Augmented intelligence instead of artificial intelligence.

Over the last years, significant and highly visible results have been achieved using Artifical Intelligence (AI). Based on those, AI is the reigning world champion in games such as checkers, chess, Jeopardy, Go or Quake 3. However, there are still several disciplines in which humans remain leading, mainly because AI lacks qualities like experience, creativity or leadership. Exactly those qualities make humans great in innovation and design. Instead of focusing on improving AI, I advocate to use AI to augment humans and human intelligence. In such a way, humancentred intelligent systems as well as intelligent user interfaces can be created that either amplify existing or create new human skills and capabilities. Such augmented humans and augmented intelligence will allow humans to exceed their capabilities, go beyond current human capabilities and offer new experiences. Athletes could, e.g., receive real-time feedback on how to optimally use their resources. Police agents could, e.g., based on their current location and environment receive notifications on urgent tasks to be performed. Over the last years, I have explored the design of augmented humans and intelligence in the domains of sports, health and safety & security. In my talk, I present my recent research and discuss resulting design recommendations for augmented humans and intelligence. I conclude with an agenda for future research on augmented humans and intelligence.

Interview with Deborah DiSanzo of IBM Watson Health

In addition to the public health and economic calamity, the coronavirus disease 2019 (COVID-19) pandemic has had multifaceted effects on the practice of dermatology. However, times of great crisis are also times of transformative change. Over the last 2 months, more progress has been made in the adoption of teledermatology than over the last two decades—the Centers for Medicare and Medicaid Services (CMS) has provided compensation parity for teledermatology visits and waived potential penalties for health insurance portability and accountability act (HIPAA) violations when treating patients through modalities such as FaceTime and Skype.1 Similarly, the American Academy of Dermatology has issued guidelines to practices for implementing teledermatology workflows.1 In effect, dermatologists have embraced a digital future. Just as telemedicine represents one pillar of dermatology's digital future, augmented intelligence (AuI), otherwise known as artificial intelligence, represents another promising component of dermatology's digital future. Recent advances in AuI have generated both hype as well as hope that AuI algorithms will be able to detect and diagnose skin disease.2 For example, numerous direct-to-consumer smartphone apps have been developed to evaluate lesions for potential melanoma. However, a recent systematic review noted high rates of false negatives and wide variability in accuracy, with sensitivities ranging from 7% to 73% and specificities ranging from 37% to 94%.3 This substantial variation in performance highlights the caution we must exercise in embracing new technologies. Dermatologists should critically appraise AuI applications in the same way we appraise any new medication or procedure. However, this does not mean dermatologists should ignore AuI. Rather, dermatologists should reimagine how AuI can be used. Although AuI is not yet capable of automating diagnoses, AuI is capable of streamlining administrative tasks. For example, Mayo Clinic has developed an AuI algorithm to automatically organize dermatology images stored within the institution's electronic health record.4 This enables researchers to quickly assemble a cohort of specific image types for research rather than manually sift through individual patient encounters. Another algorithm could be used to ensure patients are taking sufficient quality images for store-and-forward teledermatology. In light of the COVID-19 pandemic, many patients find themselves using teledermatology for the first time. Therefore, an assistive algorithm could alert patients if the image they have uploaded is blurry or does not have adequate lighting. Similarly, dermatologists face significant administrative challenges as they look to reopen their in-person practices amidst the pandemic. AuI algorithms could utilize electronic health record data to stratify patients based on their risk factors and assist in triaging patients for in-person vs virtual visits. Furthermore, this would provide dermatology practices valuable information to optimize scheduling and staffing. Although less awe-inspiring than automated diagnoses of melanoma, AuI applications for administrative tasks would still provide significant efficiency gains for our health care system. Dermatologists have a long-standing history of pioneering advances in digital medicine, dating back to at least 1972 when the first teledermatology platform was implemented in the United States.5 Therefore, as we emerge from the pandemic, dermatologists are uniquely positioned to lead a digital transformation in health care. This transformation has the potential to reduce administrative burdens and to improve patient care. The authors declare that they have no conflicts of interest. All authors contributed to the conception, critical revision, and drafting of this manuscript. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Augmented Intelligence (AI) combines human and artificial intelligence to enhance decision-making. This paper reviews AI concepts, applications, and collaboration models like human-in-the-loop AI and cognitive computing AI. It examines AI's role in improving human judgment, handling large datasets, and making routine decisions. The paper explores AI's impact on sectors like healthcare through use cases in blood glucose monitoring. It applies the McKinsey 4D framework to implement AI for glucose monitoring. The paper also discusses emerging models like Hybrid Augmented Intelligence (HAI) that integrate human cognition within AI systems for optimal performance. Overall, it underscores AI's potential in complementing human capabilities and driving innovation across industries with responsible design.

Augmented intelligence (AuI) integrates human intelligence (HI) and artificial intelligence (AI) to harness their strengths and mitigate their weaknesses. The combination of HI and AI has seen to improve both human and machine capabilities, and achieve a better performance compared to separate HI and AI approaches. In this paper, we present a survey of literature to understand how AuI has been applied in the literature, including the roles of HI and AI, AI approaches, features, and applications. Due to the limited literature related to this topic, we also present a survey of expert opinion to answer four main questions to understand the experts’ implications of AuI, including: a) the definition of AuI and the significance of HI in AuI; b) the roles of HI in AuI; c) the current and future applications of AuI in research, industry, and public, as well as the advantages and shortcomings of AuI; and d) end users’ view of the application of AuI. We also present recommendations to improve AuI, and provide a comparison between the findings from the surveys of both literature and expert opinion. The discussion of this paper shows the promising potential of AuI compared to separate HI and AI approaches.

Every decade technology makes revolutionary shifts that become the new platforms on which application technology is built. Artificial intelligence (AI) is no different. AI has moved from 1st Generation shallow learning and handcrafted features to 2nd Generation deep learning, which has been effective at learning patterns. We have now entered the 3rd Generation of AI which is machine reasoning-driven – where the machine can interpret decision making algorithm, even if it has the black-box nature. Explainable artificial intelligence and augmented intelligence are the main part of the 3rd Generation of AI. The role of Professor Lotfi Zadeh and Professor Rafik Aliev in these AI generations are tremendous.Professor L. Zadeh was the founder of the theory of fuzzy sets and linguistic variables, the “father” of fuzzy logic and approximate reasoning, the author of the theory of possibility and general theory of uncertainty, the creator of Z-numbers theory and generalized restrictions, the ancestor of granular and soft computing. His ideas and theories not only opened a new epoch in the development of scientific thought, free from the limitations of narrow scientific directions and contributing to their synergy. They made a significant contribution to the development of new information and cognitive technologies, led to the creation of effective industrial technologies, such as fuzzy computers and processors, fuzzy regulators, fuzzy clustering and recognition systems, and many others. Professor L. Zadeh has been deservedly included in the IEEE Computer Society's gallery of fame scientists who have made pioneering contributions to the field of artificial intelligence and intelligent systems.The role of L. Zadeh in AI is also hard to overestimate especially in the l focus on the concept of soft computing, originally combining hybrid models based on fuzzy sets, neural networks, and soft computing. The emergent properties of these models were one of the foundations of the current hype in artificial intelligence and machine learning.For many years L. Zadeh fruitfully cooperated with Professor Rafik Aliev. The result of their research was fundamental scientific achievements published in several joint monographs. The generalized theory of stability created by L. Zadeh and R. Aliev jointly has been recognized as a major contribution to the development of mathematics, management theory, dynamic economics and a number of other sciences. The main scientific directions of Professor Rafik Aliev’s research are the theory of decision-making in terms of high uncertainty, the theory of coordination in complex social, economic, and technical systems, fuzzy logic, and neurocomputing. Applied fields of his scientific results are economics, conflictology, space objects and technical systems.These areas of modern artificial intelligence are referred to augmented intelligence (intelligence decision-making). Augmented intelligence is a design pattern for a human-centered partnership model where humans and artificial intelligence work together to improve cognitive functions, including learning, decision-making, and new experiences. The business value forecast for augmented intelligence in 2025 will be 44 percent of all AI business applications.Explainable artificial intelligence now represents a key area of research in artificial intelligence and an unusually promising one in which many fuzzy logics could become crucial. Research in the of area of explainable artificial intelligence can be divided into three stages, which correlates with the 3 generation of AI: in the first stage (starting from 1970) expert systems were developed; in the second stage (mid-1980s), the transition was made from expert systems to knowledge-based systems; and in the third phase (since 2010), deep architectures of artificial neural networks, which required new global research on the construction of explainable systems, have been studied. On each stage the ideas of L. Zadeh and R. Aliev played the important roles. They were the first ones in explainable artificial intelligence field with neuro-fuzzy systems – a synergistic combination of fuzzy logic and neural networks, providing the first interpretable AI system based on neural network learning. Since 2011 L. Zadeh had been involved in Z Advanced Computing, Inc. (ZAC), the pioneer Cognitive Explainable Artificial Intelligence (Cognitive XAI). The Cognitive Explainable-AI approach also use the results of Prof. Rafik Aliev, Prof. Ronald Yager, Prof. Mo Jamshidi.Nowadays, explainable and augmented intelligence are prominent and fruitful research fields where many of Zadeh’s and R. Aliev’s contributions can become crucial if they are carefully considered and thoroughly developed. It is worth noting that about 30% of publications in Scopus related to XAI, dated back to 2017 or earlier, came from authors well recognized in the fuzzy logic field. This is mainly due to the commitment of the fuzzy community to produce interpretable intelligent systems since interpretability is deeply rooted in the fundamentals of fuzzy logic.

A surprising vision of how human intelligence will coevolve with digital technology and revolutionize how we think and behave. It is natural for us to fear artificial intelligence. But does Siri really want to kill us? Perhaps we are falling into the trap of projecting human traits onto the machines we might build. In Evolutionary Intelligence,Neuman offers a surprisingly positive vision in which computational intelligence compensates for the well-recognized limits of human judgment, improves decision making, and actually increases our agency. In artful, accessible, and adventurous prose, Neuman takes the reader on an exciting, fast-paced ride, all the while making a convincing case about a revolution in computationally augmented human intelligence. Neuman argues that, just as the wheel made us mobile and machines made us stronger, the migration of artificial intelligence from room-sized computers to laptops to our watches, smart glasses, and even smart contact lenses will transform day-to-day human decision making. If intelligence is the capacity to match means with ends, then augmented intelligence can offer the ability to adapt to changing environments as we face the ultimate challenge of long-term survival. Tapping into a global interest in technology's potential impacts on society, economics, and culture, Evolutionary Intelligence demonstrates that our future depends on our ability to computationally compensate for the limitations of a human cognitive system that has only recently graduated from hunting and gathering.

Background“Artificial intelligence” (AI) is often referred to as “augmented human intelligence” (AHI). The latter term implies that computers support—rather than replace—human decision-making. It is unclear whether the terminology used affects attitudes and perceptions in practice.MethodsIn the context of a quality improvement project implementing AI/AHI-based decision support in a regional health system, we surveyed staff’s attitudes about AI/AHI, randomizing question prompts to refer to either AI or AHI.ResultsNinety-three staff completed surveys. With a power of 0.95 to detect a difference larger than 0.8 points on a 5-point scale, we did not detect a significant difference in responses to six questions regarding attitudes when respondents were alternatively asked about AI versus AHI (mean difference range: 0.04–0.22 points; p > 0.05).ConclusionAlthough findings may be setting-specific, we observed that use of the terms “AI” and “AHI” in a survey on attitudes of clinical staff elicited similar responses.

Artificial intelligence (AI) shows promising potential to enhance human decision-making as synergistic augmented intelligence (AuI), but requires critical evaluation for skin cancer screening in a real-world setting. To investigate the perspectives of patients and dermatologists after skin cancer screening by human, artificial and augmented intelligence. A prospective comparative cohort study conducted at the University Hospital Basel included 205 patients (at high-risk of developing melanoma, with resected or advanced disease) and 8 dermatologists. Patients underwent skin cancer screening by a dermatologist with subsequent 2D and 3D total-body photography (TBP). Any suspicious and all melanocytic skin lesions ≥3 mm were imaged with digital dermoscopes and classified by corresponding convolutional neural networks (CNNs). Excisions were performed based on dermatologist's melanoma suspicion, study-defined elevated CNN risk-scores and/or melanoma suspicion by AuI. Subsequently, all patients and dermatologists were surveyed about their experience using questionnaires, including quantification of patient's safety sense following different examinations (subjective safety score (SSS): 0-10). Most patients believed AI could improve diagnostic performance (95.5%, n = 192/201). In total, 83.4% preferred AuI-based skin cancer screening compared to examination by AI or dermatologist alone (3D-TBP: 61.3%; 2D-TBP: 22.1%, n = 199). Regarding SSS, AuI induced a significantly higher feeling of safety than AI (mean-SSS (mSSS): 9.5 vs. 7.7, p < 0.0001) or dermatologist screening alone (mSSS: 9.5 vs. 9.1, p = 0.001). Most dermatologists expressed high trust in AI examination results (3D-TBP: 90.2%; 2D-TBP: 96.1%, n = 205). In 68.3% of the examinations, dermatologists felt that diagnostic accuracy improved through additional AI-assessment (n = 140/205). Especially beginners (<2 years' dermoscopic experience; 61.8%, n = 94/152) felt AI facilitated their clinical work compared to experts (>5 years' dermoscopic experience; 20.9%, n = 9/43). Contrarily, in divergent risk assessments, only 1.5% of dermatologists trusted a benign CNN-classification more than personal malignancy suspicion (n = 3/205). While patients already prefer AuI with 3D-TBP for melanoma recognition, dermatologists continue to rely largely on their own decision-making despite high confidence in AI-results. ClinicalTrials.gov (NCT04605822).

Healthcare TransformationVol. 1, No. 4 Open AccessRobots, Augmented Intelligence, and Things Only Humans Can DoStephen K. Klasko and Editor-in-ChiefStephen K. KlaskoSearch for more papers by this author and Editor-in-ChiefSearch for more papers by this authorPublished Online:1 Dec 2016https://doi.org/10.1089/heat.2016.29028.skkAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail It's been a year of Healthcare Transformation—this is our fourth issue. We wanted to be bold and provocative, and each issue has carried ideas that could save the planet … or at least set healthcare delivery on an optimistic path. We've talked to legends, such as Andrew Young. We've talked to pioneers, such as Bernie Marcus. We've talked to leaders, such as Aneesh Chopra.In this issue, we are honored to have a rare interview with Judy Faulkner, the founder of Epic, the nation's leading (and dominant) provider of electronic health records (EHRs). Whatever you think of EHRs, there is little question that the opportunity is clear: to create the digital concierge, as she calls it, that can flag “rising risk” for thousands of patients at a time. Judy Faulkner's story is pure—the computer science student who found a problem and sought to solve it. She is the only computer scientist and woman running a major health tech company. We are delighted she agreed to imagine the future with us.In addition to Faulkner's story, this issue tackles the intersection of digital technology and human health: EHRs, wearables, high speed genetic analysis. Could your doctor be a robot?I'm going to make a prediction:The future of healthcare will not be found in artificial intelligence (AI), although AI will contribute more than we imagine. The future of healthcare will not be found in augmented reality (AR), although AR will transform our experiences.My prediction is that healthcare will be driven by augmented intelligence that places the locus of meaning back at home—back with the patient—and allows the physician to get back to the role of guide.The one thing human beings can do that no robot can do is be human. We can help patients, families, and communities make sense of what's happening. We can help build meaning, and we can build motivation, even at the end of life.To find the physician of the future requires a revolution in medical education, not incremental reform. For a century, we've selected and trained doctors to be memorization machines. If an applicant to medical school can remember 19 causes of jaundice, that applicant beat the one who could only remember 17. But we don't need that level of memorization. There will be a robot who can remember all signs of an illness, and correlate those signs with personalized genetic markers, with risk profiles, and with evidence-based protocols for treating that patient.If a robot can do that, what we need are doctors who can do what robots cannot do. We need doctors who can answer your question when you ask, “What does this mean?”I often use the case of an obstetrician delivering a baby who unexpectedly has Down syndrome. What the parents need at that moment is not someone who can define Trisomy 21 and run through a list of potential health and developmental challenges for babies with this diagnosis.What the parents need is a doctor who can say, “What this means is that you've delivered a beautiful baby. It means your baby will grow, and love, and be loved. It means we'll find parents with other beautiful babies like yours so that you can talk with them and learn from them what's unique about your child.”Each of the patient advocates profiled in this issue has faced the need to create meaning. Emily Kramer-Golinkoff, Nicole Johnson, and Mark Laabs each face rare, life-threatening, soul-destroying illnesses. None caved in. Each has found meaning disrupting traditional organizational practices and accelerating science.I'm especially struck with Emily's observation: that science is a network of communities. When we engage with patients, we reconfigure those networks. We speed and focus our work.And we find meaning.FiguresReferencesRelatedDetailsCited byAnalysis of Nurse’s Reflection on Success or Failure of Blood Withdrawal by Vein TypesAugmented Intelligence and NursingNursing Education Perspectives, Vol. 38, No. 2 Volume 1Issue 4Dec 2016 Information© Stephen K. Klasko 2016; Published by Mary Ann Liebert, Inc.To cite this article:Stephen K. Klasko and Editor-in-Chief.Robots, Augmented Intelligence, and Things Only Humans Can Do.Healthcare Transformation.Dec 2016.209-211.http://doi.org/10.1089/heat.2016.29028.skkcreative commons licensePublished in Volume: 1 Issue 4: December 1, 2016Open accessThis Open Access article is distributed under the terms of the Creative Commons Attribution Noncommercial License ( http://creativecommons.org/licenses/by-nc/4.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.PDF download

Study question What is the mean number of transfers needed to achieve a euploid transfer selected by embryologists plus ERICA’s assistance? Summary answer Augmented intelligence (ERICA plus human collaboration) outperforms both the embryologists and artificial intelligence's individual performance alone. What is known already Euploid embryos are more likely to implant successfully. Artificial intelligence (AI) could improve embryo selection over current techniques, but scepticism exists. Augmented intelligence (AuI) combines both the mathematical reproducibility of machine learning and the knowledge and experience of humans. This approach employs AI tools as an assistant, where the user shall learn to interpret the AI. A recent study suggested that embryologists assisted by AI improved the embryo selection of euploid transfers. ERICA (IVF2.0 Limited, UK) was designed to rank blastocysts according to their probability of euploidy. Study design, size, duration We prospectively studied embryo selection for ERICA alone, embryologists only and when interacting (embryologists and ERICA) in 150 synthetically generated (reconstructed on real-data) embryo transfer cycles. Embryos were ranked in order, and performance was assessed by time to identify a euploid embryo within each cycle cohort correctly. Embryologists were allowed to rank a maximum of 10 cycles per day for three weeks starting in January 2022, using a mobile phone application designed for this purpose. Participants/materials, setting, methods Using real-life cycle distributions of euploid/aneuploid blastocysts and the number of embryos in a cycle (according to ERICA’s database), we created 150 synthetic cycles, 30 for each age bracket (&amp;lt; 35, 35-37, 38-40, 41-42, and &amp;gt;42). These were randomly populated with blastocyst images preserving their actual ploidy status correspondingly. Each synthetic cycle contained between 2 to 6 authentic embryo images with at least one euploid and one aneuploid. Main results and the role of chance The total database had a euploid rate of 37.4% (n = 513), and by age brackets from 1 to 5 were 45.7% (n = 116), 43.8% (n = 105), 35.9% (n = 92), 31.2% (n = 96), and 28.8% (n = 104) respectively. The mean number of cycles analysed by each participant was 113.5 (CI: 100.8-126.2). The mean time-to-euploid transfer for embryologists alone was 2.07 (CI:2.00-2.13); for the ERICA alone was 1.86 (CI:1.82-1.91); and for embryologists assisted by ERICA was 1.62 (CI:1.55-1.68). All study groups compared to each other were statistically significant using a paired two-tailed student’s t-test (p &amp;lt; 0.001). The proportion of euploid transfer at the first try for embryologists alone was 0.40 (CI:0.37-0.43), for ERICA alone was 0.54 (CI:0.53-0.54), and for embryologists assisted by ERICA was 0.47 (CI:0.44-0.50). All study groups compared with each other were statistically significant with a paired two-tailed student’s t-test (p &amp;lt; 0.01). Limitations, reasons for caution Although our findings suggest that Aul outperforms both AI and humans alone, this study needs to be replicated with a larger cohort of embryologists with different experience levels in different countries to confirm these results. Wider implications of the findings Combining machine-human interaction through a well-designed process could improve embryo selection and reduce inter-operator variability amongst staff with different experience levels. It could also set a frame for adequate agency and accountability, and enhance trust and adoption. Trial registration number NA

This study investigates the integration of artificial intelligence (AI) and augmented intelligence (AuI) in an open distance e-learning university, focusing on lecturers’ and students’ experiences. Using qualitative methods: focus group discussions and e-mail interviews, it examines the adoption and exploration of these technologies, particularly in academic writing skills development. The research applies diffusion of innovations theory and technology acceptance model to understand the dissemination and acceptance of AI and AuI, emphasising perceived ease of use and usefulness. It contrasts perspectives between lecturers and students, revealing varied views on AI utilisation in academic writing. Despite differences, both groups express positive experiences and benefits from AI. The findings contribute to a deeper understanding of the transformative impact of AI and AuI on teaching and learning in a distance learning university. AI has far-reaching effects on lecturers, students, and policymakers as they navigate the integration of intelligent systems in distance learning contexts.

This article explores social network site (SNS) users’ understanding of the danger the integration of human intelligence and artificial intelligence (AI), termed “augmented intelligence,” presents. Augmented intelligence, a subsection of artificial intelligence (AI), aims to enhance human intelligence with AI and is heralded as a significant step in problem-solving. A crucial concern is the profound threat to SNS users’ information security. A quantitative approach examined SNS understanding regarding the diffusion of augmented intelligence into SNS users’ spaces. An online survey was administered to 165 SNS users residing in the Gauteng province of South Africa. Diffusion of Innovation (DOI) theory was used as the theoretical lens. Ethical clearance was obtained, and the data collected was anonymized and kept confidential. The article provides new insights that can help SNS users understand that a new threat to their information security in the form of augmented intelligence is emerging. Findings suggest that out of the five constructs drawn from DOI that explain the diffusion of augmented intelligence into sophisticated social engineering attacks, relative advantage, compatibility, and complexity were perceived by study participants as likely predictors of augmented intelligence adoption. Users, however, differed on exactly how the augmentation process was being achieved.

Augmented Intelligence is a system model which centre on the partnership between human general intelligence &amp; common sense with Artificial Intelligence’s exceptional processing speed and computation power to get better cognitive performance. This study can shed light on Augmented Intelligence and how it can be implemented so that we have heroes working at the peak human level possible, suffer fewer casualties, and also have equal footing in terms of technology and military strength. Nepal being open bordered country has a great threat from the border issues and with ever-rising crime and challenges in various cities creating intra threat. The study analyses to overcome this geographical challenge, operational problems, the rise of conflicts, and establish better policies, faced by security personnel with technology that actually empowers all the brave soldiers and Police officers to maintain security from both external and internal risk. In the ever-evolving technology limitations of human beings can be catastrophic, but replacing it all with Artificial Intelligence is not a practical solution as well because it would be giving the third entity all the ability that might revolt against humans themselves. The article can be summed up as, research on the practicality of Artificial Intelligence and Augmented Intelligence in national security and figure out a way for its legal adaptation. The availability of various case studies, surveys, research on various existing technology, and research papers published in renowned journals and publications has made this study possible.

The AI revolution is moving at a breakneck speed. Organizations are beginning to invest in innovative ways to monetize their data through the use of artificial intelligence. Businesses need to understand the reality of AI. To be successful, it is imperative that organizations understand that augmented intelligence is the secret to success. Augmented Intelligence: The Business Power of Human–Machine Collaboration is about the process of combining human and machine intelligence. This book provides business leaders and AI data experts with an understanding of the value of augmented intelligence and its ability to help win competitive markets. This book focuses on the requirement to clearly manage the foundational data used for augmented intelligence. It focuses on the risks of improper data use and delves into the ethics and governance of data in the era of augmented intelligence. In this book, we explore the difference between weak augmentation that is based on automating well understood processes and strong augmentation that is designed to rethink business processes through the inclusion of data, AI and machine learning. What experts are saying about Augmented Intelligence "The book you are about to read is of great importance because we increasingly rely on machine learning and AI. Therefore, it is critical that we understand the ability to create an environment in which businesses can have the tools to understand data from a holistic perspective. What is imperative is to be able to make better decisions based on an understanding of the behavior and thinking of our customers so that we can take the best next action. This book provides a clear understanding of the impact of augmented intelligence on both society and business."—Tsvi Gal, Managing Director, Enterprise Technology and Services, Morgan Stanley "Our mission has always been to help clients apply AI to better predict and shape future outcomes, empower higher value work, and automate how work gets done. I have always said, ’AI will not replace managers, but managers who use AI will replace managers who don't.’ This book delves into the real value that AI promises, to augment existing human intelligence, and in the process, dispels some of the myths around AI and its intended purpose."—Rob Thomas, General Manager, Data and AI, IBM