model that is discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper. This model script is available on GitHub and NGC. Known Issues ‣ model that is discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper. PyTorch Release 23.06 PyTorch RN-08516-001_v23.07 | 20 This model that is discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper. This model script is available on GitHub and NGC. Known Issues ‣

an expensive process both in terms of time consumption and fiscal expenditure because it involves human labelers looking at each example and assigning them a label that they believe describes it best. The example, a human labeler might perceive the digit in figure 3-2 as a 1 and another one might see it as a 7. Figure 3-2: An example of handwritten digit that can potentially confuse the human labelers to to choose a 1 or a 7 as the target label. Obtaining labels in many cases requires significant human involvement, and for that reason can be expensive and slow. Many organizations and AI labs can only

2 T: Recognizing hand-written words P: Percentage of words correctly classified E: Database of human-labeled images of handwritten words Feng Li (SDU) Overview September 6, 2023 10 / 57 What is Machine classified E: Database of emails, some with human-given labels Example 4 T: Driving on four-lane highways using vision sensors P: Average distance traveled before a human-judged error E: A sequence of images images and steering commands recorded while ob- serving a human driver Feng Li (SDU) Overview September 6, 2023 11 / 57 Why Do We Need Machine Learning? Develop systems that are too difficult/expensive

expensive undertaking. Factoring in the costs of training human labelers on a given task, and then making sure that the labels are reliable, human labeling gets very expensive very quickly. Even after that labels (though the rate of improvement eventually plateaus). However, acquiring more labels through human effort is expensive, and is unlikely to scale to the level that we want for complex tasks. To achieve can create the output by masking some part of the input itself, there is no need for any sort of human intervention for labeling. Therefore, we can simply use e-books, Wikipedia and other sources for NLU

2018 Bloomberg Finance L.P. All rights reserved. Performance – Better than Human Precision Recall Machine Human Machine Human Table Boundary 95% 94% 95% 95% Perfect Table 87% 82% 94% 94% • 48,607 pages

资料来源：《Training language models to follow instructions with human feedback》论文 ◼ InstructGPT使用来自人类反馈的强化学习方案RLHF（reinforcement learning from human feedback）， 通过对大语言模型进行微调，从而能够在参数减少的情况下，实现优于GPT-3的 功能 ✓ 3 API的用户） GPT的发展 40 ChatGPT核心技术优势 资料来源：《Training language models to follow instructions with human feedback》论文 ◼ InstructGPT与ChatGPT属于相同代际的模型，ChatGPT只是在InstructGPT的基础上增加了Chat属性，且开放了公众测试 ◼ ChatGP

learning framework. Machine learning is the study of design of algorithms, inspired from the model of human brain. Deep learning is becoming more popular in data science fields like robotics, artificial intelligence(AI) “Artificial neural network” (ANN). They are inspired from the model of human brain, which is the most complex organ of our body. The human brain is made up of more than 90 billion tiny cells called “Neurons” passes the result to another neuron and this process continues. This is the basic method used by our human brain to process huge about of information like speech, visual, etc., and extract useful information

Classes (interfaces) 5.32.1.2 familyDescription (read-only) wstring IGuestOSType::familyDescription Human readable description of the guest OS family. 5.32.1.3 id (read-only) wstring IGuestOSType::id Guest Guest OS identifier string. 5.32.1.4 description (read-only) wstring IGuestOSType::description Human readable description of the guest OS. 5.32.1.5 is64Bit (read-only) boolean IGuestOSType::is64Bit recommendedUsbHid (read-only) boolean IGuestOSType::recommendedUsbHid Returns true if using USB Human Interface Devices, such as keyboard and mouse recom- mended. 5.32.1.19 recommendedHpet (read-only)

identifier string. 5.33.1.2 familyDescription (read-only) wstring IGuestOSType::familyDescription Human readable description of the guest OS family. 5.33.1.3 id (read-only) wstring IGuestOSType::id Guest Guest OS identifier string. 5.33.1.4 description (read-only) wstring IGuestOSType::description Human readable description of the guest OS. 5.33.1.5 is64Bit (read-only) boolean IGuestOSType::is64Bit recommendedUsbHid (read-only) boolean IGuestOSType::recommendedUsbHid Returns true if using USB Human Interface Devices, such as keyboard and mouse recom- mended. 98 5 Classes (interfaces) 5.33.1.19

identifier string. 5.33.1.2 familyDescription (read-only) wstring IGuestOSType::familyDescription Human readable description of the guest OS family. 5.33.1.3 id (read-only) wstring IGuestOSType::id Guest Guest OS identifier string. 5.33.1.4 description (read-only) wstring IGuestOSType::description Human readable description of the guest OS. 5.33.1.5 is64Bit (read-only) boolean IGuestOSType::is64Bit Returns 18 recommendedUsbHid (read-only) boolean IGuestOSType::recommendedUsbHid Returns true if using USB Human Interface Devices, such as keyboard and mouse recom- mended. 98 5 Classes (interfaces) 5.33.1.19