Highlights💡 

GPT-4's Secret Has Been Revealed [Link]

Unraveling OpenAI's masterful ploy

GPT-4 was the most anticipated AI model in history.

Yet when OpenAI released it in March, they didn’t tell us anything about its size, data, internal structure, or how they trained and built it. A true black box.

As it turns out, they didn’t conceal those critical details because the model was too innovative or the architecture too moat-y to share. The opposite seems to be true if we’re to believe the latest rumors:

GPT-4 is, technically and scientifically speaking, hardly a breakthrough.

That’s not necessarily bad—GPT-4 is, after all, the best language model in existence—just… somewhat underwhelming. Not what people were expecting after a 3-year wait.

This news, yet to be officially confirmed, reveals key insights about GPT-4 and OpenAI and raises questions about AI’s true state-of-the-art—and its future.

DeepMind claims its next chatbot will rival ChatGPT [Link]

ChatGPT might’ve captured the world’s attention. But DeepMind, the Google-owned research lab, claims that its next large language model will rival — or even best — OpenAI’s.

According to a piece in Wired, DeepMind is using techniques from AlphaGo, DeepMind’s AI system that was the first to defeat a professional human player in the board game Go, to make a ChatGPT-rivaling chatbot called Gemini.

If all goes according to plan, Gemini will have the ability to plan or solve problems as well as analyze text, DeepMind CEO Demis Hassabis told Wired’s, Will Knight.

“At a high level, you can think of Gemini as combining some of the strengths of AlphaGo-type systems with the amazing language capabilities of the large models,” Hassabis said. “We also have some new innovations that are going to be pretty interesting.”

Hope, fear, and AI [Link]

We polled 2,000 people about how they’re using AI, what they want it to do, and what scares them about it the most.

AI is about to change the world — the problem is, no one's quite sure how. Some look at the past year’s rapid progress and see opportunities to remove creative constraints, automate rote work, and discover new ways to learn and teach. Others see how this tech can disrupt our lives in more damaging ways: how it can generate misinformation, destroy or diminish jobs, and, if left unchecked, pose a serious threat to our safety.

Tech leaders, lawmakers, and researchers have all been weighing in on how we should handle this emerging tech. Some industry figures, like OpenAI CEO Sam Altman, want AI giants to steer regulation, shifting the focus to perceived future threats, including the “risk of extinction.” Others, like EU politicians, are more concerned with current dangers and banning dangerous use cases (while holding back positive applications, say skeptics). Meanwhile, many small artists would just like a guarantee that they won’t be replaced by machines.

The people paid to train AI are outsourcing their work… to AI [Link]

It’s a practice that could introduce further errors into already error-prone models.

It takes an incredible amount of data to train AI systems to perform specific tasks accurately and reliably. Many companies pay gig workers on platforms like Mechanical Turk to complete tasks that are typically hard to automate, such as solving CAPTCHAs, labeling data and annotating text. This data is then fed into AI models to train them. The workers are poorly paid and are often expected to complete lots of tasks very quickly.

No wonder some of them may be turning to tools like ChatGPT to maximize their earning potential. But how many? To find out, a team of researchers from the Swiss Federal Institute of Technology (EPFL) hired 44 people on the gig work platform Amazon Mechanical Turk to summarize 16 extracts from medical research papers. Then they analyzed their responses using an AI model they’d trained themselves that looks for telltale signals of ChatGPT output, such as lack of variety in choice of words. They also extracted the workers’ keystrokes in a bid to work out whether they’d copied and pasted their answers, an indicator that they’d generated their responses elsewhere.

DeepLearning dropped a new course: ChatGPT Prompt Engineering for Developers [Link]

In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. Using the OpenAI API, you’ll be able to quickly build capabilities that learn to innovate and create value in ways that were cost-prohibitive, highly technical, or simply impossible before now. This short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) will describe how LLMs work, provide best practices for prompt engineering, and show how LLM APIs can be used in applications for a variety of tasks, including:

• Summarizing (e.g., summarizing user reviews for brevity)

• Inferring (e.g., sentiment classification, topic extraction)

• Transforming text (e.g., translation, spelling & grammar correction)

• Expanding (e.g., automatically writing emails)

In addition, you’ll learn two key principles for writing effective prompts, how to systematically engineer good prompts and also learn to build a custom chatbot. All concepts are illustrated with numerous examples, which you can play with directly in our Jupyter Notebook environment to get hands-on experience with prompt engineering.

Tools & Links 🛠️

Empower Your AI Journey: Key Resources, Software, and Innovations

Unclassified 🌀 

• Morning Brew - Get smarter in 5 minutes (it's free) [Link]

• Fiverr - Find the right freelance service, right away [Link]

• WFH.Team - Work from anywhere in the world [Link]

• Add your link here ➜

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