• 00:00 🤖 GPT-4 proved faster and cheaper than human data analysts, making it a compelling option.
• 01:35 📊 The study evaluated GPT-4 and human analysts based on data collection, visualization, and analysis, setting a framework for comparison.
• 03:56 💻 GPT-4 successfully executed tasks like database querying and visualization, demonstrating its potential as a data analysis tool.
• 05:08 📚 Incorporating online information via Google search aided GPT-4's analysis, mimicking human analysts' domain knowledge.
• 07:54 💰 GPT-4 significantly outperformed human analysts in cost efficiency, with a cost per instance remarkably lower than human counterparts.
• 08:50 ⏱️ GPT-4 excelled in speed, completing tasks faster than human analysts, contributing to its cost-effectiveness.
• 09:19 ✅ While senior data analysts outperformed GPT-4 in accuracy, GPT-4 performed comparably to junior data analysts, indicating promising potential.

• 00:29 🤖 AI technology, including ChatGPT, faces legal challenges, with notable authors like George R.R. Martin suing OpenAI for alleged unauthorized use of their works.

• 02:25 🎨 Generative AI, such as DALL-E 2, can rapidly create art, potentially bypassing the traditional creative process, leading to concerns about the originality of the generated works.

• 03:21 💰 AI programs like ChatGPT are generating significant revenue, but concerns arise as they use existing works without proper consent, credit, or payment to the original artists.

• 04:19 📘 The speaker, Sarah Silverman, shares a personal experience of her book being used to train ChatGPT without her permission, highlighting the issue of AI companies using artists' work without proper acknowledgment.

• 06:15 🛣️ The speaker advocates for establishing rules and regulations to ensure fair compensation for artists whose work is utilized by AI, drawing parallels with historical examples of regulating technology in the music industry.

Rules are for lackeys; context is for Kings.

Certainly! The quote "Rules are for lackeys, context is for kings" reflects Captain Gabriel Lorca's viewpoint on leadership and decision-making. Here's a breakdown of its meaning:

"Rules are for lackeys": This phrase suggests that rules are meant for subordinate individuals or followers who blindly adhere to them without considering the broader context. It implies that blindly following rules without considering the situation at hand can limit one's ability to make effective decisions.

"Context is for kings": Here, Captain Lorca emphasizes the importance of understanding the context surrounding a situation. He implies that leaders, symbolized by "kings," should prioritize understanding the broader context in which rules exist. By doing so, leaders can make informed decisions that take into account various factors, such as the specific circumstances, goals, and potential consequences.

In essence, Captain Lorca's quote suggests that while rules have their place, true leadership requires considering the context in order to navigate complex situations effectively. It encourages leaders to go beyond rigid adherence to rules and regulations, recognizing that each situation may demand a nuanced understanding to achieve the best outcomes.

Interesting list of jobs and industries that could be affected/are being affected by the possibilities of ChatGPT and LLMs.

Hmm...kinda poor summary from ChatGPT 3.5...

• 🎮 A superhuman AI named Gobot won against the world champion in the board game Go in 2016.
• 🧑‍🤝‍🧑 In January 2023, AI researchers allowed an amateur human to beat one of these superhuman Go AI bots named Catego, with a nearly 100% win rate.
• 🤖 There are three main categories of artificial intelligence: Narrow AI, General AI or AGI, and superintelligence.
• 🤔 AI research today is focused on making AI better than narrow but not quite general intelligence, and far from sci-fi level AI.
• 🌐 Large language models, like Chat GPT, have been the subject of news stories almost every day, overshadowing the recent news that a human finally bested the best AI Go bot named Catego.
• 🤯 The flaw that allowed a human to beat Catego applies to all of today's most widely used AI systems, especially Chat GPT.
• 🔍 The fundamental problem is that we don't actually know how these systems work on the inside because that's how we design them.

The web page discusses the Harper.ai plugin, which allows users to use their chat jibiri to browse and extract data from documents, summarize web pages and YouTube videos, generate outlines, create comparison tables, and extract data in table format. Users can also select different models to use, depending on their needs. The plugin has a wide range of applications, and the possibilities are endless.

HARPA AI is a Chrome extension for web automation that includes AI-powered features such as summarizing and rewriting content, extracting and monitoring web pages and data, and integrating with Google Search to provide AI-assisted search results. It also includes an AI writer, page-aware GPT prompts, and the ability to track competitor websites. The tool can be used for various purposes such as marketing, copywriting, productivity, HR, product development, and spreadsheets. HARPA AI uses a hybrid AI engine built on top of ChatGPT and its own machine learning to automate tasks on the web. The tool is safe and does not store data on remote servers or show ads, and requests made to ChatGPT from HARPA AI are sent to OpenAI servers for processing.

The CEO of OpenAI, Sam Altman, has confirmed that GPT-5 is not in training and will not be for some time. This is in response to an open letter calling for a pause on the training of AI systems more powerful than GPT-4. Altman emphasizes the importance of safety issues and caution in releasing GPT-5, as emergent abilities can arise spontaneously from the interactions and complexities within a system. OpenAI is testing and making GPT-4 even safer, building it on years of alignment research that they pursued in anticipation of models like GPT-4. Altman also agrees that OpenAI should release its alignment data set and alignment evaluations to other companies to ensure AI safety.

The article discusses prompt engineering, which is the process of creating inputs that determine the output generated by an AI language model. High-quality prompts can result in better output while poorly defined prompts can lead to inaccurate or negative responses. The article explains that AI language models rely on deep learning algorithms and natural language processing (NLP) to fully understand human language. There are two main learning methods for language models: supervised and unsupervised learning. The article also covers prompt categories, which include information-seeking prompts and instruction-based prompts