Conferences  >  Mathematics  >  Neural Networks and Artificial Intelligence, Machine Learning

Algorithms and Data Structures,    Software Agents and Intelligent Agents in Artificial Intelligence

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Scientific Computing and Data Science,    Information & Knowledge Management, Big Data Computing

Centre de recherches mathématiques (CRM)

This workshop will be focused on bringing together international experts in both the broad computational neuroscience with others from machine learning.

Mit dem Ziel, den Herausforderungen der Digitalisierung in der naturwissenschaftlichen Grundlagenforschung schnell und zukunftsorientiert zu begegnen und exzellente Forschung in und aus Deutschland zu fördern, legt das Bundesministerium für Bildung und Forschung (BMBF) als Teil des 2017 veröffentlichten Rahmenprogramms „Erforschung von Universum und Materie – ErUM“ den Aktionsplan ErUM-Data fest. Der Aktionsplan ErUM-Data schafft Voraussetzungen für die naturwissenschaftliche Grundlagenforschung von morgen und fördert eine koordinierte Herangehensweise und breite Verständigung. Langfristiges Ziel ist es, den Weg zu übergeordneten Lösungen zu ebnen und optimale Bedingungen für eine effiziente und finanzierbare Datenverarbeitung und -verwertung zu schaffen. Der einfache Zugang zu Daten und Werkzeugen ist eine zentrale Säule des Aktionsplans. 

Scientific Computing and Data Science,    Career, Leadership and Management

The Active Training Course "Advanced Deep Learning" is hosted by the community organization DIG-UM with support from the BMBF-funded ErUM-Data-Hub.

The intensive course on Graph Neural Networks, Transformers, Normalizing Flows and Autoencoders will be held from Monday 25.9.23 to Thursday 28.9.23. The course includes a challenge to be worked out and presented by participant subgroups. The workshop is aimed at deep-learning enthusiasts from all ErUM communities (Research on Universe and Matter) who have prior knowledge of neural networks and applied basic concepts of deep learning. 

Courses and Events for Math Students and Early Career Researchers,    Courses and Events for Physics Students

Isaac Newton Institute for Mathematical Sciences (INI)

This workshop aims to bring together researchers from number theory, automorphic representation theory and string theory to explore how automorphic forms that encode degeneracies of BPS could be related to and feed into information theoretic concepts such as quantum error corrections, applied to BPS and near-extremal black hole backgrounds, and to develop machine learning techniques to unveil some of the hitherto undefined automorphic structures underlying the generating functions for BPS black hole microstates in superstring models. This event is the first workshop of the 4-month long BLH programme at the INI centred around the same topics.

Astronomy, Astrophysics and Cosmology,    Number Theory, Arithmetic

ICMI is the premier international forum that brings together multimodal artificial intelligence (AI) and social interaction research. Multimodal AI encompasses technical challenges in machine learning and computational modeling such as representations, fusion, data and systems. The study of social interactions englobes both human-human interactions and human-computer interactions. A unique aspect of ICMI is its multidisciplinary nature which values both scientific discoveries and technical modeling achievements, with an eye towards impactful applications for the good of people and society. We are keen to showcase novel input and output modalities and interactions to the ICMI community.

ACMSC

2023 4th International Artificial Intelligence and Blockchain Conference (AIBC 2023) will be held during 27-29 October, 2023 in Aizu-Wakamatsu, Japan.

Email: amychou2020@163.com

Information Retrieval; Intelligent System Architectures; Mechatronics; Enterprise asset management with IoT and blockchain; Facilities management with IoT and blockchain; System engineering with IoT and blockchain; IoT platform based on blockchain; Alternative proof of work algorithms; Cyberattacks on blockchains; Economic impact on cyberattacks; Neural Networks; Parallel Processing; Pattern Recognition;

Frontiers in Machine Learning in Cosmology and Particle Physics 2023 is a workshop on the latest machine learning developments in the domains of cosmology and particle physics. It takes inspiration from the influential workshop series "Hammers & Nails", organised every other year by the Weizmann Institute of Science, Israel.

High Energy Physics, Particles and Fields,    Astronomy, Astrophysics and Cosmology

Open Data Science Conference

The pace of technological development has only increased in the fields of data science and AI, and everyone from engineers to CEOs need to work hard to stay at the leading edge of the industry.

To meet the needs of both our technical and business audiences, ODSC West has made some changes to our programming. For our data scientists, data engineers, and AI professionals, we’ve added an additional day, completely dedicated to training. We’ll be inviting leading experts, core practitioners, and acclaimed academics to host hands-on training sessions and workshops in the tools and techniques you need to stay at the forefront of your field. For our business minded attendees, we are expanding our popular Ai X Business and Innovation Summit with more talks and more opportunities to connect with peers and get insights on how industries are being upended by technologies like Generative AI.

Email: iryna@odsc.om

ODSC West, Data Science, Machine Learning, Conference

Are you ready to experience the leading data science & AI conference as it returns to San Francisco? Accelerate your career with hands-on training, demo talks, workshops, networking events, nine tracks, and more. The ODSC West in-person conference will feature expert-led, hands-on instruction in in-demand topics and tools, including: NLP | Python | Responsible AI | TensorFlow | PyTorch | Conversational AI | OpenAI | Cybersecurity | Data Visualization | scikit-learn | MLOps | Data Engineering | Machine Learning | and much more

Email: iryna@odsc.com

Isaac Newton Institute for Mathematical Sciences (INI)

This workshop aims to bring together researchers in solution-generating techniques and integrability, as well as Riemann-Hilbert factorization problems in gravity. It will also bring in expertise in AI and machine learning as a tool, especially in solving factorization problems, and in developing new matrix factorization methods that will be of service to solving gravitational PDEs. This event is the second workshop in a 4-month long BLH programme at the INI centred around the same topics.

TMC Academy

In this 3 day hands-on workshop with two AI experts, Prof. Erik Ranschaert and Dr. Mohammad H. Rezazade Mehrizi, participants will experience working with various forms of AI, reflect on their own experience, and gain practical experience from it. Radiologists will learn how to adapt to using such tools, how to re-organise their workflow when using one or more an AI-applications, and how they can actively engage in making such implementation a success.

Phone: [+34935500750];     Email: academy@telemedicineclinic.com

artificial intelligence, radiology, radiologist, medicine, AI

The Pacific Rim International Conference on Artificial Intelligence (PRICAI) is an annual international event which concentrates on AI theories, technologies and their applications in the areas of scientific, social, and economic importance for countries in the Pacific Rim. In the past, the conferences have been held in Nagoya (1990), Seoul (1992), Beijing (1994), Cairns (1996), Singapore (1998), Melbourne (2000), Tokyo (2002), Auckland (2004), Guilin (2006), Hanoi (2008), Daegu (2010), Kuching (2012), Gold Coast (2014), Phuket (2016), Nanjing (2018), Fiji (2019), Yokohama (2020, online), Hanoi (2021, online) and Shanghai (2022, hybrid). The 20th PRICAI (2023) will be held in hybrid mode in Jakarta, Indonesia.

Asia Society of Researchers

Welcome to the official website of the 2023 9th International Conference on Robotics and Artificial Intelligence (ICRAI 2023) which was established in Singapore in 2015. This year, ICRAI 2023 will be held in Singapore on November 17-19, 2023.

Email: adam@asr.org

Aerial and Underwater Robotics; Bio-inspired Robotics; Cable Robots, Cognitive Robotics; Cognitive Approach for Robotics; Collaborative Robotics; Collective and Social Robots; Control and Supervision Systems; Human-Robots Interfaces; Humanoid Robots; Humanoids;

Moulay Ismail University, Faculty of sciences and Technic,Morocco

The objective of bringing together innovative scientists, professors, research scholars, students and industrial experts in the field of Artificial Intelligence and Smart Environments in a common forum. The primary goal of the conference is to promote the exchange of innovative scientific information between researchers, developers, engineers, students, and practitioners. Another goal is to promote the transformation of fundamental research into institutional and industrialized research and to convert applied exploration into real-time application. Overall, the conference will provide the researchers and attendees with prospects for national and international collaboration and networking among universities and institutions from Morocco and abroad for promoting research.

Phone: [0672377651];     Email: y.farhaoui@fste.umi.ac.ma

Software Agents and Intelligent Agents in Artificial Intelligence,    Internet of Things (IoT) and Sensor Networks

ETPUB

2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI). The Conference will be held in Mexico City, Mexico during November 25-26, 2023. ISCMI 2023 is organized by India International Congress on Computational Intelligence(IICCI), and technically co sponsored by IEEE Mexico Section, IEEE Mexico Council and Mexico Chapter of IEEE Computational Intelligence Society.

Email: helen@academic.net

Ant Colony Optimization and Swarm Intelligence; Artificial Immune Systems; Artificial Intelligence; Artificial Life; Associative Memory; Automatic Annotation; Bioinformatics and Biological Computing; Case-Based and Temporal Reasoning; Cognitive Science; Connectionism; Data Fusion; Data Mining and Data Fusion;

The purpose of the event is to provide a platform for researchers, developers, practitioners, entrepreneurs and policymakers to discuss artificial intelligence applications to accelerate fusion and plasma science; and to identify representative examples and related data to be shared through international collaboration, ideally leading to coordination or joint work within the coordinated research project on the subject.

International Association of Computer Science and Information Technology

International Conference on Medical Computer Vision and Robotics (MCVR 2023) will be held in Ningbo, China during December 8-10, 2023. It is hosted by the Cixi Institute of BioMedical Engineering.

Email: aimee@iacsit.net

Medical Image Analysis; Segmentation and feature extraction in medical images; Biomedical Signal and Image Processing; Signal processing and analysis of physiological data; Hyperspectral imaging for medical diagnosis; Machine Learning and Artificial Intelligence; Deep learning for medical image analysis; Computer-Assisted Surgery; Virtual and Augmented Reality in Medicine; Medical Robotics and Automation;

California Institute of Technology (Caltech)

Automated Theorem Proving,    Optimization Theory, Operational Research

California Institute of Technology (Caltech)

This annual workshop welcomes academics and industry practitioners at the interface of theoretical physics, mathematics, and computer science to discuss research directions centred on the application of machine learning and data science to open problems in fundamental theory, and equivalently the application of techniques from theory to describe the successes of machine learning.

Co-sponsored by Sensors and Systems Society of Singapore and Beijing Institute of Control Robotics and Intelligent Technology, hosted by Robotics Society of Singapore.

The Conference Committee warmly invites you to join us at 2023 the 3rd International Conference on Robotics, Automation and Artificial Intelligence (RAAI 2023), which will be held in Singapore during December 14-16, 2023.

Email: riorob@robotics.ac.cn

Artificial intelligence; Artificial muscle actuators; Augmented reality; Chaotic systems and control; Computational intelligence; Control applications; Digital/sampled-data control; Discrete-event systems; Emerging technology; Evolutionary computation; Feature extraction; Functional safety; Fuzzy and neural systems; Ground robotics;

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The rise of Machine Learning and Artificial Intelligence in nearly all aspects of society stands in stark contrast with the irreproducibility of the literature and its inaccessibility to the general public. Therefore, we have developed OpenML, an online collaborative science platform for Machine Learning, available at http://openml.org. The aim of this workshop is to bring together developers and users of the platform to stimulate further development.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The goal of this Dagstuhl Seminar is to provide an interdisciplinary forum to discuss the fundamental principles of fault management and diagnosis, bringing together international researchers from the fields of symbolic reasoning, machine learning, and control engineering. The seminar plans to identify an integrated framework to harmonize problems and algorithms from the different fields, as well as ideas for novel, integrated solutions; and to produce a comprehensive agenda for future research.

Automated Theorem Proving,    Soft Computing, Evolutionary Computation, Metaheuristic Optimization, Fuzzy Logic

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Peer review is the best mechanism for assessing scientific validity of new research that we have so far. But this mechanism has many well-known issues, such as the different incentives of the authors and reviewers, difficulties with preserving reviewer and author anonymity, confirmation and other cognitive biases that even researchers fall prey to. These intrinsic problems are exacerbated in interdisciplinary fields like Natural Language Processing (NLP) and Machine Learning (ML), where groups of researchers may vary so much in their methodology, terminology, and research agendas, that sometimes they have trouble even recognizing each other's contributions as "research". This Dagstuhl Seminar will cover a range of topics related to organization of peer review in NLP and ML

von Karman Institute for Fluid Dynamics | Universit� libre de Bruxelles (ULB)

The integration of machine learning methods in fluid mechanics continues to grow and open new frontiers. Combining first principles and machine learning offers new avenues to improve predictions and control design, identify patterns, reduce computational costs, enhance measurement techniques, and more generally, get deeper insights into complex fluid phenomena. This lecture series provides an overview of the current state of the art and a pedagogical introduction to the fundamentals of machine learning tools for fluid dynamics. The course is in its second edition. It follows the successful lecture series held in 2020, which attracted more than 200 participants and resulted in an edited book published by Cambridge University Press.

Continuing Professional Education, Courses (general),    Thermodynamics, Fluid Dynamics and Statistical Physics

The Alan Turing Institute

This event will gather up to 12 teams from around the world to engage in a challenge using open spending data, development indicators, and the recently released web-based modelling app from the Policy Priority Inference research programme. The organisers would like to invite anyone interested in the intersection of sustainable development, artificial intelligence, open government, and computational social science to attend this one-of-a-kind event. The opening and closing sessions will be open to the public (no need to have team), while the challenger teams will work on a case study for a week with mentorship facilitated by the organisers.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Graphs and knowledge bases have been around for many decades, and research outcomes have tremendously impacted areas like mathematics, artificial intelligence, and databases. However, despite being already coined by the scientific community, technological developments and astronomical data growth make knowledge graph management a fundamental topic nowadays in various computer science areas, supporting novel applications at the science (e.g., biomedicine) and industry (e.g., Google’s Knowledge Graph) level.

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Courses and Events for Math Students and Early Career Researchers,    Probability and Statistics, Game Theory

Institute for Pure and Applied Mathematics (IPAM), Los Angeles, CA

The purpose of this workshop is to bring together neuroscientists who collect and study these data sets with mathematicians and other theorists who develop techniques to model and analyze networks, network dynamics, and dynamical processes on networks. We expect that crossing disciplinary boundaries will greatly facilitate progress, as neuroscientists working in connectomics often lack exposure to recent mathematical developments, while the biological and technical details that underlie connectomic data may not be familiar to mathematicians. This workshop will help define directions of future work in connectomics, with deep links to neuroscience, mathematics, and data science.

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The past decade has seen a rapid growth in the ability to collect large-scale spatiotemporal data sets about sports. Ideally, such data should be used to inform strategic and tactical decision making. On the one hand, strategy is the long-term planning of training sessions, signing of coaches and athletes, rotation and the plan made before a match/race. On the other hand, tactics are short-term and involve the execution and adaptation to the match/race plan. Having insights into the efficacy and feasibility of strategies and tactics is particularly important and challenging within sports because effective and novel strategy & tactics allow weaker teams or athletes to win against stronger ones. Unfortunately, the size, richness, and complexity of modern spatiotemporal sports data means that automated analysis is essential. Alas, the nature of the data has posed a number of challenges for classic analysis techniques. This has spurred the development of novel statistical and machine learning techniques in order to perform more fine-grained analysis of every action and decision during a competitive event. In this Dagstuhl Seminar, we aim to bring together sports researchers in academia and industry to understand how they are using machine learning and statistical techniques to analyze strategy and tactics.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Artificial intelligence and machine learning have made impressive strides in the last decade and – especially in the eyes of the general public – even in the last months, with innovations that have entered the daily life of billions of people. Given the magnitude of the impact of AI, the social good should not be an afterthought: market forces alone may not guarantee that these technologies benefit everyone. Instead, we believe that AI should empower those already championing humanitarian and development causes. In order to accelerate adoption of AI methods where their impact on the social good is largest, we propose to bring together non-governmental organizations working in international development and on humanitarian issues, with AI technical experts (academics, researchers, data scientists, engineers). Primary objectives of this Dagstuhl Seminar are to establish partnerships and build trust, to iterate on concrete problems in a hands-on hackathon, and to demonstrate what is feasible today via case studies.

Institute for Pure and Applied Mathematics (IPAM), UCLA

In this workshop, we will explore the statistical and computational requirements for solving various learning problems. The statistical limit is the minimum number of samples needed to solve a learning problem. In contrast, the computational limit is the minimum number of samples required for the problem to be solvable by an efficient algorithm. There is much research on the statistical requirements for many important learning problems, but the computational requirements are less well-understood. We often have large gaps between the two for several important problems (e.g., sparse linear regression). In addition, there are also gaps in our understanding of the costs of various constraints on learning, such as privacy, fairness, interpretability, robustness, and parallelization. This workshop will provide a forum to discuss the latest research and develop new ideas on the above questions. It will help build bridges between different disciplines, such as applied mathematics, statistics, optimization, and theoretical computer science, which will lead to more effective solutions to challenges in statistical inference.

Qazvin Islamic Azad University (QIAU)

With the aim of expanding specialized knowledge in the field of artificial intelligence and robotics engineering and familiarizing students and professors with the latest achievements, Islamic Azad University of Qazvin branch is organizing the 10th international conference on artificial intelligence and robotics. Acquaintance and exchange of the latest scientific information between researchers, students and faculty members of universities and higher education institutions in various fields of artificial intelligence and robotics is one of the goals of this conference.

Email: alirezamortezaei50@gmail.com

Artificial Intelligence, Robotic, IoT, Smart City, Machine Learning, Data Science, Neural Network

Robotics, Automation & Mechatronics,    Internet of Things (IoT) and Sensor Networks

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The purpose of the seminar will be to initiate a debate around both theoretical foundations and practical methodologies for a "Trustworthiness & Responsibility in AI" framework that integrates quantifiable responsibility and verifiable correctness into all stages of the software engineering process. Such a framework will allow governance and regulatory practises to be viewed not only as rules and regulations imposed from afar, but instead as an integrative process of dialogue and discovery to understand why an autonomous system might fail and how to help designers and regulators address these through proactive governance. In particular, we will consider how to reason about responsibility, blame, and causal factors affecting the trustworthiness of the system.

The Alan Turing Institute

Hosted by The Alan Turing Institute, AI UK 2024 will be an in-depth exploration of how data science and AI can be used to solve real-world challenges. Our diverse programme has been thematically structured around the latest innovations from across the AI ecosystem. With a broad range of interactive content, expect to hear the latest thinking on foundational AI, digital twins, algorithmic bias, AI ethics – and much more.

Scientific Computing and Data Science,    History and Philosophy of Science and Technology

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Autonomous systems are intended to operate without human intervention over prolonged time periods, perceive their operating environment, and adapt to changes – while pursuing defined goals or generating new ones. The perception functions process the inputs of various sensors and generate an internal model of the operating environment. By relying on this model, the decision functions plan and execute the actions required to achieve the goals of the mission. To achieve safety for an autonomous system, the engineers should ensure that the perception functions can sufficiently accurately build the model of the environment, i.e., perception and establishing a context for prediction are reliable. They also seek to ensure that the planned actions are safe, i.e., decisions do not result in actions that endanger humans or other agents in the operating environment.

Software Engineering,    Robotics, Automation & Mechatronics

From April 8 to 10, 2024 the Heinz Maier-Leibnitz Zentrum (MLZ) located in Garching near Munich will host the Machine Learning Conference for X-Ray and Neutron-Based Experiments. The first two days (April 8/9) will take place at the Community Center (Bürgerhaus) Garching with all participants. The third day (April 10) offers optional hands-on workshops and reactor tours at the site of research neutron source FRM II for all interested participants.

Mechanics, Rheology and Tribology,    Neutron Scattering

ACM

Track Chairs

machine learning, artificial intelligence, graph neural networks, graphs, deep learning, pattern recognition

RIOROB

2024 the 5th International Conference on Artificial Intelligence, Robotics and Control (AIRC 2024) which will be held during April 22-24, 2024 in The British University in Egypt, Cairo, Egypt. AIRC is an annually held international conference and it's sponsored by The British University in Egypt (BUE), hosted by The British University in Egypt (BUE). AIRC 2024 is intended to provide an international forum for those who wish to present their latest research results, innovative ideas, and experiences in the fields of Artificial Intelligence, Robotics and Control.

Email: riorob@robotics.ac.cn

Artificial Intelligence; AI architectures and applications Agents; Information retrieval and integration; Data mining; Intelligent Robotics; Robot design; Robot kinematics/dynamics/control; Control Engineering; Man-machine interactions; System integration;

von Karman Institute for Fluid Dynamics

This course gives an overview and practical hands-on experience in integrating machine learning in fluid dynamics. The course originated as a compressed version of the course Machine Learning for Fluid Dynamics, given at the Research Master program at the von Karman Institute. After a brief review of the machine learning landscape, we show how to frame problems in fluid mechanics as machine learning problems, and we explore challenges and opportunities. Attendees will be guided through a series of tutorial sessions in Python and will tackle several relevant applications: aeroacoustics' noise prediction, turbulence modelling, reduced-order modelling and forecasting, meshless integration of (partial) differential equations, super-resolution and flow control.

Continuing Professional Education, Courses (general),    Thermodynamics, Fluid Dynamics and Statistical Physics

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The fundamental and simple model of Stochastic Games was introduced in the fifties by prominent mathematicians (Shapley, Bellman) and form today a well adopted and thoroughly studied notion, with applications in computer science, mathematics, economics, and beyond. This wide adoption implies that many different research communities are involved in the study of stochastic games, oftentimes with different but related goals. Each community develops different tools towards understanding stochastic games, leading to a very broad and diverse literature with a variety of techniques and approaches. The goal of this Dagstuhl Seminar is to bring together researchers interested in the algorithmic aspects of Stochastic Games, from a theoretical as well as practical perspective.

CIRM – Centre International de Rencontres Mathématiques

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Game design and implementation are tasks which require a high amount of creativity, and which must lead to products which require a high amount of fine-tuned functionality. For example, a game “level” should not only look appealing, it should also be playable and it should be interesting to play. These are not features which can be acquired simply by “training on big data”, which is what most developments in modern artificial intelligence are based on. The goal of this Dagstuhl Seminar is to investigate to what extent modern artificial intelligence techniques can produce meaningful and functional game content, and what changes to or extensions of these techniques can improve this AI-driven creative process.

Human-Computer Interaction, User Experience,    Multimedia, Computer Graphics and Visualization, Games

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

This seminar aims at reasoning critically about how we build software and hardware for end-to-end machine learning. We hope that the discussions will lead to increased awareness for understanding the utilization of modern hardware and kickstart future developments to minimize hardware underutilization. We thus would like to bring together academics and industry across fields of data management, machine learning, systems, and computer architecture covering expertise of algorithmic optimizations in machine learning, job scheduling and resource management in distributed computing, parallel computing, and data management and processing. The outcome of the discussions in the seminar will therefore also positively impact the research groups and companies that rely on machine learning.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

AI is a disruptive force. With growing applications in fields like healthcare, transportation, game playing, finance, or robotics in general, AI systems and methods are entering our everyday lives. Such tight interaction with AI requires serious safety, correctness, and reliability considerations. Recently, the field of safety in AI has triggered a vast amount of research. Via a diverse program with ample space for open yet guided discussion, we aim to address a number of key challenges that range across all fields.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Institute 2024 has an overarching focus on Next-Generation Human-Centred AI and Cognitive Technologies; its technical programme addresses the formal & cognitive foundations for human-centred computing (for AI), and the human-centred design, development, and usability of cognitive technologies aimed at human-in-the-loop assistance & empowerment in decision-making, planning, creative-technical problem-solving, and automation. Application areas to be addressed in the institute include autonomous vehicles, cognitive robotics, social robotics, cognitive/creative design assistance technologies (e.g., architecture and built environment design, visuo-auditory narrative media design), clinical diagnostics, technology-assisted education/learning.

Institute for Pure and Applied Mathematics (IPAM), UCLA

The study of biological intelligences has been transformed by breakthroughs in experimental and observational methods. New data have expanded our appreciation of the sophisticated behaviors exhibited by human and non-human animals across a range of taxa. Traces of intelligent behavior are typically high-dimensional, with complex structure in space and time. They often involve multiple data streams: for example, positional information from individual members of a bird flock, as well as “point-of-view” recordings showing what birds are looking at, moment by moment; or electrocorticographic recordings of human brain activity, as well as detailed traces of motor behavior. These traces are often buried beneath complex noise environments: individual animal movements must be pulled out of extensive visual clutter; whale song must be isolated from ocean noise. This complex, high-dimensional data requires entirely new approaches to data representation, integration, and analysis. This workshop will focus on the challenges raised by high-throughput, high-dimensional studies of intelligent behavior. It will bring together experts in animal cognition, computational neuroscience, and cognitive science with mathematicians and computer scientists focused on relevant methods in machine learning, network science, high-dimensional statistics, and information theory.

Part of the Long Program Mathematics of Intelligences

Institute for Pure and Applied Mathematics (IPAM), UCLA

Neural networks have confounded traditional ML beliefs about the dangers of overfitting and the need for regularization. They have also given rise to many new empirical findings around transfer learning, adversarial examples, compressibility, scaling laws (relating the size of datasets, models, and compute), grokking, and so on. What is needed to explain and predict all this is a rich new theory of learning capable of addressing the delicate interplay between model, data, and optimizers at large scale. This workshop will bring together top researchers driving the frontiers of this work with experts in both theory and experiment for natural intelligence. The result will be a scholarly discussion on how to frame questions about learning and how to distill the similarities and differences between learning with biological and artificial systems.

Part of the Long Program Mathematics of Intelligences

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Courses and Events for Math Students and Early Career Researchers,    Software Agents and Intelligent Agents in Artificial Intelligence

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The vision of NFDI4DS is to support all steps of the complex and interdisciplinary research data lifecycle in Data Science and Artificial Intelligence. The overarching objective of NFDI4DS is the development, establishment, and sustainment of a national research data infrastructure for the Data Science and Artificial Intelligence community. The key idea is to work towards increasing the transparency, reproducibility and fairness of Data Science and Artificial Intelligence projects, by making all digital artifacts available, interlinking them, and offering innovative tools and services. Within this Dagstuhl Research Meeting, the NFDI4DS partners will reflect on their progress so far, and will work on their long-term strategy.

Information & Knowledge Management, Big Data Computing,    Scientific Computing and Data Science

Institute for Pure and Applied Mathematics (IPAM), UCLA

This workshop will draw together researchers creating new algorithms and architectures (e.g., active symbol architectures, evolutionary programming approaches, neural program synthesis) with mathematicians and theoretical computer scientists who specialize in non-convex optimization, the theory of programming languages, type theory, proof theory, and category theory. It aims to promote cross-fertilization between these paradigms and more traditional approaches, while stimulating the development of rigorous foundations for evolutionary computing, program synthesis, and other naturalistic approaches to AI.

Part of the Long Program Mathematics of Intelligences

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Courses and Events for Math Students and Early Career Researchers,    Astronomy, Astrophysics and Cosmology

Institute for Pure and Applied Mathematics (IPAM), UCLA

This workshop will bring together leading experts in the mathematical modeling of collective intelligence. While the workshop will explore collective intelligence in many different systems, substrates (both natural and artificial), and scales, it will focus on three broadly applicable modeling frameworks: dynamical systems, which have been applied to systems as various as brains and insect swarms; statistical physics, which has illuminated the behavior of flocking birds and deep learning architectures; and game theory, which has been applied to animal, human, and AI collectives. The collective intelligence domain cultivates the further development of formal tools for treating compositionality; the emergence of new capacities and collective degrees of freedom at multiple scales; and the higher-level institutions that coordinate the intelligent behavior of lower-level parts.

Part of the Long Program Mathematics of Intelligences