ChatGPT Custom Instructions: A Major, Easy Hack for Data Scientists

Thanks to Shaan Khosla for tipping me off to a crazy easy hack to get markedly better results from GPT-4: providing Custom Instructions that prompt the algorithm to iterate upon its own output while critically evaluating and improving it.

Here's Shaan's full Custom Instructions text, which he himself has been iterating on in recent months:

"I need you to help me with a task. To help me with the task, first come up with a detailed outline of how you think you should respond, then critique the ideas in this outline (mention the advantages, disadvantages, and ways it could be improved), then use the original outline and the critiques you made to come up with your best possible solution.

"Overall, your tone should not be overly dramatic. It should be clear, professional, and direct. Don't sound robotic or like you're trying to sell something. You don't need to remind me you're a large language model, get straight to what you need to say to be as helpful as possible. Again, make sure your tone is clear, professional, and direct - not overly like you're trying to sell something."

Try it out! If you haven't used Custom Instructions before, in today's episode I talk you through how to set it up and explain why this approach is so effective. In the video version, I provide a screenshare that makes getting started foolproof.

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

ChatGPT Code Interpreter: 5 Hacks for Data Scientists

The ChatGPT Code Interpreter is surreal: It creates and executes Python code for whatever task you describe, debugs its own runtime errors, displays charts, does file uploads/downloads, and suggests sensible next steps all along the way.

Whether you write code yourself today or not, you can take advantage of GPT-4's stellar natural-language input/output capabilities to interact with the Code Interpreter. The mind-blowing experience is equivalent to having an expert data analyst, data scientist or software developer with you to instantaneously respond to your questions or requests.

As an example of these jaw-dropping capabilities (and given the data science-focused theme of my show), I use today's episode demonstrate the ChatGPT Code Interpreter's full automation of data analysis and machine learning. If you watch the episode on YouTube, you can even see the Code Interpreter hands-on in action while I interact with it solely with natural language.

Over the course of today's episode/video, the Code Interpreter:
1. Receives a sample data file that I provide it.
2. Uses natural language to describe all of the variables that are in the file.
3. Performs a four-step Exploratory Data Analysis (EDA), including histograms, scatterplots that compare key variables and key summary statistics (all explained in natural language).
4. Preprocesses all of my variables for machine learning.
5. Selects an appropriate baseline ML model, trains it and quantitatively evaluates its performance.
6. Suggests alternative models and approaches (e.g., grid search) to get even better performance and then automatically carries these out.
7. Optionally provides Python code every step of the way and is delighted to answer any questions I have about the code.

The whole process is a ton of fun and, again, requires no coding abilities to use (the "Code Interpreter" moniker could be misleadingly intimidating to non-coding folks). Even as an experienced data scientist, however, I would estimate that in many everyday situations use of the Code Interpreter could decrease my development time by a crazy 90% or more.

The big caveat with all of this is whether you're comfortable sharing your code with OpenAI. I wouldn't provide proprietary company code to it without clearing it with your firm first and — if you do use proprietary code with it — turn "Chat history & training" off in your ChatGPT Plus settings. To circumnavigate the data-privacy issue entirely, you could alternatively try Meta's newly-released "Code Llama — Instruct 34B" Large Language Model on your own infrastructure. Code Llama won't, however, be as good as the Code Interpreter in many circumstances and will require some technical savvy to get it up and running.

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

Open-source “ChatGPT”: Alpaca, Vicuña, GPT4All-J, and Dolly 2.0

Want a GPT-4-style model on your own hardware and fine-tuned to your proprietary language-generation tasks? Today's episode covers the key open-source models (Alpaca, Vicuña, GPT4All-J, and Dolly 2.0) for doing this cheaply on a single GPU 🤯

We begin with a retrospective look at Meta AI's LLaMA model, which was introduced in episode #670. LLaMA, with its 13 billion parameters, achieves performance comparable to GPT-3 while being significantly smaller and more manageable. This efficiency makes it possible to train the model on a single GPU, democratizing access to advanced AI capabilities.

The focus then shifts to four models that surpass LLaMA in terms of power and sophistication: Alpaca, Vicuña, GPT4All-J, and Dolly 2.0. Each of these models presents a unique blend of innovation and practicality, pushing the boundaries of what's possible with AI:

Alpaca

Developed by Stanford researchers, Alpaca is an evolution of the 7 billion parameter LLaMA model, fine-tuned with 52,000 examples of instruction-following natural language. This model excels in mimicking GPT-3.5's instruction-following capabilities, offering high performance at a fraction of the cost and size.

Vicuña

Vicuña, a product of collaborative research across multiple institutions, builds on both the 7 billion and 13 billion parameter LLaMA models. It's fine-tuned on 70,000 user-shared ChatGPT conversations from the ShareGPT repository, achieving GPT-3.5-like performance with unique user-generated content.

GPT4All-J

GPT4All-J, released by Nomic AI, is based on EleutherAI's open source 6 billion parameter GPT-J model. It's fine-tuned with an extensive 800,000 instruction-response dataset, making it an attractive option for commercial applications due to its open-source nature and Apache license.

Dolly 2.0

Dolly 2.0, from database giant Databricks, builds upon EleutherAI's 12 billion parameter model. It's fine-tuned with 15,000 human-generated instruction response pairs, offering another open source, commercially viable option for AI applications.

These models represent a significant shift in the AI landscape, making it economically feasible for individuals and small teams to train and deploy powerful language models. With a few hundred to a few thousand dollars, it's now possible to create proprietary, ChatGPT-like models tailored to specific use cases.

The advancements in AI models that can be trained on a single GPU mark a thrilling era in data science. These developments not only showcase the rapid progression of AI technology but also significantly lower the barrier to entry, allowing a broader range of users to explore and innovate in the field of artificial intelligence.

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

GPT-4: Apocalyptic stepping stone?

The final episode in our trilogy on GPT-4 is on the risks posed by the model today and the potentially existential risks posed by the models it paves the way for. Our guest for this is Jeremie Harris, a world leader on A.I. safety.

Jeremie:
• Is co-founder of Gladstone AI, an advisor to US and Canadian government entities on A.I. risk.
• Co-hosts the "Last Week in A.I.", the premier podcast on ML news.
• Wrote the new (released this week!) book "Quantum Physics Made Me Do It" that covers human consciousness and speculates on the future of A.I.
• Co-founded SharpestMinds, a Y Combinator-backed A.I.-career mentorship platform.

In today's episode, Jeremie details:
• How GPT-4 is a “dual-use technology” — capable of tremendous good but it can also be wielded malevolently.
• How RLHF — reinforcement learning from human feedback — has made GPT-4 outputs markedly more aligned with the outputs humans would like to see, but how this doesn’t necessarily mean we’re in the clear with respect to A.I. acting in the broader interest of humans.
• Emerging approaches for how we might ensure A.I. is aligned with humans, not only today but — critically — as machines overtake human intelligence, the “singularity” event that may occur in the coming decades, or even in the coming years.

The SuperDataScience GPT-4 trilogy is comprised of:
• #666 (last Friday): a ten-minute GPT-4 overview by yours truly.
• #667 (Tuesday): world-leading A.I. monetization expert Vin Vashishta on the unprecedented commercial opportunity of GPT-4.
• #668 (today): GPT-4 risks

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

Harnessing GPT-4 for your Commercial Advantage

Episode two in our trilogy on GPT-4 is dedicated to how you can leverage GPT-4 to your commercial benefit. In it, I'm joined by Vin Vashishta — perhaps the best person on the planet for covering A.I. monetization.

Vin:
• Is Founder of V Squared, a consultancy that specializes in monetizing machine learning by helping Fortune 100 companies with A.I. strategy.
• Is the creator of a four-hour course on “GPT Monetization Strategy” which teaches how to build new A.I. products, startups, and business models with GPT models like ChatGPT and GPT-4.
• Is author of the forthcoming book “From Data To Profit: How Businesses Leverage Data to Grow Their Top and Bottom Lines”, which will be published by Wiley.

Today’s episode will be broadly appealing to anyone who’d like to drive commercial value with the powerful GPT-4 model that is taking the world by storm.

In this episode, Vin details:
• What makes GPT-4 so much more commercially useful than any previous A.I. model.
• The levels of A.I. capability that have been unleashed by GPT-4 and how we can automate or augment specific types of human tasks with these new capabilities.
• The characteristics that enable individuals and organizations to best take advantage of foundation models like GPT-4 enabling them overtake their competitors commercially.

The SuperDataScience GPT-4 trilogy is comprised of:
• #666 (last Friday): a ten-minute GPT-4 overview by yours truly.
• #667 (today): GPT-4 commercial opportunities.
• #668 (this Friday): world-leading A.I.-safety expert Jeremie Harris joins me to detail the (existential!) risks of GPT-4 and the models it paves the way for.

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

GPT-4 Has Arrived

SuperDataScience episode #666 — appropriate for an algorithm that has folks (quixotically) signing a letter to pause all A.I. development. In this first episode of the GPT-4 trilogy; in ten minutes, I introduces GPT-4's staggering capabilities.

A Leap in AI Safety and Accuracy

GPT-4 marks a significant advance over its predecessor, GPT-3.5, in terms of both safety and factual accuracy. It is reportedly 82% less likely to respond with disallowed content and 40% more likely to produce factually correct responses. Despite improvements, challenges like sociodemographic biases and hallucinations persist, although they are considerably reduced.

Academic and Professional Exam Performance

The prowess of GPT-4 becomes evident when revisiting queries initially tested on GPT-3.5. Its ability to summarize complex academic content accurately and its human-like response quality are striking. In one test, GPT-4’s output was mistaken for human writing by GPTZero, an AI detection tool, underscoring its sophistication. In another test, the uniform bar exam, GPT-4 scored in the 90th percentile, a massive leap from GPT-3.5's 10th percentile.

Multimodality

GPT-4 introduces multimodality, handling both language and visual inputs. This capability allows for innovative interactions, like recipe suggestions based on fridge contents or transforming drawings into functional websites. This visual aptitude notably boosted its performance in exams like the Biology Olympiad, where GPT-4 scored in the 99th percentile.

The model also demonstrates proficiency in numerous languages, including low-resource ones, outperforming other major models in most languages tested. This linguistic versatility extends to its translation capabilities between these languages.

The Secret Behind GPT-4’s Success

While OpenAI has not disclosed the exact number of model parameters in GPT-4, it's speculated that they significantly exceed GPT-3's 175 billion. This increase, coupled with more and better-curated training data, and the ability to handle vastly more context (up to 32,000 tokens), are likely contributors to GPT-4's enhanced performance.

Reinforcement Learning from Human Feedback (RLHF)

GPT-4 incorporates RLHF, a method that refines its output based on user feedback, allowing it to align more closely with desired responses. This approach has already proven effective in previous models like InstructGPT.

GPT-4 represents a monumental step in AI development, balancing unprecedented capabilities with improved safety measures. Its impact is far-reaching, offering new possibilities in various fields and highlighting the importance of responsible AI development and use. As we continue to explore its potential, the conversation around AI safety and ethics becomes increasingly vital.
The SuperDataScience GPT-4 trilogy is comprised of:
• #666 (today): an introductory overview by yours truly
• #667 (Tuesday): world-leading A.I.-monetization expert Vin Vashishta joins me to detail how you can leverage GPT-4 to your commercial advantage
• #668 (next Friday): world-leading A.I.-safety expert Jeremie Harris joins me to detail the (existential!) risks of GPT-4 and the models it paves the way for

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

NLP with GPT Architectures (ChatGPT, GPT-4, and other LLMs)

Large Language Models have revolutionized the field of Natural Language Processing, powering mind blowing tools like ChatGPT and GPT-4. Today, we released the recording of a half-day conference I hosted on the topic.

In partnership with my publisher Pearson, the "A.I. Catalyst" conference was held earlier this month in the O'Reilly Media platform. It has now been cleaned up and released for anyone to view as a standalone three-hour video. In it, we cover the full Large Language Model (LLM) lifecycle from development to deployment.

The presenters are at the absolute vanguard on their topics:
• Sinan Ozdemir: The A.I. entrepreneur and author introduces the theory behind Transformer Architectures and LLMs like BERT, GPT, and T5.
• Melanie Subbiah: A first author on the original GPT-3 paper, Melanie leads interactive demos of the broad range of LLM capabilities.
• Shaan Khosla: A data scientist on my team at Nebula.io, he details practical tips on training, validating, and productionizing LLMs.

If you don't have access to the O'Reilly online platform through your employer or school, you can use my special code "SDSPOD23" to get a 30-day trial and enjoy the video for free!

Check it out here: learning.oreilly.com/videos/catalyst-conference-nlp/9780138224912/

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.