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Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
AI Computer Wraps up 4-1 Victory Against Human Champion Nature Reports from Alphago's Victory in Seoul
The Go Files: AI computer wraps up 4-1 victory against human champion Nature reports from AlphaGo's victory in Seoul. Tanguy Chouard 15 March 2016 SEOUL, SOUTH KOREA Google DeepMind Lee Sedol, who has lost 4-1 to AlphaGo. Tanguy Chouard, an editor with Nature, saw Google-DeepMind’s AI system AlphaGo defeat a human professional for the first time last year at the ancient board game Go. This week, he is watching top professional Lee Sedol take on AlphaGo, in Seoul, for a $1 million prize. It’s all over at the Four Seasons Hotel in Seoul, where this morning AlphaGo wrapped up a 4-1 victory over Lee Sedol — incidentally, earning itself and its creators an honorary '9-dan professional' degree from the Korean Baduk Association. After winning the first three games, Google-DeepMind's computer looked impregnable. But the last two games may have revealed some weaknesses in its makeup. Game four totally changed the Go world’s view on AlphaGo’s dominance because it made it clear that the computer can 'bug' — or at least play very poor moves when on the losing side. It was obvious that Lee felt under much less pressure than in game three. And he adopted a different style, one based on taking large amounts of territory early on rather than immediately going for ‘street fighting’ such as making threats to capture stones. This style – called ‘amashi’ – seems to have paid off, because on move 78, Lee produced a play that somehow slipped under AlphaGo’s radar. David Silver, a scientist at DeepMind who's been leading the development of AlphaGo, said the program estimated its probability as 1 in 10,000. -
In-Datacenter Performance Analysis of a Tensor Processing Unit
In-Datacenter Performance Analysis of a Tensor Processing Unit Presented by Josh Fried Background: Machine Learning Neural Networks: ● Multi Layer Perceptrons ● Recurrent Neural Networks (mostly LSTMs) ● Convolutional Neural Networks Synapse - each edge, has a weight Neuron - each node, sums weights and uses non-linear activation function over sum Propagating inputs through a layer of the NN is a matrix multiplication followed by an activation Background: Machine Learning Two phases: ● Training (offline) ○ relaxed deadlines ○ large batches to amortize costs of loading weights from DRAM ○ well suited to GPUs ○ Usually uses floating points ● Inference (online) ○ strict deadlines: 7-10ms at Google for some workloads ■ limited possibility for batching because of deadlines ○ Facebook uses CPUs for inference (last class) ○ Can use lower precision integers (faster/smaller/more efficient) ML Workloads @ Google 90% of ML workload time at Google spent on MLPs and LSTMs, despite broader focus on CNNs RankBrain (search) Inception (image classification), Google Translate AlphaGo (and others) Background: Hardware Trends End of Moore’s Law & Dennard Scaling ● Moore - transistor density is doubling every two years ● Dennard - power stays proportional to chip area as transistors shrink Machine Learning causing a huge growth in demand for compute ● 2006: Excess CPU capacity in datacenters is enough ● 2013: Projected 3 minutes per-day per-user of speech recognition ○ will require doubling datacenter compute capacity! Google’s Answer: Custom ASIC Goal: Build a chip that improves cost-performance for NN inference What are the main costs? Capital Costs Operational Costs (power bill!) TPU (V1) Design Goals Short design-deployment cycle: ~15 months! Plugs in to PCIe slot on existing servers Accelerates matrix multiplication operations Uses 8-bit integer operations instead of floating point How does the TPU work? CISC instructions, issued by host. -
The Evolution of Intelligence
Review From Homo Sapiens to Robo Sapiens: The Evolution of Intelligence Anat Ringel Raveh * and Boaz Tamir * Faculty of interdisciplinary studies, S.T.S. Program, Bar-Ilan University, Ramat-Gan 5290002, Israel; [email protected] (A.R.R.); [email protected] (B.T.) Received: 30 October 2018; Accepted: 18 December 2018; Published: 21 December 2018 Abstract: In this paper, we present a review of recent developments in artificial intelligence (AI) towards the possibility of an artificial intelligence equal that of human intelligence. AI technology has always shown a stepwise increase in its capacity and complexity. The last step took place several years ago with the increased progress in deep neural network technology. Each such step goes hand in hand with our understanding of ourselves and our understanding of human cognition. Indeed, AI was always about the question of understanding human nature. AI percolates into our lives, changing our environment. We believe that the next few steps in AI technology, and in our understanding of human behavior, will bring about much more powerful machines that are flexible enough to resemble human behavior. In this context, there are two research fields: Artificial Social Intelligence (ASI) and General Artificial Intelligence (AGI). The authors also allude to one of the main challenges for AI, embodied cognition, and explain how it can viewed as an opportunity for further progress in AI research. Keywords: Artificial Intelligence (AI); artificial general intelligence (AGI); artificial social intelligence (ASI); social sciences; singularity; complexity; embodied cognition; value alignment 1. Introduction 1.1. From Intelligence to Super-Intelligence In this paper we present a review of recent developments in AI towards the possibility of an artificial intelligence equals that of human intelligence. -
Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: a Review
information Review Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review Ammar Arbaaeen 1,∗ and Asadullah Shah 2 1 Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia 2 Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia; [email protected] * Correspondence: [email protected] Abstract: For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on Citation: Arbaaeen, A.; Shah, A. NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on Ontology-Based Approach to open domains, this study investigates the closed domain. -
Lecture 10: Recurrent Neural Networks
Lecture 10: Recurrent Neural Networks Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 1 May 4, 2017 Administrative A1 grades will go out soon A2 is due today (11:59pm) Midterm is in-class on Tuesday! We will send out details on where to go soon Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 2 May 4, 2017 Extra Credit: Train Game More details on Piazza by early next week Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 3 May 4, 2017 Last Time: CNN Architectures AlexNet Figure copyright Kaiming He, 2016. Reproduced with permission. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 4 May 4, 2017 Last Time: CNN Architectures Softmax FC 1000 Softmax FC 4096 FC 1000 FC 4096 FC 4096 Pool FC 4096 3x3 conv, 512 Pool 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 Pool Pool 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 Pool Pool 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256 Pool Pool 3x3 conv, 128 3x3 conv, 128 3x3 conv, 128 3x3 conv, 128 Pool Pool 3x3 conv, 64 3x3 conv, 64 3x3 conv, 64 3x3 conv, 64 Input Input VGG16 VGG19 GoogLeNet Figure copyright Kaiming He, 2016. Reproduced with permission. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 5 May 4, 2017 Last Time: CNN Architectures Softmax FC 1000 Pool 3x3 conv, 64 3x3 conv, 64 3x3 conv, 64 relu 3x3 conv, 64 3x3 conv, 64 F(x) + x 3x3 conv, 64 .. -
Image Retrieval Algorithm Based on Convolutional Neural Network
Advances in Intelligent Systems Research, volume 133 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016) Image Retrieval Algorithm Based on Convolutional Neural Network Hailong Liu 1, 2, Baoan Li 1, 2, *, Xueqiang Lv 1 and Yue Huang 3 1Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science & Technology University, Beijing 100101, China 2Computer School, Beijing Information Science and Technology University, Beijing 100101, China 3Xuanwu Hospital Capital Medical University, 100053, China *Corresponding author Abstract—With the rapid development of computer technology low, can’t meet the needs of people. In order to overcome this and the increasing of multimedia data on the Internet, how to difficulty, the researchers from the image itself, and put quickly find the desired information in the massive data becomes forward the method of image retrieval based on content. The a hot issue. Image retrieval can be used to retrieve similar images, method is to extract the visual features of the image content: and the effect of image retrieval depends on the selection of image color, texture, shape etc., the image database to be detected features to a certain extent. Based on deep learning, through self- samples for similarity matching, retrieval and sample images learning ability of a convolutional neural network to extract more are similar to the image. The main process of the method is the conducive to the high-level semantic feature of image retrieval selection and extraction of features, but there are "semantic using convolutional neural network, and then use the distance gap" [2] between low-level features and high-level semantic metric function similar image. -
Knowledge Elicitation: Methods, Tools and Techniques Nigel R
This is a pre-publication version of a paper that will appear as Shadbolt, N. R., & Smart, P. R. (2015) Knowledge Elicitation. In J. R. Wilson & S. Sharples (Eds.), Evaluation of Human Work (4th ed.). CRC Press, Boca Raton, Florida, USA. (http://www.amazon.co.uk/Evaluation-Human-Work-Fourth- Wilson/dp/1466559616/). Knowledge Elicitation: Methods, Tools and Techniques Nigel R. Shadbolt1 and Paul R. Smart1 1Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK. Introduction Knowledge elicitation consists of a set of techniques and methods that attempt to elicit the knowledge of a domain expert1, typically through some form of direct interaction with the expert. Knowledge elicitation is a sub-process of knowledge acquisition (which deals with the acquisition or capture of knowledge from any source), and knowledge acquisition is, in turn, a sub-process of knowledge engineering (which is a discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems). Although the elicitation, representation and transmission of knowledge can be considered a fundamental human activity – one that has arguably shaped the entire course of human cognitive and social evolution (Gaines, 2013) – knowledge elicitation had its formal beginnings in the early to mid 1980s in the context of knowledge engineering for expert systems2. These systems aimed to emulate the performance of experts within narrowly specified domains of interest3, and it initially seemed that the design of such systems would draw its inspiration from the broader programme of research into artificial intelligence. In the early days of artificial intelligence, much of the research effort was based around the discovery of general principles of intelligent behaviour. -
LSTM-In-LSTM for Generating Long Descriptions of Images
Computational Visual Media DOI 10.1007/s41095-016-0059-z Vol. 2, No. 4, December 2016, 379–388 Research Article LSTM-in-LSTM for generating long descriptions of images Jun Song1, Siliang Tang1, Jun Xiao1, Fei Wu1( ), and Zhongfei (Mark) Zhang2 c The Author(s) 2016. This article is published with open access at Springerlink.com Abstract In this paper, we propose an approach by means of text (description generation) is a for generating rich fine-grained textual descriptions of fundamental task in artificial intelligence, with many images. In particular, we use an LSTM-in-LSTM (long applications. For example, generating descriptions short-term memory) architecture, which consists of an of images may help visually impaired people better inner LSTM and an outer LSTM. The inner LSTM understand the content of images and retrieve images effectively encodes the long-range implicit contextual using descriptive texts. The challenge of description interaction between visual cues (i.e., the spatially- generation lies in appropriately developing a model concurrent visual objects), while the outer LSTM that can effectively represent the visual cues in generally captures the explicit multi-modal relationship images and describe them in the domain of natural between sentences and images (i.e., the correspondence language at the same time. of sentences and images). This architecture is capable There have been significant advances in of producing a long description by predicting one description generation recently. Some efforts word at every time step conditioned on the previously rely on manually-predefined visual concepts and generated word, a hidden vector (via the outer LSTM), and a context vector of fine-grained visual cues (via sentence templates [1–3]. -
Generative Linguistics and Neural Networks at 60: Foundation, Friction, and Fusion*
Generative linguistics and neural networks at 60: foundation, friction, and fusion* Joe Pater, University of Massachusetts Amherst October 3, 2018. Abstract. The birthdate of both generative linguistics and neural networks can be taken as 1957, the year of the publication of foundational work by both Noam Chomsky and Frank Rosenblatt. This paper traces the development of these two approaches to cognitive science, from their largely autonomous early development in their first thirty years, through their collision in the 1980s around the past tense debate (Rumelhart and McClelland 1986, Pinker and Prince 1988), and their integration in much subsequent work up to the present. Although this integration has produced a considerable body of results, the continued general gulf between these two lines of research is likely impeding progress in both: on learning in generative linguistics, and on the representation of language in neural modeling. The paper concludes with a brief argument that generative linguistics is unlikely to fulfill its promise of accounting for language learning if it continues to maintain its distance from neural and statistical approaches to learning. 1. Introduction At the beginning of 1957, two men nearing their 29th birthdays published work that laid the foundation for two radically different approaches to cognitive science. One of these men, Noam Chomsky, continues to contribute sixty years later to the field that he founded, generative linguistics. The book he published in 1957, Syntactic Structures, has been ranked as the most influential work in cognitive science from the 20th century.1 The other one, Frank Rosenblatt, had by the late 1960s largely moved on from his research on perceptrons – now called neural networks – and died tragically young in 1971. -
The History Began from Alexnet: a Comprehensive Survey on Deep Learning Approaches
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches Md Zahangir Alom1, Tarek M. Taha1, Chris Yakopcic1, Stefan Westberg1, Paheding Sidike2, Mst Shamima Nasrin1, Brian C Van Essen3, Abdul A S. Awwal3, and Vijayan K. Asari1 Abstract—In recent years, deep learning has garnered I. INTRODUCTION tremendous success in a variety of application domains. This new ince the 1950s, a small subset of Artificial Intelligence (AI), field of machine learning has been growing rapidly, and has been applied to most traditional application domains, as well as some S often called Machine Learning (ML), has revolutionized new areas that present more opportunities. Different methods several fields in the last few decades. Neural Networks have been proposed based on different categories of learning, (NN) are a subfield of ML, and it was this subfield that spawned including supervised, semi-supervised, and un-supervised Deep Learning (DL). Since its inception DL has been creating learning. Experimental results show state-of-the-art performance ever larger disruptions, showing outstanding success in almost using deep learning when compared to traditional machine every application domain. Fig. 1 shows, the taxonomy of AI. learning approaches in the fields of image processing, computer DL (using either deep architecture of learning or hierarchical vision, speech recognition, machine translation, art, medical learning approaches) is a class of ML developed largely from imaging, medical information processing, robotics and control, 2006 onward. Learning is a procedure consisting of estimating bio-informatics, natural language processing (NLP), cybersecurity, and many others. -
Evolutionary Psychology As of September 15
Evolutionary Psychology In its broad sense, the term ‘evolutionary psychology’ stands for any attempt to adopt an evolutionary perspective on human behavior by supplementing psychology with the central tenets of evolutionary biology. The underlying idea is that since our mind is the way it is at least in part because of our evolutionary past, evolutionary theory can aid our understanding not only of the human body, but also of the human mind. In the narrow sense, Evolutionary Psychology (with capital ‘E’ and ‘P’, to distinguish it from evolutionary psychology in the broad sense) is an adaptationist program which regards our mind as an integrated collection of cognitive mechanisms that are adaptations , i.e., the result of evolution by natural selection. Adaptations are traits present today because they helped to solve recurrent adaptive problems in the past. Evolutionary Psychology is interested in those adaptations that have evolved in response to characteristically human adaptive problems like choosing and securing a mate, recognizing emotional expressions, acquiring a language, distinguishing kin from non-kin, detecting cheaters or remembering the location of edible plants. Its purpose is to discover and explain the cognitive mechanisms that guide current human behavior because they have been selected for as solutions to these adaptive problems in the evolutionary environment of our ancestors. 1. Historic and Systematic Roots 1a. The Computational Model of the Mind 1b. The Modularity of Mind 1c. Adaptationism 2. Key Concepts and Arguments 2a. Adaptation and Adaptivity 1 2b. Functional Analysis 2c. The Environment of Evolutionary Adaptedness 2d. Domain-specificity and Modularity 2e. Human Nature 3.