如何高效合并音视频文件(时间短消耗资源少)(二)

英语字幕

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Good morning. We have a banger for you2
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today. We're going to launch chatbt3
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agent. But before jumping into that, I'd4
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like to ask the team to introduce5
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themselves. Starting with Yosh.6
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Hi, I'm Yash. I work on agent team and7
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before that I used to work on operator.8
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Hi, I'm Jing. I work on agents research9
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previously on deep research.10
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Hi, I'm Casey. I'm a researcher on11
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agents formerly operator.12
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Hi, I'm Issa. I'm a researcher on agent13
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formerly on deep research.14
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So we we started launching agents15
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earlier this year. Uh we launched deep16
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research, we launched operator and17
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people were very excited about this.18
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People could see that now uh AI was19
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going off to do complex tasks for them.20
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But it became clear to us that what21
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people really wanted was for us to bring22
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those capabilities and more together.23
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People wanted a unified agent that could24
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go off, use its own computer and do real25
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complex tasks for them, that could uh26
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seamlessly transition from thinking27
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about something to taking actions to28
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using lots of tools using the terminal,29
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clicking around the web, even producing30
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things like spreadsheets and slides and31
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and much more. And wanted people want to32
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be able to do this over a long time33
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horizon and a sort of for universal34
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tasks. So the team has been working35
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super hard to bring that together. And36
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today we have chat with the agent. Um,37
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it's probably easier to show it to you38
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than to keep talking about it. It is one39
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of the feel the aon moments for me to40
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watch it work. So, let's take a look.41
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Awesome. Thanks, Sam. Hello, everyone.42
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Very excited to share chat GBD agent43
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with everybody. And as Sam said, let's44
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just dive right into the demo. Okay, so45
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we are on Chad GBD as we all know and46
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love. And to turn on the agent mode, you47
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just click the tools menu and select48
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agent. You can also just type agent in49
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the composer bar and it'll take you to50
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agent mode. Um, Edward and I have a51
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wedding to go to later this year. Uh,52
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it's for one of our mutual friends.53
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Should we should we have the Asian54
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planet?55
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Yeah, let's do it. I need an outfit. And56
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don't forget the gift.57
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Okay, great. We won't forget the gift.58
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Um, it's a little bit of a longer59
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prompt, so I have it copied in my60
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buffer, so I'm just going to go ahead61
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and paste it. Um, okay. So, let's see.62
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Let's see what it says. Our friends are63
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getting married later this year, as I64
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said, Minia and Sarah. And we want the65
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agent to help us find an outfit that66
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matches the dress code. uh propose a few67
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options. Nice mid luxury taking into68
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account venue and weather. We also want69
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to find us some hotels and as Edward70
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said, don't forget the gift. Um so let's71
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see and72
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send the prompt away. As Sam said, agent73
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uses a computer. Uh so in the beginning74
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it sets up its environment. It it you75
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know it'll take a minute or two or not76
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really 5 seconds to set up its77
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environment. And in this case, as you78
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see, it understands the prompt. It's79
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asking for me for a clarification. I'm80
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just going to let it just continue and81
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work. Anyway, um I think it got confused82
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by saying, "Oh, where's the um what83
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exactly is the time of the date of the84
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wedding?" I think it'll figure out using85
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the website. Okay, cool. So, now it's86
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kicked off. It's starting the process,87
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the prompt, and it's open up a browser.88
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And to walk you through what's89
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happening, here's90
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Yeah. So, as mentioned, we gave the91
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agent access to its own virtual92
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computer, and the computer has many93
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different tools installed, and it can94
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choose which to use as it's working95
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through the task. So, in chat GPT, you96
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can see a visualization of the agent's97
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computer screen, and you can see98
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overlaid its chain of thought in text,99
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and that's what it's thinking as it's100
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working through the task and deciding101
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what to do next. We gave the agent102
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access to two different ways to browse103
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the internet. First, we gave it a text104
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browser, and this is similar to the deep105
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research tool. And this is what lets it106
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really efficiently and quickly read many107
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web pages um um and search for them. And108
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we also gave it access to a visual109
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browser. And this is similar to the110
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operator tool. And this is what lets it111
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actually interact with the UI of a web112
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page. So it can um drag things. It can113
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use the cursor to click around. It can114
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open UI components. It can fill out115
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forms and enter text and text areas.116
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It's very flexible. So those two tools117
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are very complimentary. And then we also118
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gave it access to its own terminal so119
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that it can run code and it can also120
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generate and analyze files like slide121
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decks and spreadsheets. And then through122
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the terminal it's also able to call123
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APIs. So both public APIs and APIs to124
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access your private data sources like125
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Google Drive, Google Calendar, GitHub,126
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SharePoint and many others um and only127
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if you explicitly connect them similar128
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to deep research connectors. And then it129
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also has access to the image gen API so130
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it can create nice visuals for um slide131
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decks and other things as it's working132
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through its tasks.133
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How is deciding which tools to use here?134
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Yes, we train the model to move between135
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these capabilities with reinforcement136
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learning. This is the first model we137
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trained that has access to this unified138
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tool box. A text browser, a GUI browser139
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and a terminal all in one virtual140
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machine. To guide its learning, we141
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created hard tasks that require using142
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all these tools. This allows the model143
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not only to learn how to use these144
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tools, but also when to use which tool145
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depending on the task at hand. At the146
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beginning of the training, the model147
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might attempt to use all these tools to148
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solve a relatively simple problem. Over149
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time, as we reward the model for solving150
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problems correctly and efficiently, the151
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model will have smarter tool choice.152
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For example, if you ask a model to uh153
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find a restaurant with specific154
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requirements and make a reservation, the155
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model may typically just start a deep156
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research in the text browser to find157
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some candidates, then switch to the GUI158
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browser to view photos of food, uh check159
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availability, and complete the booking.160
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Similarly, for creative task like161
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creating an artifact, the model will162
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first search online for public163
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resources, then switch to the terminal164
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to do some code editing to compile the165
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artifact and finally verify the final166
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outputs in the GUI browser. With this,167
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we truly feel like we brought together168
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the best of deep research and operator169
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and added some extra sparkle.170
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That's right. Yeah. So to put this171
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project in context, I want to give a bit172
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of history. So a few months ago, we173
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shipped operator in January and this was174
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our agent that lets you do online tasks175
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like book reservations and um send176
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emails and then two weeks later we177
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shipped deep research and deep research178
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is a tool that lets you do in-depth179
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internet research and output highquality180
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um um research reports. And after launch181
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we realized that actually these two182
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approaches are actually deeply183
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complimentary.184
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Um for example operator has some trouble185
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reading super long articles. Um it has186
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to scroll. It takes a long time. But187
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that's something that deep research is188
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good at. Conversely operator uh uh deep189
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research isn't as good at interacting190
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with web pages interactive elements191
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visual uh highly visual web pages but192
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that's something that operator excels193
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at. So uh yeah we felt these approaches194
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were complimentary and then we we were195
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also looking at some customer feedback.196
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So for example one of our most highly197
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requested features for deep research was198
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the ability to log into websites and199
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access authenticated sources. That's200
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something that operator can do.201
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I've been waiting for that for a long202
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time.203
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Yeah.204
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Um another thing is that we were looking205
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at the prompts that people were trying206
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for operator and we saw that they were207
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actually more deep research type208
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prompts. for example, plan a trip and209
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then book it. And so, yeah, we we really210
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feel like we're bringing the best of211
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both worlds here. And on a personal212
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note, we've all been friends for a213
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while, and it's really exciting to be214
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working together. So, speaking of215
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matches made in heaven, how is the216
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wedding planning going?217
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It's amazing to watch. This is an218
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example of a task I hate doing. This can219
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like ruin like, you know, multiple hours220
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for me as I get sucked into these rabbit221
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holes. So, just watching this as you222
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guys have been talking click through223
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this and just like do the whole thing is224
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really quite remarkable. Yeah, totally.225
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Um, looks like it started off by226
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figuring out the weather. One of the227
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cool features, um, is that, you know, as228
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some of these tasks may take a little229
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bit longer, you can just go back and see230
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what it was doing. So, that's what we're231
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exactly going to do. Looks like it went232
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through the website to use the text233
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browser. Interestingly, for that, now234
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it's looking through the suits for235
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Edward. I think it'll find something236
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good. Here you can see it switched over237
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to actually a visual browser to make238
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sure suit will look really good on239
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Edward.240
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And now looks like yeah, it's got241
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chugging along, figuring out what to do.242
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Um, and still on suits and now probably243
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getting to the gifts section. Um, okay,244
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cool. So, this is going to take a while.245
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As Sam said, these tasks sometimes can246
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take a long time. So, it's going to247
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continue doing hopefully much faster248
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than we will do. Um, should we do249
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something else while it's doing it? I250
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think the team really wanted the um251
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stickers, some stickers for the for the252
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launch. Should we do that?253
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Yeah, cool.254
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All right. So, we have a team mascot,255
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which is one of our colleagues, Bunny256
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Doodle. really really cute tell you. Um257
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and we're going to try and bring um get258
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some laptop stickers for everybody. Uh259
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one of the favorite features for agent260
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is given that trajectories can take 15261
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minutes, 20 minutes, 30 minutes262
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depending on the complexity of the task.263
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Um a lot of times the you might need to264
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help the agent. Agent might need to ask265
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you clarifications, confirmations and266
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things like that. Um so I love to use it267
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on the go. So I'm going to use my mobile268
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phone to actually send the query this269
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time and then see how it goes.270
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Okay, so let's see. Okay, so we are on271
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Chad Gibbdi. Uh I have already selected272
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the agent mode. I've also inputed our uh273
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cute mascot and I'm going to quickly274
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paste a query. So query says make some275
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swag for the team one by one laptop276
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stickers and order 500 of them. I'll277
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also say I like sticker mule278
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which we have used in the past and send279
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it off.280
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Okay. So, just like it was doing on the281
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web, it's going to take some time, think282
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about like what's it doing, and it'll283
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kick off kick off the query. And as it's284
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going, it'll take some time to kick it285
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off. Is it Oh, there we go. So, it'll286
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start working on it. Looks like it's287
00:10:12,480 --> 00:10:14,720
starting to create the anime art. It'll288
00:10:14,720 --> 00:10:16,640
probably use image that Isa referred289
00:10:16,640 --> 00:10:18,399
earlier on to create hopefully an anime290
00:10:18,399 --> 00:10:20,240
art. We'll see how it comes out. While291
00:10:20,240 --> 00:10:21,760
that's going, anything else we want to292
00:10:21,760 --> 00:10:22,399
do?293
00:10:22,399 --> 00:10:24,720
Oh, yeah. I also need a pair of shoes294
00:10:24,720 --> 00:10:26,320
because my shoes got damaged.295
00:10:26,320 --> 00:10:27,360
How did they get damaged?296
00:10:27,360 --> 00:10:28,560
Uh, by the rain297
00:10:28,560 --> 00:10:30,000
in SF.298
00:10:30,000 --> 00:10:30,800
Yes.299
00:10:30,800 --> 00:10:32,160
Cool. All right. Uh, well, let's get300
00:10:32,160 --> 00:10:34,240
Edward a pair of shoes as well. So, oh,301
00:10:34,240 --> 00:10:40,320
can you also find us um pair of men's302
00:10:40,320 --> 00:10:43,519
dress black shoes in size303
00:10:43,519 --> 00:10:44,240
9.5?304
00:10:44,240 --> 00:10:46,000
9.5.305
00:10:46,000 --> 00:10:47,920
So, one of the key capabilities of the306
00:10:47,920 --> 00:10:49,920
model is being able to interrupt. I307
00:10:49,920 --> 00:10:51,920
think you know as trajectories take long308
00:10:51,920 --> 00:10:53,760
time or whatever time it's really309
00:10:53,760 --> 00:10:56,720
important for us to for it to feel very310
00:10:56,720 --> 00:10:59,120
multi-turn so the users can interject311
00:10:59,120 --> 00:11:01,120
user can direct it user can give it more312
00:11:01,120 --> 00:11:02,640
guidance less guidance whatever we want313
00:11:02,640 --> 00:11:04,320
to do and that's what we're doing here314
00:11:04,320 --> 00:11:07,040
we essentially the the model was315
00:11:07,040 --> 00:11:08,720
chugging along figuring out all the316
00:11:08,720 --> 00:11:10,240
things that we had asked before and in317
00:11:10,240 --> 00:11:12,320
this case we essentially said hey can318
00:11:12,320 --> 00:11:16,000
you also uh get us a pair of men's black319
00:11:16,000 --> 00:11:18,160
shoes and now it's thinking and soon320
00:11:18,160 --> 00:11:19,839
enough hopefully it'll take that into321
00:11:19,839 --> 00:11:22,000
account and keep going uh into its322
00:11:22,000 --> 00:11:23,600
trajectory. There we go. So, it said323
00:11:23,600 --> 00:11:25,120
acknowledge the interruption. It said,324
00:11:25,120 --> 00:11:26,880
"Okay, cool. I'll also research men's325
00:11:26,880 --> 00:11:29,600
black shoes in size 9.5." Um, and then326
00:11:29,600 --> 00:11:31,680
it'll probably get on its way. Um, but327
00:11:31,680 --> 00:11:33,120
maybe Issa can tell us a little bit more328
00:11:33,120 --> 00:11:34,240
about how that works.329
00:11:34,240 --> 00:11:36,320
Yeah, sure. So, as you can see, the330
00:11:36,320 --> 00:11:38,079
agent is very collaborative, and this331
00:11:38,079 --> 00:11:39,920
was really important to us when we were332
00:11:39,920 --> 00:11:41,200
training the model and building the333
00:11:41,200 --> 00:11:42,880
product. If you were asking another334
00:11:42,880 --> 00:11:44,399
person to do a task for you that would335
00:11:44,399 --> 00:11:45,519
take them a really long time to336
00:11:45,519 --> 00:11:46,959
complete, you'd probably give them some337
00:11:46,959 --> 00:11:48,800
instructions to start and then they338
00:11:48,800 --> 00:11:50,640
might ask you some clarifying questions339
00:11:50,640 --> 00:11:52,320
and then they'd start the task and maybe340
00:11:52,320 --> 00:11:53,600
realize, oh, they need more341
00:11:53,600 --> 00:11:55,440
clarification from you or they need your342
00:11:55,440 --> 00:11:56,880
permission to sign into something or do343
00:11:56,880 --> 00:11:58,560
something on your behalf and then you344
00:11:58,560 --> 00:12:00,240
might realize, oh, I forgot to mention345
00:12:00,240 --> 00:12:02,640
this thing or um what's your status? How346
00:12:02,640 --> 00:12:04,240
are you doing? Can I help redirect you347
00:12:04,240 --> 00:12:05,760
if you're getting along the wrong path348
00:12:05,760 --> 00:12:07,760
or something? And so similarly for these349
00:12:07,760 --> 00:12:09,680
really longrunning agentic tasks, it's350
00:12:09,680 --> 00:12:11,519
very important that both the user and351
00:12:11,519 --> 00:12:13,600
the agent are able to initiate352
00:12:13,600 --> 00:12:15,519
communication with each other so that um353
00:12:15,519 --> 00:12:17,200
the agent is able to most effectively354
00:12:17,200 --> 00:12:19,360
help you with your tasks. And so this is355
00:12:19,360 --> 00:12:20,560
something that we actually trained into356
00:12:20,560 --> 00:12:22,320
the model. We trained it to be able to357
00:12:22,320 --> 00:12:24,160
ask clarifying questions, not every358
00:12:24,160 --> 00:12:26,240
single time like deep research. Um we359
00:12:26,240 --> 00:12:28,800
also asked it we also trained it to be360
00:12:28,800 --> 00:12:30,560
interruptible as Yash just showed. And361
00:12:30,560 --> 00:12:32,000
also sometimes it will ask you for362
00:12:32,000 --> 00:12:33,519
clarification and confirmation363
00:12:33,519 --> 00:12:35,680
mid-trajectory.364
00:12:35,680 --> 00:12:38,079
Yeah. And part of working with agent is365
00:12:38,079 --> 00:12:40,480
that well sometimes it'll make mistakes.366
00:12:40,480 --> 00:12:42,079
And that's why we felt it was important367
00:12:42,079 --> 00:12:44,079
to train the model to ask you for368
00:12:44,079 --> 00:12:45,920
confirmation at the last step of369
00:12:45,920 --> 00:12:49,279
important steps. Um so for example maybe370
00:12:49,279 --> 00:12:51,519
before it's going to send the email um371
00:12:51,519 --> 00:12:53,440
it'll ask you to take a look at the372
00:12:53,440 --> 00:12:54,720
draft and whether it makes sense and373
00:12:54,720 --> 00:12:56,079
whether there are any embarrassing374
00:12:56,079 --> 00:12:59,200
typos. Um, and if there are, then you375
00:12:59,200 --> 00:13:01,360
can either ask it to fix it or you can376
00:13:01,360 --> 00:13:03,440
directly take over the browser and jump377
00:13:03,440 --> 00:13:06,079
right into the um, agents environment378
00:13:06,079 --> 00:13:09,040
and correct it yourself. And that way it379
00:13:09,040 --> 00:13:10,720
feels collaborative and you can um,380
00:13:10,720 --> 00:13:13,680
really work with the agent.381
00:13:13,680 --> 00:13:15,120
Should we look at maybe one more demo?382
00:13:15,120 --> 00:13:17,279
We've got this uh, sort of fun tradition383
00:13:17,279 --> 00:13:19,600
in live streams of using uh, using our384
00:13:19,600 --> 00:13:21,120
newest models to sort of evaluate385
00:13:21,120 --> 00:13:23,040
themselves or do something kind of meta.386
00:13:23,040 --> 00:13:24,240
Anything like that we could do?387
00:13:24,240 --> 00:13:27,440
Yeah, let's do it.388
00:13:27,440 --> 00:13:28,320
So um389
00:13:28,320 --> 00:13:29,440
I think people would love to know how390
00:13:29,440 --> 00:13:30,320
good the model is.391
00:13:30,320 --> 00:13:33,920
Yes. So this is a prompt we previously392
00:13:33,920 --> 00:13:36,880
gave the a agent yesterday. So basically393
00:13:36,880 --> 00:13:38,959
it asks the model to pull its own394
00:13:38,959 --> 00:13:40,959
evalution number from our Google job395
00:13:40,959 --> 00:13:43,440
connector and make some slides. So we396
00:13:43,440 --> 00:13:44,959
want to keep it simple like no397
00:13:44,959 --> 00:13:47,360
introduction no conclusion just present398
00:13:47,360 --> 00:13:50,000
the results with in the charts. As you399
00:13:50,000 --> 00:13:52,160
can see now the model is connecting to400
00:13:52,160 --> 00:13:55,120
the Google Drive API and uh then search401
00:13:55,120 --> 00:13:57,600
within API it right now it looks like402
00:13:57,600 --> 00:13:59,920
the first result is very relevant. So403
00:13:59,920 --> 00:14:02,720
it's reading the first result.404
00:14:02,720 --> 00:14:04,959
Now it's reading the first result uh in405
00:14:04,959 --> 00:14:07,920
details. Uh let's accelerate this uh406
00:14:07,920 --> 00:14:12,800
replay. So then the model might read407
00:14:12,800 --> 00:14:15,279
from the result again and write some408
00:14:15,279 --> 00:14:16,959
code.409
00:14:16,959 --> 00:14:19,519
So here you can see that the model is410
00:14:19,519 --> 00:14:21,920
using the image generation model called411
00:14:21,920 --> 00:14:24,480
image generation tool to generate some412
00:14:24,480 --> 00:14:28,079
decorations for the slides.413
00:14:28,079 --> 00:14:30,160
And let's see what's the first slide the414
00:14:30,160 --> 00:14:33,399
model made.415
00:14:33,920 --> 00:14:35,920
So here the model is writing some code416
00:14:35,920 --> 00:14:38,399
that will be compiled to be the final417
00:14:38,399 --> 00:14:41,120
slides. So this is the first slide the418
00:14:41,120 --> 00:14:44,160
model make in this demo which looks okay419
00:14:44,160 --> 00:14:46,240
but it's not polished enough.420
00:14:46,240 --> 00:14:48,240
One of the key feature in reinforcement421
00:14:48,240 --> 00:14:50,160
learning is that the model will re422
00:14:50,160 --> 00:14:52,240
review its own results and refine the423
00:14:52,240 --> 00:14:55,120
results to to deliver a good final424
00:14:55,120 --> 00:14:57,839
results. Let's see what's the finally425
00:14:57,839 --> 00:15:00,320
what the model give us.426
00:15:00,320 --> 00:15:04,000
We can click skip and then the model427
00:15:04,000 --> 00:15:07,519
give us a good uh PowerPoint file. So428
00:15:07,519 --> 00:15:09,040
it's a real PowerPoint that you can429
00:15:09,040 --> 00:15:14,040
download and open it in any software.430
00:15:14,639 --> 00:15:19,279
Let's open it in uh in the office. So431
00:15:19,279 --> 00:15:22,160
let's present the slides the model just432
00:15:22,160 --> 00:15:23,839
generated.433
00:15:23,839 --> 00:15:27,120
First are two intelligence benchmarks.434
00:15:27,120 --> 00:15:30,480
Humanities last exam is a benchmark that435
00:15:30,480 --> 00:15:33,519
measures AI's ability to solve a broad436
00:15:33,519 --> 00:15:37,120
range of subjects on hard problems. We437
00:15:37,120 --> 00:15:40,320
evaluate the models with two settings438
00:15:40,320 --> 00:15:43,440
with and without tool use.439
00:15:43,440 --> 00:15:45,920
We can see that the agent modes the raw440
00:15:45,920 --> 00:15:48,720
intelligence is already pretty nice and441
00:15:48,720 --> 00:15:50,880
with access to all tools nearly double442
00:15:50,880 --> 00:15:54,720
the performance to 42%.443
00:15:54,720 --> 00:15:56,720
When evaluating models on humanity's444
00:15:56,720 --> 00:15:59,360
last exam, especially with the browsing445
00:15:59,360 --> 00:16:01,759
ability, we have a two-layer446
00:16:01,759 --> 00:16:04,399
decontamination that ensure that the447
00:16:04,399 --> 00:16:07,680
model doesn't cheat on this benchmark.448
00:16:07,680 --> 00:16:10,079
Front TMS is a benchmark that measures449
00:16:10,079 --> 00:16:11,839
advanced mathematical reasoning ability450
00:16:11,839 --> 00:16:13,680
of models.451
00:16:13,680 --> 00:16:16,000
Different from our baseline of mini and452
00:16:16,000 --> 00:16:18,560
03 which use Python with function453
00:16:18,560 --> 00:16:21,440
coding. We give the agent model all454
00:16:21,440 --> 00:16:23,440
available tools like a browser, a455
00:16:23,440 --> 00:16:26,320
computer and a terminal. The agent456
00:16:26,320 --> 00:16:29,360
achieves new state art of 27% on this457
00:16:29,360 --> 00:16:31,440
benchmark with the help of all these458
00:16:31,440 --> 00:16:34,440
tools.459
00:16:34,639 --> 00:16:36,880
Next, we evaluated the model on two460
00:16:36,880 --> 00:16:39,519
agentic benchmarks. Web arena is a461
00:16:39,519 --> 00:16:41,519
benchmark that measures web agents462
00:16:41,519 --> 00:16:43,600
ability so to solve real world web463
00:16:43,600 --> 00:16:47,279
tasks. The agent model improves over464
00:16:47,279 --> 00:16:51,360
previous O3 model that powers the core.465
00:16:51,360 --> 00:16:54,399
Browse comp is a benchmark we introduced466
00:16:54,399 --> 00:16:56,240
earlier this year that measures the467
00:16:56,240 --> 00:16:58,880
browsing agents ability to search and468
00:16:58,880 --> 00:17:02,320
find uh how to locate information.469
00:17:02,320 --> 00:17:03,839
The agent model significantly470
00:17:03,839 --> 00:17:06,160
outperforms 03 and deep research on this471
00:17:06,160 --> 00:17:11,679
benchmark achieving 69% pass rate.472
00:17:11,679 --> 00:17:14,559
Finally, we care about how the users473
00:17:14,559 --> 00:17:16,959
will benefit from our model in the real474
00:17:16,959 --> 00:17:19,919
world. Spreadsheet bench is a benchmark475
00:17:19,919 --> 00:17:21,919
that measures the model's ability to476
00:17:21,919 --> 00:17:24,400
edit spreadsheets derived from the real477
00:17:24,400 --> 00:17:28,079
world use case. Here the agent model478
00:17:28,079 --> 00:17:30,480
with the liberal office and the computer479
00:17:30,480 --> 00:17:34,000
tool can already solve 30% of the task480
00:17:34,000 --> 00:17:36,480
when we give the model the access to the481
00:17:36,480 --> 00:17:39,840
raw Excel file in the terminal which482
00:17:39,840 --> 00:17:44,000
further boost the performance to 45%.483
00:17:44,000 --> 00:17:46,000
Finally we evated the model on an484
00:17:46,000 --> 00:17:48,000
internal banking benchmark. The bench485
00:17:48,000 --> 00:17:49,760
this benchmark evaluated the model's486
00:17:49,760 --> 00:17:52,559
ability to to conduct first to third487
00:17:52,559 --> 00:17:55,679
year investment bank uh banking analyst488
00:17:55,679 --> 00:17:58,799
tasks such as like putting together a489
00:17:58,799 --> 00:18:00,559
three statement financial model for490
00:18:00,559 --> 00:18:04,000
Fortune uh 500 company in this491
00:18:04,000 --> 00:18:06,160
benchmark. The agent model significantly492
00:18:06,160 --> 00:18:08,080
outperforms the previous deep research493
00:18:08,080 --> 00:18:11,760
and all three models. As you can see494
00:18:11,760 --> 00:18:13,919
this model is one of the most powerful495
00:18:13,919 --> 00:18:16,080
model we've ever trained.496
00:18:16,080 --> 00:18:18,960
It's not only good on benchmarks, it's497
00:18:18,960 --> 00:18:22,480
also capable of reasoning, browsing, and498
00:18:22,480 --> 00:18:24,720
tackling real world tasks at a level499
00:18:24,720 --> 00:18:28,480
that we cannot imagine three months ago.500
00:18:28,480 --> 00:18:31,600
That's right. Um, as Edward said, um, we501
00:18:31,600 --> 00:18:32,799
think we've trained a very powerful502
00:18:32,799 --> 00:18:35,280
model and a lot of the power comes from503
00:18:35,280 --> 00:18:38,240
its ability to browse the internet. And504
00:18:38,240 --> 00:18:40,240
as we know, the internet can be a scary505
00:18:40,240 --> 00:18:42,400
place. There are all sorts of hackers506
00:18:42,400 --> 00:18:45,120
trying to steal your information, scams,507
00:18:45,120 --> 00:18:48,480
uh fishing attempts. Um and agent isn't508
00:18:48,480 --> 00:18:51,120
immune to all these things. Um one509
00:18:51,120 --> 00:18:53,360
particular thing we're worried about is510
00:18:53,360 --> 00:18:55,520
a new uh attack called prompt511
00:18:55,520 --> 00:18:57,120
injections.512
00:18:57,120 --> 00:18:59,840
This is where let's say you ask agent to513
00:18:59,840 --> 00:19:02,080
buy you a book and you give it your514
00:19:02,080 --> 00:19:04,400
credit card information to do that.515
00:19:04,400 --> 00:19:06,240
Agent might stumble upon a malicious516
00:19:06,240 --> 00:19:08,559
website that asks it, "Oh, enter your517
00:19:08,559 --> 00:19:10,400
credit card information here. it'll help518
00:19:10,400 --> 00:19:12,799
you with your task. An agent, which is519
00:19:12,799 --> 00:19:15,200
trained to be helpful, might decide520
00:19:15,200 --> 00:19:18,080
that's a good idea.521
00:19:18,080 --> 00:19:19,760
We've done a lot of work to try to522
00:19:19,760 --> 00:19:22,320
ensure that this doesn't happen. We've523
00:19:22,320 --> 00:19:24,240
trained our model to ignore suspicious524
00:19:24,240 --> 00:19:27,120
instructions on on suspicious websites.525
00:19:27,120 --> 00:19:29,039
We've also have uh we also have layers526
00:19:29,039 --> 00:19:32,000
of monitors that kind of peer over the527
00:19:32,000 --> 00:19:33,760
agent's shoulder and watch it as it's528
00:19:33,760 --> 00:19:36,480
going um and stop the trajectory if529
00:19:36,480 --> 00:19:38,799
anything looks suspicious. We can even530
00:19:38,799 --> 00:19:41,919
update these in real time if new attacks531
00:19:41,919 --> 00:19:44,160
are found in the wild.532
00:19:44,160 --> 00:19:45,919
That said though, you know, this is a533
00:19:45,919 --> 00:19:47,760
cutting edge product. This is a new534
00:19:47,760 --> 00:19:50,000
surface and we can't stop everything.535
00:19:50,000 --> 00:19:51,280
And so that's why I feel it's very536
00:19:51,280 --> 00:19:52,559
important for the audience to be aware537
00:19:52,559 --> 00:19:55,360
of the risks involved in using agent.538
00:19:55,360 --> 00:19:57,440
And um we encourage users to be539
00:19:57,440 --> 00:19:59,520
proactive in kind of thinking about how540
00:19:59,520 --> 00:20:01,120
they share their information. You know,541
00:20:01,120 --> 00:20:02,880
if it's highly sensitive information,542
00:20:02,880 --> 00:20:06,799
maybe don't share that. um maybe um uh543
00:20:06,799 --> 00:20:08,799
use our features like takeover mode to544
00:20:08,799 --> 00:20:10,799
directly input your credit credit card545
00:20:10,799 --> 00:20:12,880
information into the browser instead of546
00:20:12,880 --> 00:20:15,679
um giving it to agent. Um we feel like547
00:20:15,679 --> 00:20:18,640
we've built a very powerful product but548
00:20:18,640 --> 00:20:20,480
again it's important for our users to549
00:20:20,480 --> 00:20:21,760
understand the risk involved.550
00:20:21,760 --> 00:20:23,280
Yeah, I really want to emphasize that I551
00:20:23,280 --> 00:20:25,520
think this is a new level of capability552
00:20:25,520 --> 00:20:27,120
in AI. It's a new way to use AI, but553
00:20:27,120 --> 00:20:28,799
there will be a new set of attacks that554
00:20:28,799 --> 00:20:30,799
come with that. And society and the555
00:20:30,799 --> 00:20:33,120
technology will have to evolve and learn556
00:20:33,120 --> 00:20:34,320
how we're going to mitigate things that557
00:20:34,320 --> 00:20:36,159
we can't even really imagine yet. Uh, as558
00:20:36,159 --> 00:20:37,360
people start doing more and more work559
00:20:37,360 --> 00:20:39,679
this way. Before I wrap up, should we560
00:20:39,679 --> 00:20:41,840
check in on some of the tasks you kicked561
00:20:41,840 --> 00:20:42,080
off?562
00:20:42,080 --> 00:20:46,159
Yeah, let's do it. Um, okay. So, I am563
00:20:46,159 --> 00:20:48,240
going to open a new tab and make sure564
00:20:48,240 --> 00:20:51,840
that we can see the progress of our um,565
00:20:51,840 --> 00:20:55,679
stickers as well. Okay. Let's see. All566
00:20:55,679 --> 00:20:58,159
right. So, sounds like stickers are567
00:20:58,159 --> 00:21:00,880
ready. Let me see what it actually Okay.568
00:21:00,880 --> 00:21:03,200
So, cool thing. This is sort of the end569
00:21:03,200 --> 00:21:06,720
end result of the took about 7 minutes.570
00:21:06,720 --> 00:21:08,480
Highly likely figured out everything.571
00:21:08,480 --> 00:21:09,840
We'll go back and look at the trajectory572
00:21:09,840 --> 00:21:11,679
and see how it did. But at the end573
00:21:11,679 --> 00:21:13,679
result, it looks like it's added to the574
00:21:13,679 --> 00:21:15,360
cart. This is the subtotal. I can just575
00:21:15,360 --> 00:21:17,360
go ahead and look at it and then figure576
00:21:17,360 --> 00:21:20,000
out uh I can just take over at this577
00:21:20,000 --> 00:21:21,600
point as Casey said to enter my credit578
00:21:21,600 --> 00:21:23,039
card information and then place the579
00:21:23,039 --> 00:21:25,200
order really quickly. model is asking580
00:21:25,200 --> 00:21:27,120
for confirmations, etc. as it's supposed581
00:21:27,120 --> 00:21:29,280
to do. Let's just quickly browse through582
00:21:29,280 --> 00:21:31,039
the trajectory and see what it actually583
00:21:31,039 --> 00:21:33,280
did. Oh, it looks like it generated some584
00:21:33,280 --> 00:21:35,840
stickers. Oh, look at that. That's what585
00:21:35,840 --> 00:21:38,880
it generated sticker. Cool. So, yeah,586
00:21:38,880 --> 00:21:40,640
that's the task. I think I can at this587
00:21:40,640 --> 00:21:42,559
point finish up by myself or I can ask588
00:21:42,559 --> 00:21:43,919
the model to actually go ahead and do it589
00:21:43,919 --> 00:21:46,720
for me as well. Let's check on the590
00:21:46,720 --> 00:21:49,840
wedding. Okay, great. Looks like it just591
00:21:49,840 --> 00:21:52,720
finished in the nick of time. Uh, okay,592
00:21:52,720 --> 00:21:55,520
cool. So in this case, as as we said, we593
00:21:55,520 --> 00:21:57,840
were looking for hotel, stress, uh594
00:21:57,840 --> 00:22:01,919
suits, and also shoes. So it's come out595
00:22:01,919 --> 00:22:03,520
with a pretty comprehensive report. It596
00:22:03,520 --> 00:22:05,840
looks like wedding venue, date, when it597
00:22:05,840 --> 00:22:10,240
is with the Zilla links, dress codes. It598
00:22:10,240 --> 00:22:11,600
figured out like what the suit599
00:22:11,600 --> 00:22:12,960
recommendation should be, where you can600
00:22:12,960 --> 00:22:14,799
buy. Now I can go ahead and buy myself601
00:22:14,799 --> 00:22:17,120
or I can ask the agent to go and buy for602
00:22:17,120 --> 00:22:20,960
me. Um also figured out footwear hurdle603
00:22:20,960 --> 00:22:23,360
options. It actually looked through all604
00:22:23,360 --> 00:22:27,120
the oop sorry it looked through all the605
00:22:27,120 --> 00:22:29,360
availability. You can see actually it606
00:22:29,360 --> 00:22:31,440
gives screenshots of what it checked. In607
00:22:31,440 --> 00:22:33,120
this case we use booking.com and it's608
00:22:33,120 --> 00:22:35,280
able to do that. Also has gift609
00:22:35,280 --> 00:22:37,360
suggestions etc. And next step I can ask610
00:22:37,360 --> 00:22:39,760
it as you said the agent says hey if you611
00:22:39,760 --> 00:22:41,520
need assistance purchasing any item or612
00:22:41,520 --> 00:22:42,960
have any further adjustments let me know613
00:22:42,960 --> 00:22:44,880
so we can do that. Um, and I want to614
00:22:44,880 --> 00:22:46,320
show one last demo which we didn't615
00:22:46,320 --> 00:22:48,640
really run live but I think it's really616
00:22:48,640 --> 00:22:51,280
cool and especially because the folks617
00:22:51,280 --> 00:22:52,880
who are getting married are really into618
00:22:52,880 --> 00:22:57,679
MLB. U so we asked the agent uh to go619
00:22:57,679 --> 00:22:59,679
and build an optimal itinary for620
00:22:59,679 --> 00:23:02,640
visiting all 30 MLB stadiums in just in621
00:23:02,640 --> 00:23:05,200
case you're thinking of a satical uh and622
00:23:05,200 --> 00:23:08,159
then design the optimal route prioritize623
00:23:08,159 --> 00:23:10,960
Hello Kitty nights and whatnot and624
00:23:10,960 --> 00:23:12,400
present a final plan as a detailed625
00:23:12,400 --> 00:23:13,520
spreadsheet. I'll really quickly run626
00:23:13,520 --> 00:23:15,440
through this. Um I think it's just so627
00:23:15,440 --> 00:23:18,240
fun to see. So again like as we have628
00:23:18,240 --> 00:23:20,720
thrown shown throughout the the live629
00:23:20,720 --> 00:23:23,919
stream it uses a multitude of tools uses630
00:23:23,919 --> 00:23:26,240
container the terminal use using the631
00:23:26,240 --> 00:23:28,799
browser working through all the details.632
00:23:28,799 --> 00:23:30,400
It'll probably use again back to the633
00:23:30,400 --> 00:23:33,200
browser figuring out Hello Kitty nights634
00:23:33,200 --> 00:23:36,559
and then sports stadium and whatnot. Oh635
00:23:36,559 --> 00:23:39,520
let's see did I miss the Oh go map.636
00:23:39,520 --> 00:23:42,080
building a map using code to actually637
00:23:42,080 --> 00:23:43,919
build it out and then overall we get638
00:23:43,919 --> 00:23:46,159
like a pretty solid result I think at639
00:23:46,159 --> 00:23:48,880
the end takes 25 minutes to work where640
00:23:48,880 --> 00:23:50,400
does the season start and what not you641
00:23:50,400 --> 00:23:51,919
have a spreadsheet that you can quickly642
00:23:51,919 --> 00:23:55,760
view inside just right inside Chad GBD643
00:23:55,760 --> 00:23:57,919
you can map the journey cool looking map644
00:23:57,919 --> 00:24:00,400
I guess and that's it so this is Chad645
00:24:00,400 --> 00:24:02,240
GBD agent we hope you really like it and646
00:24:02,240 --> 00:24:04,000
over to Sam647
00:24:04,000 --> 00:24:05,919
amazing work all of you and and to your648
00:24:05,919 --> 00:24:07,440
teams this is I think uh really649
00:24:07,440 --> 00:24:08,720
something that's going to help people650
00:24:08,720 --> 00:24:10,720
get worked done uh and have more time to651
00:24:10,720 --> 00:24:12,240
do the things they want to do. Um I652
00:24:12,240 --> 00:24:13,520
think it's it's really amazing how much653
00:24:13,520 --> 00:24:15,360
you've brought together to deliver this654
00:24:15,360 --> 00:24:17,760
experience and watching the agent sort655
00:24:17,760 --> 00:24:19,120
of use the internet, make these656
00:24:19,120 --> 00:24:20,640
spreadsheets, make PowerPoints, whatever657
00:24:20,640 --> 00:24:22,960
else uh and do all this work is is quite658
00:24:22,960 --> 00:24:26,000
amazing. We're going live today for pro659
00:24:26,000 --> 00:24:28,880
plus and team users. Pro users will get660
00:24:28,880 --> 00:24:30,720
uh 400 queries a month plus some team661
00:24:30,720 --> 00:24:32,720
users will get 40 a month. Uh the662
00:24:32,720 --> 00:24:34,000
rollout should be finished by the end of663
00:24:34,000 --> 00:24:36,159
the day for pro and very soon for plus664
00:24:36,159 --> 00:24:38,400
and team users. will try to be live for665
00:24:38,400 --> 00:24:40,799
enterprise and edu by the end of this666
00:24:40,799 --> 00:24:43,360
month. As Casey mentioned, although this667
00:24:43,360 --> 00:24:45,360
is an extremely exciting new technology,668
00:24:45,360 --> 00:24:48,080
there are new risks. Uh people learned669
00:24:48,080 --> 00:24:49,520
how to use the internet generally pretty670
00:24:49,520 --> 00:24:50,880
safely, although of course there are671
00:24:50,880 --> 00:24:52,880
still scammers and other attacks. People672
00:24:52,880 --> 00:24:54,559
are going to need to learn to use AI673
00:24:54,559 --> 00:24:56,080
agents. Uh and societyy's going to need674
00:24:56,080 --> 00:24:57,919
to learn to build up defenses against675
00:24:57,919 --> 00:25:00,080
attacks on AI agents as well. So we're676
00:25:00,080 --> 00:25:02,080
starting with a very robust system, lots677
00:25:02,080 --> 00:25:04,240
of warnings. We will relax that over678
00:25:04,240 --> 00:25:05,679
time as people get more comfortable with679
00:25:05,679 --> 00:25:07,600
it. But we do want people to treat this680
00:25:07,600 --> 00:25:09,919
as a new technology and a new risk681
00:25:09,919 --> 00:25:12,080
surface and use all of the caution that682
00:25:12,080 --> 00:25:14,799
Casey talked about. Um, but that said,683
00:25:14,799 --> 00:25:16,720
we hope you'll love it. Uh, this is684
00:25:16,720 --> 00:25:18,159
still very early. We will improve it685
00:25:18,159 --> 00:25:20,640
rapidly and we're excited to see where686
00:25:20,640 --> 00:25:22,640
it all goes. So, congrats again. Thank687
00:25:22,640 --> 00:25:26,440
you very much. Hope you enjoy.
字幕中英文转换的网址

中文字幕:

1
00:00:06,480 --> 00:00:08,400
早上好。我们为您准备了美味佳肴。2
00:00:08,400 --> 00:00:09,840
今天。我们将推出 ChatBT3
00:00:09,840 --> 00:00:11,519
经纪人。但在开始之前,我4
00:00:11,519 --> 00:00:12,559
喜欢请团队介绍5
00:00:12,559 --> 00:00:14,080
他们自己。从 Yosh 开始。6
00:00:14,080 --> 00:00:17,840
嗨,我是 Yash。我在代理团队工作,7
00:00:17,840 --> 00:00:20,080
在此之前我曾从事过操作员工作。8
00:00:20,080 --> 00:00:22,560
你好,我是 Jing。我负责经纪人研究9
00:00:22,560 --> 00:00:24,400
之前曾进行过深入研究。10
00:00:24,400 --> 00:00:26,000
嗨,我是 Casey。我是一名研究员11
00:00:26,000 --> 00:00:27,920
代理商原为运营商。12
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你好,我是Issa。我是一名特工研究员13
00:00:30,560 --> 00:00:32,640
以前进行过深入研究。14
00:00:32,640 --> 00:00:34,880
所以我们开始推出代理15
00:00:34,880 --> 00:00:36,800
今年早些时候。我们推出了深度16
00:00:36,800 --> 00:00:38,879
研究,我们推出了运营商和17
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人们对此感到非常兴奋。18
00:00:40,160 --> 00:00:42,480
人们可以看到,现在人工智能19
00:00:42,480 --> 00:00:44,640
去为他们完成复杂的任务。20
00:00:44,640 --> 00:00:46,079
但我们清楚地认识到21
00:00:46,079 --> 00:00:48,000
人们真正想要的是让我们带来22
00:00:48,000 --> 00:00:49,760
将这些功能和更多功能结合在一起。23
00:00:49,760 --> 00:00:51,920
人们想要一个统一的代理,可以24
00:00:51,920 --> 00:00:55,039
出发,使用自己的计算机并进行实际操作25
00:00:55,039 --> 00:00:57,360
对他们来说很复杂的任务,这可能呃26
00:00:57,360 --> 00:00:59,359
无缝过渡到思考27
00:00:59,359 --> 00:01:01,520
关于某事采取行动28
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使用终端中的大量工具,29
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在网络上点击,甚至制作30
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比如电子表格和幻灯片31
00:01:06,880 --> 00:01:08,960
以及更多。并希望人们想要32
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能够长期做到这一点33
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地平线和一种普遍的34
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任务。因此团队一直在努力35
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很难把这些结合起来。而且36
00:01:16,400 --> 00:01:18,080
今天我们和经纪人聊了聊。嗯,37
00:01:18,080 --> 00:01:19,680
给你看可能更容易38
00:01:19,680 --> 00:01:21,439
而不是继续谈论它。这是39
00:01:21,439 --> 00:01:23,360
感受我此刻的感受40
00:01:23,360 --> 00:01:25,280
观察它的工作原理。那么,让我们来看看吧。41
00:01:25,280 --> 00:01:27,840
太棒了!谢谢,Sam。大家好。42
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非常高兴与 GBD 代理分享聊天43
00:01:29,920 --> 00:01:31,600
和大家一起。正如萨姆所说,让我们44
00:01:31,600 --> 00:01:33,759
直接进入演示。好的,45
00:01:33,759 --> 00:01:36,159
众所周知,我们位于乍得 GBD,46
00:01:36,159 --> 00:01:39,119
爱。要打开代理模式,你47
00:01:39,119 --> 00:01:40,880
只需单击工具菜单并选择48
00:01:40,880 --> 00:01:43,280
代理人。您也可以直接输入代理人49
00:01:43,280 --> 00:01:45,040
作曲家栏,它会带你到50
00:01:45,040 --> 00:01:47,520
代理模式。嗯,爱德华和我有一个51
00:01:47,520 --> 00:01:49,360
今年晚些时候要去参加婚礼。呃,52
00:01:49,360 --> 00:01:51,119
这是我们共同的朋友之一的礼物。53
00:01:51,119 --> 00:01:52,560
我们应该有亚洲54
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行星?55
00:01:53,280 --> 00:01:55,680
好的,我们开始吧。我需要一套衣服。还有56
00:01:55,680 --> 00:01:56,799
别忘了礼物。57
00:01:56,799 --> 00:01:58,719
好的,太好了。我们不会忘记礼物的。58
00:01:58,719 --> 00:02:00,240
嗯,有点长59
00:02:00,240 --> 00:02:01,680
提示,所以我把它复制到我的60
00:02:01,680 --> 00:02:02,799
缓冲区,所以我要继续61
00:02:02,799 --> 00:02:05,759
然后粘贴。嗯,好的。那么,我们看看。62
00:02:05,759 --> 00:02:07,360
让我们看看它说了什么。我们的朋友是63
00:02:07,360 --> 00:02:08,640
今年晚些时候结婚,因为我64
00:02:08,640 --> 00:02:10,720
米妮娅和莎拉说道。我们希望65
00:02:10,720 --> 00:02:12,879
经纪人帮我们找到一套66
00:02:12,879 --> 00:02:15,520
符合着装要求。呃,推荐几个67
00:02:15,520 --> 00:02:17,840
选项。不错的中型豪华酒店,68
00:02:17,840 --> 00:02:21,040
考虑到场地和天气。我们还希望69
00:02:21,040 --> 00:02:23,280
帮我们找到一些酒店,就像爱德华70
00:02:23,280 --> 00:02:25,760
说,别忘了礼物。嗯,那我们71
00:02:25,760 --> 00:02:27,840
看到和72
00:02:27,840 --> 00:02:30,319
把提示发送出去。正如 Sam 所说,73
00:02:30,319 --> 00:02:32,640
使用电脑。呃,一开始74
00:02:32,640 --> 00:02:34,959
它会设置它的环境。它会75
00:02:34,959 --> 00:02:38,000
知道这需要一两分钟还是不知道76
00:02:38,000 --> 00:02:39,680
只需 5 秒钟即可设置77
00:02:39,680 --> 00:02:41,440
环境。在这种情况下,正如你78
00:02:41,440 --> 00:02:43,840
瞧,它理解了提示。它79
00:02:43,840 --> 00:02:46,319
要求我澄清。我80
00:02:46,319 --> 00:02:48,000
就让它继续下去吧81
00:02:48,000 --> 00:02:51,120
工作。总之,嗯,我觉得搞混了82
00:02:51,120 --> 00:02:54,239
说“哦,那个什么83
00:02:54,239 --> 00:02:55,680
正是日期的时间84
00:02:55,680 --> 00:02:57,200
婚礼?“我想它会弄清楚使用85
00:02:57,200 --> 00:02:59,840
网站。好的,很酷。所以,现在86
00:02:59,840 --> 00:03:01,760
开始了。它正在启动这个过程,87
00:03:01,760 --> 00:03:03,920
提示,然后打开一个浏览器。88
00:03:03,920 --> 00:03:04,959
并引导你了解89
00:03:04,959 --> 00:03:06,800
正在发生的事情,这里是90
00:03:06,800 --> 00:03:09,040
是的。正如之前提到的,我们给了91
00:03:09,040 --> 00:03:10,879
代理访问自己的虚拟92
00:03:10,879 --> 00:03:13,280
计算机,并且计算机有很多93
00:03:13,280 --> 00:03:14,720
安装了不同的工具,它可以94
00:03:14,720 --> 00:03:16,239
选择使用哪个95
00:03:16,239 --> 00:03:18,640
完成任务。因此,在聊天 GPT 中,你96
00:03:18,640 --> 00:03:21,360
可以看到代理的可视化97
00:03:21,360 --> 00:03:23,680
电脑屏幕上,你可以看到98
00:03:23,680 --> 00:03:25,519
用文字覆盖其思路,99
00:03:25,519 --> 00:03:27,200
这就是它的想法,因为它100
00:03:27,200 --> 00:03:28,480
完成任务并决定101
00:03:28,480 --> 00:03:30,799
下一步该做什么?我们给了经纪人102
00:03:30,799 --> 00:03:32,400
可以使用两种不同的方式浏览103
00:03:32,400 --> 00:03:34,560
互联网。首先,我们给它一个文本104
00:03:34,560 --> 00:03:36,159
浏览器,这类似于深度105
00:03:36,159 --> 00:03:38,000
研究工具。这就是它106
00:03:38,000 --> 00:03:40,159
真正高效、快速地阅读许多107
00:03:40,159 --> 00:03:43,440
网页,嗯,嗯,然后搜索它们。还有108
00:03:43,440 --> 00:03:45,040
我们还允许它访问视觉109
00:03:45,040 --> 00:03:46,319
浏览器。这类似于110
00:03:46,319 --> 00:03:48,239
操作员工具。这就是它111
00:03:48,239 --> 00:03:50,159
实际与网页的 UI 进行交互112
00:03:50,159 --> 00:03:52,720
页面。所以它可以拖动东西。它可以113
00:03:52,720 --> 00:03:54,879
使用光标点击。它可以114
00:03:54,879 --> 00:03:57,280
打开 UI 组件。它可以填写115
00:03:57,280 --> 00:03:59,920
表格并输入文本和文本区域。116
00:03:59,920 --> 00:04:02,560
它非常灵活。所以这两个工具117
00:04:02,560 --> 00:04:04,720
非常赞赏。然后我们也118
00:04:04,720 --> 00:04:06,720
让它访问自己的终端,119
00:04:06,720 --> 00:04:08,720
它可以运行代码,也可以120
00:04:08,720 --> 00:04:10,640
生成并分析幻灯片等文件121
00:04:10,640 --> 00:04:12,879
卡片和电子表格。然后通过122
00:04:12,879 --> 00:04:14,560
它还可以调用终端123
00:04:14,560 --> 00:04:17,840
API。因此,公共 API 和 API124
00:04:17,840 --> 00:04:19,840
访问您的私人数据源,例如125
00:04:19,840 --> 00:04:22,479
Google 云端硬盘、Google 日历、GitHub、126
00:04:22,479 --> 00:04:25,360
SharePoint 和许多其他127
00:04:25,360 --> 00:04:26,960
如果你明确地将它们联系起来128
00:04:26,960 --> 00:04:28,960
深入研究连接器。然后它129
00:04:28,960 --> 00:04:31,680
也可以访问图像生成 API,因此130
00:04:31,680 --> 00:04:34,240
它可以为幻灯片创建漂亮的视觉效果131
00:04:34,240 --> 00:04:36,080
甲板和其他东西在工作时132
00:04:36,080 --> 00:04:38,240
通过其任务。133
00:04:38,240 --> 00:04:40,800
如何决定在这里使用哪些工具?134
00:04:40,800 --> 00:04:42,560
是的,我们训练模型在135
00:04:42,560 --> 00:04:44,160
这些能力通过强化136
00:04:44,160 --> 00:04:46,080
学习。这是我们的第一个模型137
00:04:46,080 --> 00:04:48,880
接受过培训的人员可以访问这个统一138
00:04:48,880 --> 00:04:52,000
工具箱。一个文本浏览器,一个 GUI 浏览器139
00:04:52,000 --> 00:04:53,840
以及一个虚拟的终端140
00:04:53,840 --> 00:04:57,120
机器。为了指导它的学习,我们141
00:04:57,120 --> 00:04:59,360
创建需要使用142
00:04:59,360 --> 00:05:01,919
所有这些工具。这使得模型143
00:05:01,919 --> 00:05:04,000
不仅要学习如何使用这些144
00:05:04,000 --> 00:05:06,160
工具,以及何时使用哪种工具145
00:05:06,160 --> 00:05:08,400
取决于手头的任务。在146
00:05:08,400 --> 00:05:10,400
训练开始时,模型147
00:05:10,400 --> 00:05:12,880
可能会尝试使用所有这些工具来148
00:05:12,880 --> 00:05:15,600
解决一个相对简单的问题。结束149
00:05:15,600 --> 00:05:17,840
时间,因为我们奖励模型解决150
00:05:17,840 --> 00:05:20,560
正确有效地解决问题,151
00:05:20,560 --> 00:05:24,080
模型将有更智能的工具选择。152
00:05:24,080 --> 00:05:27,360
例如,如果你要求一个模特呃153
00:05:27,360 --> 00:05:29,039
找到有特定154
00:05:29,039 --> 00:05:31,919
要求并进行预订,155
00:05:31,919 --> 00:05:34,479
模型通常可能只是开始深度156
00:05:34,479 --> 00:05:36,160
在文本浏览器中搜索157
00:05:36,160 --> 00:05:39,039
一些候选人,然后切换到 GUI158
00:05:39,039 --> 00:05:42,160
浏览器查看食物照片,呃检查一下159
00:05:42,160 --> 00:05:45,600
确认是否有空位,并完成预订。160
00:05:45,600 --> 00:05:48,000
同样,对于创造性任务,161
00:05:48,000 --> 00:05:50,160
创建一个工件,模型将162
00:05:50,160 --> 00:05:51,680
首先在网上搜索公众163
00:05:51,680 --> 00:05:54,479
资源,然后切换到终端164
00:05:54,479 --> 00:05:57,039
进行一些代码编辑来编译165
00:05:57,039 --> 00:05:59,919
工件并最终验证最终166
00:05:59,919 --> 00:06:02,960
在 GUI 浏览器中输出。这样,167
00:06:02,960 --> 00:06:05,600
我们真的感觉我们团结在一起168
00:06:05,600 --> 00:06:08,240
深度研究和运营商的最佳169
00:06:08,240 --> 00:06:11,759
并增添了一些额外的光彩。170
00:06:11,759 --> 00:06:14,000
没错。是的。所以这么说吧171
00:06:14,000 --> 00:06:15,520
项目背景,我想提供一点172
00:06:15,520 --> 00:06:18,000
历史。几个月前,我们173
00:06:18,000 --> 00:06:20,960
一月份发货了操作员,这是174
00:06:20,960 --> 00:06:23,120
我们的代理可让您执行在线任务175
00:06:23,120 --> 00:06:25,759
比如预订并发送176
00:06:25,759 --> 00:06:27,840
两周后我们177
00:06:27,840 --> 00:06:29,919
进行了深入研究和深入研究178
00:06:29,919 --> 00:06:31,919
是一个可以让你深入179
00:06:31,919 --> 00:06:35,759
互联网研究和高质量输出180
00:06:35,759 --> 00:06:39,280
嗯嗯研究报告。发布后181
00:06:39,280 --> 00:06:41,039
我们意识到实际上这两个182
00:06:41,039 --> 00:06:42,319
方法实际上很深刻183
00:06:42,319 --> 00:06:44,160
免费。184
00:06:44,160 --> 00:06:46,400
嗯,比如说操作员遇到了一些麻烦185
00:06:46,400 --> 00:06:48,720
阅读超长文章。嗯,它有186
00:06:48,720 --> 00:06:50,400
滚动。这需要很长时间。但是187
00:06:50,400 --> 00:06:51,759
这是需要深入研究的188
00:06:51,759 --> 00:06:56,240
擅长。相反,运算符呃呃深189
00:06:56,240 --> 00:06:58,240
研究并不擅长互动190
00:06:58,240 --> 00:07:00,319
带有网页交互元素191
00:07:00,319 --> 00:07:03,199
视觉呃高度视觉化的网页,但是192
00:07:03,199 --> 00:07:04,800
这是运营商擅长的193
00:07:04,800 --> 00:07:08,639
嗯。嗯,是的,我们觉得这些方法194
00:07:08,639 --> 00:07:11,120
是免费的,然后我们195
00:07:11,120 --> 00:07:13,120
还查看了一些客户的反馈。196
00:07:13,120 --> 00:07:14,880
例如,我们最受推崇的197
00:07:14,880 --> 00:07:17,120
深入研究所要求的功能是198
00:07:17,120 --> 00:07:18,960
登录网站的能力和199
00:07:18,960 --> 00:07:20,960
访问经过身份验证的来源。200
00:07:20,960 --> 00:07:22,880
操作员可以做的事情。201
00:07:22,880 --> 00:07:24,000
我已经等待很久了202
00:07:24,000 --> 00:07:24,560
时间。203
00:07:24,560 --> 00:07:26,160
是的。204
00:07:26,160 --> 00:07:28,479
嗯,另一件事是,我们正在寻找205
00:07:28,479 --> 00:07:29,840
在人们尝试的提示下206
00:07:29,840 --> 00:07:31,520
对于操作员,我们看到他们207
00:07:31,520 --> 00:07:32,880
实际上是更深入的研究类型208
00:07:32,880 --> 00:07:35,199
提示。例如,计划一次旅行,209
00:07:35,199 --> 00:07:38,240
然后预订。所以,是的,我们真的210
00:07:38,240 --> 00:07:39,360
感觉我们正在带来最好的211
00:07:39,360 --> 00:07:41,440
两个世界。在个人方面212
00:07:41,440 --> 00:07:42,800
请注意,我们都是朋友了213
00:07:42,800 --> 00:07:44,160
而这真的非常令人兴奋214
00:07:44,160 --> 00:07:46,479
一起工作。所以,说到215
00:07:46,479 --> 00:07:48,960
天作之合,216
00:07:48,960 --> 00:07:50,319
婚礼筹划进行得如何?217
00:07:50,319 --> 00:07:51,759
看起来棒极了。这是218
00:07:51,759 --> 00:07:53,599
我讨厌做某件事的例子。这可以219
00:07:53,599 --> 00:07:55,520
就像毁掉几个小时一样220
00:07:55,520 --> 00:07:56,960
对我来说,当我被这些兔子吸进去时221
00:07:56,960 --> 00:07:58,160
洞。所以,当你看着这个的时候,222
00:07:58,160 --> 00:07:59,520
伙计们一直在谈论点击223
00:07:59,520 --> 00:08:01,199
这就像做整件事一样224
00:08:01,199 --> 00:08:03,360
真的非常了不起。是的,完全是。225
00:08:03,360 --> 00:08:06,560
嗯,看起来它开始于226
00:08:06,560 --> 00:08:08,560
了解天气。其中之一227
00:08:08,560 --> 00:08:11,280
很酷的功能,嗯,你知道,作为228
00:08:11,280 --> 00:08:12,560
其中一些任务可能需要一点时间229
00:08:12,560 --> 00:08:14,160
再过一会儿,你就可以回去看看230
00:08:14,160 --> 00:08:15,759
它在做什么。所以,这就是我们要做的231
00:08:15,759 --> 00:08:17,199
确实会这么做。看起来232
00:08:17,199 --> 00:08:18,720
通过网站使用文本233
00:08:18,720 --> 00:08:21,039
浏览器。有趣的是,现在234
00:08:21,039 --> 00:08:22,400
它正在检查西装235
00:08:22,400 --> 00:08:23,919
爱德华。我想它会找到一些东西236
00:08:23,919 --> 00:08:25,360
很好。在这里你可以看到它切换了237
00:08:25,360 --> 00:08:27,199
实际上是一个可视化浏览器238
00:08:27,199 --> 00:08:28,960
穿上这套西装一定会很好看239
00:08:28,960 --> 00:08:31,280
愛德華。240
00:08:31,280 --> 00:08:34,560
现在看起来是的,它有241
00:08:34,560 --> 00:08:36,880
努力前行,思考该做什么。242
00:08:36,880 --> 00:08:39,599
嗯,现在仍然穿着西装,可能243
00:08:39,599 --> 00:08:41,919
去礼品区吧。嗯,好的,244
00:08:41,919 --> 00:08:43,279
太棒了。所以,这需要一段时间。245
00:08:43,279 --> 00:08:44,959
正如 Sam 所说,这些任务有时可以246
00:08:44,959 --> 00:08:46,160
需要很长时间。所以,它将会247
00:08:46,160 --> 00:08:47,680
继续做,希望能更快248
00:08:47,680 --> 00:08:49,760
比我们做的要多。嗯,我们应该249
00:08:49,760 --> 00:08:51,600
在它做这件事的时候还做了其他什么?我250
00:08:51,600 --> 00:08:53,519
我认为球队真的想要251
00:08:53,519 --> 00:08:55,279
贴纸,一些贴纸252
00:08:55,279 --> 00:08:56,480
发射。我们应该这么做吗?253
00:08:56,480 --> 00:08:57,279
是的,很酷。254
00:08:57,279 --> 00:08:59,040
好的。我们有一个球队吉祥物,255
00:08:59,040 --> 00:09:00,320
这是我们的一位同事,Bunny256
00:09:00,320 --> 00:09:03,279
涂鸦。真的很可爱告诉你。嗯257
00:09:03,279 --> 00:09:06,080
我们将努力258
00:09:06,080 --> 00:09:08,480
给大家一些笔记本电脑贴纸。呃259
00:09:08,480 --> 00:09:10,480
代理最喜欢的功能之一260
00:09:10,480 --> 00:09:13,120
假设轨迹可能需要 15261
00:09:13,120 --> 00:09:15,040
分钟、20分钟、30分钟262
00:09:15,040 --> 00:09:17,120
取决于任务的复杂性。263
00:09:17,120 --> 00:09:19,120
嗯,很多时候你可能需要264
00:09:19,120 --> 00:09:20,560
帮助经纪人。经纪人可能需要询问265
00:09:20,560 --> 00:09:22,480
您的澄清、确认和266
00:09:22,480 --> 00:09:25,040
诸如此类。嗯,所以我喜欢用它267
00:09:25,040 --> 00:09:26,640
在路上。所以我要用我的手机268
00:09:26,640 --> 00:09:28,160
手机实际发送查询269
00:09:28,160 --> 00:09:30,240
时间,然后看看进展如何。270
00:09:30,240 --> 00:09:32,880
好的,那我们看看。好的,我们继续271
00:09:32,880 --> 00:09:35,519
Chad Gibbdi。呃,我已经选好了272
00:09:35,519 --> 00:09:38,560
代理模式。我还输入了我们的呃273
00:09:38,560 --> 00:09:40,560
可爱的吉祥物,我要快点274
00:09:40,560 --> 00:09:43,040
粘贴一个查询。查询说做一些275
00:09:43,040 --> 00:09:45,279
为团队逐一赠送笔记本电脑276
00:09:45,279 --> 00:09:47,920
贴纸,并订购500张。我会277
00:09:47,920 --> 00:09:52,959
还说我喜欢贴纸骡子278
00:09:52,959 --> 00:09:55,279
我们过去使用过并发送279
00:09:55,279 --> 00:09:57,200
把它关掉。280
00:09:57,200 --> 00:10:00,080
好的。所以,就像在281
00:10:00,080 --> 00:10:02,080
网络,这需要一些时间,想想282
00:10:02,080 --> 00:10:04,080
它在做什么,它会283
00:10:04,080 --> 00:10:07,120
开始开始查询。因为它是284
00:10:07,120 --> 00:10:08,880
继续,这需要一些时间285
00:10:08,880 --> 00:10:11,200
关掉。是吗?哦,我们走了。所以,它会286
00:10:11,200 --> 00:10:12,480
开始着手吧。看起来287
00:10:12,480 --> 00:10:14,720
开始创作动画艺术。它将288
00:10:14,720 --> 00:10:16,640
可能使用 Isa 提到的图像289
00:10:16,640 --> 00:10:18,399
希望能够制作一部动画290
00:10:18,399 --> 00:10:20,240
艺术。我们拭目以待。291
00:10:20,240 --> 00:10:21,760
就这样,还有什么我们想做的292
00:10:21,760 --> 00:10:22,399
做?293
00:10:22,399 --> 00:10:24,720
哦,是的。我还需要一双鞋294
00:10:24,720 --> 00:10:26,320
因为我的鞋子损坏了。295
00:10:26,320 --> 00:10:27,360
它们是怎么受损的?296
00:10:27,360 --> 00:10:28,560
呃,因为下雨297
00:10:28,560 --> 00:10:30,000
在旧金山。298
00:10:30,000 --> 00:10:30,800
是的。299
00:10:30,800 --> 00:10:32,160
酷。好吧。呃,好吧,我们开始吧300
00:10:32,160 --> 00:10:34,240
爱德华也给我买了一双鞋。所以,哦,301
00:10:34,240 --> 00:10:40,320
你也可以找到我们嗯一对男士的302
00:10:40,320 --> 00:10:43,519
穿着黑色鞋子尺码303
00:10:43,519 --> 00:10:44,240
9.5304
00:10:44,240 --> 00:10:46,000
9.5.305
00:10:46,000 --> 00:10:47,920
因此,306
00:10:47,920 --> 00:10:49,920
模型能够中断。我307
00:10:49,920 --> 00:10:51,920
你知道,因为轨迹需要很长时间308
00:10:51,920 --> 00:10:53,760
时间或任何时间,它真的309
00:10:53,760 --> 00:10:56,720
对我们来说很重要,因为感觉非常310
00:10:56,720 --> 00:10:59,120
多轮,以便用户可以插入311
00:10:59,120 --> 00:11:01,120
用户可以直接它用户可以给它更多312
00:11:01,120 --> 00:11:02,640
指导 更少指导 无论我们想要什么313
00:11:02,640 --> 00:11:04,320
我们要做的事情,这就是我们在这里做的事情314
00:11:04,320 --> 00:11:07,040
我们本质上的模型是315
00:11:07,040 --> 00:11:08,720
努力弄清楚所有316
00:11:08,720 --> 00:11:10,240
我们之前问过的事情317
00:11:10,240 --> 00:11:12,320
在这种情况下,我们基本上说,嘿,可以318
00:11:12,320 --> 00:11:16,000
你也给我们买一双男士黑色319
00:11:16,000 --> 00:11:18,160
鞋子,现在它正在思考,很快320
00:11:18,160 --> 00:11:19,839
希望它能考虑到这一点321
00:11:19,839 --> 00:11:22,000
帐户并继续进入其322
00:11:22,000 --> 00:11:23,600
轨迹。就是这样。所以,它说323
00:11:23,600 --> 00:11:25,120
承认打扰。它说,324
00:11:25,120 --> 00:11:26,880
“好的,很酷。我也会研究一下男士的325
00:11:26,880 --> 00:11:29,600
9.5码的黑色鞋子。嗯,然后326
00:11:29,600 --> 00:11:31,680
它可能会继续前进。嗯,但是327
00:11:31,680 --> 00:11:33,120
也许 Issa 可以告诉我们更多328
00:11:33,120 --> 00:11:34,240
关于它是如何运作的。329
00:11:34,240 --> 00:11:36,320
是的,当然。所以,正如你所看到的,330
00:11:36,320 --> 00:11:38,079
经纪人非常合作,而且331
00:11:38,079 --> 00:11:39,920
对我们来说真的很重要332
00:11:39,920 --> 00:11:41,200
训练模型并构建333
00:11:41,200 --> 00:11:42,880
产品。如果你问的是另一个334
00:11:42,880 --> 00:11:44,399
为您完成一项任务的人335
00:11:44,399 --> 00:11:45,519
花了很长时间336
00:11:45,519 --> 00:11:46,959
完成,你可能会给他们一些337
00:11:46,959 --> 00:11:48,800
开始的说明,然后他们338
00:11:48,800 --> 00:11:50,640
可能会问你一些澄清问题339
00:11:50,640 --> 00:11:52,320
然后他们就开始任务,也许340
00:11:52,320 --> 00:11:53,600
意识到,哦,他们需要更多341
00:11:53,600 --> 00:11:55,440
你需要澄清,或者他们需要你的342
00:11:55,440 --> 00:11:56,880
允许登录或做某事343
00:11:56,880 --> 00:11:58,560
为你做一些事情,然后你344
00:11:58,560 --> 00:12:00,240
可能会意识到,哦,我忘了说345
00:12:00,240 --> 00:12:02,640
这件事,或者你的状态怎么样?346
00:12:02,640 --> 00:12:04,240
你好吗?我可以帮你转接一下吗?347
00:12:04,240 --> 00:12:05,760
如果你走错了路348
00:12:05,760 --> 00:12:07,760
或者其他什么?同样,对于这些349
00:12:07,760 --> 00:12:09,680
真正长期运行的代理任务,它是350
00:12:09,680 --> 00:12:11,519
非常重要的是,用户和351
00:12:11,519 --> 00:12:13,600
代理人能够发起352
00:12:13,600 --> 00:12:15,519
互相沟通,以便353
00:12:15,519 --> 00:12:17,200
代理人能够最有效地354
00:12:17,200 --> 00:12:19,360
帮助你完成任务。所以这是355
00:12:19,360 --> 00:12:20,560
我们实际上训练过的东西356
00:12:20,560 --> 00:12:22,320
模型。我们训练它能够357
00:12:22,320 --> 00:12:24,160
提出澄清问题,不是每个358
00:12:24,160 --> 00:12:26,240
像深入研究这样的一次性研究。嗯,我们359
00:12:26,240 --> 00:12:28,800
还问了它我们还训练它360
00:12:28,800 --> 00:12:30,560
就像 Yash 刚才展示的那样,是可中断的。并且361
00:12:30,560 --> 00:12:32,000
有时它还会要求你362
00:12:32,000 --> 00:12:33,519
澄清和确认363
00:12:33,519 --> 00:12:35,680
中段轨迹。364
00:12:35,680 --> 00:12:38,079
是的。和经纪人合作的一部分是365
00:12:38,079 --> 00:12:40,480
有时它会犯错误。366
00:12:40,480 --> 00:12:42,079
这就是为什么我们觉得这很重要367
00:12:42,079 --> 00:12:44,079
训练模型来向你询问368
00:12:44,079 --> 00:12:45,920
最后一步确认369
00:12:45,920 --> 00:12:49,279
重要的步骤。嗯,比如说370
00:12:49,279 --> 00:12:51,519
在发送电子邮件之前371
00:12:51,519 --> 00:12:53,440
它会要求你看一下372
00:12:53,440 --> 00:12:54,720
草案以及它是否有意义,373
00:12:54,720 --> 00:12:56,079
是否有任何尴尬374
00:12:56,079 --> 00:12:59,200
拼写错误。嗯,如果有的话,那么你375
00:12:59,200 --> 00:13:01,360
您可以要求它修复它,或者您可以376
00:13:01,360 --> 00:13:03,440
直接接管浏览器并跳转377
00:13:03,440 --> 00:13:06,079
直接进入代理环境378
00:13:06,079 --> 00:13:09,040
并自己纠正。这样379
00:13:09,040 --> 00:13:10,720
感觉合作,你可以,嗯,380
00:13:10,720 --> 00:13:13,680
真正与代理商合作。381
00:13:13,680 --> 00:13:15,120
我们是否应该再看一个演示?382
00:13:15,120 --> 00:13:17,279
我们有这个呃,有点有趣的传统383
00:13:17,279 --> 00:13:19,600
在直播中使用我们的384
00:13:19,600 --> 00:13:21,120
最新模型的评估385
00:13:21,120 --> 00:13:23,040
他们自己或者做一些元的事情。386
00:13:23,040 --> 00:13:24,240
我们能做类似的事情吗?387
00:13:24,240 --> 00:13:27,440
是的,我们开始吧。388
00:13:27,440 --> 00:13:28,320
只有一个389
00:13:28,320 --> 00:13:29,440
我想人们很想知道390
00:13:29,440 --> 00:13:30,320
这个模型很好。391
00:13:30,320 --> 00:13:33,920
是的。这是我们之前提出的一个提示。392
00:13:33,920 --> 00:13:36,880
昨天给了经纪人。所以基本上393
00:13:36,880 --> 00:13:38,959
它要求模型自己394
00:13:38,959 --> 00:13:40,959
来自我们 Google 工作的评估编号395
00:13:40,959 --> 00:13:43,440
连接器并制作一些幻灯片。所以我们396
00:13:43,440 --> 00:13:44,959
想要保持简单,就像没有397
00:13:44,959 --> 00:13:47,360
引言 没有结论 只是提出398
00:13:47,360 --> 00:13:50,000
图表中的结果。正如你399
00:13:50,000 --> 00:13:52,160
现在可以看到模型正在连接到400
00:13:52,160 --> 00:13:55,120
Google Drive API 然后搜索401
00:13:55,120 --> 00:13:57,600
在 API 中它现在看起来像402
00:13:57,600 --> 00:13:59,920
第一个结果非常相关。所以403
00:13:59,920 --> 00:14:02,720
它正在读取第一个结果。404
00:14:02,720 --> 00:14:04,959
现在它正在读取第一个结果405
00:14:04,959 --> 00:14:07,920
细节。呃,让我们加速这个呃406
00:14:07,920 --> 00:14:12,800
重播。那么模型可能会读407
00:14:12,800 --> 00:14:15,279
从结果中再次写出一些408
00:14:15,279 --> 00:14:16,959
代码。409
00:14:16,959 --> 00:14:19,519
所以在这里你可以看到模型是410
00:14:19,519 --> 00:14:21,920
使用名为411
00:14:21,920 --> 00:14:24,480
图像生成工具来生成一些412
00:14:24,480 --> 00:14:28,079
幻灯片的装饰。413
00:14:28,079 --> 00:14:30,160
让我们看看第一张幻灯片是什么414
00:14:30,160 --> 00:14:33,399
模型制作。415
00:14:33,920 --> 00:14:35,920
所以这里的模型正在写一些代码416
00:14:35,920 --> 00:14:38,399
将被编译为最终版本417
00:14:38,399 --> 00:14:41,120
幻灯片。这是第一张幻灯片418
00:14:41,120 --> 00:14:44,160
此演示中的模型看起来不错419
00:14:44,160 --> 00:14:46,240
但还不够精致。420
00:14:46,240 --> 00:14:48,240
强化的关键特征之一421
00:14:48,240 --> 00:14:50,160
学习是模型将重新422
00:14:50,160 --> 00:14:52,240
审查自己的结果并改进423
00:14:52,240 --> 00:14:55,120
取得好成绩424
00:14:55,120 --> 00:14:57,839
结果。让我们看看最终结果如何425
00:14:57,839 --> 00:15:00,320
模型给了我们什么。426
00:15:00,320 --> 00:15:04,000
我们可以点击跳过,然后点击模型427
00:15:04,000 --> 00:15:07,519
给我们一个好的PowerPoint文件。所以428
00:15:07,519 --> 00:15:09,040
这是一个真正的 PowerPoint,你可以429
00:15:09,040 --> 00:15:14,040
下载并在任何软件中打开它。430
00:15:14,639 --> 00:15:19,279
我们在办公室里打开它吧。所以431
00:15:19,279 --> 00:15:22,160
让我们展示一下幻灯片模型432
00:15:22,160 --> 00:15:23,839
生成。433
00:15:23,839 --> 00:15:27,120
首先是两个情报基准。434
00:15:27,120 --> 00:15:30,480
人文学科的期末考试是435
00:15:30,480 --> 00:15:33,519
衡量人工智能解决广泛问题的能力436
00:15:33,519 --> 00:15:37,120
一系列关于难题的主题。我们437
00:15:37,120 --> 00:15:40,320
用两种设置评估模型438
00:15:40,320 --> 00:15:43,440
无论是否使用工具。439
00:15:43,440 --> 00:15:45,920
我们可以看到代理模式原始440
00:15:45,920 --> 00:15:48,720
智力已经相当不错了,441
00:15:48,720 --> 00:15:50,880
所有工具的使用率几乎翻倍442
00:15:50,880 --> 00:15:54,720
性能提升至42%443
00:15:54,720 --> 00:15:56,720
在评估人类的模型时444
00:15:56,720 --> 00:15:59,360
上次考试,尤其是浏览445
00:15:59,360 --> 00:16:01,759
能力,我们有两层446
00:16:01,759 --> 00:16:04,399
净化,确保447
00:16:04,399 --> 00:16:07,680
模型在这个基准上没有作弊。448
00:16:07,680 --> 00:16:10,079
前 TMS 是衡量449
00:16:10,079 --> 00:16:11,839
高级数学推理能力450
00:16:11,839 --> 00:16:13,680
模型。451
00:16:13,680 --> 00:16:16,000
与我们的迷你基准不同,452
00:16:16,000 --> 00:16:18,560
03 使用 Python 函数453
00:16:18,560 --> 00:16:21,440
编码。我们给代理模型所有454
00:16:21,440 --> 00:16:23,440
可用的工具,如浏览器、455
00:16:23,440 --> 00:16:26,320
计算机和终端。代理456
00:16:26,320 --> 00:16:29,360
在这方面取得了 27% 的新状态457
00:16:29,360 --> 00:16:31,440
借助所有这些458
00:16:31,440 --> 00:16:34,440
工具。459
00:16:34,639 --> 00:16:36,880
接下来,我们在两个方面评估了模型460
00:16:36,880 --> 00:16:39,519
代理基准。Web 竞技场是一个461
00:16:39,519 --> 00:16:41,519
衡量网络代理的基准462
00:16:41,519 --> 00:16:43,600
能够解决现实世界的网络问题463
00:16:43,600 --> 00:16:47,279
任务。代理模型改进了464
00:16:47,279 --> 00:16:51,360
为核心提供动力的先前的 O3 模型。465
00:16:51,360 --> 00:16:54,399
浏览公司是我们推出的基准466
00:16:54,399 --> 00:16:56,240
今年早些时候,467
00:16:56,240 --> 00:16:58,880
浏览代理搜索能力和468
00:16:58,880 --> 00:17:02,320
查找呃如何定位信息。469
00:17:02,320 --> 00:17:03,839
代理模型显著470
00:17:03,839 --> 00:17:06,160
优于03并对此进行深入研究471
00:17:06,160 --> 00:17:11,679
基准测试通过率为69%472
00:17:11,679 --> 00:17:14,559
最后,我们关心的是用户473
00:17:14,559 --> 00:17:16,959
将在现实中受益于我们的模型474
00:17:16,959 --> 00:17:19,919
世界。电子表格工作台是一个基准475
00:17:19,919 --> 00:17:21,919
衡量模型的能力476
00:17:21,919 --> 00:17:24,400
编辑来自真实477
00:17:24,400 --> 00:17:28,079
世界用例。这里是代理模型478
00:17:28,079 --> 00:17:30,480
拥有自由的办公室和电脑479
00:17:30,480 --> 00:17:34,000
工具已经可以解决 30% 的任务480
00:17:34,000 --> 00:17:36,480
当我们让模型访问481
00:17:36,480 --> 00:17:39,840
终端中的原始 Excel 文件482
00:17:39,840 --> 00:17:44,000
进一步提升性能至45%483
00:17:44,000 --> 00:17:46,000
最后,我们在484
00:17:46,000 --> 00:17:48,000
内部银行基准。基准485
00:17:48,000 --> 00:17:49,760
该基准评估了该模型的486
00:17:49,760 --> 00:17:52,559
能够进行第一到第三487
00:17:52,559 --> 00:17:55,679
年度投资银行 uh 银行分析师488
00:17:55,679 --> 00:17:58,799
诸如组装489
00:17:58,799 --> 00:18:00,559
三表财务模型490
00:18:00,559 --> 00:18:04,000
财富 500 强公司491
00:18:04,000 --> 00:18:06,160
基准。代理模型显著492
00:18:06,160 --> 00:18:08,080
优于之前的深入研究493
00:18:08,080 --> 00:18:11,760
以及所有三个模型。正如你所见494
00:18:11,760 --> 00:18:13,919
这个模型是最强大的模型之一495
00:18:13,919 --> 00:18:16,080
我们曾经训练过的模型。496
00:18:16,080 --> 00:18:18,960
它不仅在基准测试中表现出色,而且497
00:18:18,960 --> 00:18:22,480
还具有推理、浏览和498
00:18:22,480 --> 00:18:24,720
在一定程度上解决现实世界的任务499
00:18:24,720 --> 00:18:28,480
这是三个月前我们无法想象的。500
00:18:28,480 --> 00:18:31,600
没错。嗯,就像爱德华说的,嗯,我们501
00:18:31,600 --> 00:18:32,799
我认为我们已经训练了一支非常强大的502
00:18:32,799 --> 00:18:35,280
模型,很大一部分力量来自于503
00:18:35,280 --> 00:18:38,240
浏览互联网的能力。并且504
00:18:38,240 --> 00:18:40,240
我们知道,互联网可能是一个可怕的505
00:18:40,240 --> 00:18:42,400
那里有各种各样的黑客506
00:18:42,400 --> 00:18:45,120
试图窃取您的信息、诈骗、507
00:18:45,120 --> 00:18:48,480
呃,钓鱼尝试。嗯,经纪人没有508
00:18:48,480 --> 00:18:51,120
对所有这些事情都免疫。嗯,一个509
00:18:51,120 --> 00:18:53,360
我们特别担心的是510
00:18:53,360 --> 00:18:55,520
一种名为“prompt”的新攻击511
00:18:55,520 --> 00:18:57,120
注射。512
00:18:57,120 --> 00:18:59,840
假设你要求代理人513
00:18:59,840 --> 00:19:02,080
给你买一本书,你给它你的514
00:19:02,080 --> 00:19:04,400
信用卡信息即可实现这一点。515
00:19:04,400 --> 00:19:06,240
代理可能会偶然发现恶意516
00:19:06,240 --> 00:19:08,559
网站询问,“哦,输入你的517
00:19:08,559 --> 00:19:10,400
信用卡信息在这里。这会有帮助518
00:19:10,400 --> 00:19:12,799
完成你的任务。代理519
00:19:12,799 --> 00:19:15,200
受过培训,可以提供帮助,可能会决定520
00:19:15,200 --> 00:19:18,080
这是个好主意。521
00:19:18,080 --> 00:19:19,760
我们做了很多工作,试图522
00:19:19,760 --> 00:19:22,320
确保这种情况不会发生。我们已经523
00:19:22,320 --> 00:19:24,240
训练我们的模型忽略可疑524
00:19:24,240 --> 00:19:27,120
有关可疑网站的说明。525
00:19:27,120 --> 00:19:29,039
我们也有呃,我们也有层526
00:19:29,039 --> 00:19:32,000
监视着527
00:19:32,000 --> 00:19:33,760
特工的肩膀,看着它528
00:19:33,760 --> 00:19:36,480
如果529
00:19:36,480 --> 00:19:38,799
任何事看起来都很可疑。我们甚至可以530
00:19:38,799 --> 00:19:41,919
如果有新的攻击,请实时更新这些531
00:19:41,919 --> 00:19:44,160
在野外发现。532
00:19:44,160 --> 00:19:45,919
尽管如此,你知道,这是一个533
00:19:45,919 --> 00:19:47,760
尖端产品。这是一个新的534
00:19:47,760 --> 00:19:50,000
表面,我们无法阻止一切。535
00:19:50,000 --> 00:19:51,280
所以我觉得这非常536
00:19:51,280 --> 00:19:52,559
让观众意识到这一点很重要537
00:19:52,559 --> 00:19:55,360
使用代理所涉及的风险。538
00:19:55,360 --> 00:19:57,440
我们鼓励用户539
00:19:57,440 --> 00:19:59,520
积极思考如何540
00:19:59,520 --> 00:20:01,120
他们分享信息。你知道,541
00:20:01,120 --> 00:20:02,880
如果是高度敏感的信息,542
00:20:02,880 --> 00:20:06,799
也许不要分享这个。嗯也许嗯呃543
00:20:06,799 --> 00:20:08,799
使用我们的功能(例如接管模式)544
00:20:08,799 --> 00:20:10,799
直接输入您的信用卡545
00:20:10,799 --> 00:20:12,880
信息到浏览器中,而不是546
00:20:12,880 --> 00:20:15,679
嗯,把它交给经纪人。嗯,我们觉得547
00:20:15,679 --> 00:20:18,640
我们已经打造了一款非常强大的产品,但是548
00:20:18,640 --> 00:20:20,480
再次强调,对于我们的用户来说549
00:20:20,480 --> 00:20:21,760
了解所涉及的风险。550
00:20:21,760 --> 00:20:23,280
是的,我真的想强调一下551
00:20:23,280 --> 00:20:25,520
认为这是一种新的能力水平552
00:20:25,520 --> 00:20:27,120
在人工智能领域。这是一种使用人工智能的新方法,但是553
00:20:27,120 --> 00:20:28,799
将会有一系列新的攻击554
00:20:28,799 --> 00:20:30,799
随之而来。社会和555
00:20:30,799 --> 00:20:33,120
技术必须不断发展和学习556
00:20:33,120 --> 00:20:34,320
我们将如何缓解557
00:20:34,320 --> 00:20:36,159
我们甚至还无法想象。呃,因为558
00:20:36,159 --> 00:20:37,360
人们开始做越来越多的工作559
00:20:37,360 --> 00:20:39,679
这边走。在我结束之前,我们应该560
00:20:39,679 --> 00:20:41,840
检查你踢出的一些任务561
00:20:41,840 --> 00:20:42,080
离开?562
00:20:42,080 --> 00:20:46,159
好的,我们开始吧。嗯,好的。所以我563
00:20:46,159 --> 00:20:48,240
打开新标签页并确保564
00:20:48,240 --> 00:20:51,840
我们可以看到我们的进展,565
00:20:51,840 --> 00:20:55,679
还有贴纸。好的。我看看。所有566
00:20:55,679 --> 00:20:58,159
对。所以,听起来贴纸567
00:20:58,159 --> 00:21:00,880
准备好了。让我看看它到底怎么样。好的。568
00:21:00,880 --> 00:21:03,200
太棒了。这算是个结局了569
00:21:03,200 --> 00:21:06,720
最终结果耗时约 7 分钟。570
00:21:06,720 --> 00:21:08,480
很可能已经弄清楚了一切。571
00:21:08,480 --> 00:21:09,840
我们将回过头来看一下轨迹572
00:21:09,840 --> 00:21:11,679
看看效果如何。但最后573
00:21:11,679 --> 00:21:13,679
结果,它看起来像是被添加到574
00:21:13,679 --> 00:21:15,360
购物车。这是小计。我可以575
00:21:15,360 --> 00:21:17,360
继续看,然后弄清楚576
00:21:17,360 --> 00:21:20,000
我可以接手这个577
00:21:20,000 --> 00:21:21,600
正如凯西所说,输入我的信用578
00:21:21,600 --> 00:21:23,039
卡信息,然后放置579
00:21:23,039 --> 00:21:25,200
订购非常快。模特正在询问580
00:21:25,200 --> 00:21:27,120
确认等,因为它应该581
00:21:27,120 --> 00:21:29,280
要做。我们先快速浏览一下582
00:21:29,280 --> 00:21:31,039
看看它实际上583
00:21:31,039 --> 00:21:33,280
确实。哦,看起来它生成了一些584
00:21:33,280 --> 00:21:35,840
贴纸。哦,看看这个。这就是585
00:21:35,840 --> 00:21:38,880
它生成了贴纸。很酷。所以,是的586
00:21:38,880 --> 00:21:40,640
这就是任务。我想我可以587
00:21:40,640 --> 00:21:42,559
我自己完成,或者我可以问588
00:21:42,559 --> 00:21:43,919
真正继续执行的模型589
00:21:43,919 --> 00:21:46,720
对我来说也是如此。让我们检查一下590
00:21:46,720 --> 00:21:49,840
婚礼。好的,太好了。看起来591
00:21:49,840 --> 00:21:52,720
及时完成了。嗯,好吧,592
00:21:52,720 --> 00:21:55,520
很酷。所以在这种情况下,正如我们所说的,我们593
00:21:55,520 --> 00:21:57,840
正在寻找酒店,压力很大,呃594
00:21:57,840 --> 00:22:01,919
西装,还有鞋子。所以它出来了595
00:22:01,919 --> 00:22:03,520
一份相当全面的报告。它596
00:22:03,520 --> 00:22:05,840
看起来像婚礼场地、日期、时间597
00:22:05,840 --> 00:22:10,240
是与 Zilla 链接,着装规范。它598
00:22:10,240 --> 00:22:11,600
弄清楚了这套衣服599
00:22:11,600 --> 00:22:12,960
建议应该是,你可以600
00:22:12,960 --> 00:22:14,799
买。现在我可以自己买了601
00:22:14,799 --> 00:22:17,120
或者我可以请代理去买602
00:22:17,120 --> 00:22:20,960
我。嗯,也解决了鞋类障碍603
00:22:20,960 --> 00:22:23,360
选项。它实际上查看了所有604
00:22:23,360 --> 00:22:27,120
哎呀,抱歉,它查看了所有的605
00:22:27,120 --> 00:22:29,360
可用性。你实际上可以看到606
00:22:29,360 --> 00:22:31,440
提供检查结果的屏幕截图。在607
00:22:31,440 --> 00:22:33,120
在这种情况下,我们使用 booking.com,它是608
00:22:33,120 --> 00:22:35,280
能够做到这一点。也有天赋609
00:22:35,280 --> 00:22:37,360
建议等。下一步我可以问610
00:22:37,360 --> 00:22:39,760
正如你所说,经纪人说,嘿,如果你611
00:22:39,760 --> 00:22:41,520
需要协助购买任何物品或612
00:22:41,520 --> 00:22:42,960
有任何进一步的调整请告诉我613
00:22:42,960 --> 00:22:44,880
这样我们就可以做到。嗯,我想614
00:22:44,880 --> 00:22:46,320
展示最后一个我们没有展示的演示615
00:22:46,320 --> 00:22:48,640
真的现场直播,但我认为这真的616
00:22:48,640 --> 00:22:51,280
很酷,尤其是因为人们617
00:22:51,280 --> 00:22:52,880
即将结婚的人真的很喜欢618
00:22:52,880 --> 00:22:57,679
MLB。所以我们叫经纪人去619
00:22:57,679 --> 00:22:59,679
并制定最佳行程620
00:22:59,679 --> 00:23:02,640
参观所有 30 个 MLB 体育场621
00:23:02,640 --> 00:23:05,200
如果你正在考虑一个讽刺的呃和622
00:23:05,200 --> 00:23:08,159
然后设计最优路线,优先考虑623
00:23:08,159 --> 00:23:10,960
Hello Kitty 之夜等等624
00:23:10,960 --> 00:23:12,400
提出最终计划作为详细的625
00:23:12,400 --> 00:23:13,520
电子表格。我会很快运行626
00:23:13,520 --> 00:23:15,440
通过这个。嗯,我觉得这太627
00:23:15,440 --> 00:23:18,240
很有趣。所以再次像我们一样628
00:23:18,240 --> 00:23:20,720
在整个直播中展示629
00:23:20,720 --> 00:23:23,919
流它使用多种工具使用630
00:23:23,919 --> 00:23:26,240
集装箱终端使用使用631
00:23:26,240 --> 00:23:28,799
浏览器处理所有细节。632
00:23:28,799 --> 00:23:30,400
它可能会再次使用回到633
00:23:30,400 --> 00:23:33,200
浏览器搞清楚 Hello Kitty 之夜634
00:23:33,200 --> 00:23:36,559
然后还有体育场等等。哦635
00:23:36,559 --> 00:23:39,520
让我们看看我是否错过了 Oh go 地图。636
00:23:39,520 --> 00:23:42,080
使用代码构建地图来实际637
00:23:42,080 --> 00:23:43,919
将其构建出来然后我们总体上得到638
00:23:43,919 --> 00:23:46,159
我认为这是一个相当可靠的结果639
00:23:46,159 --> 00:23:48,880
最终需要 25 分钟才能完成640
00:23:48,880 --> 00:23:50,400
赛季开始了,你641
00:23:50,400 --> 00:23:51,919
有一个电子表格,你可以快速642
00:23:51,919 --> 00:23:55,760
查看内部,恰好位于 Chad GBD 内部643
00:23:55,760 --> 00:23:57,919
你可以绘制旅程很酷的地图644
00:23:57,919 --> 00:24:00,400
我想就是这样了,这就是乍得645
00:24:00,400 --> 00:24:02,240
GBD 代理我们希望您真的喜欢它,646
00:24:02,240 --> 00:24:04,000
交给 Sam647
00:24:04,000 --> 00:24:05,919
你们都做得很棒,648
00:24:05,919 --> 00:24:07,440
团队这是我认为呃真的649
00:24:07,440 --> 00:24:08,720
一些能够帮助人们的东西650
00:24:08,720 --> 00:24:10,720
完成工作,有更多的时间651
00:24:10,720 --> 00:24:12,240
做他们想做的事。嗯,我652
00:24:12,240 --> 00:24:13,520
想想这真是太神奇了653
00:24:13,520 --> 00:24:15,360
你们齐心协力完成了这项任务654
00:24:15,360 --> 00:24:17,760
体验和观察代理排序655
00:24:17,760 --> 00:24:19,120
使用互联网,使这些656
00:24:19,120 --> 00:24:20,640
电子表格、制作 PowerPoint 等等657
00:24:20,640 --> 00:24:22,960
否则呃,做所有这些工作是相当658
00:24:22,960 --> 00:24:26,000
太棒了。我们今天要为专业版直播659
00:24:26,000 --> 00:24:28,880
plus 和团队用户。Pro 用户将获得660
00:24:28,880 --> 00:24:30,720
呃,每月 400 个查询,加上一些团队661
00:24:30,720 --> 00:24:32,720
用户每月可获得 40 美元。呃662
00:24:32,720 --> 00:24:34,000
部署工作应在年底前完成663
00:24:34,000 --> 00:24:36,159
Pro 版即将面世,Plus 版也即将面世664
00:24:36,159 --> 00:24:38,400
和团队用户。将尝试直播665
00:24:38,400 --> 00:24:40,799
企业和教育机构666
00:24:40,799 --> 00:24:43,360
月。正如 Casey 提到的,尽管这667
00:24:43,360 --> 00:24:45,360
是一项极其令人兴奋的新技术,668
00:24:45,360 --> 00:24:48,080
有新的风险。呃,人们学到了669
00:24:48,080 --> 00:24:49,520
如何使用互联网一般很漂亮670
00:24:49,520 --> 00:24:50,880
安全地,当然也有671
00:24:50,880 --> 00:24:52,880
诈骗者和其他攻击。人们672
00:24:52,880 --> 00:24:54,559
需要学习使用人工智能673
00:24:54,559 --> 00:24:56,080
特工。呃,社会需要674
00:24:56,080 --> 00:24:57,919
学会建立防御机制675
00:24:57,919 --> 00:25:00,080
攻击人工智能代理。所以我们676
00:25:00,080 --> 00:25:02,080
从一个非常强大的系统开始,很多677
00:25:02,080 --> 00:25:04,240
警告。我们将放宽678
00:25:04,240 --> 00:25:05,679
随着人们越来越习惯679
00:25:05,679 --> 00:25:07,600
但我们确实希望人们能够680
00:25:07,600 --> 00:25:09,919
作为一项新技术和新风险681
00:25:09,919 --> 00:25:12,080
表面并采取所有谨慎措施682
00:25:12,080 --> 00:25:14,799
凯西说过。嗯,不过话说回来,683
00:25:14,799 --> 00:25:16,720
希望你会喜欢。呃,这是684
00:25:16,720 --> 00:25:18,159
还为时过早。我们会改进685
00:25:18,159 --> 00:25:20,640
我们很高兴看到686
00:25:20,640 --> 00:25:22,640
一切顺利。所以,再次祝贺。谢谢687
00:25:22,640 --> 00:25:26,440
非常感谢。希望你喜欢。

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