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Rise of Artificial Intelligence in and its Implications on Educational Systems and Practices

Historical Overview

The long and now rapidly flowing Artificial Intelligence (AI) river which courses through the global technoscape has several milestones worth noting, especially as they impact the current speed and course of this river. And it is both the speed and direction to which this paper intends to focus. As most readers know, AI originated in the early 1950s, with the  work of Frank Rosenblatt [1] and his concept of the perceptron, or neural network to mimic the brain. This concept, in turn, was extended by Geoffrey Hinton in the 1980s [2] to that of the multi-layered neural network, which eventuated in the first self-driving car –  built by one Dean Pomerleau.[3]. A decade later Yan Lecun [4] extended the system to recognise handwritten digits. From there the river slowed somewhat until 2006 [5] when superfast chips and massive datasets unleashed the power of Hinton’s algorithms and AI began to more easily identify images, recognize speech and support language translation. Some six decades later, in 2012, machine learning and neural nets became front page news when Hinton [6] was able to demonstrate on the 10 million image data set of ImageNet a reduction in  image classification errors by 20%. This has led to substantive adoption of AI in industry and finance, not to mention medical education.

Schools of Thought

Currently discussion surrounding the nature and function of AI is replete with fierce evangelists on both sides of what is regarded by many as a two-edged sword. One side sees AI as having the potential to enhance human capacity and transform the way we live, work and experience life on the planet. This includes AI researchers like Geoffrey Hinton, Yoshua Bengio, Yan Lecun, Andrew Ng, Chris Bishop, Max Tegmark and Ray Kurzweill [7]. The other side consists of those who warn of the very imminent danger of AI, with respect to loss of individual freedom and control, as well as the destruction of many jobs and livelihoods within the global economy. Included in this group are the likes of Elon Musk, Stuart Russell, Martin Ford[8], Nick Bostrom, Sam Harris, Peter Haas and Yuval Harari[9]. The core concern of the latter group is the worry that we are building AI systems about which we make incorrect assumptions re: the capabilities and intelligence of AI – which in narrow fields is already light years ahead of humans, i.e., computation, reading ability, pattern recognition, to name a few; and that without proper ethical guidelines, principles and processes, we may in fact be building something which could lead to our extermination, and/ or replace the many practical advantages of democracy, because, ironically, they do what we ask them to do to the detriment of humanity. As Norbert Wiener presaged in 1960: ”put a purpose into a machine, better be absolutely certain the purpose is what you desire.”[10]

Current Capabilities and Behaviours of AI

While we accept that Advanced General Intelligence (AGI) may remain a distinct and distant possibility, we also argue that it is important to highlight and keep in view what the current behaviors and capabilities of Narrow AI are, while working towards both understanding and controlling their evolution and application, especially within the Health Services[11-13] and the Medical Education sectors. Building on the general approaches and successes of Machine Learning, i.e., supervised, unsupervised and reinforcement learning, it is increasingly accepted in the Health Sciences that the applications gaining most traction and funding, both for ongoing research, development and application are in four specific areas: (i) recommending what one should buy online,  (ii) spotting spam and detecting credit card fraud, (iii) recognizing who and what is in a photo, and (iv) interacting with virtual assistants like Alexa and Siri.  Increasingly these AI behaviors are spilling over into medicine in a number of areas. For example, the photo identification in images and video is leading to speedier identification of pathologies and supporting assistive prediction. Another example is its application in biometrics, enabling real time tracking for diagnosis and management of chronic diseases. Perhaps more interesting is that is being used in areas where doctor shortages exist and patient needs are growing. Yet another application is through the use of elder care robots that can detect and interpret signals from the brains of the elderly to support both patient and caregiver.

There is compelling data to support the idea that the recent advances in both deep learning, i.e., unsupervised systems, could have significant impact on how our social, political and economic narratives evolve. Increasingly, more questions are being raised with respect to the impact upon and control over systems and services formally managed by humans. The arguments build around the rapid advances in Advanced General Intelligence (AGI), and the attendant requirements for the training and monitoring of AI. These significant evolutionary improvements in machine and deep learning techniques, as well as the inability of developers to explain how and why AI is generating its own operational biases, suggest that adoption of AI reflects a clearer understanding of principles and processes guiding such research directions.

Medical Education

Medical Education is education relevant to human health among any type of learner, including health professionals, students in the health professions, and patients.

With respect to Medical Education and our own research in this area, Natural Artificial Intelligence (NAI) enables a number of innovative and paradigm shifting experiences. For the first time we can now explore the extent to which an AI tutor can support learning and teaching.  We are now looking at being able to program AI to support personalized and adaptive learning for each and every learner. Specifically, within a fully digitized curriculum we are examining the extent to which AI can now dynamically generate learner profiles, based on extensive knowledge of student prior learning history, current learning activities within accessed learning resources, knowledge gained through quizzes and assessment results – including Work Placed Based Assessments. With this information we are then able to explore the degree to which AI can interact with learners to provide targeted content, meaningful feedback and dynamic visualization of curriculum progress and associated mastery of specific and general competency associated with becoming a medical practitioner.  Of particular interest to us is the degree to which the current affordances of AI can then be extended to support the application of such tracked performance to digital learning such as Virtual Patients[14]

Conclusion

The paper highlighted key directions research and implementation of AI has taken to date in health services and medical education. It also underlined the degree to which current supervised and unsupervised learning on the part of AI agents may impact medical systems, services and training, and suggested strongly that moral and ethical responsibilities consonant with generally accepted medical values and principles need be evident before AI gains traction in medical decision making, an area that directly impacts quality of care and patient safety.

 

References

  1. Rosenblatt, F., The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 1958. 65(6): p. 386.
  2. Rumelhart, D.E., G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors. nature, 1986. 323(6088): p. 533.
  3. Pomerleau, D.A., ALVINN: an autonomous land vehicle in a neural network, in Advances in neural information processing systems 1, S.T. David, Editor. 1989, Morgan Kaufmann Publishers Inc. p. 305-313.
  4. LeCun, Y., et al., Backpropagation applied to handwritten zip code recognition. Neural computation, 1989. 1(4): p. 541-551.
  5. Hinton, G.E., S. Osindero, and Y.-W. Teh, A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 2006. 18(7): p. 1527-1554.
  6. Krizhevsky, A., I. Sutskever, and G.E. Hinton. Imagenet classification with deep convolutional neural networks. in Advances in neural information processing systems. 2012.
  7. Kurzweil, R., The Singularity is near: When Humans Transcend Biology. 2005, New York: : Viking.
  8. Ford, M., Rise of the Robots: Technology and the Threat of a Jobless Future. . 2016, New York, NY, USA: Basic Books Inc.
  9. Harari, Y.N., Homo Deus: A Brief History of Tomorrow. 2016, London Harvill Secker.
  10. Wiener, N., Some Moral and Technical Consequences of Automation. Science, 1960. 131(3410): p. 1355-1358.
  11. Jiang F, J.Y., Zhi H, et al Artificial intelligence in healthcare: past, present and future Stroke and Vascular Neurology 2017.
  12. Wahl B, C.-G.A., Germann S, et al Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Global Health 2018(3:e000798).
  13. Gašević, D., et al., Internet High Educ, 2016. 28(null): p. 68.
  14. Kononowicz AA, Z.N., Edelbring S, Corral J, Hege I, Virtual patients–what are we talking about? A framework to classify the meanings of the term in healthcare education. BMC Med Educ, 2015(Feb 1;15:11).

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CTC Global Singapore, a premier end-to-end IT solutions provider, is a fully owned subsidiary of ITOCHU Techno-Solutions Corporation (CTC) and ITOCHU Corporation.

Since 1972, CTC has established itself as one of the country’s top IT solutions providers. With 50 years of experience, headed by an experienced management team and staffed by over 200 qualified IT professionals, we support organizations with integrated IT solutions expertise in Autonomous IT, Cyber Security, Digital Transformation, Enterprise Cloud Infrastructure, Workplace Modernization and Professional Services.

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Planview has one mission: to build the future of connected work. Our solutions enable organizations to connect the business from ideas to impact, empowering companies to accelerate the achievement of what matters most. Planview’s full spectrum of Portfolio Management and Work Management solutions creates an organizational focus on the strategic outcomes that matter and empowers teams to deliver their best work, no matter how they work. The comprehensive Planview platform and enterprise success model enables customers to deliver innovative, competitive products, services, and customer experiences. Headquartered in Austin, Texas, with locations around the world, Planview has more than 1,300 employees supporting 4,500 customers and 2.6 million users worldwide. For more information, visit www.planview.com.

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SIRIM is a premier industrial research and technology organisation in Malaysia, wholly-owned by the Minister​ of Finance Incorporated. With over forty years of experience and expertise, SIRIM is mandated as the machinery for research and technology development, and the national champion of quality. SIRIM has always played a major role in the development of the country’s private sector. By tapping into our expertise and knowledge base, we focus on developing new technologies and improvements in the manufacturing, technology and services sectors. We nurture Small Medium Enterprises (SME) growth with solutions for technology penetration and upgrading, making it an ideal technology partner for SMEs.

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HashiCorp provides infrastructure automation software for multi-cloud environments, enabling enterprises to unlock a common cloud operating model to provision, secure, connect, and run any application on any infrastructure. HashiCorp tools allow organizations to deliver applications faster by helping enterprises transition from manual processes and ITIL practices to self-service automation and DevOps practices. 

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IBM is a leading global hybrid cloud and AI, and business services provider. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity and service.

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