role—and predict its future role—in enterprise. They highlighted the complementary nature of China and the United States’ AI entrepreneurial environments and asserted that AI will continue to drive companies and societies into the future. KEY TAKEAWAYS Startups and large enterprises face different opportunities and challenges in AI. Enterprises and NOVEMBER 2018 30

“As we know for AI, training data is like our fuel to the car, like the blood source.” Joy Tang startups occupy different roles in the economy. As companies’ scale, scope and time in business differ, so do their strengths and weaknesses. Although artificial intelligence has increased competition between startups and enterprises, with both being AI-focused in applying its technology into their businesses, AI implementation has led startups and companies to witness different opportunities and hurdles. AI startups benefit from their focus and collaborations. AI companies’ narrow focus makes them more agile in the changing market. Joy Tang notes the importance of being niche in a sector, highlighting the narrowness of fashion recognition and its accompanying technological difficulty as an example. Being niche creates accuracy, which enables AI companies to become more advanced as they focus on


solving a single problem well. To help solve problems, AI startups develop symbiotic relationships with small, medium and large enterprise clients: smaller AI startups give their models—their software development kit (SDK) and underlying application program interface (API)—in exchange for the enterprise’s data. Software companies like Markable. AI thrive in this AI ecosystem by shortening enterprises’ technology development time while receiving needed and diverse data. Since data is “the blood source” of AI, such relationships are vital. Large companies see benefits in efficiency improvements. One of companies’ greatest AI opportunities is improving logistics and operations. Specifically, Terry Song highlights JD’s “4 Deployment of Inventories” strategy as a way to improve customer experiences by cutting down delivery times. Rather than having one large inventory warehouse at the outskirt of urban areas, this approach localizes travel time by deploying 4 separate inventory warehouses throughout the city. By cutting down on transportation times, consumers can receive orders within the same day.

“Usually when we see the demand, we go out and the first reaction is to find a see if there’s a very specialized solution that fits our need to give us that market window.” Terry Song Enterprises struggle with AI’s short market window. Since AI is fast-paced and disruptive, products fall in and out of demand. By the time an enterprise recognizes a key industry or product and decides to hire people and build a team, two or three years have passed and the company has missed the market window. Companies can overcome this challenge by finding startups with a very specialized company product and teaming together. AI companies face challenges with worker pay. Startups’ largest AI struggle relates to paying its workers. While startups are at risk of losing their shares to investors to keep fundraising, the employed engineers, data scientists and developers require adequate pay


since they are highly qualified and essential to the company’s existence. As there are talent and qualification scarcities in AI, startups need to make competitive offers. Artificial intelligence talent is scarce. Artificial intelligence requires an advanced skill set. Because of this, companies need qualified and educated data scientists, developers and engineers to develop and operate artificial intelligence technology. As AI bleeds into enterprises in terms of developing AI products and using AI to make more efficient and automate company processes, businesses compete against each other for talented and qualified employees— making talent recruitment and retention a pressing topic in regards to AI in enterprise. Universities are essential to recruitment. There are four main universities where AI talent originates: Stanford, Carnegie Mellon, Massachusetts Institute of Technology (MIT) and University of California, Berkeley. Anis Uzzaman and Julie Choi recognize that AI talent does not come from elsewhere, with Chinese computer


has caused Chinese AI talent to be less focused than American AI talent. As Chinese talent is funneled into large enterprises in China, their skills are at risk of becoming irrelevant as they do a single task in the large company but AI advances quickly in the outside world.

scientists attending these universities after their bachelor degrees as well. Because of this, AI startups and various enterprises go to universities when seeking talent. Tang specifically found talent by reading CVPR papers related to deformable object recognition and hired the students and professors that wrote the reports. International universities are emerging as potential recruitment sources. Though the top four American AI universities definitely remain the top sources for AI talent, the University of Auckland, Shaun Paga highlights, plays a large role in providing Soul Machines with qualified employees. The company’s talent is not only trained in data and computer science, but have PhDs in diverse areas of psychology, neuroscience and neurology. Silicon Valley, Anis Uzzaman notes, is attempting to tap into Canadian markets in Toronto and Ontario because of the University of Toronto and McGill University’s noteworthy computer science programs. The narrow sources of AI talent, Choi asserts, has diminished creativity in AI. With more universities in New Zealand and Canada being viewed as talent sources, an AI “renaissance” could possibly occur. Empowering AI talent retains them. As AI becomes more widespread, more enterprises seek qualified AI

“We think about AI and technology and how we’re going to be able to build relationships with machines and with brands in a new way.” Shaun Paga developers, engineers and computer and data scientists. To retain such workers startups and large enterprises offer different benefits. Startups, Tang asserts, give workers more ownership, control and creative licensing over technologies and products than any large firm. Having AI-trained and focused leadership is also a perk of startups: when AI talent is asked to create training data, AI’s fuel, workers are not pleased. Recognizing the importance of such data to the algorithm helps startups retain their employees. China’s high company valuations and AI culture is attracting more talent. Although Chinese computer science students come to the United States for school and stay to work, Chinese AI workers are beginning to return to China to create AI companies. This is because of China’s friendly AI culture, a high growth rate, access to abundant data and Chinese AI companies’ high valuations—being valued three to five times more than their Bay Area counterparts. This, Tang notes,

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Large enterprises have acclimated to the AI ecosystem. Well established companies have adapted to AI technologies in order to reap artificial intelligence’s benefits. Even though large enterprises’ scale can inhibit quick adaptivity, these companies recognize the importance of AI in remaining relevant and competitive in today’s technological global economy. Large enterprises’ have focused their resources into applying AI to their business models, products and operations, bettering technology and their companies in the process. Intel is more than hardware and CPU. Intel, the hardware company that invented processors, continues to play an important role in the tech industry. Intel has increasingly become an AI company as it provides the CPU and silicon needed to power artificial intelligence, with Microsoft’s use of Intel’s Field Programmable Gate Arrays (FPGAs) serving as an example. As AI models are the sum of data, compute and software, Intel strives to connect all parts of the equation. Specifically, the company is working with various organizations to create models in the form of PoCs and open sourcing tools for the data science community to accelerate enterprises’ AI development. Additionally, Intel is improving software data and usage to help enterprises maximize their CPU usage and quicken their models. AI is central to JD’s business. The E-commerce company has adjusted its business over the past five years to make AI the core of its operations. Approximately 200 billion dollars of gross merchandise volume (GMV) was on JD’s platform in 2017. As that amount grows at an annual rate of 30 to 40 percent, the company recognizes it has reached a scale where it physically cannot do anything manually. This, coupled


with rising labor costs, has pushed JD to use AI in fully automating the company’s warehouses in order to increase operating efficiency. AI’s future depends on investment and human engagement. To fuel AI in enterprise, companies need money. As venture capitalists look for companies to invest in, they take an anticipatory approach to determine which companies will profit moving forward, as well as which products will remain relevant. Although there are many uncertainties in this line of work, technologies that improve the human experience and humanAI engagements will take center stage as artificial intelligence continues to disrupt enterprises and economies. Future AI should prioritize trust and engagement. Soul Machines has made a new interface for AI. Although artificial intelligence pushes humans into more passive positions in many activities, this technology focuses on humans and human interaction, as they are the intended users. Soul Machines fully autonomous digital being, prioritizes trust and engagement when interacting with humans by detecting voice tones and facial expressions. By giving technologies a higher emotional quotient, AI will be more adopted in the future. Venture capitalists are looking to automation and blockchain. AI’s fast paced and adaptable nature corresponds to venture capitalists’ forward thinking, but adds difficulty to VC’s line of work. The largest hurdle that venture capitalists face is anticipating what is next in making instantaneous investment decisions. Although there are many uncertainties, Uzzaman forecasts that automation in the robotic control space will be the next big thing to invest in. Blockchain also plays a role moving forward if coupled with AI correctly since AI is past experience and blockchain applies that experience to making it secure in the future. If a company can unite a blockchain-based distribution and data transfer with AI-based future analytics, they will receive investments. ●




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