In this talk, we explore the application of various ML architectures including language models to the problem of predicting SQL Query cost, i.e., CPU time and wall time to execute a SQL query command given only the query text. We show how a publicly available pretrained language model with fine tuning and appropriate data preprocessing can achieve state of the art performance on this highly valuable task. And we discuss a scalable approach to using these models in a real time machine learning framework that can generate fresh predictions from continuously integrated data without retraining.

I'm a machine learning generalist with backgrounds in numerical science, approximation theory, and optimization. My interests span the gamut from foundations and proven error bounds to designing effective and ethically sensitive ML workflows for important social issues. I have long term research ambitions related to generalized artificial intelligence, and have a Ph.D. in Computer Science from Virginia Polytechnic Institute and State University.

Professor of Electrical Engineering at Tel Aviv University. Before joining Tel Aviv University, he worked for four years at the Networking Center of Bell Labs, Holmdel, NJ. He published seminal papers in the fields of caching, routing, IP hijack attacks, and network measurements. In recent years, he has been working on usage of AI to solve networking problems in the areas of traffic classification and detection of routing manipulations.

A great battle over the morality of deploying Autonomous Weapons Systems (AWS) is being waged around the globe. The ultimate moral concern surrounds human dignity: is a human being demeaned by a machine deciding and in fact taking their life?  In this talk I argue that human dignity has different definitions based on context.

Mois Navon is one of the founding engineers of Mobileye, where he designed the EyeQ family of SoC (System On a Chip) - the chip powering the autonomous vehicle revolution.  Mois is also an ordained rabbi who has published numerous articles on Jewish law and lore.  Working at the intersection of Torah U’Madda, he is currently pursuing his PhD at the department of Jewish Philosophy at Bar Ilan University where his thesis seeks to apply Jewish philosophy to address the ethical questions arising in the field of artificial intelligence.  In this vein, he teaches a course on “Ethics in Big Data and Artificial Intelligence” at Ben Gurion University and Yeshiva University.  

In the past few years, the European Union recognized the need to regulate artificial intelligence as it is a technology whose main characteristics and functionalities will enable a complete transformation of our society. One of many reasons why artificial intelligence is an industry game changer is its ability to instantly provide personalized online experiences based on vast amounts of available data.
 

Dr. Alžběta Solarczyk Krausová is Head of the Center for Innovations and Cyberlaw Research (CICeRo)  at the Institute of State and Law of the Czech Academy of Sciences, Head of Regulatory Unit of Central European Digital Media Observatory (CEDMO), Chair of the Czech Republic’s AI Observatory & Forum, a founder and Chair of the SOLAIR Conference (Society, Law, Artificial Intelligence and Robotics), an external lecturer at the Charles University in Prague, an executive editor of The Lawyer Quarterly, and a member of the Appeal Commission at the Office for Personal Data Protection of the Czech Republic. She has acted as a member of the Expert Group on Liability and New Technologies at the European Commission, a member of OECD’s network of experts ONE AI, and a Czech expert in negotiating the UNESCO’s Draft Recommendation on Ethics of AI. In February 2022, she became a member of the GPAI Data Governance Working Group. Her research focuses on legal aspects of artificial intelligence, robotics, brain-computer interfaces, and merging technology with organic life. 

Since 2017, she has contributed to the formulation of future regulation of AI both on the international as well as the national level. She is the main author of Czech proposals for international regulation of AI submitted to UNCITRAL and UNIDROIT (2017), a co-author of the EU report Liability for Artificial Intelligence and other emerging digital technologies(2019), and a co-author of the non-paper of the Czech Republic Regulatory Framework for Artificial Intelligence in the European Union (2020). She is the main author of the Analysis of Legal and Ethical Aspects Related to Development of AI and Its Applications (2018) for the Office of the Government of the Czech Republic and a co-author of the National Artificial Intelligence Strategy of the Czech Republic (2019).

The novel introduction of Artificial Intelligence (AI) into in-vitro fertilization (IVF) technologies (AI(i)IVF) is, at the same time, medically promising and ethically disconcerting. The new possibilities and complexities generated by involving intelligent, powerful algorithms in human creation and reproduction, raise a host of ethical issues for IVF patients, practitioners – embryologists in particular, and society at large. This paper seeks to identify and analyze various, as yet unexplored ethical implications of AI(i)IVF. We first originally contemplate a suitable framework for the ethical discussion. We suggest that a combination of the long-standing bioethics and the currently evolving AI ethics (nuanced, yet considerably overlapping) sets of principles should be thus applied: human-centric AI ethics principles – applied to the technological architecture of the AI(i)IVF tool, and the patient-centric principles of bioethics – applied to personal and societal aspects of implementing AI(i)IVF. We then respectively consider selected ethical principles to analyze the ethical merits and caveats of implementing AI(i)IVF, particularly for embryo evaluation and selection, while consistently debating whether putting a higher threshold for AI(i)IVF ethicality, than the one set for traditional (non-AI) IVF, is indeed warranted or justified. 
 

Dr. Sivan Tamir is Head of Bioethics & Genetic Policy Unit at KSM – Maccabi Research and Innovation Center. She is also a teaching fellow at the Faculty of Law, and a research fellow in the International Center for Health, Law, and Ethics at the University of Haifa. Dr. Tamir is a researcher in the fields of medical law and bioethics, specialising in genethics, AI ethics, big data ethics, clinical research ethics and reproductive ethics. She is the former Coordinator of the National Helsinki Committee for Human Medical Research (Ministry of Health), and a former researcher in the Genetic Policy & Bioethics Unit, at the Gertner Institute for Epidemiology & Health Policy Research (Sheba Medical Centre) and at the Israel Tech Policy Institute. Dr. Tamir is a member of the Advisory Committee on Bioethics for the Israel Academy of Sciences and Humanities. 

As the predictive power of AI gradually replaces human decision makers, calls for a "right for explanation" of automated systems gain simultaneous support. Accordingly, the technological domain entrusted with executing this right is continuously sharpening the professional tool, often related to as "explainability" or "interpretability", to generate explanations for end-users and decision subjects. This discussion explores the rudimentary question: Does explainability benefit end users? Leveraging insights from the legal and ML domains, a multidisciplinary analysis uncovers key complexities in the quest to generate users' explainability, and critically questions the intuitive appeal of AI explanations as a means to assist users in an automated world. 

Hofit Wasserman Rozen is a research fellow at Shamgar Center, TAU Faculty of Law and a lawyer whose practice concentrates on the regulation of Artificial Intelligence systems. Hofit is currently a PhD candidate at Tel-Aviv University (Law and Engineering faculties). Her research focuses on AI regulation and ethics, specifically on the legal and ML aspects of Explainability and the legal right for explanation of AI systems. Hofit is a licensed attorney in Israel, and her LLB and LLM (both with honors) are from Tel-Aviv university.
 

The importance of detecting the existence of disease-related biomarkers in medical imaging data is undoubted as in most cases it may ameliorate patient care. Training a vision model to accurately annotate medical imaging requires a large dataset of annotated medical imaging. However, the existence of annotated volumetric imaging datasets, such as, 3D magnetic resonance imaging (MRI) scans, 3D optical coherence tomography (OCT) scans, and ultrasound (US) videos, is very limited as their annotation is both time- and cost-prohibitive. Per-se 3D-based vision models are thus practically bounded by a performance glass ceiling. In this work we present SLIViT, a 2D-based deep-learning heuristic framework that leverages transfer learning and vision transformers to accurately detect disease-related biomarkers in (potentially any) 3D medical imaging, given a low-sized 3D dataset for training. To exemplify SLIViT’s generic utility for diagnosing disease-related biomarkers in volumetric medical imaging, we applied it to three different data modalities tackling both classification and regression problems. We first show SLIViT’s performance superiority compared to both state-of-the-art methods and junior clinicians in classifying four different Age-related Macular Degeneration biomarkers (ROC AUC > 0.8) in two independent 3D OCT datasets. We then show SLIViT’s overperformance in measuring heart ejection fraction in US videos and show that with only an eighth of the original training dataset it can reproduce current state-of-the-art achievements (R^2 = 0.5). Lastly, we challenge SLIViT with a 3D MRI dataset where the order of the slices within each volume is not recorded. We show that even under these settings, we can accurately (R^2 = 0.7) model proton density fat fraction, a well-known biomarker for nonalcoholic fatty liver disease.

Oren is a Postdoctoral Researcher in Prof. Eran Halperin’s lab at the Henry Samueli School of Engineering and David Geffen School of Medicine at the University of California, Los Angeles (UCLA). Oren develops practical AI applications for the healthcare domain, including deep-learning methods for electronic medical records and biomedical imaging data. Oren did his Ph.D. in Bioinformatics under the supervision of Prof. Tal Pupko at the Shmunis School of Biomedicine and Cancer Research at Tel Aviv University. Prior to that, he received his B.Sc. and M.Sc. (both cum laude) in Computer Sciences with a specialization in Bioinformatics at Tel Aviv University. 

We propose an algorithm for learning a conditional generative model of a molecule given a target.  Specifically, given a receptor molecule that one wishes to bind to, the conditional model generates candidate ligand molecules that may bind to it.  The distribution should be invariant to rigid body transformations that act \textit{jointly} on the ligand and the receptor; it should also be invariant to permutations of either the ligand or receptor atoms.  Our learning algorithm is based on a continuous normalizing flow.  We establish semi-equivariance conditions on the flow which guarantee the aforementioned invariance conditions on the conditional distribution.  We propose a graph neural network architecture which implements this flow, and which is designed to learn effectively despite the vast differences in size between the ligand and receptor.  We evaluate our method on the CrossDocked2020 dataset, attaining a significant improvement in binding affinity over competing methods.

Daniel Freedman is the Lead of Verily Research (Verily = Google Life Sciences).  He received the A.B. degree in physics from Princeton University in 1993, and the Ph.D. degree in engineering sciences from Harvard University, in 2000. From 2000 to 2009, he served as Professor of Computer Science at RPI (Rensselaer Polytechnic Institute).  Since 2009, he has worked in a number of corporate research positions, most prominently at Microsoft Research, Google Research, and Verily Research.  His current research focuses include computational molecular design, novel imaging modalities, physics and deep learning, and medical applications of computer vision.  He has received the National Science Foundation CAREER Award and the Fulbright Fellowship.