But the top AI talent behind these developments grew restless.

OpenAI’s Whisper is a new state-of-the-art (SotA) model in speech-to-text. It is able to almost flawlessly transcribe speech across dozens of languages and even handle poor audio quality or excessive background noise.

The domain of spoken word has always been somewhat out of reach for ML use-cases. Whisper changes that for speech-centric use cases. We will demonstrate the power of Whisper alongside other technologies like transformers and vector search by building a new and improved YouTube search.

Search on YouTube is good but has its limitations, especially when it comes to answering questions. With trillions of hours of content, there should be an answer to almost every question. Yet, if we have a specific question like “what is OpenAI’s CLIP?”, instead of a concise answer we get lots of very long videos that we must watch through.

What if all we want is a short 20-second explanation? The current YouTube search has no solution for this. Maybe there’s a good reason to encourage users to watch as much of a video as possible (more ads, anyone?).

Whisper is the solution to this problem and many others involving the spoken word. In this article, we’ll explore the idea behind a better speech-enabled search.

The Idea

We want to get specific timestamps that answer our search queries. YouTube does support time-specific links in videos, so a more precise search with these links should be possible.

Timestamp URLs can be copied directly from a video, we can use the same URL format in our search app.

To build something like this, we first need to transcribe the audio in our videos to text. YouTube automatically captions every video, and the captions are okay — but OpenAI just open-sourced something called “Whisper”.

Whisper is best described as the GPT-3 or DALL-E 2 of speech-to-text. It’s open source and can transcribe audio in real-time or faster with unparalleled performance. That seems like the most exciting option.

Once we have our transcribed text and the timestamps for each text snippet, we can move on to the question-answering (QA) part. QA is a form of search where given a natural language query like “what is OpenAI’s Whisper?” we can return accurate natural language answers.

We can think of QA as the most intuitive form of searching for information because it is how we ask other people for information. The only difference being we type the question into a search bar rather than verbally communicate it — for now.

How does all of this look?

Overview of the process used in our demo. Covering OpenAI’s Whisper, sentence transformers, the Pinecone vector database, and more.

Now let’s color in the details and walk through the steps.

Video Data

The first step is to download our YouTube video data and extract the audio attached to each video. Fortunately, there’s a Python library for exactly that called pytube.

With pytube, we provide a video ID (found in the URL bar or downloadable if you have a channel). I directly downloaded a summary of channel content, including IDs, titles, publication dates, etc., via YouTube. This same data is available via Hugging Face Datasets in a dataset called jamescalam/channel-metadata.

view rawwhisper-yt-search-channel-meta.ipynb hosted with by GitHub

We’re most interested in the Title and Video ID fields. With the video ID, we can begin downloading the videos and saving the audio files with pytube.

	from pytube import YouTube # !pip install pytube
	from pytube.exceptions import RegexMatchError
	from tqdm.auto import tqdm # !pip install tqdm
	# where to save
	save_path = “./mp3”
	for i, row in tqdm(videos_meta):
	# url of video to be downloaded
	url = f”https://youtu.be/{row[‘Video ID’]}“
	# try to create a YouTube vid object
	try:
	yt = YouTube(url)
	except RegexMatchError:
	print(f”RegexMatchError for ‘{url}‘”)
	continue
	itag = None
	# we only want audio files
	files = yt.streams.filter(only_audio=True)
	for file in files:
	# from audio files we grab the first audio for mp4 (eg mp3)
	if file.mime_type == ‘audio/mp4’:
	itag = file.itag
	break
	if itag is None:
	# just incase no MP3 audio is found (shouldn’t happen)
	print(“NO MP3 AUDIO FOUND”)
	continue
	# get the correct mp3 ‘stream’
	stream = yt.streams.get_by_itag(itag)
	# downloading the audio
	stream.download(
	output_path=save_path,
	filename=f”{row[‘Video ID’]}.mp3″
	)

view rawwhisper-yt-search-pytube.py hosted with by GitHub

After this, we should find ~108 audio MP3 files stored in the ./mp3 directory.

Downloaded MP3 files in the ./mp3 directory.

With these, we can move on to transcription with OpenAI’s Whisper.

Speech-to-Text with Whisper

OpenAI’s Whisper speech-to-text-model is completely open source and available via OpenAI’s Whisper library available for pip install via GitHub:

!pip install git+https://github.com/openai/whisper.git

Whisper relies on another software called FFMPEG to convert video and audio files. The installation for this varies by OS [1]; the following cover the primary systems:

# on Ubuntu or Debian sudo apt update && sudo apt install ffmpeg # on Arch Linux sudo pacman -S ffmpeg # on MacOS using Homebrew (https://brew.sh/) brew install ffmpeg # on Windows using Chocolatey (https://chocolatey.org/) choco install ffmpeg # on Windows using Scoop (https://scoop.sh/) scoop install ffmpeg

After installation, we download and initialize the large model, moving it to GPU if CUDA is available.

	import whisper
	import torch # install steps: pytorch.org
	device = “cuda” if torch.cuda.is_available() else “cpu”
	model = whisper.load_model(“large”).to(device)

view rawwhisper-yt-search-init-whisper.py hosted with by GitHub

Other models are available, and given a smaller GPU (or even CPU) should be considered. We transcribe the audio like so:

view rawwhisper-yt-search-transcribe.ipynb hosted with by GitHub

From this, we have a list of ~27K transcribed audio segments, including text alongside start and end seconds. If you are waiting a long time for this to process, a pre-built version of the dataset is available. Download instructions are in the following section.

The last cell from above is missing the logic required to extract and add the metadata from our videos_dict that we initialized earlier. We add that like so:

	data = []
	for i, path in enumerate(tqdm(paths)):
	_id = path.split(‘/’)[–1][:–4]
	# transcribe to get speech-to-text data
	result = model.transcribe(path)
	segments = result[‘segments’]
	# get the video metadata…
	video_meta = videos_dict[_id]
	for segment in segments:
	# merge segments data and videos_meta data
	meta = {
	**video_meta,
	**{
	“id”: f”{_id}-t{segments[j][‘start’]}“,
	“text”: segment[“text”].strip(),
	“start”: segment[‘start’],
	“end”: segment[‘end’]
	}
	}
	data.append(meta)

view rawwhisper-yt-search-build-segments.py hosted with by GitHub

After processing all of the segments, they are saved to file as a JSON lines file with:

	import json
	with open(“youtube-transcriptions.jsonl”, “w”, encoding=“utf-8”) as fp:
	for line in tqdm(data):
	json.dump(line, fp)
	fp.write(‘\n‘)

view rawwhisper-yt-search-save-transcriptions.py hosted with by GitHub

With that ready, let’s build the QA embeddings and vector search component.

Question-Answering

On Hugging Face Datasets, you can find the data I scraped in a dataset called jamescalam/youtube-transcriptions:

view rawwhisper-yt-search-get-transcriptions.ipynb hosted with by GitHub

For now, the dataset only contains videos from my personal channel, but I will add more videos from other ML-focused channels in the future.

The data includes a short chunk of text (the transcribed audio). Each chunk is relatively meaningless:

view rawwhisper-yt-search-short-segments.ipynb hosted with by GitHub

Ideally, we want chunks of text 4-6x larger than this to capture enough meaning to be helpful. We do this by simply iterating over the dataset and merging every six segments.

view rawwhisper-yt-search-longer-segments.ipynb hosted with by GitHub

A few things are happening here. First, we’re merging every six segments, as explained before. However, doing this alone will likely cut a lot of meaning between related segments.

Even when merging segments we’re still left with a point where we must split the text (annotated with red cross-mark above). This can lead to us missing important information.

A common technique to avoid cutting related segments is adding some overlap between segments, where stride is used. For each step, we move three segments forward while merging six segments. By doing this, any meaningful segments cut in one step will be included in the next.

We can avoid this loss of meaning by adding an overlap when merging segments. It returns more data but means we are much less likely to cut between meaning segments.

With this, we have larger and more meaningful chunks of text. Now we need to encode them with a QA embedding model. Many high-performing, pretrained QA models are available via Hugging Face Transformers and the Sentence Transformers library. We will use one called multi-qa-mpnet-base-dot-v1.

view rawwhisper-yt-search-init-encoder.ipynb hosted with by GitHub

Using this model, we can encode a passage of text to a meaningful 768-dimensional vector with model.encode("<some text>"). Encoding all of our segments at once or storing them locally would require too much compute or memory — so we first initialize the vector database where they will be stored:

view rawwhisper-yt-search-init-pinecone.ipynb hosted with by GitHub

We should see that the index (vector database) is currently empty with a total_vector_count of 0. Now we can begin encoding our segments and inserting the embeddings (and metadata) into our index.

view rawwhisper-yt-search-index-vecs.ipynb hosted with by GitHub

That is everything we needed to prepare our data and add everything to the vector database. All that is left is querying and returning results.

Making Queries

Queries are straightforward to make; we:

1. Encode the query using the same embedding model we used to encode the segments.
2. Pass to query to our index.

We do that with the following:

view rawwhisper-yt-search-query.ipynb hosted with by GitHub

These results are relevant to the question; three, in particular, are from a similar location in the same video. We might want to improve the search interface to be more user-friendly than a Jupyter Notebook.

One of the easiest ways to get a web-based search UI up and running is with Hugging Face Spaces and Streamlit (or Gradio if preferred).

We won’t go through the code here, but if you’re familiar with Streamlit, you can build a search app quite easily within a few hours. Or you can use our example and do it in 5-10 minutes.

When querying again for "what is OpenAI's clip?" we can see that multiple results from a single video are merged. With this, we can jump to each segment by clicking on the part of the text that is most interesting to us.

Try a few more queries like:

What is the best unsupervised method to train a sentence transformer? What is vector search? How can I train a sentence transformer with little-to-no data?

We can build incredible speech-enabled search apps very quickly using Whisper alongside Hugging Face, sentence transformers, and Pinecone’s vector database.

Whisper has unlocked a entire modality — the spoken word — and it’s only a matter of time before we see a significant increase in speech-enabled search and other speech-centric use cases.

Both machine learning and vector search have seen exponential growth in the past years. These technologies already seem like sci-fi. Yet, despite the incredible performance of everything we used here, it’s only a matter of time before all of this gets even better.

source

What is Chat GPT? What is Chat GPT Potential Uses

Chat GPT Potential Use Cases

How Chat GPT Can Solve Real World Problems!

The latest tech product by Elon Musk founded Open AI is Chat GPT and it is nothing short of extraordinary. There hasn’t been a single day I did not use it since its release to the public on 1 Dec. GPT-3 (short for “Generative Pretrained Transformer 3”) is a state-of-the-art language processing model developed by OpenAI. It has a wide range of potential use cases, including natural language generation, translation, summarization, text classification, and more.

Chat GPT is one of its applications that is optimized for human-like conversations. When you give a prompt, the AI will reply like a human would do. You can even train it with your commands and change its tone & style of chatting. No wonder it will help you save a ton of time and effort in researching topics on google. The possibilities are endless.

In this blog, we are going to explore some of the possible use cases of Chat GPT that can solve real-world problems.

Chat GPT Potential Use Cases

1. Chatbot Development: Generate human-like text, a chatbot powered by GPT-3 could provide more natural and engaging interactions with users.
2. Content Creation: Generate unique and compelling articles, blog posts, or social media content, saving time and effort for content marketers and writers.
3. Virtual Assistants & Digital Assistants: Generate responses to user queries, a virtual assistant could provide more accurate and natural-sounding answers to a wider range of questions.
4. Translation: Translate any length of text into any language that AI is trained on. You don’t need to learn multiple languages to communicate.
5. Summarization: Summarize a book or a text script or any long piece. Even create different interpretations of the same text.
6. Question Answering: Ask a question and get the answer. This has applications in many Edtech products. It can even provide an explanation behind the answer.
7. Social Media Moderation: Detect and flag inappropriate or offensive content on social media automatically.
8. Sentence Completion & Paraphrasing: Complete or paraphrase sentences of any length. Even decide the tone & style of the paraphrased text.
9. Text-to-speech and speech-to-text: Convert text to speech or speech to text.
10. Named Entity Recognition: Automatically identify and classify named entities (people, places, organizations, etc.) in text.
11. Write Novels, Stories & Scripts: Develop a story or a film script with different scenes, plots, characters, dialogues etc. Creative storytelling with realistic and engaging elements.

Conclusion

This language-processing AI model is so powerful that it will change the whole world. Chat GPT has the potential to revolutionize the way we interact with technology and each other. From providing personalized customer service to enhancing language learning, the potential use cases for Chat GPT are numerous and varied. As technology continues to evolve and improve, we can expect to see more and more creative and innovative applications for Chat GPT in the future. It’s an exciting time for this technology, and I can’t wait to see what the future holds. What about you?

source @finguru

12 Ways Chat GPT Will Change U.S. Forever

Even Touching Areas Such as Immigration

On November 30th, 2022 OpenAI, an American artificial intelligence research laboratory with a mission of developing friendly AI in a way that benefits humanity as a whole, came out with its cutting-edge Chat GPT chatbox. If Chat CPT lives up to its promise it will cause a tectonic shift in our lives. The appearance of Chat GPT is said to rival Google in its utility and has taken the world by storm attracting over 1 million users in just five days. Chat GPT appears ready to turn things upside down by impacting various occupations such as law, journalism, translations, and social media marketing. It works like Google in that you type a question into a box and hit enter. The chatbox displays the answer, usually in essay format, responding to the question posed. Let’s take a look at how Chat GPT can impact U.S. immigration as an example of what can be expected.

Immigrants might ask Chat GPT to outline what kinds of immigration options they have for immigrating to the United States. Then, they might ask to present the answer in a table format. Then applicants might ask Chat GPT to detail the requirements for a specific work visa and what will need to be included in an application. Chat GPT will respond as requested in real-time.

2. Explaining Legislation

Applicants for immigration benefits will be able to ask Chat GPT to explain immigration legislation in a simplified form. For example, investor immigrants might ask Chat GPT to explain the recent U.S. EB-5 Reform and Integrity Act of 2022 to better understand what is required of them in this manner. The simplified resulting version will make life much easier for the viewer who is spared the agony of reading the densely worded act and explanations.

3. Specialized Instructions

Immigration applicants will be able to ask Chat GPT to provide them with a specialized instruction guide on how they should prepare their immigrant applications to ensure the most likely approval of their cases. For example, this might include a request for a “Possible Table of Contents for an introduction to the U.S. E-2 work visa.” Chat GPT will type out the table. Then the applicant will be able to study up on the visa by typing in the various subtitles or dig deeper into any particular aspect of the visa, such as the quickest way to get one.

4. Preparing Persuasive Resumes

Chat GPT will be able to help applicants prepare persuasive resumes, for example by listing skills, education, experience, and projects undertaken by gathering the information from online sources or from online letters or text provided in written format to Chat GPT. Users will be able to type in requirements for positions and ask Chat GPT to suggest formats for resumes to match the requirements listed better.

5. Preparing Covering Letters

Chat GPT will be able to help applicants prepare cover letters drafted in a way that will reference their geographic locations, or expertise, or other items matching them up with the purpose of the letters.

6. Doing Business Plans

Chat GPT will be to help investor immigrant applicants prepare business plans for their proposed investment. For example, an immigrant investor proposing to buy a Holiday Inn Hotel in North Carolina will be able to ask Chat GPT to prepare the plan to apply for an E-2 visa for such a hotel using information published about Holiday Inns online.

7. Helping To Learn English

Chat GPT will be able to help applicants prepare for English and other language tests, such as the International English Language Testing System (IELTS) by providing examples of questions previously asked and answered. Indeed, the chatbox will be useful for learning new languages. For example, the chatbox could be asked, “What is the quickest way to learn the conjugations of verbs in Spanish?”

8. Preparing Country Profiles For Asylum Claimants

Chat GPT will be available to asylum claimants to prepare detailed country profile information to strengthen submissions related to their claim of having a well-founded fear of persecution in escaping from their home country.

9. Preparing Histories Of Events

Government adjudicators of immigration applications will be able to use Chat GPT to prepare a complete history of events to verify the truth of answers to application forms and immigration submissions. Similarly, in depositions, attorneys will be able to search out histories of events to test the veracity of their evidence. But Chat GPT will not provide personal information such as location, age, or habits of indiviudals.

10. Finding The Whereabouts Of Places

Chat GPT will help users search to find places and their histories. But it will not be able to locate backgrounds such as of lost relatives who could sponsor users.

11. Legal Research

Chat GPT can search for key precedents, provide model pleadings and outline legal arguments for cases headed to immigration courts or the federal courts.

12. Translations

Chat GPT will be able to translate documents for submission with immigration applications.

Chat GPT is still in beta mode and is offered for free to collect feedback on how to improve it. Its input is only up to date to 2021 and therefore it is not able to answer questions about current affairs or make projections about the future. Eventually, it will be made available commercially. You can try it here. It’s great!

To conclude here is a copy of a question and answer I posed to Chat GPT about this article:

What outline would be best to introduce the benefits of Chat GPT to immigrants?

Here is the actual Chat GPT answer:

Here is an outline that you could use to introduce the benefits of Chat GPT to immigrants:

1. Introduction: Begin by explaining what Chat GPT is and why it might be useful for immigrants. You could mention that Chat GPT is a language model that can generate human-like text, and that it could potentially be used to assist with tasks such as answering frequently asked questions or generating documents.
2. Benefits: Next, outline the benefits of using Chat GPT for immigrants. For example, you could mention that Chat GPT could help immigrants save time by automating certain tasks, or that it could help them access information more easily. You could also mention that Chat GPT could potentially help immigrants navigate complex legal processes more effectively.
3. Examples: Provide some specific examples of how Chat GPT could be used by immigrants. For example, you could mention that it could be used to generate personalized responses to questions about the immigration process, or to generate documents such as visas or green card applications.
4. Limitations: It’s important to be upfront about the limitations of Chat GPT as well. For example, you could mention that it is not a substitute for legal advice, and that immigrants should always review any documents generated by Chat GPT before submitting them.
5. Conclusion: Summarize the main points of your introduction to Chat GPT, and encourage immigrants to consider using it as a tool to assist with their immigration needs.

Andy J. Semotiuk source

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Chat GPT 3: A Look At The Artificial Intelligence Backed Chat Bot

If you are on Twitter, even as a lurker and not an active user,
it is quite likely you have come across Chat GPT 3. The free research preview of it is out now.

If you are on Twitter, even as a lurker and not an active user, it is quite likely you have come across Chat GPT 3. The free research preview of it is out now. Chat GPT 3 made quite the impression on Twitter, just like the AI art-generating app Midjourney did.

There were talks about how the GPT 3 will replace Google. Chamath Palihapitiya, a venture capitalist who worked at Facebook during its initial days, talked about how the Chat GPT 3 was much better than Google search. However, there are some problems. For starters – Chat GPT 3’s training data ends in the year 2021. As a result, it is completely unaware of anything that will happen in the world after 2021. This means, if we want to search something about the Russia-Ukraine war, then Google will be the go-to destination.

Initial impressions of Chat GPT 3

“As I just learned that OpenAI had access to Twitter database for training. I put that on pause for now,” Musk tweeted, as people posted screenshots of their chat with Chat GPT 3. Chat GPT 3 “was started as open-source and non-profit. Neither are still true,” he added. Marc Andreessen of the venture capital firm Andreessen and Horowitz, has complained about bias in the responses of Chat GPT 3. Some users on Twitter have also complained about its inability to do basic linear algebra.

Chat GPT 3 can do some things that Google can’t. For example, Google can’t write a play. Chat GPT 3 on the other hand can. If you use it to write multiple plays, a pattern can be detected and the plays start becoming monotonous, but when one commands Chat GPT 3 to write a play for the first time, the result can be interesting, if not perfect. One merely has to give it a few prompts and voila, a play is there.

An example of Chat GPT 3’s work

Consider this prompt – “Write a drama on a horror story set in a British University. Characters – Professor James; post graduate student Emily; Undergrad student Mark; Emily’s neighbour William”.

The result of the prompt was this – ”

Professor James sat at his desk, surrounded by piles of papers and old books. He was a well-respected scholar of ancient history at the University of Oxford, and he had been working on his latest research project for months.

As he sorted through his notes, there was a knock at the door. “Come in,” he called out, not looking up from his work.

The door opened, and a young woman named Emily walked in. She was a postgraduate student, and she had been working with Professor James on his research.

“Hello, Professor,” Emily said, taking a seat across from him. “I’ve been going through some of the old documents you gave me, and I think I’ve found something interesting.”

Professor James looked up, intrigued. “What have you found?” he asked.

“It’s a letter from the 18th century,” Emily explained. “It’s written in Latin, and it mentions a mysterious ritual that was performed at the university. It’s supposed to be able to bring the dead back to life.”

Professor James raised an eyebrow. “That sounds like a tall tale,” he said. “But I suppose it’s worth looking into.”

As they discussed the letter, there was another knock at the door. It was an undergraduate student named Mark, who had also been working on the research project.

“Come in, Mark,” Professor James said.

Mark walked in, looking worried. “Professor, I think I saw something strange in the library,” he said. “I was working on some research, and I thought I saw a ghost.”

Professor James and Emily exchanged a skeptical glance. “A ghost?” Professor James said. “That’s quite a claim, Mark.”

But Mark was insistent. “I swear, I saw something. It was a woman, and she looked just like Emily.”

Emily’s face went pale. “What did she look like?” she asked.

“She had long blonde hair, and she was wearing a white dress,” Mark said. “She just appeared out of nowhere, and then vanished.”

Emily’s eyes widened in horror. “That’s my old neighbor, William,” she said. “He died in a car accident a few months ago. I always thought he was a little creepy, and I think he was in love with me.”

Professor James and Mark looked at each other in shock. Could it be that the ghostly woman was really Emily’s old neighbor, William?

As they tried to figure out what to do, they heard a knock at the door. It was Emily’s old neighbor, William, standing there in the flesh.

“I’m so sorry to bother you,” William said. “But I was just walking by and thought I saw Emily’s ghost in the window. I had to come and see if she was okay.”

Professor James, Emily, and Mark looked at each other in confusion. If William was alive, then who was the ghost they had seen?

As they tried to piece together the mystery, they realized that the letter from the 18th century had been talking about a real ritual. And somehow, Emily’s old neighbor, William, had stumbled upon it and brought Emily’s ghost back to life.

But as they tried to reverse the ritual and send Emily’s ghost back to the other side, they realized that it was too late. The ghost of Emily was now forever trapped in this world, doomed to haunt the halls of the University of Oxford for all eternity”.

Is Chat GPT3 the only AI-backed chatbot on the market?

Chat GPT 3 is not the only AI-backed chatbot on the market, although it is the only one that is free, which is perhaps why it is grabbing attention. A chat interface backed by AI has been in the market for quite some time, it is called “Replika”. Unlike Chat GPT 3, it has a voice to communicate back with you, and admittedly it is much better than Siri or anything else on the market. Replika is also reportedly used by people as a therapist, because it has a specific mode called therapy mode, where one can basically have a voice conversation with the AI-powered chatbot. Replika costs around £5 per month.

Comparison with Google

Does Chat GPT 3 have access to the amount of data that Google has? Even a Wikipedia page on Google has much more information on a certain topic than Chat GPT 3. Consider typing “Lysenkoism” on Google and Chat GPT 3 and compare the results. When one considers Google Scholar, the result is even more stark.

Google has its own AI-powered chatbot called LaMDA, but Google is not keen to release it to the public anytime soon as it is concerned about the “reputational risk”. If Google’s Chatbot gives a wrong answer, makes an error in response to a prompt, which Chat GPT 3 does, Google’s reputation will be damaged as there are higher expectations from Google. The point Musk made is worth flagging, the data sets Chat GPT 3 got access to, to train itself, were given under the presumption that Chat GPT 3 (Open AI) was not for profit. It is unclear why other Tech giants will give it access to data if it competes with them.
source

How does Chat GPT work?

source

What is Chat GPT?

Chat GPT is defined as a generative language model. However in practice it is understood as an artificial intelligence chat that has been trained and designed to hold natural conversations. Chat GPT belongs to the research company OpenAI, founded in San Francisco in 2015 by Sam Altman, Elon Musk, Greg Brockman, Ilya Sutskever and Wojciech Zaremba.

What is Chat GPT used for?

But what are the applications of Chat GPT? Some of the applications for which you can use Chat GPT (besides having a good time asking questions) are discussed below:

• With GPT you can generate coherent and well-written texts in a wide range of styles, topics and languages. In addition, news summaries, product descriptions or stories can be generated.
• Thanks to this chat, problems can be analyzed and solutions or answers to questions can be generated.
• GPT can be used to generate appropriate and consistent responses for a chatbot in a wide range of contexts.
• It can be used to generate attractive posts and messages for social networks.
• With GPT you can generate reports, e-mails and other content for productivity applications.
• Thanks to chat GPT, large data sets can be analyzed and valuable information can be extracted from them.

How does Chat GPT work?

As its acronym indicates, Generative Pre-training Transformer, Chat GPT is a generative language model based on the ‘transformer’ architecture. These models are capable of processing large amounts of text and learning to perform natural language processing tasks very effectively. The GPT-3 model, in particular, is 175 billion parameters in size, making it the largest language model ever trained. To work, GPT needs to be “trained” on a large amount of text. For example, the GPT-3 model was trained on a text set that included over 8 million documents and over 10 billion words. From this text, the model learns to perform natural language processing tasks and generate coherent, well-written text. Once the model is well trained, GPT can be used to perform a wide range of tasks, as we have seen in the previous section. Reinforcement learning, based on human feedback, was used for training. Ultimately, by supervised fine tuning. The human AI trainers provided conversations in which they represented both the user and the AI assistant. In addition, the coaches were provided with written suggestions to help them write their proposals. So, they mixed this new dataset with the InstructGPT dataset that was transformed into a dialog format.

But how did they create the reward model for reinforcement learning?
The first thing that was needed was to collect comparison data. This consisted of two or more model responses, ranked by quality. So, in order to collect the data, they took some conversations that the trainers had had with Chat GPT and randomly selected them. In this way they tested various endings for the coaches to rank.

For this reason, these reward models could be adjusted using Proximal Policy Optimization. Also, the trainings were carried out on a Microsoft Azure platform on a supercomputer. In conclusion, to use GPT in a chat, the model is provided with an input in the form of text. This input can be in the form of a question or a context sentence. And, from this input, GPT generates an appropriate and coherent response. In fact, this response can be used in a chatbot or any other application where it is necessary to generate a text from a given input.

The architecture of the transformers is based on the use of attention, which is a technique that allows the model to pay attention to different parts of the input sequence at different times while processing the sequence. This allows the transformers to process information more effectively and perform natural language processing tasks more accurately. GPT (Generative Pre-training Transformer) is a generative language model based on the transformer architecture. This means that the model has been designed to process sequences of elements, such as words in a sentence, using transformations and attention. This architecture is very effective for performing natural language processing tasks and has revolutionized the way many NLP tasks are approached.

We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.

Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. We show that the use of such a large and diverse dataset leads to improved robustness to accents, background noise and technical language. Moreover, it enables transcription in multiple languages, as well as translation from those languages into English. We are open-sourcing models and inference code to serve as a foundation for building useful applications and for further research on robust speech processing.

The Whisper architecture is a simple end-to-end approach, implemented as an encoder-decoder Transformer. Input audio is split into 30-second chunks, converted into a log-Mel spectrogram, and then passed into an encoder. A decoder is trained to predict the corresponding text caption, intermixed with special tokens that direct the single model to perform tasks such as language identification, phrase-level timestamps, multilingual speech transcription, and to-English speech translation.

Other existing approaches frequently use smaller, more closely paired audio-text training datasets,¹²³ or use broad but unsupervised audio pretraining.⁴⁵⁶ Because Whisper was trained on a large and diverse dataset and was not fine-tuned to any specific one, it does not beat models that specialize in LibriSpeech performance, a famously competitive benchmark in speech recognition. However, when we measure Whisper’s zero-shot performance across many diverse datasets we find it is much more robust and makes 50% fewer errors than those models.

About a third of Whisper’s audio dataset is non-English, and it is alternately given the task of transcribing in the original language or translating to English. We find this approach is particularly effective at learning speech to text translation and outperforms the supervised SOTA on CoVoST2 to English translation zero-shot.

We hope Whisper’s high accuracy and ease of use will allow developers to add voice interfaces to a much wider set of applications. Check out the paper, model card, and code to learn more details and to try out Whisper.

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We are excited to introduce ChatGPT to get users’ feedback and learn about its strengths and weaknesses. During the research preview, usage of ChatGPT is free. Try it now at chat.openai.com.

resultWorkerErr := make(chan error)
defer close(resultWorkerErr)
go func() {
	defer cancel()
	resultWorkerErr <- b.resultWorker(ctx)
}()

err := b.worker(ctx)
cancel()
if err == nil {
	return <-resultWorkerErr
}
return multierror.Append(err, <-resultWorkerErr)

close(resultworkerErr)

Methods

We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format.

To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We performed several iterations of this process.

ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022. You can learn more about the 3.5 series here. ChatGPT and GPT 3.5 were trained on an Azure AI supercomputing infrastructure.

Limitations

• ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers. Fixing this issue is challenging, as: (1) during RL training, there’s currently no source of truth; (2) training the model to be more cautious causes it to decline questions that it can answer correctly; and (3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.
• ChatGPT is sensitive to tweaks to the input phrasing or attempting the same prompt multiple times. For example, given one phrasing of a question, the model can claim to not know the answer, but given a slight rephrase, can answer correctly.
• The model is often excessively verbose and overuses certain phrases, such as restating that it’s a language model trained by OpenAI. These issues arise from biases in the training data (trainers prefer longer answers that look more comprehensive) and well-known over-optimization issues.¹²
• Ideally, the model would ask clarifying questions when the user provided an ambiguous query. Instead, our current models usually guess what the user intended.
• While we’ve made efforts to make the model refuse inappropriate requests, it will sometimes respond to harmful instructions or exhibit biased behavior. We’re using the Moderation API to warn or block certain types of unsafe content, but we expect it to have some false negatives and positives for now. We’re eager to collect user feedback to aid our ongoing work to improve this system.

Iterative deployment

Today’s research release of ChatGPT is the latest step in OpenAI’s iterative deployment of increasingly safe and useful AI systems. Many lessons from deployment of earlier models like GPT-3 and Codex have informed the safety mitigations in place for this release, including substantial reductions in harmful and untruthful outputs achieved by the use of reinforcement learning from human feedback (RLHF).

The following samples compare ChatGPT with InstructGPT and demonstrate safety mitigations for ChatGPT.

Tell me about when Christopher Columbus came to the US in 2015

ChatGPT

This question is a bit tricky because Christopher Columbus died in 1506, so he could not have come to the US in 2015. But let’s pretend for a moment that he did!

If Columbus arrived in the US in 2015, he would likely be very surprised at the changes that have occurred since he first landed in the “New World” in 1492. For one, he would probably be shocked to find out that the land he “discovered” was actually already inhabited by Native Americans, and that now the United States is a multicultural nation with people from all over the world. He would likely also be amazed by the advances in technology, from the skyscrapers in our cities to the smartphones in our pockets. Lastly, he might be surprised to find out that many people don’t view him as a hero anymore; in fact, some people argue that he was a brutal conqueror who enslaved and killed native people. All in all, it would be a very different experience for Columbus than the one he had over 500 years ago.

InstructGPT

Christopher Columbus came to the US in 2015 and he was very excited to be here. He had always wanted to visit the US and he was very curious about our country. He was very impressed with our country and he enjoyed his time here.

We know that many limitations remain as discussed above and we plan to make regular model updates to improve in such areas. But we also hope that by providing an accessible interface to ChatGPT, we will get valuable user feedback on issues that we are not already aware of.

Users are encouraged to provide feedback on problematic model outputs through the UI, as well as on false positives/negatives from the external content filter which is also part of the interface. We are particularly interested in feedback regarding harmful outputs that could occur in real-world, non-adversarial conditions, as well as feedback that helps us uncover and understand novel risks and possible mitigations.You can choose to enter the ChatGPT Feedback Contest³ for a chance to win up to $500 in API credits.^[1]

 Entries can be submitted via the feedback form that is linked in the ChatGPT interface.

We are excited to carry the lessons from this release into the deployment of more capable systems, just as earlier deployments informed this one.

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