TECHNOLOGY BRIEF: A BOARD PRIMER ON ARTIFICIAL INTELLIGENCE

2 > A BOARD PRIMER ON ARTIFICIAL INTELLIGENCE

Making sense of artificial intelligence (AI) 3

Is the current AI hype valid? 5

How does AI work? 7

What opportunities does AI present? 9

What are the risks of AI? 12

How can AI be secured? 15

First steps to responsible AI 17

Recommended reading 19

CONTENTS

A BOARD PRIMER ON ARTIFICIAL INTELLIGENCE > 3

MAKING SENSE OF ARTIFICIAL INTELLIGENCE (AI)

FIGURE 1. A brief history of AI

ARTIFICIAL INTELLIGENCECan a computer be programmedwith a well-defined set of rulesto solve a problem?

1956

1950

Alan Turingcreated theTuring Test.

1940-1956:The Birth of AI

1958

1959

1961 1966

1966

1994

1997

2000

2004

2011

2016

2017

20181994-present:Modern Age

1987-1994:Second Winter

1980-1987:AI Boom

1974-1980AI Winter

1956-1974:The GoldenYears

Conference heldat DartmouthCollege wherethe term ArtificialIntelligence wascoined.

ELIZA, an artificialconversational“therapist” wascreated.

Honda Asimo, apersonal robot,was released.

IBM’s Watsonbeat the best Jeopardy player.

Kismet, a socialmachine capableof expressingemotions wasintroduced.

Introductionof VirtualAgents such as Siri, GoogleNow, andIPSoft’s Amelia.

Google’sAutoML allowed to generate AI.

Shakey becamethe first mobilerobot “aware” ofits surroundings.

IBM’s Deep Bluedefeated the humanchess champion.

Boom of ExpertMachines inindustry likethe R1/XCONto help salesrepresentativesavoid errorsin productsuggestions.

Two roboticcars drove longdistance on thehighway.

Perceptron, thefoundation ofneural networks,was introduced.

Samuel’s checkersprogram usedMachine Learningto beat humanplayers.

IBM’s Shoeboxperformedarithmetic byvoice command.

MACHINE LEARNINGCan a computer learn to solve a problem using algorithms and statistical models and trained with data?

DEEP LEARNINGCan a computer analyze unstructured data and train itself to solve a problem with minimal external interference?

Artificial Intelligence (AI) is all around us—from satellite navigation systems that use real-time traffic information to calculate the fastest route home, and virtual assistants that monitor and control a room’s environment, to recommendations for the next movie to watch. Given the many uses of AI, defining this technology can present a challenge.

This report defines AI as a collection of technologies used to train a machine to emulate human tasks through learning and automation. The technologies enable a machine to sense its environment, assess any relevant factors, act on that information, and learn how to improve future performance.

The first generation of AI relied on engineers to hard-code intelligence into systems using a series of rules. In these early systems, “learning” took the form of improvements made to the computer code—or algorithm—over time. Early pioneer Arthur Samuel first demonstrated his Checkers-Playing Program on television in 1956, which went on to defeat the checkers master Robert Nealey in 1962. In 1997, after nearly 40 years in the field, IBM’s Deep Blue computer beat the reigning world chess champion Garry Kasparov—in what was hailed as the ultimate battle of man versus machine—during their famous rematch (see Figure 1).

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MAKING SENSE OF ARTIFICIAL INTELLIGENCE (AI)

More modern AI relies on a process called machine learning—the ability to decide which actions are required to complete a task by analyzing data, rather than solely relying on a code to act in a pre-determined way. In short, machine learning enables a machine to train itself to complete tasks independently. Whether we are talking about predictive systems that can forecast what is likely to happen, natural language processing that can comprehend speech and text in realtime, machine vision that can understand visual inputs with high levels of accuracy, or optimized research and information retrieval, these activities are all based on machine learning (see Figure 2).

FIGURE 2. Machine learning is at the heart of modern AI

Predictive Systems

Natural LanguageUnderstanding

ExportSystems

MachineVision

Robotics

InformationRetrieval

KnowledgeRepresentation

Search andOptimization

MACHINELEARNING

A BOARD PRIMER ON ARTIFICIAL INTELLIGENCE > 5

As previously noted, AI is far from a new idea. In fact, the term “artificial intelligence” was coined as early as 1956. AI’s history is one characterized by waves of optimism, disappointment, and inertia. These waves have even been dubbed “AI winters” (see Figure 1). It has been observed that previous breakthroughs have only partly lived up to the hype generated, and none have managed to kick-start the technology into the mainstream. So, what is different this time?

AI is what economists call a general-purpose technology. 1 We believe that the significance lies in the fact that this technology can cause disruption not only through direct contributions to society, but also through spill-over effects, which can help enable a vast range of complementary innovations. Electricity, for example, made possible factory electrification, telegraphic communication, and computers. Similarly, the internal combustion engine gave rise to the automobile, the airplane, and modern transportation and logistics networks. AI could offer the same potential impact as electricity and the internal combustion engine; that is, the potential to transform the way we work and live.

Most businesses today can experience an unprecedented period of technology innovation through and across various industries, much of it tied to AI. AI applications can make use of virtually unlimited processing power in the cloud. When factoring in the decreasing cost of storage (down from US$500,000 a gigabyte in 1980 to three cents a gigabyte in 2015) 2 as well as the exponential growth in data sets that can be used to train AI, a compelling picture emerges for the potential capabilities of this technology. This “democratization of AI”—broader access to the opportunities and benefits for non-experts—can provide a powerful

1 ExplAIned:AGuideforExecutives,Accenture:https://www.accenture.com/gb-en/insights/artificial-intelligence/artificial-intelligence-explained-executives

2 Ibid.

IS THE CURRENT AI HYPE VALID?

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IS THE CURRENT AI HYPE VALID?

foundation for the technology to reach critical mass for mainstream adoption 3 (see Figure 3).

FIGURE 3. The mainstream deployment of AI 4

3 The Democratization of AI Is Putting Powerful Tools in the Hands of Non-Experts, Singularity Hub: https://singularityhub.com/2018/02/19/the-democratization-of-ai-is-putting-powerful-tools-in-the-hands-of-non-experts/

4 AI momentum, maturity & models for success; SAS, Accenture, and Intel with Forbes Insights: https://www.accenture.com/t20180919T202227Z__w__/us-en/_acnmedia/PDF-86/Accenture-AI-Momentum-Final.pdf

22%

24%

27%

36%

37%

47%

51%

60%

61%

66%

71%

31%

39%

34%

28%

26%

25%

26%

19%

20%

16%

10%

Fleet/Mobile Facilities

Expediting Transactions

Production-Floor Systems

Logistics and Supply Chain

Monitoring Through External Devices/Systems

Operational Improvement

Security/Fraud Detection

HR/Workforce Management

Customer Relations/Interactions (i.e. Chatbots)

Marketing/Sales

External Communication (Marketing, Social Media, PR)

0% 20% 40% 60% 80% 100%

Deploying Considering

In which of the following functional areas are you deploying or considering deploying AI?

A BOARD PRIMER ON ARTIFICIAL INTELLIGENCE > 7

HOW DOES AI WORK?

The ability to learn is a fundamental characteristic of AI (see Figure 4). Identifying which actions are required to complete a task by analyzing data, rather than being explicitly coded to act in a pre-determined way, is what makes the modern form of AI “intelligent.”

FIGURE 4. Steps in AI machine learning process

But there is a limit to intelligence with traditional machine learning processes—improvements can reach a plateau and performance can tail off. Improved understanding of how the brain works to obtain knowledge has led to the development of a new field in machine learning—called deep learning—which is more scalable and theoretically has no limit to intelligence.

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HOW DOES AI WORK?

Most machine learning models are “black boxes.” As the accuracy and complexity of these models continue to grow, many of the behaviors they exhibit may defy any comprehensive human understanding. This lack of transparency can create security vulnerabilities; for example, if the data used by an AI engine is manipulated, the machine’s learning and decision making will be altered. Similarly, if algorithms are not properly secured, a malicious actor can alter specific outcomes.

5 AI momentum, maturity & models for success; SAS, Accenture, and Intel with Forbes Insights: https://www.accenture.com/t20180919T202227Z__w__/us-en/_acnmedia/PDF-86/Accenture-AI-Momentum-Final.pdf

Learning with AlphaGo 5

When the best AI systems apply deep learning as a means to learn from large amounts of data, the results can be extraordinary. AlphaGo, the AI developed by Google DeepMind, became the first computer program to defeat a professional human player at the highly-complex board game Go. AlphaGo was taught the rules of play, and then shown thousands of different human-versus-human games, so that it could discern the winning strategies on its own. The result: victory over the legendary world Go champion, Lee Sedol.

But even that was not the end of DeepMind’s Go success. The company subsequently developed a second, even more powerful, version of AlphaGo. AlphaGo Zero learned winning strategies simply by playing games against itself, rather than using data from human players.

The latest iteration of the AI AlphaZero has gone even further. AlphaZero proved it could learn chess by playing games against itself, surpassing human levels of skill in just four hours. The feat is particularly noteworthy, given that AlphaZero was not specifically designed to play chess.

A BOARD PRIMER ON ARTIFICIAL INTELLIGENCE > 9

AI is likely to become a new driver of economic value for organizations. However, businesses can find it difficult to leverage this technology without first understanding the opportunities it presents. To set a clearer path forward, corporate leaders can consider the following: review and, where appropriate, introduce automation into business processes; assess how AI can augment employees’ current work; and avoid concentrating or limiting this technology within particular business units or functions.

Consider what processes to automate: AI is one of the new frontiers of automation. With the advent of self-learning autonomous systems that mimic human behavior—exploiting machine learning, computer vision, as well as knowledge representation and reasoning—AI could take automation beyond rules-based predictable work into tasks that require human judgment. That would open up a huge number of new automation opportunities. The potential use cases vary from the automation of manufacturing or service processes using mechanical robots, to that of administrative or service processes comprising digital and manual inputs using Robotic Process Automation (RPA).

The company AMP Robotics has created a robotic system called Cortex which uses computer vision to rapidly pick recyclable materials from a conveyor belt of waste products. The system is driven by an AI, called Neuron, capable of distinguishing materials that can be recycled from those that cannot—even if they are dirty or mixed with other materials—using a video stream.6

Augmenting employees’ current work: AI can unlock new levels of efficiency to enhance organizations’ use of their resources. In practice, this can translate to augmenting employees’ judgment and help to enhance the customer experience.

6 Robots for Recycling, AMP Robotics: https://www.amprobotics.com/amp-cortex

WHAT OPPORTUNITIES DOES AI PRESENT?

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WHAT OPPORTUNITIES DOES AI PRESENT?

Machine learning enables AI systems to analyze and extract meaning from large and highly complex data sets at unmatched speeds. AI can see patterns, similarities, and anomalies, where human experts see none.

Researchers at the University of Nottingham have created an AI that can predict which patients are likely to have a stroke or heart attack within 10 years. The AI performed better than the standard methods of prediction. While impressive, this should not be taken as a sign that human expertise will be superseded any time soon. The best results are still achieved when human experts work together with AI, each bringing their unique capabilities to bear on a problem. 7

Businesses can also use AI to help improve customers’ experiences. That could mean using digital assistants and chatbots to engage customers through social media and digital platforms. It could also mean making personalized product or service recommendations on an eCommerce site.

When South American airline Avianca wanted to enhance the travel experience of its 28 million passengers, it created Carla, a Facebook Messenger chatbot assistant, which uses AI to help customers manage their travel arrangements.8 By holding natural-sounding conversations with Carla on a messaging platform already familiar to them, Avianca’s customers were given a quick and intuitive way to check in, review itineraries and flight status, and monitor weather and other updates from the airline. Carla also cut the average check-in time for customers by half.

7 Confirmed:AICanPredictHeartAttacksandStrokesMoreAccuratelyThanDoctors,Futurism:https://futurism.com/confirmed-ai-can-predict-heart-attacks-and-strokes-more-accurately-than-doctors

8 ExplAIned:AGuideforExecutives,Accenture:https://www.accenture.com/gb-en/insights/artificial-intelligence/artificial-intelligence-explained-executives

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Diffusing innovation: AI innovations can diffuse through various businesses and sectors to help create new business models and opportunities. The implications for our personal and professional lives are likely to be far-reaching.

Autonomous electric vehicles, for example, could completely overturn our current thinking on transportation. From automotive companies, and logistics, to global oil prices, the direct effects of a shift to autonomous vehicles is likely to be profound. These shifts could also affect vehicle safety design, insurance needs, and medical or emergency response. Additionally, this could impact road network design, parking needs, car dealerships, and oil taxes. The sheer scale of the societal change from an AI innovation in just one industry can be transformative.

Technology can only enable an existing strategy; it cannot replace it. To be able to effectively exploit AI, leaders should first identify the business case for adoption or implementation. Executives can discuss what outcomes are expected, how success will be measured, anticipated returns on investment, and whether these goals align with the organization’s broader priorities.

WHAT OPPORTUNITIES DOES AI PRESENT?

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The huge opportunities and benefits that AI offers do not come risk-free. To help ensure their companies are well-positioned to exploit these opportunities, organizations should embark on their AI journeys with a clear-eyed view of the likely risks.

Cyber risks fall into two broad categories: data integrity and algorithm manipulation. The learning and decision-making capabilities of AI can be altered by threat actors modifying the data used in the training process. The algorithms themselves should also be protected from manipulations by threat actors hoping to change the outcomes of AI systems for malevolent purposes.

The power and danger of AI

A team of Harvard pathologists created an AI-based technique to identify breast cancer cells. The technique worked well, scoring 92 percent accuracy, but still fell short of human pathologists, who typically achieve precision rates of around 96 percent. The biggest surprise came when humans and AI combined forces. Together, they accurately identified 99.5 percent of cancerous biopsies,9 indicating that their diagnostic contributions were to some degree complementary, not duplicative.

Another team of researchers recently used a form of deep learning AI that has been used to generate realistic imagery. The researchers showed how this technique could be used to efficiently manipulate high resolution 3D medical images. 10 Using medical imagery freely available from the Internet, the researchers trained one AI algorithm to inject images of cancer tumors and trained the other to remove images of cancer tumors from hospital computerized tomography (CT) scans.

Radiologists misdiagnosed 99 percent of the altered scans that showed malignant tumors, and 94 percent of altered images that had cancerous images removed. The researchers were also able to fool AI-based algorithms that are used to help radiologists in their diagnosis.

9 World Cancer Research Fund International: https://www.wcrf.org/dietandcancer/cancer-trends/breast-cancer-statistics

10 IsraeliResearchersShowMedicalScansVulnerabletoFakeTumors,TheTimesofIsrael: https://www.timesofisrael.com/israeli-researchers-show-medical-scans-vulnerable-to-fake-tumors/

WHAT ARE THE RISKS OF AI?

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Four principal risks should be considered in the near-term. These risks relate to trust and transparency, liability, control, and security. As directors engage their management teams around these issues, they should consider the following:

TRUST AND TRANSPARENCY

How can the company demonstrate to its stakeholders (including employees, investors, clients, suppliers and regulators) that AI is safe to use? How does the company avoid biases, unconscious or not, being coded into the AI-enabled systems? The answers to these questions usually lie in transparency and accountability. Complex forms of AI are often, by their very nature, “black boxes.” In other words, they operate in ways that can make it hard to explain how they arrived at the results produced. This is a challenge and new approaches are needed to offer better explanations of the processes underlying AI decisions. Decisions taken by AI must be open to interrogation and, when necessary, appeal.

LIABILITY

What happens when AI makes an error—or even breaks the law? Who, for example, should be held legally liable if an autonomous vehicle injures a pedestrian with no driver at the wheel? Executive leaders should carefully monitor changes in legislative and regulatory requirements to ensure compliance.

WHAT ARE THE RISKS OF AI?

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WHAT ARE THE RISKS OF AI?

CONTROL

What happens when a machine takes over a process? Under what circumstances might a human need to take this back? How should companies go about building that option into AI-enabled systems? Careful thought is needed about when and how control is shared or transferred between humans and AI.

SECURITY

How do companies prevent unauthorized or malicious manipulation of AI? As the growth of AI into all sectors increases, security becomes paramount and is compounded by the current lack of protection to both AI models and the data used to train them.

A BOARD PRIMER ON ARTIFICIAL INTELLIGENCE > 15

Much of the current investment in cybersecurity is dedicated to securing the infrastructure underpinning AI models. 11 This includes patching vulnerabilities in software and systems, implementing robust access management to ensure employees only engage with the necessary information to do their jobs, and prioritizing the security of the firm’s most valuable data assets. The adoption of AI systems generally creates entirely new areas of infrastructure to secure the AI models themselves and requires security practices to mitigate against these vulnerabilities.

AI cyberattacks, in most cases, succeed by predicting the decisions machine learning models will make and then manipulating subsequent data sets to produce the attacker’s desired outcomes—rather than the programmed decisions. These breaches can also take the form of “poisoning attacks,” wherein the machine learning model itself is manipulated.

As organizations navigate through these multifaceted challenges, corporate executives should also take into consideration the following areas to help ensure robust and secure AI governance:

LIMIT THE AI LEARNING RATE

The amount of time and effort to train a model is determined by how quickly data examples can be submitted into a system. Limiting the rate at which an AI system will accept new data sets can increase the time and effort required by malicious actors to “poison” a model and produce different outputs. Limiting the volume of data to be ingested in an AI system over a set period can act as a major deterrent.

11 KnowYourThreat:AIisthenewattacksurface,Accenture:https://www.accenture.com/gb-en/insights/artificial-intelligence/adversarial-ai

HOW CAN AI BE SECURED?

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HOW CAN AI BE SECURED?

VALIDATE AND PROTECT AI INPUT

Data integrity—the ability to identify, validate and maintain data throughout its lifecycle—is fundamental to AI security. Extra care is required when using the Internet as a source of data. It is much easier for a threat actor to post toxic data on the Internet than it is to break into a system and modify it. In assessing data integrity practices, both around protection and validation, companies should carefully focus on inputs into AI models and confirm that these originate from identifiable and trusted sources. Doing so provides an important defense against an adversary’s ability to fool a model.

STRUCTURE AI MODELS FOR RESILIENCE

Restricting access to AI models by limiting the ability to make ad hoc changes is one of the most effective forms of defense. One advantage of machine learning is its tolerance for “dirty data.” Machine learning’s ability to learn and improve over time enables suspect data to be processed with far greater resilience. If properly executed, it is also possible to structure the machine learning models to provide some natural resistance to adversarial attack.

TRAIN AI TO RECOGNIZE ATTACKS

AI models learn by example. If enough malicious examples are inserted into data during the training phase, a machine learning algorithm can eventually learn how to interpret toxic data and reject adversarial attacks. Business continuity and disaster recovery are also vital. Organizations should understand how to “relearn” and recover without negatively impacting the business.

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As attacks on AI continue to emerge, organizations’ future security strategies should take account of all risks and threats to AI. The emphasis should be on engineering resilient modelling structures and strengthening critical models against cyberattack by malicious threat actors.

Data integrity is also a fundamental requirement to help secure AI from malevolent influence. But there is no substitution for basic cybersecurity hygiene; companies should aim to: identify high-value assets and harden them to cyberattack; encourage timeliness in the patching of systems; and implement robust access management processes to ensure access is limited to those with the necessary permissions. Perhaps most importantly, organizations should be prepared for the worst-case scenario with robust business continuity and disaster recovery plans. Practicing these regularly can help ensure the organization is resilient to potential attacks.

As artificial intelligence becomes more sophisticated, it could start to make or assist decisions that have a greater impact on individual lives. This will raise ethical challenges as people adjust to the larger and more prominent role of automated decision making in society.

To pressure-test their management’s preparedness to assess and mitigate the risks associated with AI, directors can ask the following questions:

1. What are the opportunities for using AI to automate, augment and innovate what we do and the way we work? Do we have the prerequisite processes, talent and other capabilities in place to exploit the value of AI over time? If not, how (fast) will we develop these necessary conditions?

2. Are we considering process-oriented and functional changes to our operating model in addition to automation?

FIRST STEPS TO RESPONSIBLE AI

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FIRST STEPS TO RESPONSIBLE AI

3. Have we fully assessed the risks of using AI in our business, particularly the unintended consequences these may present?

4. Have we taken the right steps to develop AI in a transparent and responsible way without accidental bias?

5. Do we understand the people implications of our AI strategy? How are we ensuring we have the adequate training and skills in place for the ethical use of AI in our organization?

6. Are the results from our AI-enabled capabilities explainable to our broad set of stakeholders?

7. How are we monitoring the use of AI to track any potential issues within our organization and with our customers and suppliers?

8. What governance processes are in place to assess our AI usage and results against the initial purpose of deployment in the organization?

9. Are our AI models and training data secure against outside influence and manipulation?

10. What is our response plan to mitigate the impact of an attack or hack against our AI system(s)?

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Human + Machine: Reimagining Work in the Age of AI

by

Paul R. Daugherty and H. James Wilson

RECOMMENDED READING

CONTACT USRobert KressManaging Director, Accenture Securityrobert.e.kress@accenture.com

ABOUT ACCENTUREAccenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations. Combining unmatched experience and specialized skills across more than 40 industries and all business functions—underpinned by the world’s largest delivery network—Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders. With approximately 482,000 people serving clients in more than 120 countries, Accenture drives innovation to improve the way the world works and lives. Visit us at www.accenture.com.

ABOUT ACCENTURE SECURITYAccenture Security helps organizations build resilience from the inside out, so they can confidently focus on innovation and growth. Leveraging its global network of cybersecurity labs, deep industry understanding across client value chains and services that span the security lifecycle, Accenture protects organization’s valuable assets, end-to-end. With services that include strategy and risk management, cyber defense, digital identity, application security and managed security, Accenture enables businesses around the world to defend against known sophisticated threats, and the unknown. Follow us @AccentureSecure on Twitter or visit the Accenture Security blog.

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