OpenAI’s DALL-E 3, Image Chat and Web Search

Today's episode details three big releases from OpenAI: (1) DALL-E 3 text-to-image model, which "exactly" adheres to your prompt. (2) Image-to-text chat. (3) Real-time web search integrated into ChatGPT (which seems to lag behind Google's Bard).

So, first, DALL-E 3 text-to-image generation:
• Appears to generate images that are on par with Midjourney V5, the current state-of-the-art.
• The big difference is that apparently DALL-E 3 will actually generate images that adhere “exactly” to the text you provide.
• In contrast, the incumbent models in the state of the art typically ignore words or key parts of the description even though the quality is typically stunning.
• This adherence to prompts extends even to language that you’d like to include in the image, which is mega.
• Watch today's YouTube version for examples of all the above.

In addition, using Midjourney is a really bizarre user experience because it's done through Discord where you provide prompts and get results alongside dozens of other people at the same time. DALL-E 3, in contrast, will be within the slick ChatGPT Plus environment, which could completely get rid of the need to develop text-to-image prompt-engineering expertise in order to get great results. Instead, you can simply have an iterative back-and-forth conversation with ChatGPT to produce the image of your dreams.

Next up is image-to-text chat in ChatGPT Plus:
• We've known this was coming for a while.
• Works stunningly well in the test I've done so far.
• Today's YouTube version also shows an example of this.

Finally, real-time web search with Bing is now integrated into ChatGPT Plus:
• In my personal (anecdotal tests), this lagged behind Google's Bard.
• Bard is also free, so if real-time web search is what you're after, there doesn't seem to be a reason to pay for ChatGPT Plus. That said, for state-of-the-art general chat plus now image generation and text-to-image chat (per the above), ChatGPT Plus is well worth the price tag.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Five Ways to Use ChatGPT for Data Science

Back in Episode #646, we focused on how anyone can extract commercial value from ChatGPT today — whether ye be a technical data science practitioner or not. In today’s episode, it’s exclusively the technical practitioners’ turn: In today’s episode, I’ve got five specific ways that ChatGPT can be used for data science.

Use case #1 is code generation. While ChatGPT was designed primarily as a tool for generating natural language (while, in contrast, OpenAI’s Codex algorithm was designed explicitly for generating code — you can hear all about it in Episode #584), the friendly, conversational UI of ChatGPT nevertheless comes in handy for rapidly generating code. And it can do so in all of the primary software languages for data science, including Python, R, and SQL. ChatGPT’s code is not always going to be perfect, but for quick ideas on how you could be extracting features from your data, implementing an algorithm, or creating a data visualization, ChatGPT is a great tool for getting started.

Use case #2 is translating code between programming languages. Not only can ChatGPT convert your natural-language input into code, it can also translate between programming languages. So if you, for example, are expert at Python but unfamiliar with an R code snippet you found online that you’d like to understand and implement in Python, you could ask ChatGPT to convert the R code into Python for you. Because ChatGPT has training data from many different programming languages, you can now convert perhaps any unfamiliar code you come across into a familiar target programming language of your choice.

Use case #3 is code troubleshooting. Not only can ChatGPT help you with generating code, you can use it to explain errors that you’re coming across and provide suggestions as to how to fix it. You can even request ChatGPT to rewrite your code for you so that it’s bug-free.

Use case #4 is providing library suggestions. In Python or R, there are countless open-source libraries of code available to you. With ChatGPT, you can now quickly identify which library or libraries are best-suited to a particular task you’d like to perform with your code.

Finally, use case #5 is article summarization. A seemingly endless number of fascinating articles on machine learning innovations are published on ArXiV each week. Poring through each of the articles that interests you is likely to be impossible, but with ChatGPT you can instantly have articles summarized and key information extracted, making it much easier for you to stay on top of the latest data science developments.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Open-Source Tools for Natural Language Processing

In today's episode, the brilliant Vincent Warmerdam regales us with invaluable ideas and open-source software libraries for developing A.I. (particularly Natural Language Processing) applications. Enjoy!

Vincent:
• Is an ML Engineer at Explosion, the German software company that specializes in developer tools for A.I. and NLP such as spaCy and Prodigy.
• Is renowned for several open-source tools of his own, including Doubtlab.
• Is behind an educational platform called Calmcode that has over 600 short and conspicuously enjoyable video tutorials about software engineering concepts.
• Was Co-Founder and Chair of PyData Amsterdam.
• Has delivered countless amusing and insightful PyData talks.
• Holds a Masters in Econometrics and Operations Research from Vrije Universiteit Amsterdam (VU Amsterdam)).

Today’s episode will appeal primarily to technical listeners as it focuses primarily on ideas and open-source software libraries that are indispensible for data scientists, particularly those developing A.I. or NLP applications.

In this episode, Vincent details:
• The prompt recipes he developed to enable OpenAI GPT architectures to perform tremendously helpful NLP tasks such as data labeling.
• The super-popular open-source libraries he’s developed on his own as well as with Explosion.
• The software tools he uses daily including several invaluable open-source packages made by other folks.
• How both linguistics and operations research are extremely useful fields to be a better NLP practitioner and ML practitioner, respectively.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.