Types of Generative AI Using Generative AI Research Guides at University of Tennessee Knoxville

Recent extensions have addressed this problem by conditioning each latent variable on the others before it in a chain, but this is computationally inefficient due to the introduced sequential dependencies. For example, business users could explore product marketing imagery using text descriptions. They could further refine these results using simple commands or suggestions. Design tools will seamlessly embed more useful recommendations directly into workflows. Training tools will be able to automatically identify best practices in one part of the organization to help train others more efficiently.

• This article discusses those benefits and how companies can build inclusivity into the design of this technology.
• Jokes aside, generative AI allows computers to abstract the underlying patterns related to the input data so that the model can generate or output new content.
• Semantic web applications could use generative AI to automatically map internal taxonomies describing job skills to different taxonomies on skills training and recruitment sites.

For example, in March 2022, a deep fake video of Ukrainian President Volodymyr Zelensky telling his people to surrender was broadcasted on Ukrainian news that was hacked. Though it could be seen to the naked eye that the video was fake, it got to social media and caused a lot of manipulation. We can enhance images from old movies, upscaling them to 4k and beyond, generating more frames per second (e.g., 60 fps instead of 23), and adding color to black and white movies. Using this approach, you can transform people’s voices or change the style/genre of a piece of music. For example, you can “transfer” a piece of music from a classical to a jazz style. Transformers work through sequence-to-sequence learning where the transformer takes a sequence of tokens, for example, words in a sentence, and predicts the next word in the output sequence.

So understanding the use cases that will deliver the most value to your industry is key

Again, the key proposed advantage is efficiency because generative AI tools can help users reduce the time they spend on certain tasks so they can invest their energy elsewhere. That said, manual oversight and scrutiny of generative AI models remains highly important. One example might be teaching genrative ai a computer program to generate human faces using photos as training data. Over time, the program learns how to simplify the photos of people’s faces into a few important characteristics — such as size and shape of the eyes, nose, mouth, ears and so on — and then use these to create new faces.

Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs. What’s more, the models usually have random elements, which means they can produce a variety of outputs from one input request—making them seem even more lifelike. As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny.

Applications of Generative AI

The MIT Technology Review described Generative AI as one of the most promising advances in the world of AI in the past decade. This means that things like images, music, and code can be generated based only on a text description of what the user wants. Generative AI can be run on a variety of models, which use different mechanisms to train the AI and create outputs. These include generative adversarial networks (GANs), transformers, and Variational AutoEncoders (VAEs). Transformer-based models, such as OpenAI’s GPT (Generative Pre-trained Transformer) series, have revolutionized natural language processing.

Generative AI’s popularity is accompanied by concerns of ethics, misuse, and quality control. Because it is trained on existing sources, including those that are unverified on the internet, generative AI can provide misleading, inaccurate, and fake information. Even when a source is provided, that source might have incorrect information or may be falsely linked.

The readability of the summary, however, comes at the expense of a user being able to vet where the information comes from. There are many generative AI tools that can automate parts of the research process and make long, complex texts easier to decipher. This type of AI often analyses research papers that users upload to extract key information or summarise a paper.

Many results of generative AI are not transparent, so it is hard to determine if, for example, they infringe on copyrights or if there is problem with the original sources from which they draw results. If you don’t know how the AI came to a conclusion, you cannot reason about why it might be wrong. Early implementations of generative AI vividly illustrate its many limitations. Some of the challenges generative AI presents result from the specific approaches used to implement particular use cases. For example, a summary of a complex topic is easier to read than an explanation that includes various sources supporting key points.

We expect this space to evolve rapidly and will continue to roll out our research as that happens. To stay up to date on this topic, register for our email alerts on “artificial intelligence” here. Since the release of ChatGPT in November 2022, it’s been all over the headlines, and businesses are racing to capture its value. Within the technology’s first few months, McKinsey research found that generative AI (gen AI) features stand to add up to $4.4 trillion to the global economy—annually.