BOND May 2025 Trends – Artificial IntelligenceTrends – Artificial Intelligence (AI) May 30, 2025 Mary Meeker / Jay Simons / Daegwon Chae / Alexander Krey2 Context We set out to compile foundational faster…and the machines can outpace us. The pace and scope of change related to the artificial intelligence technology evolution is indeed unprecedented, as supported by the data. This document is filled with the world’s organized, connected and accessible information being supercharged by artificial intelligence, accelerating computing power, and semi-borderless capital…all driving massive change. Sport

Practices SAP SAP Data Intelligence 3 on Rancher Kubernetes Engine 2 using VMware vSAN and vSphere SUSE Linux Enterprise Server 15 SP4 Rancher Kubernetes Engine 2 SAP Data Intelligence 3 Dr. Ulrich Schairer Klinger, SAP Solutions Architect (SUSE) 1 SAP Data Intelligence 3 on Rancher Kubernetes Engine 2 using VMware vSAN and vSphere SAP Data Intelligence 3 on Rancher Kubernetes Engine 2 using VMware vSAN SAP Data Intelligence 3 is the tool set to govern big amounts of data, and it runs ful- ly containerized. This document describes the installation and configuration of SAP Data Intelligence 3 deployed

actions, and innovation, higher efficiency, cost savings, and revenue growth powered by data-driven intelligence. For example: FMCG & Manufacturing need real-time production dashboards and historical analytics discoverability, accelerating decision-making, and improving operational efficiency. AI acts as the intelligence layer, optimizing both search and analytics by making them faster, smarter, and more intuitive workloads or schema adjustments. 5.3 Data Intelligence CrateDB combines real-time data, AI integration, advanced analytics to empower businesses with data intelligence for smarter decisions and more efficient

undergone rapid development, offering a glimpse into the dawn of Artificial General Intelligence (AGI). In general, the intelligence of an LLM tends to improve as the number of parameters increases, allowing it large models with longtermism, aiming to progressively approach the goal of artificial general intelligence. • In our ongoing exploration, we are dedicated to devising methods that enable further scaling Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI

星际争霸与人工智能 阿里巴巴认知计算实验室 龙海涛 Why StarCraft? Challenge Problems for Artificial Intelligence Imperfect Information Huge State and Action Space Long-Term Planning Temporal and Spatial Reasoning Deep Reinforcement Learning What is next? • All above are single AI agent • But, true human intelligence embraces social and collective wisdom • How large-scale multiple AI agents could learn human-level collaborations (or competitions) from their experiences? Cooperative Hunting Artificial Collective Intelligence https://arxiv.org/abs/1703.10069 Multiagent Bidirectionally-Coordinated Net (BiCNet) Neuroscience

innovation and business innovation are going hand in hand in the Open Source 3.0 era. Artificial intelligence, open source data, and blockchain are some examples of emerging innovations on a technical level configuring and managing microservices. Baidu is also on the list with two projects, mainly artificial intelligence. The two projects are the core framework, Paddle, and the associated tool libraries on the deep 智慧城市和电子商务等行业。 Zilliz is a next-generation data processing and analytics platform for artificial intelligence that provides the underlying technology for application-oriented enterprises. Mega, Zilliz's

be trained to be fairly accurate over a longer term. Figure 1-1: Relation between Artificial Intelligence, Machine Learning, and Deep Learning. Deep learning is one possible way of solving machine learning learning problems. Machine learning in turn is one approach towards artificial intelligence. Deep learning with neural networks has been the dominant methodology of training new machine learning models rectifier neural networks." Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 2011. Figure 1-2: Growth of parameters

highly valued than salary  Embraces and addresses conflict (Crucial conversations, Positive Intelligence, HTWFIP)Agile for Defense Agile Coaching by Darin Plum Aug 6, 2014 2 Books 1. “Good Patterson, Grenny, McMillan, and Switzler 5. “Coaching Agile Teams” by Lyssa Adkins 6. “Positive Intelligence” by Shirzad Chamine “Coaching Agile Teams” by Lyssa Adkins

机器学习 内容生产者 内容消费者 内容采集 （上传加速） 内容分发 海量存储 海量富媒体数据的业务布局 视觉智能 Vision Intelligence 数据智能 Data Intelligence 海量存储 ���� ���� ���� 弹性计算 ��� ��� 智能网络 API �� �� CDN ��� ������� ���

Edge with K3s and SUSE RGS based on existing intelligence gathered in advance. Logistically, this requires high lev- els of coordination, or intelligence can be compromised and quickly become out- dated