Trends Artificial Intelligence
Admin Costs Margins Marketing Spend Effectivity ROIC Revenues Sales Productivity Customer Service Production / Output Revenue-Focused Cost-Focused ‘Traditional’ Enterprise AI Adoption = Rising Erica Virtual Assistant (6/18) Note: We assume a start at zero users from Erica’s launch in 6/18. Pilot users excluded. Source: Bank of America (2/21, 4/24, 2/25) Bank of America Erica Virtual Assistant 10/21 3/22 8/22 1/23 6/23 11/23 4/24 9/24 2/25 Cumulative Client Interactions with Erica Virtual Assistant (MM) Note: Erica is a conversational AI built into Bank of America’s mobile app that helps0 码力 | 340 页 | 12.14 MB | 4 月前3
OpenAI - AI in the Enterpriseplatform, introduced a new AI assistant to streamline customer service. Within a few months, the assistant was handling two-thirds of all service chats—doing the work of hundreds of agents and cutting average invested heavily in our API to make it easier to customize and fine-tune models—whether as a self-service approach or using our tools and support. We worked closely with Lowe’s, the Fortune 50 home improvement team Uses it to answer 40,000 questions a year on policies, compliance, and more. The Customer Service team Automates the sentiment analysis of NPS surveys. 16 AI in the EnterpriseAnd the wins continue0 码力 | 25 页 | 9.48 MB | 5 月前3
Dynamic Model in TVMmodel in TVM ● Support Any-dim in typing ● Use shape function to compute the type at runtime ● Virtual machine as a new runtime for Relay ● Dynamic codegen (WIP) ○ Kernel dispatch for a single op ○ Data dependent© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Relay virtual machine Relay Executable relay.vm.compile Relay Object (hardware independent) Code segment VM a data type using the entries from a register. AllocClosure Allocates a closure with a lowered virtual machine function. If Jumps to the true or false offset depending on the condition. Goto Unconditionally0 码力 | 24 页 | 417.46 KB | 5 月前3
OctoML OSS 2019 11 8areas: o Core Infrastructure Improvements to TVM o_uTVM: support for microcontrollers in TVM o_ Virtual Machine and dynamic NNs support (w/ AWS folks) o_ Improved NLP support, with focus on transformers o QQ octoML BERT has many reshape operations, which are currently implemented using copy, 10 Virtual Machine e Many improvements from contributors at UW, AWS, and OctoML. e Initial implementation0 码力 | 16 页 | 1.77 MB | 5 月前3
TVM: Where Are We Goingremote_mod = remote.load_module(“mylib.so") func = remote_mod[“npufunction0"] func(remote_a, remote_b)Virtual Machine: Supporting Dynamic Workload Dynamic shape workloads More runtime objects: Arrays, Tuples0 码力 | 31 页 | 22.64 MB | 5 月前3
PAI & TVM Meetup - Shanghai 20191116buffer to hide memory load latency 。 storage align to reduce bank conflicts of shared memory 。 Virtual threads for data reuse (on going) Performance on V100 (FP16) 计算平台事业部 COMPUTING PLATFORM 512, 160 码力 | 26 页 | 5.82 MB | 5 月前3
OpenAI 《A practical guide to building agents》sequence of steps that must be executed to meet the user’s goal, whether that's resolving a customer service issue, booking a restaurant reservation, committing a code change, or generating a report. Applications judgment, exceptions, or context-sensitive decisions, for example refund approval in customer service workflows. 02 Difficult-to-maintain rules: Systems that have become unwieldy due to extensive and records, or sending messages. Send emails and texts, update a CRM record, hand-off a customer service ticket to a human. Orchestration Agents themselves can serve as tools for other agents—see the0 码力 | 34 页 | 7.00 MB | 6 月前3
DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Modelcompared with dense DeepSeek 67B. Inference Efficiency. In order to efficiently deploy DeepSeek-V2 for service, we first convert its parameters into the precision of FP8. In addition, we also perform KV cache DeepSeek-V2 based on the prompt and generation length distribution from the actually deployed DeepSeek 67B service. On a single node with 8 H800 GPUs, DeepSeek-V2 achieves a generation throughput exceeding 50K tokens based on the overall score. Models marked with * represent that we evaluate them through their API service or open-weighted model, instead of referring to the results reported in their original papers. Suffixes0 码力 | 52 页 | 1.23 MB | 1 年前3
TVM@AliOS2 _ _ 10 9.86 。, Online Service 8 8 6.952 。 C++0 码力 | 27 页 | 4.86 MB | 5 月前3
开源中国 2023 大模型(LLM)技术报告这些先进的 AI 模型, 快速完成从模型到应用的跨越,如 、 等。 : 大模型聚合平台主要用于整合和管理多个大型机器学习模型,在聚合平台之上,衍生出 MaaS(Model-as-a- Service,大模型即服务)的服务模式——通过提供统一的接口和框架,以更高效地部署、运行和优化这些模型, 。 :其它开发相关的 LLM 工具,如云原生构建多模态AI应用的工具 Jina,嵌入式数据库 txtai0 码力 | 32 页 | 13.09 MB | 1 年前3
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