What is generative AI?
Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. Recent breakthroughs in the field have the potential to drastically change the way we approach content creation.
Generative AI systems fall under the broad category of machine learning, and here’s how one such system—ChatGPT—describes what it can do:
Ready to take your creativity to the next level? Look no further than generative AI! This nifty form of machine learning allows computers to generate all sorts of new and exciting content, from music and art to entire virtual worlds. And it’s not just for fun—generative AI has plenty of practical uses too, like creating new product designs and optimizing business processes. So why wait? Unleash the power of generative AI and see what amazing creations you can come up with!
Did anything in that paragraph seem off to you? Maybe not. The grammar is perfect, the tone works, and the narrative flows.
What are ChatGPT and DALL-E?
That’s why ChatGPT—the GPT stands for generative pretrained transformer—is receiving so much attention right now. It’s a free chatbot that can generate an answer to almost any question it’s asked. Developed by OpenAI, and released for testing to the general public in November 2022, it’s already considered the best AI chatbot ever. And it’s popular too: over a million people signed up to use it in just five days. Starry-eyed fans posted examples of the chatbot producing computer code, college-level essays, poems, and even halfway-decent jokes. Others, among the wide range of people who earn their living by creating content, from advertising copywriters to tenured professors, are quaking in their boots.
While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown—as are the risks. But there are some questions we can answer—like how generative AI models are built, what kinds of problems they are best suited to solve, and how they fit into the broader category of machine learning. Read on to get the download.
What’s the difference between machine learning and artificial intelligence?
Artificial intelligence is pretty much just what it sounds like—the practice of getting machines to mimic human intelligence to perform tasks. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites.
Machine learning is a type of artificial intelligence. Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction. The unmanageably huge volume and complexity of data (unmanageable by humans, anyway) that is now being generated has increased the potential of machine learning, as well as the need for it.
What are the main types of machine learning models?
Machine learning is founded on a number of building blocks, starting with classical statistical techniques developed between the 18th and 20th centuries for small data sets. In the 1930s and 1940s, the pioneers of computing—including theoretical mathematician Alan Turing—began working on the basic techniques for machine learning. But these techniques were limited to laboratories until the late 1970s, when scientists first developed computers powerful enough to mount them.
Until recently, machine learning was largely limited to predictive models, used to observe and classify patterns in content. For e