Professor Moshe BenBassat

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Professor Moshe BenBassat- Short Biography
Spanning an academic career of over 20 years, with positions at Tel Aviv University, USC, and UCLA, Professor BenBassat made significant contributions in multiple areas of Mathematics and Computer Science involving Optimization, Pattern Recognition, Artificial Intelligence, Statistics and Machine Learning, disciplines which are the foundations of what is known today as Data Science. His research was supported by NIH, NSF, DARPA, NASA, BMD (Ballistic Missile Defence Agency), ARI, Israel Defense Forces and others. Professor BenBassat has published extensively in leading scientific journals and industry magazines, led many executives training classes, and has lectured in in numerous events worldwide.
In 1996 Moshe coined the term “service chain optimization” aimed at integrated holistic optimization of service operations. He founded ClickSoftware and together with his associates developed a comprehensive set of business concepts and processes, decision making optimization algorithms, and software products to improve customer service and business performance of service organizations. These provide the service sector what ‘supply chain optimization’ provides the manufacturing sector. Since its inception, ClickSoftware has been widely recognized as the world leader in its field, serving numerous industry leaders of the service sector including Telecommunication, Electricity, Gas, Water, Medical Equipment, Office Equipment, and more. Moshe led ClickSoftware until his retirement in 2015, at which point ClickSoftware was acquired by Francisco Partners for close to ½ Billion dollars.
Professor BenBassat also founded Plataine which is focused on intelligent automation for smart manufacturing, leveraging Industrial Internet of Things (IIoT) and Artificial Intelligence technologies to increase visibility and optimized decision making on the factory floor. With specific focus on discrete manufacturing optimization, Plataine is known today as the industry leader for IIoT in the fast growing Aerospace industry, among others.

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Double Deep Machine Learning

arXiv (Cornell University), Nov 17, 2017

Very important breakthroughs in data-centric deep-learning algorithms led to impressive performan... more Very important breakthroughs in data-centric deep-learning algorithms led to impressive performance in 'transactional' point applications of Artificial Intelligence (AI) such as Face Recognition, or EKG classification. With all due appreciation, however, 'knowledge-blind' data-only machine learning algorithms have severe limitations for nontransactional AI applications, such as medical diagnosis beyond the EKG results. Such applications require deeper and broader knowledge in their problem-solving capabilities, e.g. integrating anatomy and physiology knowledge with EKG results and other patient's findings. Following a review and illustrations of such limitations for several real-life AI applications, we point at ways to overcome them. The proposed Wikipedia for Smart Machines initiative aims at building repositories of software structures that represent humanity's science & technology knowledge in various parts of life; knowledge that we all learn in schools, universities and during our professional life. Target readers for these repositories are smart machines; not human. AI software developers will have these Reusable Knowledge structures readily available, hence, the proposed name ReKopedia. Big Data is by now a mature technology, it is time to focus on 'Big Knowledge'. Some will be derived from data, some will be obtained from mankind's gigantic repository of knowledge. Wikipedia for smart machines along with the new Double Deep Learning approach offer a paradigm for integrating data-centric deep learning algorithms with algorithms that leverage deep knowledge, e.g. evidential reasoning and causality reasoning. The resulting synergies establish broader and deeper foundations that will enable us to scale faster the AI field. For illustration, a project is described to produce ReKopedia knowledge modules for medical diagnosis of about 1,000 disorders. We are now in the second AI 'spring' after a long AI 'winter'. To avoid sliding again into an AI winter, it is essential that we rebalance the roles of data and knowledge. Data is important, but knowledge-deep, basic, and commonsense-is equally important.

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Double Deep Machine Learning

ArXiv, 2017

Very important breakthroughs in data-centric machine learning algorithms led to impressive perfor... more Very important breakthroughs in data-centric machine learning algorithms led to impressive performance in transactional point applications such as detecting anger in speech, alerts from a Face Recognition system, or EKG interpretation. Non-transactional applications, e.g. medical diagnosis beyond the EKG results, require AI algorithms that integrate deeper and broader knowledge in their problem-solving capabilities, e.g. integrating knowledge about anatomy and physiology of the heart with EKG results and additional patient findings. Similarly, for military aerial interpretation, where knowledge about enemy doctrines on force composition and spread helps immensely in situation assessment beyond image recognition of individual objects. The Double Deep Learning approach advocates integrating data-centric machine self-learning techniques with machine-teaching techniques to leverage the power of both and overcome their corresponding limitations. To take AI to the next level, it is essent...

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Disease Labeling via Machine Learning is NOT quite the same as Medical Diagnosis

A key step in medical diagnosis is giving the patient a universally recognized label (e.g. Append... more A key step in medical diagnosis is giving the patient a universally recognized label (e.g. Appendicitis) which essentially assigns the patient to a class(es) of patients with similar body failures. However, two patients having the same disease label(s) with high probability may still have differences in their feature manifestation patterns implying differences in the required treatments. Additionally, in many cases, the labels of the primary diagnoses leave some findings unexplained. Medical diagnosis is only partially about probability calculations for label X or Y. Diagnosis is not complete until the patient overall situation is clinically understood to the level that enables the best therapeutic decisions. Most machine learning models are data centric models, and evidence so far suggest they can reach expert level performance in the disease labeling phase. Nonetheless, like any other mathematical technique, they have their limitations and applicability scope. Primarily, data cent...

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Multimembership and Multiperspective Classification: Introduction, Applications, and a Bayesian Model

IEEE Transactions on Systems, Man, and Cybernetics

Patten recognition problems are indwuced in which 1) the object to be recognized may simultaneous... more Patten recognition problems are indwuced in which 1) the object to be recognized may simultaneously belong to several classes, which is called a muhimembership classMication (MMC), and 2) the object to be recognized has to be classified with respect to different groups of clases, which is called a mukierspective classification (MPC). Possible applications of MMC and MPC models in medicine, bioengineering, military, and management are discussed. A Bayesian approach is developed which includes classification rules, feature selection, and performance measures I.

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Applications of Adaptive Programming Technology (APT) to Command Group Training and Performance Improvement

~~~~~~~~~~ ' " ' "v and. S-I, '~~~I • I 'i;2F% ht ' C% 1 A riz. This s p e c if i c c ff~~r wa~-'... more ~~~~~~~~~~ ' " ' "v and. S-I, '~~~I • I 'i;2F% ht ' C% 1 A riz. This s p e c if i c c ff~~r wa~-'~~. I-2-! c-3 u r8d .'r Army rr o i i '~-t • ' i-1 1 ;I *74F as basic research ro I a ' '~-1-1w a3~' vr r e q u t r c~~n t s ~~~ C o n t rac t : AHc I)-r 7~~~-fl O47 t. . • ;~'r '~~' 1 V I .~~c • 12k':; 1! '~1 i"i t'y 3~~! I ; hc HuI~~n Fa. ! c~r n ' A echn Ical A rea .~r84~~~~

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Knowledge Requirements and Management in Expert Decision Support Systems for (Military) Situation Assessment

Situation assessment tasks, e.g., medical diagnosis, battlefield reading, corporation status asse... more Situation assessment tasks, e.g., medical diagnosis, battlefield reading, corporation status assessment for merger or acquisition purposes, are formulated as a general family of problem solving tasks. The generic nature of this family task as a multimembership hierarchical pattern recognition problem is characterized and the types of decision support systems (DSS) are identified. The focus is on knowledge representation and elicitation, although issues related to inference mechanisms, system structure, and expert-machine-user interface are also discussed. Two types of knowledge are distinguished: global knowledge and local knowledge. Global knowledge is required to determine directions on which to focus attention, while local knowledge is required for assessing the validity of a specific alternative based on a given set of findings. Global knowledge is represented as a network of relevancy pointers between alternatives and features. Attached to the links of this network are weights by which the strength of relevancy is evaluated and global directions (hypotheses) for situation analysis are determined. For local knowledge, it seems that in most practical problems multiple representation techniques would be required to characterize adequately the alternatives by means of their relevant features. The presentation is accompanied by examples for military situation assessment. However, comparable examples from medical and business applications are also cited. In fact, many of the ideas presented here have already been implemented in the MEDAS system-a medical DSS for emergency and critical care medicine.

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A Framework for Control Strategies in Uncertain Inference Networks

Control Strategies for hierarchical tree-like probabilistic inference networks are formulated and... more Control Strategies for hierarchical tree-like probabilistic inference networks are formulated and investigated. Strategies that utilize staged look-ahead and temporary focus on subgoals are formalized and refined using the Depth Vector concept that serves as a tool for defining the 'virtual tree' regarded by the control strategy. The concept is illustrated by four types of control strategies for three-level trees that are characterized according to their Depth Vector, and according to the way they consider intermediate nodes and the role that they let these nodes play. INFERENTI is a computerized inference system written in Prolog, which provides tools for exercising a variety of control strategies. The system also provides tools for simulating test data and for comparing the relative average performance under different strategies.

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Multi-Dimensional Resource Optimization in the Manufacture of Substantially Two-Dimensional Elements

Composite Application Enabling Workflow Between Unmodified Constituent Applications

Pattern recognition and reduction of dimensionality

Development of signal processing algorithms for ultrasonic detection of coal seam interfaces. Final report

Intelligent computer based training

1997 Ieee Autotestcon Proceedings Autotestcon 97 Ieee Systems Readiness Technology Conference Systems Readiness Supporting Global Needs and Awareness in the 21st Century, Sep 22, 1997

FEG structures for representation and recognition of 3-D polyhedral objects

Ijcv, 1996

The paper proposes a fast O(n2.5) recognition algorithm for partially occluded 3D polyhedral obje... more

Implementation of Hierarchical Treatment Protocols for a Computer Based Medical Decision Assistance System

Crit Care Med, 1981

Entropies, Probability of Error, and Feature Selection

Iandc, 1978

Mutual Adaptiveness of Man and Machine in Information Acquisition Tasks

Method and system for assigning human resources to provide services

Centralized Commerce and the Implementation of Enterprise Software Applications

Taxonomy, Structure, and Implementation of Evidential Reasoning

A human-oriented decision making cycle for solving evidential reasoning problems is described and... more A human-oriented decision making cycle for solving evidential reasoning problems is described and illustrated for a military situation assessment problem. The implementation of this cycle may serve as the basis for an expert system shell for evidential reasoning; i.e. a situation assessment processor.

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Method and system for sharing knowledge

Professor Moshe BenBassat

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