Keynote: AI Innovations and their Intel Applications
Dr. Amitai Armon
10:05 - 10:30
10:30 - 11:05
Beyond End-to-End: the Case of Multi-text Summarization
11:05 - 11:25
Two Tales of Retrieval
11:25 - 11:45
Moving Away from One-size-fits-all: People-centric Natural Language Processing
Prof. Rada Mihalcea
11:45 - 12:10
Moderator: Dr. Jacob Mendel
Dr. Mendel was the General Manager Cybersecurity COE at Intel. He is a serial cybersecurity entrepreneur; He has been the CEO and Co-Founder of SCsquare Ltd., where he founded a business enabler for advanced cybersecurity technologies. Dr. Mendel holds 16 approved patents in the area of cybersecurity. His career in cybersecurity over the past 20 years is a unique mixture of broad practical experience and research expertise. His practice included extensive involvement in cybersecurity offensive projects, research and development, business development and product management. Proven worldwide track records in secure operating systems, digital rights management (DRM), security certification (CC, FIPS, EMV), penetration test, reverses engineering, Machine Learning, Blockchain, IoT security and Smart Grid cybersecurity. His academic research topics includes: economic perspective of cybersecurity attacks, Quantum, Blockchain technology with a special focus on cybersecurity attacks, privacy issues and business continuation under cyber-attack.
Jack leads Sandbox, developing quantum-inspired physics- and A.I.-based tools and applications that run on today’s classical computing platforms. Sandbox focuses on enterprise SaaS solutions at the intersection of machine learning and physics.
Jack is the author of Quantum Computing: An Applied Approach, published by Springer. This work, now in its Second Edition, is one of the leading textbooks in the field and is used both in PhD programs and corporate training sessions.
Jack is a serial entrepreneur and founder of several tech companies, including EarthWeb/Dice (NYSE: DHX), which he led through a record breaking IPO. He also co-founded Vista Research which he then sold to Standard and Poors/McGraw-Hill.
Jack is a trustee of the X Prize Foundation and has been a board member of Trickle Up, which helps thousands of entrepreneurs start small businesses each year. His foundation, The Hidary Foundation, is dedicated to medical oncology research and has supported work at Sloan Kettering and UCSF.
Jack has been recognized for his leadership by organizations such as the World Economic Forum, HealthCorps and Young Presidents’ Organization. Jack studied neuroscience at Columbia and subsequently received the Stanley Fellowship in Clinical Neuroscience at NIH where he worked on functional brain imaging and neural networks.
There is a growing demand for AI systems that justify their decisions. In my talk, I will present recent results on adding explainability to black box algorithms as well as ways to design transparent AI methods. We would discuss the implications of such methods in science and technology and how explainability can provide a feedback mechanism that enhances the performance of AI methods.
Prof. Lior Wolf
Prof. Wolf is a full professor at the School of Computer Science at Tel Aviv University. His research focuses on computer vision and deep learning.
AI research keeps accelerating, with dozens of thousands of papers published each year. How does this relate to applying AI in the industry? The talk will start with a brief overview of key recent AI techniques that could apply to a wide variety of real-life problems. It will then provide examples for utilizing such new approaches to develop innovative AI-based products internally at Intel. Intel’s internal AI group (IT AI) transforms the company’s critical operations using AI, from processor architecture and design, through manufacturing to sales. This affects almost every processor that Intel provides, yielding both a 9-digit value for the company and better products for its customers. The talk will discuss video, text and tabular data use-cases, which increase the products quality and the operational efficiency. One of these applications was developed in collaboration with the Stanford AI lab.
Dr. Amitai Armon
Dr. Amitai Armon is the Chief Data Scientist of Intel’s internal AI group, a global group of over 200 experts that uses AI to upgrade and transform Intel’s critical operations. The group develops and deploys AI solutions across the company, from processor architecture and design, through manufacturing to sales. This yields both a 9-digit value for Intel and better products for its customers. Prior to joining Intel in 2013, Amitai was the co-Founder and Director of Research at TaKaDu, a data-science company that received multiple international awards, including the World Economic Forum Technology Pioneers award (Davos Summit). He previously was a visiting research scholar at the Los-Alamos National Lab (USA). Amitai has over 20 years of experience in performing and leading data-science work, including service as a team-leader in an elite technology unit. He holds a PhD in computer-science from Tel Aviv University, where he previously completed his BSc with honors at the age of 18.
Supervised NLP tasks are often addressed today in an end-to-end manner, where a model is trained (or fine tuned) on input-output instances of the full task. However, such an approach may exhibit limitations when applied to fairly complex tasks. In this talk, I will discuss an alternative approach where a complex task is decomposed to its inherent subtasks, each addressed by a targeted model, and will illustrate it for the challenging application of multi-document summarization (MDS). Notably, the decomposition approach becomes particularly appealing when targeted training data for modeling specific subtasks can be derived automatically from the originally available “end-to-end” training data for the full task, as I will show for the MDS case. Additionally, I will describe a separate contribution, namely a targeted Cross-Document Language Model (CDLM). This model is pre-trained specifically to model cross-document relationships supporting diverse tasks in this setting, and is also leveraged within our decomposed MDS architecture.
Ido Dagan is a Professor at the Department of Computer Science at Bar-Ilan University, Israel, the founder of the Natural Language Processing (NLP) Lab at Bar-Ilan, the founder and head of the nationally funded Bar-Ilan University Data Science Institute, and a Fellow of the Association for Computational Linguistics (ACL). His interests are in applied semantic processing, focusing on textual inference, natural open semantic representations, consolidation and summarization of multi-text information, and interactive text summarization and exploration. Dagan and colleagues initiated and promoted textual entailment recognition (RTE, later aka NLI) as a generic empirical task. He was the President of the ACL in 2010 and served on its Executive Committee during 2008-2011. In that capacity, he led the establishment of the journal Transactions of the Association for Computational Linguistics, which became one of two premiere journals in NLP. Dagan received his B.A. summa cum laude and his Ph.D. (1992) in Computer Science from the Technion. He was a research fellow at the IBM Haifa Scientific Center (1991) and a Member of Technical Staff at AT&T Bell Laboratories (1992-1994). During 1998-2003 he was co-founder and CTO of FocusEngine and VP of Technology of LingoMotors, and has been regularly consulting in the industry. His academic research has involved extensive industrial collaboration, including funds from IBM, Google, Thomson-Reuters, Bloomberg, Intel and Facebook, as well as collaboration with local companies under funded projects of the Israel Innovation Authority.
The field of NLP has undergone a revolution in the past few years geared by the use of very large language models (LMs) that can learn to perform language understanding tasks given only a few examples. In this talk, I will describe two recent projects that use neural retrievers to address some shortcomings of large LMs. First, while large LMs do well given a short piece of text, it is difficult to capitalize on their advantages for tasks that are at the corpus level (say all of Wikipedia). I will describe a self-supervised method for retrieving paragraphs from a large corpus, such as Wikipedia, that performs well even without any training examples. Second, the behavior of very large LMs strongly depends on the examples that are given to them as input (this is often termed in-context learning). We present a method for retrieving examples from the training set that maximizes the performance of the LM on a downstream language understanding task. Our approaches make it easier to build new question answering models for arbitrary corpora and to interact with large LMs that are provided as a service by commercial companies.
Jonathan Berant is an associate professor at the School of Computer Science at Tel Aviv University. Jonathan earned a Ph.D. in Computer Science at Tel Aviv University under the supervision of Prof. Ido Dagan. Jonathan was a post-doctoral fellow at Stanford University, working with Prof. Christopher Manning and Prof. Percy Liang, and subsequently a post-doctoral fellow at Google Research, Mountain View. Jonathan received several awards and fellowships including the Rothschild fellowship, the ACL 2011 Best Student Paper award, EMNLP 2014 Best Paper award, and NAACL 2019 Best Resource Paper award, as well as several honorable mentions. Jonathan is currently and ERC grantee.
The typical approach in natural language processing is to use one-size-fits-all representations, obtained from training one model on very large text collections. While this approach is effective for those people whose language style is well represented in the data, it fails to account for variations between people, and thus may lead to worse performance for those in the minority. In this talk, I will challenge the one-size-fits-all assumption, and show that (1) we can identify words that are used in significantly different ways by speakers from different cultures; and (2) we can effectively use information about the people behind the words to build better natural language processing models.
Prof. Rada Mihalcea
Rada Mihalcea is the Janice M. Jenkins Collegiate Professor of Computer Science and Engineering at the University of Michigan and the Director of the Michigan Artificial Intelligence Lab. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the Journals of Computational Linguistics, Language Resources and Evaluations, Natural Language Engineering, Journal of Artificial Intelligence Research, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a program co-chair for EMNLP 2009 and ACL 2011, and a general chair for NAACL 2015 and *SEM 2019. She currently serves as ACL President. She is the recipient of a Presidential Early Career Award for Scientists and Engineers awarded by President Obama (2009), an ACM Fellow (2019) and a AAAI Fellow (2021). In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania.