AI EDUCATION: What is Generative AI?


In our ongoing series “AI EDUCATION”, we delve into various topics around artificial intelligence to help our readers better understand the sector, because it’s an admittedly complex one. 

In this post, we delve into “generative AI”, a topic that has gotten MASSIVE press coverage at the onset of 2024, and also look at one of the main drivers of the media frenzy, chip-maker Nvidia, and their part in this conversation.  Numerous sources for the content in this post are cited at the end, so you can do additional research on the topic. Enjoy!  

Generative AI – A Brief Overview

Generative AI is a type of artificial intelligence technology that broadly describes machine learning systems capable of generating text, images, code, or other types of content, often in response to a prompt entered by a user. It uses a computing process known as deep learning to analyze patterns in large sets of data and then replicates this to create new data that appears human-generated. 

The origin of Generative AI can be traced back to the mid-1950s when the concepts of artificial intelligence and machine learning were beginning to take shape. Early versions of Generative AI were the Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs). These statistical models were designed to generate new sequences of datasets based on manual input. 

Business Uses of Generative AI

Generative AI has uses across a wide range of business practices, including software development, healthcare, finance, entertainment, customer service, sales and marketing, art, writing, fashion, and product design. Businesses like Walmart are already using GenAI to assist customers. Generative AI can create original content such as text, images, audio, code, and video. In fact, DWN uses generative AI for images and content, especially for our new brand AI & Finance.  We like to adjust text & ordering and add in personal elements to make it feel more “comfortable” for our readers

Separately, there are a myriad of uses for generative AI in finance. This is mainly because the financial sector draws upon enormous amounts of data to function, and therefore it’s a natural fit to take advantage of Generative AI. It can add contextual awareness and human-like decision-making to enterprise and finance workflows, potentially dramatically changing how work is conducted. 

Generative AI’s Recent Popularity

The popularity of generative AI in the past 12 months is due to a trifecta of factors, including advances in deep learning such as generative adversarial networks; much more available data available to train models; and more powerful graphics processing unit computers that help accelerate the training. Generative AI exploded onto the scene in late 2022 when OpenAI, a San Francisco-based tech company, released Dall-E, an image generator, and ChatGPT, an AI chatbot, that allowed anyone to use them to create art or text.

Nvidia’s Role in Generative AI

The recent stock price movement at Nvidia has indeed also influenced the dialogue around generative AI. Nvidia’s stock has rallied by almost 3.3x over the past 12 months to about $790 per share, taking Nvidia’s market cap near the $2 trillion mark. This surge in Nvidia’s stock price is largely attributed to the company’s significant role in the AI sector, particularly in generative AI. 

Nvidia’s GPUs (graphics processing units), which are crucial for training and operating AI systems due to their ability to carry out immense data crunching required for tools like chatbots, have seen a surge in demand. This is especially true with the rise of generative AI platforms like ChatGPT. The company’s strong demand for its products is a clear sign that the technology is thriving, and investors have followed suit. 

The company’s recent earnings also exceeded expectations, with revenues of $22.1bn against expectations of $20.6bn. Nvidia’s CEO, Jensen Huang, stated that the demand for generative AI had reached a “tipping point”. This has led to a positive impact on the company’s earnings results and is expected to have an even greater impact on results this quarter. 

The stock market’s response to Nvidia’s success indicates the financial sector’s recognition of the potential of generative AI. The surge in Nvidia’s stock price and the subsequent dialogue around it underscores the growing importance and influence of generative AI in the tech industry and beyond. However, it’s important to note that while the stock price can reflect market sentiment, it doesn’t necessarily reflect the full potential or limitations of the technology itself. 

In conclusion, Nvidia’s recent stock price movement has brought generative AI into sharper focus, highlighting its potential and influencing discussions around its applications and future developments. However, as with any technology, the dialogue around generative AI will continue to evolve as the technology itself progresses.

Written by DWN Staff & CoPilot

Article Sources (links are included so you can do more research on the topic, if you’d like):

  1. Generative AI Defined: How It Works, Benefits and Dangers – TechRepublic
  2. Generative artificial intelligence – Wikipedia
  3. The history of Generative AI (GenAI) – WeAreBrain
  4. 5 Generative AI Best Practices For Enterprise Businesses
  5. The Impact of Generative AI in Finance | Deloitte US
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  9. Explained: Generative AI | MIT News | Massachusetts Institute of Technology
  10. A Brief History of Generative AI – Medium
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  12. Generative AI: How It Works, History, and Pros and Cons – Investopedia
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  17. Understanding the Ethics of Generative AI in Business
  18. Generative AI in Finance | Deloitte US
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  25. Role of NVIDIA GPUs in Advancing Generative AI
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  27. AMD stock replicates the movement of Nvidia stock before surge
  28. Why Super Micro Computer Stock Is Plummeting Amid Nvidia and SoundHound AI Developments Today
  29. Key Market Movement: Nvidia Earnings Stir Market Excitement, Fed Minutes Awaited, and HSBC Faces Sharp Decline
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