Automated Journalism is Here:
Meet the AI-Driven Podcast Taking on the Stock Market

In a world where news travels at lightning speed and the stock market can be unforgiving, one startup is changing the game with an AI-powered podcast that delivers real-time, full-coverage analysis of every major event in the market.

The RowSheet Podcast is a brand new venture that has been around for less than two months, but it's already making waves in the industry. What sets it apart is that it's 100% automated and AI-generated, with no human involvement whatsoever.

Created and run by a 28-year-old entrepreneur, the RowSheet Podcast uses advanced machine learning techniques to ingest information from internet forums like Y Combinator and /r/wallstreetbets, as well as social media platforms like Twitter. The podcast can be generated from start to finish in less than three minutes and is heavily leveraged by OpenAI's GPT-3, an advanced language model.

The company behind the RowSheet Podcast is an AI-focused startup that's building a large database of scraped data to train its neural network. The goal is to develop its own large language model that can analyze vast amounts of market data and make accurate predictions. The podcast is just a demonstration of the company's analytical capabilities.

While the RowSheet Podcast is targeted towards day traders, the company's long-term goal is to consult with finance companies and provide valuable insights into the stock market. To achieve this, the company is seeking funding to further develop its technology and expand its offerings.

Despite its newness, the RowSheet Podcast is already making an impact, generating income from advertising. As the company continues to grow and innovate, it's clear that automated journalism is not just a possibility, but a reality. The future of news may be more AI-driven than we ever imagined.

Our Methodlogies:

First, we begin with raw data, meaning we start with raw RSS feeds and HTML pages published at the correct timestamp range, which we parse out the text content separated from other content that might be on the page such as advertisements and other info. To achieve this, we use web scraping techniques to collect data from various sources, such as news outlets and online news aggregators.

Once we have the raw data, we use Named Entity Recognition (NER) and Sentiment Analysis to extract key information from the text. NER helps us to identify and categorize named entities such as people, organizations, and locations in the collected data. Sentiment analysis helps us to extract subjective information from the collected data such as opinions, attitudes, and emotions. This helps us to identify the most critical news events in the last 90 minutes, which we use as the basis for our podcast.

Next, we use text classification and topic modeling to further refine the data. Text classification helps us to automatically categorize the collected data based on its content. Topic modeling helps us to discover abstract topics that occur in the collected data. These techniques help us to filter out irrelevant or duplicate information and focus on the most relevant news events.

Once we have identified the critical news events, we use language generation techniques to generate a concise transcript of the events. Language generation is a technique that uses algorithms and machine learning to generate natural language text. We use this technique to create a transcript of the most critical news events in the last 90 minutes.

Finally, we use hyper-realistic AI-powered text-to-speech to generate a human-like voiceover for the transcript. We use advanced text-to-speech technology that leverages deep learning and neural networks to create a voice that sounds natural and realistic. This helps us to provide a high-quality listening experience for our audience.

In summary, our process involves collecting raw data using web scraping techniques, using NER and sentiment analysis to extract key information, using text classification and topic modeling to refine the data, using language generation to create a concise transcript of the news events, and using hyper-realistic AI-powered text-to-speech to generate a human-like voiceover for the transcript. This allows us to provide our listeners with a 5-minute podcast that delivers real-time, full-coverage analysis of every event from every major outlet, all in one place.