Advancing the Future of AI IBM Research AI February 2021 Introduction Trusted AI Scaling and Automating AI
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The Evolution of Ibm Research Looking Back at 50 Years of Scientific Achievements and Innovations
FEATURES THE EVOLUTION OF IBM RESEARCH LOOKING BACK AT 50 YEARS OF SCIENTIFIC ACHIEVEMENTS AND INNOVATIONS l Chris Sciacca and Christophe Rossel – IBM Research – Zurich, Switzerland – DOI: 10.1051/epn/2014201 By the mid-1950s IBM had established laboratories in New York City and in San Jose, California, with San Jose being the first one apart from headquarters. This provided considerable freedom to the scientists and with its success IBM executives gained the confidence they needed to look beyond the United States for a third lab. The choice wasn’t easy, but Switzerland was eventually selected based on the same blend of talent, skills and academia that IBM uses today — most recently for its decision to open new labs in Ireland, Brazil and Australia. 16 EPN 45/2 Article available at http://www.europhysicsnews.org or http://dx.doi.org/10.1051/epn/2014201 THE evolution OF IBM RESEARCH FEATURES he Computing-Tabulating-Recording Com- sorting and disseminating information was going to pany (C-T-R), the precursor to IBM, was be a big business, requiring investment in research founded on 16 June 1911. It was initially a and development. Tmerger of three manufacturing businesses, He began hiring the country’s top engineers, led which were eventually molded into the $100 billion in- by one of world’s most prolific inventors at the time: novator in technology, science, management and culture James Wares Bryce. Bryce was given the task to in- known as IBM. vent and build the best tabulating, sorting and key- With the success of C-T-R after World War I came punch machines. -
IBM Research AI Residency Program
IBM Research AI Residency Program The IBM Research™ AI Residency Program is a 13-month program Topics of focus include: that provides opportunity for scientists, engineers, domain experts – Trust in AI (Causal modeling, fairness, explainability, and entrepreneurs to conduct innovative research and development robustness, transparency, AI ethics) on important and emerging topics in Artificial Intelligence (AI). The program aims at creating and investigating novel approaches – Natural Language Processing and Understanding in AI that progress capabilities towards significant technical (Question and answering, reading comprehension, and real-world challenges. AI Residents work closely with IBM language embeddings, dialog, multi-lingual NLP) Research scientists and are expected to fully complete a project – Knowledge and Reasoning (Knowledge/graph embeddings, within the 13-month residency. The results of the project may neuro-symbolic reasoning) include publications in top AI conferences and journals, development of prototypes demonstrating important new AI functionality – AI Automation, Scaling, and Management (Automated data and fielding of working AI systems. science, neural architecture search, AutoML, transfer learning, few-shot/one-shot/meta learning, active learning, AI planning, As part of the selection process, candidates must submit parallel and distributed learning) a 500-word statement of research interest and goals. – AI and Software Engineering (Big code analysis and understanding, software life cycle including modernize, build, debug, test and manage, software synthesis including refactoring and automated programming) – Human-Centered AI (HCI of AI, human-AI collaboration, AI interaction models and modalities, conversational AI, novel AI experiences, visual AI and data visualization) Deadline to apply: January 31, 2021 Earliest start date: June 1, 2021 Duration: 13 months Locations: IBM Thomas J. -
Treatment and Differential Diagnosis Insights for the Physician's
Treatment and differential diagnosis insights for the physician’s consideration in the moments that matter most The role of medical imaging in global health systems is literally fundamental. Like labs, medical images are used at one point or another in almost every high cost, high value episode of care. Echocardiograms, CT scans, mammograms, and x-rays, for example, “atlas” the body and help chart a course forward for a patient’s care team. Imaging precision has improved as a result of technological advancements and breakthroughs in related medical research. Those advancements also bring with them exponential growth in medical imaging data. The capabilities referenced throughout this document are in the research and development phase and are not available for any use, commercial or non-commercial. Any statements and claims related to the capabilities referenced are aspirational only. There were roughly 800 million multi-slice exams performed in the United States in 2015 alone. Those studies generated approximately 60 billion medical images. At those volumes, each of the roughly 31,000 radiologists in the U.S. would have to view an image every two seconds of every working day for an entire year in order to extract potentially life-saving information from a handful of images hidden in a sea of data. 31K 800MM 60B radiologists exams medical images What’s worse, medical images remain largely disconnected from the rest of the relevant data (lab results, patient-similar cases, medical research) inside medical records (and beyond them), making it difficult for physicians to place medical imaging in the context of patient histories that may unlock clues to previously unconsidered treatment pathways. -
The Impetus to Creativity in Technology
The Impetus to Creativity in Technology Alan G. Konheim Professor Emeritus Department of Computer Science University of California Santa Barbara, California 93106 [email protected] [email protected] Abstract: We describe the technical developments ensuing from two well-known publications in the 20th century containing significant and seminal results, a paper by Claude Shannon in 1948 and a patent by Horst Feistel in 1971. Near the beginning, Shannon’s paper sets the tone with the statement ``the fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected *sent+ at another point.‛ Shannon’s Coding Theorem established the relationship between the probability of error and rate measuring the transmission efficiency. Shannon proved the existence of codes achieving optimal performance, but it required forty-five years to exhibit an actual code achieving it. These Shannon optimal-efficient codes are responsible for a wide range of communication technology we enjoy today, from GPS, to the NASA rovers Spirit and Opportunity on Mars, and lastly to worldwide communication over the Internet. The US Patent #3798539A filed by the IBM Corporation in1971 described Horst Feistel’s Block Cipher Cryptographic System, a new paradigm for encryption systems. It was largely a departure from the current technology based on shift-register stream encryption for voice and the many of the electro-mechanical cipher machines introduced nearly fifty years before. Horst’s vision directed to its application to secure the privacy of computer files. Invented at a propitious moment in time and implemented by IBM in automated teller machines for the Lloyds Bank Cashpoint System. -
Proceedings of the Rexx Symposium for Developers and Users
SLAC-R-95-464 CONF-9505198-- PROCEEDINGS OF THE REXX SYMPOSIUM FOR DEVELOPERS AND USERS May 1-3,1995 Stanford, California Convened by STANFORD LINEAR ACCELERATOR CENTER STANFORD UNIVERSITY, STANFORD, CALIFORNIA 94309 Program Committee Cathie Dager of SLAC, Convener Forrest Garnett of IBM Pam Taylor of The Workstation Group James Weissman Prepared for the Department of Energy under Contract number DE-AC03-76SF00515 Printed in the United States of America. Available from the National Technical Information Service, U.S. Department of Commerce, 5285 Port Royal Road, Springfield, Virginia 22161. DISTRIBUTION OF THIS DOCUMENT IS UNLIMITED ;--. i*-„r> ->&• DISCLAIMER This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, make any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. DISCLAIMER Portions -
IBM Cloud Pak for Data
IBM Cloud Pak for Data IBM Cloud Pak for Data Intelligently automate your data and AI strategy to connect the right data, to the right people, at the right time, from anywhere. Contents Introduction – IBM Cloud Pak for Data: Any data. Any cloud. Anywhere In today’s uncertain environment every organization must become smarter and more responsive to intelligently operate and engage the – The next generation of world, resiliently respond to market changes, flexibly optimize costs, IBM Cloud Pak for Data and innovate. Fueled by data, AI is empowering leading organizations to transform and deliver value. A recent study found that data-driven – Deployment models for organizations are 178% more likely to outperform in terms of revenue IBM Cloud Pak for Data and profitability. – Top use cases for IBM Cloud However, to successfully scale AI throughout your business, you Pak for Data must overcome data complexity. Companies today are struggling to manage and maintain vast amounts of data spanning public, private – Next steps and on-premises clouds. 70 percent of global respondents stated their company is pulling from over 20 different data sources to inform their AI, BI, and analytics systems. In addition, one third cited data complexity and data siloes as top barriers to AI adoption. Further compounding the complexity of these hybrid data landscapes is the fact that the lifespan of that data—the time that it is most relevant and valuable—is drastically shrinking. 1 The solution? An agile and resilient cloud-native platform that enables clients to predict and automate outcomes with trusted data and AI. -
Z/OS Resilience Enhancements
17118: z/OS Resilience Enhancements Harry Yudenfriend IBM Fellow, Storage Development March 3, 2015 Insert Custom Session QR if Desired. Agenda • Review strategy for improving SAN resilience • Details of z/OS 1.13 Enhancements • Enhancements for 2014 Session 16896: IBM z13 and DS8870 I/O Innovations for z Systems for additional information on z/OS resilience enhancements. © 2015 IBM Corporation 3 SAN Resilience Strategy • Clients should consider exploiting technologies to improve SAN resilience – Quick detection of errors – First failure data capture – Automatically identify route cause and failing component – Minimize impact on production work by fencing the failing resources quickly – Prevent errors from impacting production work by identifying problem links © 2015 IBM Corporation 4 FICON Enterprise QoS • Tight timeouts for quicker recognition of lost frames and more responsive recovery • Differentiate between lost frames and congestion • Explicit notification of SAN fabric issues • End-to-End Data integrity checking transparent to applications and middleware • In-band instrumentation to enable – SAN health checks – Smart channel path selection – Work Load Management – Autonomic identification of faulty SAN components with Purge-Path-Extended – Capacity planning – Problem determination – Identification of Single Points of Failure – Real time configuration checking with reset event and self description • No partially updated records in the presence of a failure • Proactive identification of faulty links via Link Incident Reporting • Integrated management software for safe switching for z/OS and storage • High Integrity, High Security Fabric © 2015 IBM Corporation 5 System z Technology Summary • Pre-2013 Items a) IOS Recovery option: RECOVERY,LIMITED_RECTIME b) z/OS 1.13 I/O error thresholds and recovery aggregation c) z/OS message to identify failing link based on LESB data d) HCD Generation of CONFIGxx member with D M=CONFIG(xx) e) Purge Path Extended (LESB data to SYS1.LOGREC) f) zHPF Read Exchange Concise g) CMR time to differentiate between congestion vs. -
Introduction to the Cell Multiprocessor
Introduction J. A. Kahle M. N. Day to the Cell H. P. Hofstee C. R. Johns multiprocessor T. R. Maeurer D. Shippy This paper provides an introductory overview of the Cell multiprocessor. Cell represents a revolutionary extension of conventional microprocessor architecture and organization. The paper discusses the history of the project, the program objectives and challenges, the design concept, the architecture and programming models, and the implementation. Introduction: History of the project processors in order to provide the required Initial discussion on the collaborative effort to develop computational density and power efficiency. After Cell began with support from CEOs from the Sony several months of architectural discussion and contract and IBM companies: Sony as a content provider and negotiations, the STI (SCEI–Toshiba–IBM) Design IBM as a leading-edge technology and server company. Center was formally opened in Austin, Texas, on Collaboration was initiated among SCEI (Sony March 9, 2001. The STI Design Center represented Computer Entertainment Incorporated), IBM, for a joint investment in design of about $400,000,000. microprocessor development, and Toshiba, as a Separate joint collaborations were also set in place development and high-volume manufacturing technology for process technology development. partner. This led to high-level architectural discussions A number of key elements were employed to drive the among the three companies during the summer of 2000. success of the Cell multiprocessor design. First, a holistic During a critical meeting in Tokyo, it was determined design approach was used, encompassing processor that traditional architectural organizations would not architecture, hardware implementation, system deliver the computational power that SCEI sought structures, and software programming models. -
APPLIED MACHINE LEARNING for CYBERSECURITY in SPAM FILTERING and MALWARE DETECTION by Mark Sokolov December, 2020
APPLIED MACHINE LEARNING FOR CYBERSECURITY IN SPAM FILTERING AND MALWARE DETECTION by Mark Sokolov December, 2020 Director of Thesis: Nic Herndon, PhD Major Department: Computer Science Machine learning is one of the fastest-growing fields and its application to cy- bersecurity is increasing. In order to protect people from malicious attacks, several machine learning algorithms have been used to predict the malicious attacks. This research emphasizes two vulnerable areas of cybersecurity that could be easily ex- ploited. First, we show that spam filtering is a well known problem that has been addressed by many authors, yet it still has vulnerabilities. Second, with the increase of malware threats in our world, a lot of companies use AutoAI to help protect their systems. Nonetheless, AutoAI is not perfect, and data scientists can still design better models. In this thesis I show that although there are efficient mechanisms to prevent malicious attacks, there are still vulnerabilities that could be easily exploited. In the visual spoofing experiment, we show that using a classifier trained on data using Latin alphabet, to classify a message with a combination of Latin and Cyrillic let- ters leads to much lower classification accuracy. In Malware prediction experiment, our model has been able to predict malware attacks on Microsoft computers and got higher accuracy than any well known Auto AI. APPLIED MACHINE LEARNING FOR CYBERSECURITY IN SPAM FILTERING AND MALWARE DETECTION A Thesis Presented to The Faculty of the Department of Computer Science East Carolina University In Partial Fulfillment of the Requirements for the Degree Master of Science in Software Engineering by Mark Sokolov December, 2020 Copyright Mark Sokolov, 2020 APPLIED MACHINE LEARNING FOR CYBERSECURITY IN SPAM FILTERING AND MALWARE DETECTION by Mark Sokolov APPROVED BY: DIRECTOR OF THESIS: Nic Herndon, PhD COMMITTEE MEMBER: Rui Wu, PhD COMMITTEE MEMBER: Mark Hills, PhD CHAIR OF THE DEPARTMENT OF COMPUTER SCIENCE: Venkat Gudivada, PhD DEAN OF THE GRADUATE SCHOOL: Paul J. -
IBM Infosphere
Software Steve Mills Senior Vice President and Group Executive Software Group Software Performance A Decade of Growth Revenue + $3.2B Grew revenue 1.7X and profit 2.9X + $5.6B expanding margins 13 points $18.2B$18.2B $21.4B$21.4B #1 Middleware Market Leader * $12.6B$12.6B Increased Key Branded Middleware 2000 2006 2009 from 38% to 59% of Software revenues Acquired 60+ companies Pre-Tax Income 34% Increased number of development labs Margin globally from 15 to 42 27% 7 pts Margin 2010 Roadmap Performance Segment PTI Growth Model 12% - 15% $8.1B$8.1B 21% 6 pts • Grew PTI to $8B at a 14% CGR Margin • Expanded PTI Margin by 7 points $5.5B$5.5B $2.8B$2.8B ’00–’06’00–’06 ’06–’09’06–’09 Launched high growth initiatives CGRCGR CGRCGR 12%12% 14%14% • Smarter Planet solutions 2000 2006 2009 • Business Analytics & Optimization GAAP View © 2010 International Business Machines Corporation * Source: IBM Market Insights 04/20/10 Software Will Help Deliver IBM’s 2015 Roadmap IBM Roadmap to 2015 Base Growth Future Operating Portfolio Revenue Acquisitions Leverage Mix Growth Initiatives Continue to drive growth and share gain Accelerate shift to higher value middleware Capitalize on market opportunity * business • Middleware opportunity growth of 5% CGR Invest for growth – High growth products growing 2X faster than rest of • Developer population = 33K middleware Extend Global Reach – Growth markets growing 2X faster than major markets • 42 global development labs with skills in 31 – BAO opportunity growth of 7% countries Acquisitions to extend -
Innovator Exercise Steps to Perform This Part of the Exercise We Will Be
Innovator Predict Employee Turnover using IBM Watson Studio Build, train and evaluate Machine Learning models Exercise steps To perform this part of the exercise we will be using a feature in Watson Studio called AutoAI. AutoAI is an automated machine learning tool that helps to quickly get started and work with machine learning models. It helps in cleaning the data, preparing the data, feature engineering, hyper parameter optimization, model experimentation and then finally choosing the right model to deploy for your data. 1. Let’s start by adding an AutoAI experiment asset type to the project. Click on the Add to project button in the Assets tab of your project and click on AutoAI Experiment. 2. Name the model “Predicting Employee Turnover” and click on Associate a Machine Learning service instance to create a new Machine Learning instance on your cloud. Innovator Predict Employee Turnover using IBM Watson Studio Build, train and evaluate Machine Learning models 3. A new tab will open with the details of the Machine Learning instance. Make sure you’re creating a New Machine Learning instance. Innovator Predict Employee Turnover using IBM Watson Studio Build, train and evaluate Machine Learning models 4. Again, make sure that the lite plan is selected. 5. Leave everything as default, scroll down to the bottom and create the Machine Learning instance. Innovator Predict Employee Turnover using IBM Watson Studio Build, train and evaluate Machine Learning models 6. Confirm the creation of the instance. 7. Once the Machine Learning instance is created, you should be back on the Create an AutoAI experiment details screen. -
John Backus (1924–2007) Inventor of Science’S Most Widespread Programming Language, Fortran
NEWS & VIEWS NATURE|Vol 446|26 April 2007 OBITUARY John Backus (1924–2007) Inventor of science’s most widespread programming language, Fortran. John Backus, who died on 17 March, was developed elsewhere around the same time, a pioneer in the early development of however, Speedcoding produced programs IBM/AP computer programming languages, and was that were uneconomically slow. subsequently a leading researcher in so- Backus proposed to his manager the called functional programming. He spent his development of a system he called the entire career with IBM. Formula Translator — later contracted to Backus was born on 3 December 1924 in Fortran — in autumn 1953 for the model Philadelphia, and was raised in Wilmington, 704 computer, which was soon to be Delaware. His father had been trained as a launched. The unique feature of Fortran was chemist, but changed careers to become a that it would produce programs that were partner in a brokerage house, and the family 90% as good as those written by a human became wealthy and socially prominent. As a programmer. Backus got the go-ahead, and child, Backus enjoyed mechanical tinkering was allocated a team of ten programmers and loved his chemistry set, but showed for six months. Designing a translator that little scholastic interest or aptitude. He was produced efficient programs turned out to be sent to The Hill School, an exclusive private a huge challenge, and, by the time the system high school in Pottstown, Pennsylvania. was launched in April 1957, six months had His academic performance was so poor that become three years.