charts that also go up-and-to-the right. Creators / bettors / consumers are taking advantage of global internet rails that are accessible to 5.5B citizens via connected devices; ever-growing digital of their hefty free cash flows toward AI in efforts to drive growth and fend off attackers. And global competition – especially related to China and USA tech developments – is acute. The outline for Competition + Open-Source Momentum + China’s Rise • AI & Physical World Ramps = Fast + Data-Driven • Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before • AI & Work

EnterpriseA new way 
 to work As an AI research and deployment company, OpenAI prioritizes partnering with global companies because our models will increasingly do their best work with sophisticated, complex, EnterpriseLesson 1 Start with evals How Morgan Stanley iterated to ensure quality and safety As a global leader in financial services, Morgan Stanley is a relationship business. Not surprisingly, there earlier you start, the more your organization benefits from compounding improvements. Klarna, a global payments network and shopping platform, introduced a new AI assistant to streamline customer service

with JSON format Prompt Engineering February 2025 21 There are some benefits in returning JSON objects from a prompt that extracts data. In a real-world application I don’t need to manually create this format, I can already return the data in a sorted order (very handy when working with datetime objects), but most importantly, by prompting for a JSON format it forces the model to create a structure output returned in a structured format like JSON or XML. There are some benefits in returning JSON objects from a prompt that extracts data. In a real-world application I don’t need to manually create this

NDArray | Rd | tuplelclosure AST Nodes Cross language suppPort Easy to introduce new runtime objects (trees, graphs) Direct access from other languages QQ octoML HTVM Overview *。 Plug directly into

func(remote_a, remote_b)Virtual Machine: Supporting Dynamic Workload Dynamic shape workloads More runtime objects: Arrays, Tuples, Trees, ADTs Minimum runtime for dynamic models Credit: Jared Roesch, Haichen Shen

sub-tree to TensorCore Intrinsics Pattern Matching 计算平台事业部 shared/global lecal 印16/int8 - fpl6/ints ecal

frontend.from_mxnet(block, shape={input_name: input_shape}, dtype=dtype) tvm.relay.transform.dispatch_global_func(mod, "main", {input_name: input_shape}, tvm.relay.vm.exp_dispatcher) vmc = relay.backend.vm