Generative AI and the use of large language models (LLMs) are changing the way we work, create, play, and live. As we have witnessed in the past few years, there is significant progress in training LLMs to have a deep understanding of the semantics of language so that such models begin to perform ''reasoning''. (Human) Reasoning is the process of applying logic to derive conclusions based on new or existing information with the goal of finding the truth. Reasoning is a form of high-level human intelligence. There are many types of reasoning: mathematical reasoning, common sense reasoning, temporal reasoning, among others. Multi-hope reasoning with LLM is an emerging capability for LLMs with tens of billions of parameters. Such ''reasoning models'', including Sonnet 3.7, Chat GPT O1, have powered important application areas such as AI4coding, agentic workflow, among others. The first KDD AI Reasoning Day is a special event that we organize in order to increase the awareness of this important research topic for the research community. We bring leaders from industry and academia to present the latest progresses on improving LLM's reasoning capability and enabling reasoning for different application development.

In recent years, there have been exciting and accelerated developments in AI with novel developments in foundation models, deep learning, new AI applications across numerous verticals, and more. In addition, the pace of adoption of these innovations driven by both academic and industry research labs has sped up with both big tech companies and startups looking to deliver value-differentiated products and services. With Generative AI (GenAI) garnering significant attention, the second edition of the I2S workshop focuses on two aspects: First, bringing together AI thought leaders from academia, big tech, and startups to discuss the opportunities, use-case themes, challenges, and risks of GenAI in various business verticals; and Second, bringing together startup founders to share experiences and lessons learned in commercializing GenAI innovations into successful enterprises highlighting challenges through the entire commercial journey - from productization to acquiring customers, building a team, and securing funding.

Environmental problems such as air pollution monitoring and prevention, flood detection and prevention, land use, forest management, river water quality, wastewater treatment supervision, etc. are more complex than typical real-world problems usually AI faces to. This added complexity rises from several aspects, such as the randomness shown by most of environmental processes involved, the 2D/3D nature of involved problems, the temporal aspects, the spatial aspects, the inexactness of the information, etc. In fact, environmental problems belong to the most difficult problems with a lot of inexactness and uncertainty, and possibly conflicting objectives to be solved according to several classifications such as the one by Funtowicz & Ravetz (Funtowicz & Ravetz, 1999), which states that there are 3 kinds of problems. Also, they are non-structured problems in the classification proposed by H. Simon (Simon, 1966). All this complexity means that to effectively solve those problems a lot of knowledge is needed. This knowledge can be theoretical knowledge expressed in mechanistic models, such as the Gravidity Newton's Theory, or it can be empirical knowledge that can be expressed by means of empirical models, originated by some data and observations (data-driven knowledge) or by the expertise gathered by people when coping with such problems (model-driven knowledge, particularly expert-based knowledge). The KDD 2024 Special Day for AI for environment brings together researchers and practitioners to present their perspective on this very timely topic on how AI can be used for good, and improving the environment where we all live in.

There is huge value in making software development more productive with AI. An important component of this vision is the capability to translate natural language to a programming language ("NL2Code") and thus to significantly accelerate the speed at which code is written. This workshop gathers researchers, practitioners, and users from industry and academia that are working on NL2Code, specifically on the problem of using large language models to convert statements posed in a human language to a formal programming language.

In recent years, the AI community has witnessed an exciting acceleration in innovation across foundation models, deep learning, new AI applications across numerous verticals, and more. In addition, AI innovations driven by both academic and industry research labs have rapidly been adopted by big tech companies and startups to deliver value-differentiated products and services. For many machine learning researchers looking at commercializing their work, one of the frequently wondered questions is - "How do I kickstart a startup that can commercialize my research innovations?". For many ML practitioners in the KDD community, there is always curiosity on how big tech and startups take AI research and innovations, and scale it to be used by millions of users. This interactive workshop aims to achieve two goals: First, the workshop will bring together invited AI thought leaders working in academia, big tech as well as startups to share their perspective on the next big AI ideas that will change the world, and deliver impact. Second, the workshop will invite startup founders (from both academia and industry) to share their journey of acquiring customers, building a team, pitching for initial funding, and commercializing their research into successful enterprises

As cloud-computing becomes more and more popular lately, we explore its potential for hyperscale seismic imaging workloads on Azure. We introduce our cloud-native fault-tolerant solution named Hyperwavve which is based on advanced cloud technologies including Docker/Container, Kubernetes and Dask. We demonstrate a large-scale 3D FWI using 1000 VMs/nodes on Azure, where Hyperwavve uses distributed containerized processes to successfully invert for the full 3D (20x20x5 km3) overthrust velocity model. We also further validate that our Hyperwavve can distribute FWI work onto 6000 (or more) VMs/nodes concurrently. Last, we show that our Python-based FWI runs on both Azure CPUs and GPUs including various architectures.

Summary We introduce DeepSeismic, an open source Github repository (https://github.com/microsoft/seismic-deeplearning) that provides implementation of deep learning algorithms for seismic facies interpretation. The repository provides composable machine learning pipelines, that enables a data scientists and geophysicists to use state-of-the-art segmentation algorithms for seismic interpretation (e.g. UNet: Ronneberger et al. (2015) , SEResNet: Hu et al. (2018) , HRNet: Sun et al. (2019) ). We provide scripts to reproduce benchmark results from running these algorithms using various public seismic datasets (Dutch F3, and Penobscot). Finally,the repository provides documentation, and quick start Jupyter notebook and Python scripts to enable the community to get started with seismic interpretation projects quickly. We believe the results in this paper provide a strong baseline on which others can build upon. To the best of our knowledge,these provide state-of-the-art result on Dutch F3 data set. We have released the code and the models in an open-source GitHub repository with permissive MIT license

This chapter discusses some of the trends in deep learning and related fields. We cover specifically which trends might be useful for what tasks as well as discuss some of the methods and ideas that could have far-reaching implications but have yet to be applied to many real-world problems. We finish by covering briefly some of the current limitations of deep learning as well as some other areas of AI that seem to hold promise for future AI applications, and discuss briefly some of the ethical and legal implications of deep learning applications.

In Chapter 1, we gave an overview of AI and the basic idea behind deep learning. We discussed how deep learning—applying artificial neural network models with a large number of layers—has yielded state-of-the art results for several research areas, such as image classification, object detection, speech recognition, and natural language processing.

Chapter 4 introduced the tools, infrastructure, and services that are available to build the next generation of intelligent applications. These together form a platform that empowers data scientists and developers to build, train, and deploy ML and deep learning models on the intelligent cloud and intelligent edge.