GPT4.0 升级后有哪些功能呢？
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GPT升级记录
Models
Overview
The OpenAI API is powered by a diverse set of models with different capabilities and price points. You can also make limited customizations to our original base models for your specific use case with fine-tuning.
MODELS | DESCRIPTION |
GPT-4 Limited beta | A set of models that improve on GPT-3.5 and can understand as well as generate natural language or code |
A set of models that improve on GPT-3 and can understand as well as generate natural language or code | |
DALL·E Beta | A model that can generate and edit images given a natural language prompt |
Beta | A model that can convert audio into text |
A set of models that can convert text into a numerical form | |
Codex Limited beta | A set of models that can understand and generate code, including translating natural language to code |
A fine-tuned model that can detect whether text may be sensitive or unsafe | |
GPT-3 | A set of models that can understand and generate natural language |
We have also published open source models including Point-E, Whisper, Jukebox, and CLIP.
Visit our model index for researchers to learn more about which models have been featured in our research papers and the differences between model series like InstructGPT and GPT-3.5.
GPT-4 is a large multimodal model (accepting text inputs and emitting text outputs today, with image inputs coming in the future) that can solve difficult problems with greater accuracy than any of our previous models, thanks to its broader general knowledge and advanced reasoning capabilities. Like , GPT-4 is optimized for chat but works well for traditional completions tasks.gpt-3.5-turbo
GPT-4 is currently in a limited beta and only accessible to those who have been granted access. Please join the waitlist to get access when capacity is available.
LATEST MODEL | DESCRIPTION | MAX TOKENS | TRAINING DATA |
gpt-4 | More capable than any GPT-3.5 model, able to do more complex tasks, and optimized for chat. Will be updated with our latest model iteration. | 8,192 tokens | Up to Sep 2021 |
gpt-4-0314 | Snapshot of from March 14th 2023. Unlike , this model will not receive updates, and will only be supported for a three month period ending on June 14th 2023. | 8,192 tokens | Up to Sep 2021 |
gpt-4-32k | Same capabilities as the base mode but with 4x the context length. Will be updated with our latest model iteration. | 32,768 tokens | Up to Sep 2021 |
gpt-4-32k-0314 | Snapshot of from March 14th 2023. Unlike , this model will not receive updates, and will only be supported for a three month period ending on June 14th 2023. | 32,768 tokens | Up to Sep 2021 |
For many basic tasks, the difference between GPT-4 and GPT-3.5 models is not significant. However, in more complex reasoning situations, GPT-4 is much more capable than any of our previous models.
GPT-3.5
GPT-3.5 models can understand and generate natural language or code. Our most capable and cost effective model in the GPT-3.5 family is which has been optimized for chat but works well for traditional completions tasks as well.gpt-3.5-turbo
LATEST MODEL | DESCRIPTION | MAX TOKENS | TRAINING DATA |
gpt-3.5-turbo | Most capable GPT-3.5 model and optimized for chat at 1/10th the cost of . Will be updated with our latest model iteration. | 4,096 tokens | Up to Sep 2021 |
gpt-3.5-turbo-0301 | Snapshot of from March 1st 2023. Unlike , this model will not receive updates, and will only be supported for a three month period ending on June 1st 2023. | 4,096 tokens | Up to Sep 2021 |
text-davinci-003 | Can do any language task with better quality, longer output, and consistent instruction-following than the curie, babbage, or ada models. Also supports inserting completions within text. | 4,097 tokens | Up to Jun 2021 |
text-davinci-002 | Similar capabilities to but trained with supervised fine-tuning instead of reinforcement learning | 4,097 tokens | Up to Jun 2021 |
code-davinci-002 | Optimized for code-completion tasks | 8,001 tokens | Up to Jun 2021 |
We recommend using over the other GPT-3.5 models because of its lower cost.gpt-3.5-turbo
OpenAI models are non-deterministic, meaning that identical inputs can yield different outputs. Setting temperature to 0 will make the outputs mostly deterministic, but a small amount of variability may remain.
Feature-specific models
While the new model is optimized for chat, it works very well for traditional completion tasks. The original GPT-3.5 models are optimized for text completion.gpt-3.5-turbo
Our endpoints for creating embeddings and editing text use their own sets of specialized models.
Finding the right model
Experimenting with is a great way to find out what the API is capable of doing. After you have an idea of what you want to accomplish, you can stay with or another model and try to optimize around its capabilities.gpt-3.5-turbogpt-3.5-turbo
You can use the GPT comparison tool that lets you run different models side-by-side to compare outputs, settings, and response times and then download the data into an Excel spreadsheet.
DALL·E is a AI system that can create realistic images and art from a description in natural language. We currently support the ability, given a prommpt, to create a new image with a certain size, edit an existing image, or create variations of a user provided image.
The current DALL·E model available through our API is the 2nd iteration of DALL·E with more realistic, accurate, and 4x greater resolution images than the original model. You can try it through the our Labs interface or via the API.
Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. The Whisper v2-large model is currently available through our API with the model name.whisper-1
Currently, there is no difference between the open source version of Whisper and the version available through our API. However, through our API, we offer an optimized inference process which makes running Whisper through our API much faster than doing it through other means. For more technical details on Whisper, you can read the paper.
Embeddings
Embeddings are a numerical representation of text that can be used to measure the relateness between two pieces of text. Our second generation embedding model, is a designed to replace the previous 16 first-generation embedding models at a fraction of the cost. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks. You can read more about our latest embedding model in the announcement blog post. text-embedding-ada-002
The Codex models are descendants of our GPT-3 models that can understand and generate code. Their training data contains both natural language and billions of lines of public code from GitHub. Learn more.
They’re most capable in Python and proficient in over a dozen languages including javascript, Go, Perl, php, Ruby, Swift, TypeScript, SQL, and even Shell.
We currently offer two Codex models:
LATEST MODEL | DESCRIPTION | MAX TOKENS | TRAINING DATA |
code-davinci-002 | Most capable Codex model. Particularly good at translating natural language to code. In addition to completing code, also supports inserting completions within code. | 8,001 tokens | Up to Jun 2021 |
code-cushman-001 | Almost as capable as Davinci Codex, but slightly faster. This speed advantage may make it preferable for real-time applications. | Up to 2,048 tokens |
For more, visit our guide on working with Codex.
新一代蓝牙5.3到底有哪些新东西
1 概述
2021年7月，蓝牙官方组织SIG释放了代码Syndney的5.3版本蓝牙核心协议文档，此版本仍在第5个大版本中，属于小功能升级，那这个版本带来了哪些功能升级呢？
1.1 AdvDataInfo in Periodic Advertising
同步广播是蓝牙5.0增加的功能，蓝牙5.0开始对蓝牙广播功能有了较大的升级，增加了可选的广播信道以及可选的广播类型，单个广播内容长度也由原本31个字节增加到255个字节。
蓝牙5.3中，在Sync广播类型中，增加了可选的AdvDataInfo(ADI)描述。
下图是5.3版本中，SYNC_IND广播支持了可选的ADI描述
而在5.2的版本中，SYNC_IND广播并不支持ADI。
从修改点看，这是一个小修改，而且目前同步广播并没有较多的应用场景，SIG修改此处，不排除后期有应用会使用此功能。
1.2 Host to Controller Encryption Key Control Enhancements
这个修改点是针对安全的，2018年曾爆出，蓝牙（经典蓝牙）加密密钥长度的可协商策略导致很大一部分芯片和蓝牙协议栈可被（Key Negotiation of Bluetooth, KNOB）攻击，攻击者可以修改加密密钥长度至1个字节，之后的整个加密将极易破解，形同虚设。
关于KNOB攻击，可参考如下文章。
https://topic.atatech.org/articles/173726
在KNOB攻击被爆出后，蓝牙官方组织SIG要求蓝牙芯片厂商，协议栈和操作系统厂商升级，以弥补这个漏洞，然后承诺将在蓝牙协议版本升级中解决。
蓝牙5.3版本中，通过增加一条加密密钥长度的控制指令来解决安全问题。
通过此条指令，操作系统可以设置最小加密密钥长度，如果协商者想协商小于此长度的加密密钥，将会被拒绝。
1.3 LE Enhanced Connection Update
基于低功耗的连接功能增强，5.3版本中增加了一个Connection Subrating的功能，这个功能通过调整连接间隔的方式，可以快速降低蓝牙的功耗。
蓝牙是一个低速设备，其在保持连接状态下的功耗与蓝牙的连接参数有较大的关系，一般无数据传输时，可以通过修改连接参数，将参数中的交互间隔扩大来降低功耗，而当需要有数据传输时，则通过降低交互间隔来提高传输带宽。
蓝牙5.3中，增加了一个快速修改的机制，不修改交互间隔，仅修改交互因子，交互因子决定了多少个交互间隔才进行一次实际的交互。
目前看，也仅仅是个小功能。
1.4 LE Channel Classification
Channel Classification是经典蓝牙在1.2就引入的功能，蓝牙使用的2.4G是一个公用频段，蓝牙技术通过跳频技术能解决频段被占用的问题，到了蓝牙1.2版本时，引入了AFH自适应跳频技术，这个技术通过标记信道的方式来标记哪些信道是干净可用的，哪些信道干扰大，然后每隔一段时间重设可用信道组来降低被干扰的风险。由于重设信道的功能只能由Master发起，Slave可以通过Channel Classification将信道状态上报给Master做参考。
到了蓝牙4.0标准后，增加了低功耗蓝牙（简称LE）技术，其中AFH跳频在低功耗蓝牙上本身支持，Master通过重设信道组的方式来降低干扰，蓝牙5.3增加了Channel Classification后，Slave设备即可和经典蓝牙一样，将信道评估状况发给Master。这有利于改善低功耗蓝牙的抗干扰能力。
1.5 Remove AMP
这可能是蓝牙5.3的最大修改点了，但这个不是增加功能，反而是在减少功能，AMP是蓝牙3.0增加了一个高速物理层HS，用于提供高达24Mbps的数据传输率，不过3.0版本中并没有自己设计一个物理层，而是直接使用了WI-FI的物理层，这为蓝牙提供了一个高速通道。
蓝牙HS高速通道一直推展不顺利，并没有什么应用使用到这个高速物理层，且高速物理层反而带来了功耗与成本问题，所以在5.3中被删除。
这个表明，蓝牙基本放弃了高速领域和其他无线技术的竞争，专注于低功耗低成本领域。
2 总结
从目前看，新出的蓝牙5.3标准并没有大范围的功能更新，甚至不如5.1和5.2更新的功能多。不过从中可以看出，即便更新，也主要是LE低功耗蓝牙做更新，经典蓝牙除非是安全问题，基本已经停更了。甚至一些不常用功能会直接删除出协议。AMP被删并不是一个个例，早在5.0版本时，一个不常用的经典蓝牙低功耗机制---Park State就被删除出协议。
另一个摆在蓝牙组织面前的问题，蓝牙标准虽在不停的推陈出新，但应用迟迟跟不上。以蓝牙5.2引入的LE Audio为例，基本仍在Demo阶段，蓝牙5.1的寻向也仅仅小规模应用，其他诸如5.0引入的2M物理层，长距离功能都应用的非常有限，反而是10年未更新的经典蓝牙大放异彩，在苹果革命性的TWS耳机Airpods后，其他各家跟进，年出货以亿计。
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