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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. -
Artificial Intelligence
TechnoVision The Impact of AI 20 18 CONTENTS Foreword 3 Introduction 4 TechnoVision 2018 and Artificial Intelligence 5 Overview of TechnoVision 2018 14 Design for Digital 17 Invisible Infostructure 26 Applications Unleashed 33 Thriving on Data 40 Process on the Fly 47 You Experience 54 We Collaborate 61 Applying TechnoVision 68 Conclusion 77 2 TechnoVision 2018 The Impact of AI FOREWORD We introduce TechnoVision 2018, now in its eleventh year, with pride in reaching a second decade of providing a proven and relevant source of technology guidance and thought leadership to help enterprises navigate the compelling and yet complex opportunities for business. The longevity of this publication has been achieved through the insight of our colleagues, clients, and partners in applying TechnoVision as a technology guide and coupling that insight with the expert input of our authors and contributors who are highly engaged in monitoring and synthesizing the shift and emergence of technologies and the impact they have on enterprises globally. In this edition, we continue to build on the We believe that with TechnoVision 2018, you can framework that TechnoVision has established further crystalize your plans and bet on the right for several years with updates on last years’ technology disruptions, to continue to map and content, reflecting new insights, use cases, and traverse a successful digital journey. A journey technology solutions. which is not about a single destination, but rather a single mission to thrive in the digital epoch The featured main theme for TechnoVision 2018 through agile cycles of transformation delivering is AI, due to breakthroughs burgeoning the business outcomes. -
Alphabet Company 2021 Report
Alphabet Company 2021 Report 22 January 2021 ALPHABET ALPHABET COMPANY COMPANY OVERVIEW Alphabet, Inc. is a holding company, which engages in the business of acquisition and operation of different companies. It operates through the Google and Other Bets segments. The Google segment includes its main Internet products such as ads, Android, Chrome, hardware, Google Cloud, Google Maps, Google Play, Search, and YouTube. The Other Bets segment consists of businesses such as Access, Calico, CapitalG, GV, Verily, Waymo, and X. The company was founded by Lawrence E. Page and Sergey Mikhaylovich Brin on October 2, 2015 and is headquartered in Mountain View, CA. The company currently falls under ‘Mega-Cap’ category with current market capitalization of 1100 B. Market capitalization usually refers to the total value of a company’s stock within the entire market. Google’s namesake search engine and YouTube video service are gateways to the internet for billions of people and have become more essential as they transact and entertain online to avoid the virus. Advertisers have turned to Google’s ad system to let shoppers know about deals and adjusted service offerings as the economy chugs along again. ALPHABET FINANCIALS - Q3 2020 The company beat estimates across the board, following its first-ever revenue decline in Q2. The results showed a strong rebound in its core advertising business, which was hit hard by customer spending pullbacks amid the Covid-19 pandemic. Total revenues of $46.2 billion in the third quarter reflect broad based growth led by an increase in advertiser spend in Search and YouTube as well as continued strength in Google Cloud and Play $46.17 BILLION On the company’s earnings call, CEO Sundar Pichai said, “This year, including this REVENUE quarter, showed how valuable Google’s founding product, search, has been to people.” Pichai said starting next quarter, it will report operating income for its cloud $16.40 business, joining Amazon in giving investors EARNINGS PER SHARE more details. -
Identificação De Textos Em Imagens CAPTCHA Utilizando Conceitos De
Identificação de Textos em Imagens CAPTCHA utilizando conceitos de Aprendizado de Máquina e Redes Neurais Convolucionais Relatório submetido à Universidade Federal de Santa Catarina como requisito para a aprovação da disciplina: DAS 5511: Projeto de Fim de Curso Murilo Rodegheri Mendes dos Santos Florianópolis, Julho de 2018 Identificação de Textos em Imagens CAPTCHA utilizando conceitos de Aprendizado de Máquina e Redes Neurais Convolucionais Murilo Rodegheri Mendes dos Santos Esta monografia foi julgada no contexto da disciplina DAS 5511: Projeto de Fim de Curso e aprovada na sua forma final pelo Curso de Engenharia de Controle e Automação Prof. Marcelo Ricardo Stemmer Banca Examinadora: André Carvalho Bittencourt Orientador na Empresa Prof. Marcelo Ricardo Stemmer Orientador no Curso Prof. Ricardo José Rabelo Responsável pela disciplina Flávio Gabriel Oliveira Barbosa, Avaliador Guilherme Espindola Winck, Debatedor Ricardo Carvalho Frantz do Amaral, Debatedor Agradecimentos Agradeço à minha mãe Terezinha Rodegheri, ao meu pai Orlisses Mendes dos Santos e ao meu irmão Camilo Rodegheri Mendes dos Santos que sempre estiveram ao meu lado, tanto nos momentos de alegria quanto nos momentos de dificuldades, sempre me deram apoio, conselhos, suporte e nunca duvidaram da minha capacidade de alcançar meus objetivos. Agradeço aos meus colegas Guilherme Cornelli, Leonardo Quaini, Matheus Ambrosi, Matheus Zardo, Roger Perin e Victor Petrassi por me acompanharem em toda a graduação, seja nas disciplinas, nos projetos, nas noites de estudo, nas atividades extracurriculares, nas festas, entre outros desafios enfrentados para chegar até aqui. Agradeço aos meus amigos de infância Cássio Schmidt, Daniel Lock, Gabriel Streit, Gabriel Cervo, Guilherme Trevisan, Lucas Nyland por proporcionarem momentos de alegria mesmo a distância na maior parte da caminhada da graduação. -
The Importance of Generation Order in Language Modeling
The Importance of Generation Order in Language Modeling Nicolas Ford∗ Daniel Duckworth Mohammad Norouzi George E. Dahl Google Brain fnicf,duckworthd,mnorouzi,[email protected] Abstract There has been interest in moving beyond the left-to-right generation order by developing alter- Neural language models are a critical compo- native multi-stage strategies such as syntax-aware nent of state-of-the-art systems for machine neural language models (Bowman et al., 2016) translation, summarization, audio transcrip- and latent variable models of text (Wood et al., tion, and other tasks. These language models are almost universally autoregressive in nature, 2011). Before embarking on a long-term research generating sentences one token at a time from program to find better generation strategies that left to right. This paper studies the influence of improve modern neural networks, one needs ev- token generation order on model quality via a idence that the generation strategy can make a novel two-pass language model that produces large difference. This paper presents one way of partially-filled sentence “templates” and then isolating the generation strategy from the general fills in missing tokens. We compare various neural network design problem. Our key techni- strategies for structuring these two passes and cal contribution involves developing a flexible and observe a surprisingly large variation in model quality. We find the most effective strategy tractable architecture that incorporates different generates function words in the first pass fol- generation orders, while enabling exact computa- lowed by content words in the second. We be- tion of the log-probabilities of a sentence. -
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1 TABLE OF CONTENTS 2 I. INTRODUCTION ...................................................................................................... 2 3 II. JURISDICTION AND VENUE ................................................................................. 8 4 III. PARTIES .................................................................................................................... 9 5 A. Plaintiffs .......................................................................................................... 9 6 B. Defendants ....................................................................................................... 9 7 IV. FACTUAL ALLEGATIONS ................................................................................... 17 8 A. Alphabet’s Reputation as a “Good” Company is Key to Recruiting Valuable Employees and Collecting the User Data that Powers Its 9 Products ......................................................................................................... 17 10 B. Defendants Breached their Fiduciary Duties by Protecting and Rewarding Male Harassers ............................................................................ 19 11 1. The Board Has Allowed a Culture Hostile to Women to Fester 12 for Years ............................................................................................. 19 13 a) Sex Discrimination in Pay and Promotions: ........................... 20 14 b) Sex Stereotyping and Sexual Harassment: .............................. 23 15 2. The New York Times Reveals the Board’s Pattern -
Vikas Sindhwani Google May 17-19, 2016 2016 Summer School on Signal Processing and Machine Learning for Big Data
Real-time Learning and Inference on Emerging Mobile Systems Vikas Sindhwani Google May 17-19, 2016 2016 Summer School on Signal Processing and Machine Learning for Big Data Abstract: We are motivated by the challenge of enabling real-time "always-on" machine learning applications on emerging mobile platforms such as next-generation smartphones, wearable computers and consumer robotics systems. On-device models in such settings need to be highly compact, and need to support fast, low-power inference on specialized hardware. I will consider the problem of building small-footprint non- linear models based on kernel methods and deep learning techniques, for on-device deployments. Towards this end, I will give an overview of various techniques, and introduce new notions of parsimony rooted in the theory of structured matrices. Such structured matrices can be used to recycle Gaussian random vectors in order to build randomized feature maps in sub-linear time for approximating various kernel functions. In the deep learning context, low-displacement structured parameter matrices admit fast function and gradient evaluation. I will discuss how such compact nonlinear transforms span a rich range of parameter sharing configurations whose statistical modeling capacity can be explicitly tuned along a continuum from structured to unstructured. I will present empirical results on mobile speech recognition problems, and image classification tasks. I will also briefly present some basics of TensorFlow: a open-source library for numerical computations on data flow graphs. Tensorflow enables large-scale distributed training of complex machine learning models, and their rapid deployment on mobile devices. Bio: Vikas Sindhwani is Research Scientist in the Google Brain team in New York City. -
Notice of 2021 Annual Meeting of Stockholders and Proxy Statement
Notice of 2021 Annual Meeting of Stockholders and Proxy Statement 1600 Amphitheatre Parkway Mountain View, California 94043 (650) 253-0000 zz DEAR STOCKHOLDERS We are pleased to invite you to participate in our 2021 Annual Meeting of Stockholders (Annual Meeting) to be held on Wednesday, June 2, 2021 at 9:00 a.m., Pacific Time. We have adopted a virtual format for our Annual Meeting to provide a consistent experience to all stockholders regardless of location. Alphabet stockholders of Class A or Class B common stock (or their proxy holders) as of the close of business on the record date, April 6, 2021 (Record Date), can participate in and vote at our Annual Meeting by visiting www. virtualshareholdermeeting.com/GOOGL21 and entering the 16-digit control number included in your Notice of Internet Availability of Proxy Materials (Notice), voting instruction form, or proxy card. All others may view the Annual Meeting through our Investor Relations YouTube channel at www.youtube.com/c/AlphabetIR. Further details regarding participation in the Annual Meeting and the business to be conducted are described in the Notice you received in the mail and in this proxy statement. We have also made available a copy of our 2020 Annual Report to Stockholders (Annual Report) with this proxy statement. We encourage you to read our Annual Report. It includes our audited financial statements and provides information about our business. We have elected to provide access to our proxy materials over the Internet under the U.S. Securities and Exchange Commission’s “notice and access” rules. We are constantly focused on improving the ways people connect with information, and believe that providing our proxy materials over the Internet increases the ability of our stockholders to connect with the information they need, while reducing the environmental impact of our Annual Meeting. -
BRKIOT-2394.Pdf
Unlocking the Mystery of Machine Learning and Big Data Analytics Robert Barton Jerome Henry Distinguished Architect Principal Engineer @MrRobbarto Office of the CTAO CCIE #6660 @wirelessccie CCDE #2013::6 CCIE #24750 CWNE #45 BRKIOT-2394 Cisco Webex Teams Questions? Use Cisco Webex Teams to chat with the speaker after the session How 1 Find this session in the Cisco Events Mobile App 2 Click “Join the Discussion” 3 Install Webex Teams or go directly to the team space 4 Enter messages/questions in the team space BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 3 Tuesday, Jan. 28th Monday, Jan. 27th Wednesday, Jan. 29th BRKIOT-2600 BRKIOT-2213 16:45 Enabling OT-IT collaboration by 17:00 From Zero to IOx Hero transforming traditional industrial TECIOT-2400 networks to modern IoT Architectures IoT Fundamentals 08:45 BRKIOT-1618 Bootcamp 14:45 Industrial IoT Network Management PSOIOT-1156 16:00 using Cisco Industrial Network Director Securing Industrial – A Deep Dive. Networks: Introduction to Cisco Cyber Vision PSOIOT-2155 Enhancing the Commuter 13:30 BRKIOT-1775 Experience - Service Wireless technologies and 14:30 BRKIOT-2698 BRKIOT-1520 Provider WiFi at the Use Cases in Industrial IOT Industrial IoT Routing – Connectivity 12:15 Cisco Remote & Mobile Asset speed of Trains and Beyond Solutions PSOIOT-2197 Cisco Innovates Autonomous 14:00 TECIOT-2000 Vehicles & Roadways w/ IoT BRKIOT-2497 BRKIOT-2900 Understanding Cisco's 14:30 IoT Solutions for Smart Cities and 11:00 Automating the Network of Internet Of Things (IOT) BRKIOT-2108 Communities Industrial Automation Solutions Connected Factory Architecture Theory and 11:00 Practice PSOIOT-2100 BRKIOT-1291 Unlock New Market 16:15 Opening Keynote 09:00 08:30 Opportunities with LoRaWAN for IOT Enterprises Embedded Cisco services Technologies IOT IOT IOT Track #CLEMEA www.ciscolive.com/emea/learn/technology-tracks.html Cisco Live Thursday, Jan. -
NEUBERGER BERMAN ALTERNATIVE FUNDS Form N-PX
SECURITIES AND EXCHANGE COMMISSION FORM N-PX Annual report of proxy voting record of registered management investment companies filed on Form N-PX Filing Date: 2017-08-24 | Period of Report: 2017-06-30 SEC Accession No. 0000898432-17-000842 (HTML Version on secdatabase.com) FILER NEUBERGER BERMAN ALTERNATIVE FUNDS Mailing Address Business Address 1290 AVENUE OF THE 1290 AVENUE OF THE CIK:1317474| IRS No.: 000000000 | State of Incorp.:DE | Fiscal Year End: 1031 AMERICAS AMERICAS Type: N-PX | Act: 40 | File No.: 811-21715 | Film No.: 171048680 NEW YORK NY 10104 NEW YORK NY 10104 (212) 476-8800 Copyright © 2017 www.secdatabase.com. All Rights Reserved. Please Consider the Environment Before Printing This Document As filed with the Securities and Exchange Commission on August 24, 2017 UNITED STATES SECURITIES AND EXCHANGE COMMISSION Washington, D.C. 20549 FORM N-PX Annual Report of Proxy Voting Record of Registered Management Investment Company Investment Company Act file number: 811-21715 NEUBERGER BERMAN ALTERNATIVE FUNDS (Exact Name of the Registrant as Specified in Charter) c/o Neuberger Berman Investment Advisers LLC 1290 Avenue of the Americas New York, New York 10104-0002 (Address of Principal Executive Offices – Zip Code) Registrant's telephone number, including area code: (212) 476-8800 Robert Conti Chief Executive Officer and President Neuberger Berman Alternative Funds c/o Neuberger Berman Investment Advisers LLC 1290 Avenue of the Americas New York, New York 10104-0002 Arthur C. Delibert, Esq. K&L Gates LLP 1601 K Street, N.W. -
Contains Forward-Looking Statements Within the Meaning of the Federal Secu
This Annual Report (including the Founders’ DIRECTORS AND OFFICERS STOCKHOLDER INFORMATION Letter) contains forward-looking statements Directors For further information about Google, within the meaning of the federal securities contact: laws. These forward-looking statements Eric Schmidt Chairman of the Board & Investor Relations include, but are not limited to, statements Chief Executive Officer Google Inc. related to improvements to our search Google Inc. 1600 Amphitheatre Parkway engine, advertising systems, and products Mountain View, California 94043 Sergey Brin [email protected] and services. These forward-looking Co-Founder & President, Technology statements are based on current expectations, Google Inc. You may also visit us by visiting the forecasts, and assumptions and involve a investor relations portion of our website at: Larry Page http://investor.google.com number of risks and uncertainties that could Co-Founder & President, Products cause actual results to differ materially from Google Inc. If you wish to receive stockholder those anticipated by these forward-looking information online, you can register at: L. John Doerr http://investor.google.com/notify.html statements. Such risks and uncertainties General Partner include a variety of factors, some of which Kleiner Perkins Caufield & Byers Google’s stock trades on the NASDAQ are beyond our control. In particular, such Global Select Market under the ticker John L. Hennessy symbol GOOG. risks and uncertainties include our ability President, Stanford University to innovate and many risks relating to Transfer Agent and Registrar successful development and marketing of Ann Mather Computershare Trust Company, N.A. Former Executive Vice President & P.O. Box 43078 technology, products, and operating systems. -
Design Perspectives on Delivery Drones
C O R P O R A T I O N Design Perspectives on Delivery Drones Jia Xu For more information on this publication, visit www.rand.org/t/RR1718z2 Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2017 RAND Corporation R® is a registered trademark. Limited Print and Electronic Distribution Rights This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND’s publications do not necessarily reflect the opinions of its research clients and sponsors. Support RAND Make a tax-deductible charitable contribution at www.rand.org/giving/contribute www.rand.org Preface Delivery drones may become widespread over the next five to ten years, particularly for what is known as the “last-mile” logistics of small, light items. Companies such as Amazon, Google, the United Parcel Service (UPS), DHL, and Alibaba have been running high-profile experiments testing drone delivery systems, and the development of such systems reached a milestone when the first commercial drone delivery approved by the Federal Aviation Administration took place on July 17, 2015.