 OpenAI - AI in the Enterprisein new ways. The power of why Making great job recommendations to job seekers is only the start of the Indeed experience. They also need to explain to the candidate why this specific job was recommended capabilities of GPT-4o mini to shape these ‘why’ statements in their emails and messages to jobseekers. Using AI, the popular ‘Invite to Apply’ feature also explains why a candidate’s background or previous similar results with 60% fewer tokens. Helping job seekers find the right jobs—and understanding why a given opportunity is right for them—is a profoundly human outcome. Indeed's team has used AI to0 码力 | 25 页 | 9.48 MB | 5 月前3 OpenAI - AI in the Enterprisein new ways. The power of why Making great job recommendations to job seekers is only the start of the Indeed experience. They also need to explain to the candidate why this specific job was recommended capabilities of GPT-4o mini to shape these ‘why’ statements in their emails and messages to jobseekers. Using AI, the popular ‘Invite to Apply’ feature also explains why a candidate’s background or previous similar results with 60% fewer tokens. Helping job seekers find the right jobs—and understanding why a given opportunity is right for them—is a profoundly human outcome. Indeed's team has used AI to0 码力 | 25 页 | 9.48 MB | 5 月前3
 Google 《Prompt Engineering v7》``` Classify the above email as IMPORTANT or NOT IMPORTANT. Let's think step by step and explain why. Continues next page... Prompt Engineering February 2025 34 Output Attempt 1 **Step 1: Identify the statement or an instruction, resulting in different outputs: • Question: What was the Sega Dreamcast and why was it such a revolutionary console? • Statement: The Sega Dreamcast was a sixth-generation video game . • Instruction: Write a single paragraph that describes the Sega Dreamcast console and explains why it was so revolutionary. For few-shot prompting with classification tasks, mix up the classes Generally0 码力 | 68 页 | 6.50 MB | 6 月前3 Google 《Prompt Engineering v7》``` Classify the above email as IMPORTANT or NOT IMPORTANT. Let's think step by step and explain why. Continues next page... Prompt Engineering February 2025 34 Output Attempt 1 **Step 1: Identify the statement or an instruction, resulting in different outputs: • Question: What was the Sega Dreamcast and why was it such a revolutionary console? • Statement: The Sega Dreamcast was a sixth-generation video game . • Instruction: Write a single paragraph that describes the Sega Dreamcast console and explains why it was so revolutionary. For few-shot prompting with classification tasks, mix up the classes Generally0 码力 | 68 页 | 6.50 MB | 6 月前3
 Dynamic Model in TVMout_shape_tensors ○ Data independent (op_attrs, input_shapes, out_ndims) -> out_shape_tensors ● Why? ○ Fuse data independent shape function together© 2019, Amazon Web Services, Inc. or its Affiliates bs < 17 17 <= bs < 33 ...© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Why do we need graph dispatcher 1. Minimal overhead: only one dispatching operation is required for each0 码力 | 24 页 | 417.46 KB | 5 月前3 Dynamic Model in TVMout_shape_tensors ○ Data independent (op_attrs, input_shapes, out_ndims) -> out_shape_tensors ● Why? ○ Fuse data independent shape function together© 2019, Amazon Web Services, Inc. or its Affiliates bs < 17 17 <= bs < 33 ...© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Why do we need graph dispatcher 1. Minimal overhead: only one dispatching operation is required for each0 码力 | 24 页 | 417.46 KB | 5 月前3
 Trends Artificial Intelligence
Jassy in 2024 Amazon Shareholder Letter – 4/25 The chance to improve lives and reimagine things is why Google has been investing in AI for more than a decade… …We see it as the most important way we can tests used are HumanEval, MATH-500, MMLU and GPQA Diamond. Source: Artificial Analysis via NBC News, ‘Why DeepSeek is different, in three charts’ (1/25)266 Rising Performance of Open-Source Models + Falling foundation model family. Imagine an AI that's not just smart, but also affordable and versatile. Here's why it's turning heads: - Multimodal Prowess: It excels in handling text, images, and even videos,0 码力 | 340 页 | 12.14 MB | 4 月前3 Trends Artificial Intelligence
Jassy in 2024 Amazon Shareholder Letter – 4/25 The chance to improve lives and reimagine things is why Google has been investing in AI for more than a decade… …We see it as the most important way we can tests used are HumanEval, MATH-500, MMLU and GPQA Diamond. Source: Artificial Analysis via NBC News, ‘Why DeepSeek is different, in three charts’ (1/25)266 Rising Performance of Open-Source Models + Falling foundation model family. Imagine an AI that's not just smart, but also affordable and versatile. Here's why it's turning heads: - Multimodal Prowess: It excels in handling text, images, and even videos,0 码力 | 340 页 | 12.14 MB | 4 月前3
 DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Modelexample of NaturalQuestions. PROMPT A woman notices that she is depressed every autumn, and wonders why. A friend suggests to her that perhaps certain changes that take place as seasons move from warm to To put the writer’s idea into real use. Q: Which question CANNOT be answered in the passage? A: Why can human listen like earthworms? Q: How can you understand Earthworms better according to this passage0 码力 | 52 页 | 1.23 MB | 1 年前3 DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Modelexample of NaturalQuestions. PROMPT A woman notices that she is depressed every autumn, and wonders why. A friend suggests to her that perhaps certain changes that take place as seasons move from warm to To put the writer’s idea into real use. Q: Which question CANNOT be answered in the passage? A: Why can human listen like earthworms? Q: How can you understand Earthworms better according to this passage0 码力 | 52 页 | 1.23 MB | 1 年前3
 Facebook -- TVM AWS Meetup Talkprimitives (over 500 aten kernels) - Interpreter methods not delivering generalized performance 2 Why TVM? XTVM for Speech Synthesis - WaveRNN-style model architecture - Autoregressive sampling net0 码力 | 11 页 | 3.08 MB | 5 月前3 Facebook -- TVM AWS Meetup Talkprimitives (over 500 aten kernels) - Interpreter methods not delivering generalized performance 2 Why TVM? XTVM for Speech Synthesis - WaveRNN-style model architecture - Autoregressive sampling net0 码力 | 11 页 | 3.08 MB | 5 月前3
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