The recent advancement in deep learning has re-opened the field of acoustics sensing for unique defense related
tasks such as drone detection, gunshot detection, ultrasonic sensing and more. Learn about the recent advances in this field and how the IAI team uses deep learning to overcome the past limitations of sound sensing to build innovative new products.

Development team leader at ELTA. Founded the IAI acoustics group in IAI's innovation center and leading the system design and algorithm development for all acoustics products. Author of several patents In the field of defense acoustics. An expert in the field of signal intelligence (SIGINT) data fusion systems, also implementing ML in these systems since 2018. Has a monthly column in Hebrew on innovative AI technology.

Despite extensive technological advances in recent years, objective and continuous assessment of physiologic measures after vaccination is rarely performed. To evaluate the safety profile of COVID-19 vaccines, we analyzed their short-term effects by using data from, first, a retrospective cohort of 250,000 random members of the second-largest health-care organization in Israel and, second, a prospective cohort (the PerMed study) of 4698 participants that wore smartwatches and filled questionnaires. Our findings further support the safety of the vaccines from subjective and objective perspectives and underscore the need for integrating wearables in clinical trials due to their higher sensitivity.

Merav Mofaz is part of the fast-track Ph.D. program for excellent students at Tel Aviv University. 
She is conducting a study focused on continuous monitoring and early detection of health and well-being-related events using smartphones and smartwatches, under the supervision of Prof. Erez Shmueli.
The work she and her coworkers did to evaluate the safety of vaccines with wearables, was published in five journals including Lancet Respiratory Medicine, in Nature group journals, and in other leading journals. 
Great interest in the results led to this research being presented to decision-makers in the US CDC, which is entrusted with assessing the safety of the COVID-19 booster vaccine.
Merav is an excellence award-winning lecturer in the Faculty of Engineering.
In addition, she works as a data scientist in the Microsoft Education group where she develops advanced models for personalized learning and the diagnosis of at-risk students.

The success of the best products in the defense sector is usually based on well-defined system requirements, even if highly complicated. "Well defined requirements" refers especially to the borders and interfaces of these systems with the customer and/or the environment.
On this talk, however, we focus on cases where these requirements and borders could not be clearly set up front, and  perhaps even should not. Such cases arise when a solution is requested to be integrated deep into an existing operational ecosystem in which the customer already operates. This is even more acute when we are dealing with AI-based solutions, which are notoriously hard to maintain after deployment. In these cases, the best option is to take the agile development process to the extreme, and agilely evolve the system requirements together with the actual system.
Three such success cases from Rafael's joint AI-hubs will be presented, in which a successful AI-based solution was developed together with the customer and deployed into his operational environment in a short time. The quick  feedback received from the customer was imperative to adjust the solution in response to changes in the operational scenario.

Roee earned his M.Sc. and Ph.D. in the School of Electrical Engineering in Tel-Aviv University, and has over 15 years of experience in developing Computer Vision algorithms. Roee joined Rafael in 2020 as the Computer Vision Research Lead of the Tel-Aviv R&D Center. Before Rafael, Roee worked at AWS on the document understanding service "Amazon Textract", and before that at GM on the hands-free driving solution "Ultra Cruise".

The intelligence directorate in the IDF, and in it the 8200 unit, has a vital mission to implement digital transformation and utilize artificial intelligence. In this age of overwhelming information from various sources, a new approach is required to discover the most insightful and trustworthy data and breaking human barriers in the intelligence process.

We are facing a climate emergency.  Temperatures are rising, rainfall patterns are changing, and sea level is rising.  As a result, we are seeing a dramatic increase in natural disasters related to both dry weather (heat waves, droughts, wildfires) and wet weather (river floods, storms, coastal flooding), with the Global South suffering most from climate change, even though the Global North is responsible for the crisis. How can AI be used for both adaptation and mitigation of climate change in the near and far future?  AI brings tools that can be used to distill raw data into actionable information, to improve predictions in sectors such as energy, agriculture and extreme weather.  AI can optimize complex systems such as heating and cooling systems or transportation schedules.  AI can be used for accelerating scientific modeling and discovery using machine learning with physics-based constraints.  However, at the same time we need to ensure that AI will not have negative impacts on the Earth’s climate.  Examples of where AI can promote climate action will be presented in the talk.

Prof. Colin Price is a Professor in the Porter School of the Environment and Earth Sciences at Tel Aviv University, and the university’s Head of the Climate Crisis Initiative (PlanNetZero).  He is a world known Atmospheric Physicist, specializing in the Earth's weather and climate, with a focus on thunderstorms, climate change and natural hazards.  He was born in Johannesburg, South Africa, in 1962.  After starting his university studies in South Africa, he transferred to Tel Aviv University in 1982 where he completed his B.Sc. and M.Sc. degrees in Geophysics and Atmospheric Sciences.  He received his Ph.D. at Columbia University, New York, in 1993, while working at NASA's Goddard Institute for Space Studies in New York. After completing a postdoctoral fellowship at the Lawrence Livermore National Laboratory in California, he returned to Israel and joined the faculty of Tel Aviv University in 1995.  He has published more than 150 scientific papers and has a team of 10+ graduate students and researchers working under his guidance on various scientific research projects.  
Website: https://www.flash.sites.tau.ac.il/ 
 

Personalization in gaming is the concept of tailoring the best gaming experience to each player based on a variety of parameters such as their preferences, habits, level, etc..  Personalization goal is to increase player engagement and create a more immersive and satisfying gaming experience. In this presentation, we will discuss the importance and benefits of personalization in gaming, and how Playtika is leveraging a human-machine team to deliver personalized experiences to its players through scalable and generic digital products. We will cover topics such as data standardization, user modeling, and human-machine collaboration in the context of Playtika's digital platforms. This presentation is intended for professionals and technical audiences with an interest in gaming, personalization, and human-machine collaboration.

Assaf founded the AI department at Playtika and implemented an ML strategy that emphasized putting people at the center and promoting autonomy in innovation. As a result, the AI department became the driving force behind the company's growth strategy. He also promoted the development of an advanced ML platform to democratize and standardize AI across the organization. The department now conducts research and develops solutions for various areas of gaming and advertising operations under his leadership. With extensive experience as a researcher in computer vision and a variety of managerial roles in data science, Assaf holds a Bachelor's and Master's degree in Electrical and Computer Engineering, with a specialization in Machine Learning from Ben Gurion University of the Negev. He is excited about meaningful and valuable work, with good people, and is passionate about the special connection between data, algorithms, and real business needs that require innovation.

Toxic content such as misinformation, cyberbullying, harassment, and child sexual abuse material has become an integral part of the online ecosystem. To ensure compliance with community standards, millions of pieces of potentially harmful content are flagged daily by social media platforms using a combination of automated and human reviews. Despite this, content moderation remains a pernicious problem for online platforms. 
 
Most platforms today use centralized AI-enabled models, whereby a common algorithm scans all posted content.  Given the wide variety of groups and communities that participate in online interactions, each with its own parlance, behavioral norms, and topics, a common algorithm applied indiscriminately across all users ignores the context and leads to errors.  
 
In this talk we propose an alternative, decentralized framework for AI-enabled content moderation that uses a more tailored algorithm augmented with a contextual layer adapted to the type of content that a given community finds acceptable.  We show that such an approach can lead to fewer false positives and decrease the load on human moderators. We conclude with thoughts on how this approach can enable self-governance by online communities and help address the issues of censorship and control raised against technology platforms and government regulators.  
 

Anjali Joshi serves as a board member of Lattice Semiconductor, a provider of low power FPGA products, and Alteryx, a provider of automated analytics solutions. She is also an Executive in Residence at INSEAD in France. Until March 2019, Anjali was Vice President of Product Management at Google, where she led the product teams for Search, Maps, Translate, News, Finance, and Global Infrastructure. Prior to Google, Anjali was executive vice president of engineering for Covad Communications, Inc., a company providing voice and data communications products and services to consumers and businesses. Before that, she held positions at AT&T Bell Labs, working in the areas of communications and high-speed data. She received her Master’s degree in Management Science and Engineering from Stanford University, a Master’s degree in Computer Engineering from the State University of New York, and a Bachelor’s degree in Electrical Engineering from IIT, Kanpur. She also completed the Executive Education Program in Medicine at Harvard University. In 2017, she was awarded the Distinguished Alumni Award from IIT, Kanpur She has served in advisory roles to many start-ups, contributed to the National Academy of Science committee on Public Health, and served on the visiting committee for the Management Science & Engineering department at Stanford University.

Head of the Data Science and R&D departments at Quantum Inc company, where high-tech solutions help companies worldwide gain critical predictions through data analytics, significantly reduce costs, and distribute workloads more efficiently through intelligent automation. Michael has in-depth expertise in Machine Learning, Computer Vision, and NLP. The main domains of Michael's experience are Healthcare, AgriTech, and Geo Analytics. During Michael's leadership, Quantum's R&D team was among the top 3 winners of the 3rd Autonomous Greenhouse Challenge (Netherlands).
Also, Michael publishes scientific publications and has several patents. Michael regularly performs at technical summits and actively realizes the company's mentoring, internship, and professional growth programs.