GenAI, or Generative AI has been all over everyone’s Twitter and LinkedIn feeds for months now. The results of generative AI have been funny, beautiful, and useful. While there have been many think pieces and social media posts about generative AI, ChatGPT, and large language models–both for and against–the technology will certainly continue to disrupt and change how we work and how we live. Regardless of your opinion of GenAI, it is something everyone should be learning about. So what is it? How can you leverage it for your team, and what are the risks? In this article, we’ll answer some of your burning questions.
What is GenAI (Generative AI)?
GenAI, short for Generative AI, refers to a category of artificial intelligence algorithms, models, and systems (like GPT-4 and DALL-E) designed to generate new and original data that resembles existing data. Generative AI is powered by large machine learning models, some of which are referred to as large language models (LLMs). Unlike traditional AI models that are typically used for specific tasks, such as prediction or classification, generative AI is focused on creating new content, whether it be images, text, audio, videos, or other types of data. Many of today’s popular models, like GPT, BERT, and LaMDa, are based on Transformer models.
In order to create or generate new data that is plausible and mimics existing data, the models must be trained on existing data, and fine-tuned. There are a variety of techniques and data to pre-train and train GenAI models. For example, the model behind ChatGPT, one of OpenAI’s most viral creations to date, was trained using Reinforcement Learning from Human Feedback (RLHF). When Google trained its LaMDa model, given its focus on producing conversation, the model was trained on dialogue.
If you want to learn more about large language models, specifically, or generative pre-trained (GPT) models, check out our blog.
GenAI Use-Cases
Generative AI has the potential to revolutionize creative industries, minimize repetitive tasks for data and machine learning teams, and aid in other applications where the ability to produce realistic and novel content is valuable. However, it also brings challenges related to ethics, as it can be used to create fake content that might be used to spread misinformation or deceive people.
Data science & analytics
The data domain is an exciting place for GenAI. In order to extract meaningful insights and glean relevant stories from messy, raw data, data teams must perform a variety of time-consuming technical tasks. Generative AI can help to greatly speed up the tedious parts of the process such as processing text data, cleaning data for particular ML models, producing functional charts during the exploratory data analysis (EDA) phase, and so much more. Part of this involves using generative AI to create code so that data teams don’t get bogged down looking through Stack Overflow for specific bug fixes. But the AI models have to be tailored for these domain specific tasks.
Einblick: an AI-native data notebook
Einblick is the AI-native data notebook that can write and fix code, create beautiful charts, build and fine-tune models, and much more. Over the past year, the Einblick team has developed a powerful reasoning agent called Einblick Prompt, which is able to build out entire data workflows in as little as one sentence. Powered by OpenAI and LangChain, Prompt is context-aware and generates code directly into every workspace. No more copy-pasting code from one app to another, and no more typing out paragraphs of detail to provide the LLM needed context.
Einblick users can test and edit code immediately, connect to various databases, and compare results. In Einblick, users are able to build out multiple data workflows side-by-side, and collaborate easily with teammates and stakeholders.
Text generation
Generative AI can be used to create different kinds of text, from paragraphs or essays to funny raps or even boilerplate emails and landing pages. The application of text generation can help users speed up administrative tasks, but also can help them change the tone of their writing, shorten or lengthen existing content, and more. Tons of apps and companies have come out with general AI assistants that leverage the newest technology to automate text-based tasks. Popular apps that are focused on using generative AI for text generation include, of course, OpenAI’s ChatGPT and Jasper AI.
Image creation & editing
Although much of the conversation around generative AI has been within the context of large language models, generative AI can create a lot more than just language. Products like Midjourney and DALL-E are creating impressive AI-generated images that mimic different time periods or art styles. Another popular application of image creation has been creating professional-looking headshots for LinkedIn and portfolio sites.
Code generation
Given the incredible ability of new large language models to create convincing bodies of text, it is no surprise that generative AI can be used to generate functional code. As with any other output of genAI, you’ll have to do your own testing to ensure the code does what you want it to. But we’ve found that these new large language models with some additional training and fine-tuning, can be great code assistants, so that programmers no longer have to spend so much time digging through documentation or figuring out the specificities of tricky syntax. For example, GitHub Copilot bills itself as “Your AI pair programmer.”
Using GenAI Responsibly
While generative AI has and certainly will continue to shake up various industries, it will become more important than ever to fact-check new content. Not everyone will do their due diligence before they hit publish on written content an AI has generated, and some people will actively create and spread fake photos, videos, and audio for various reasons. It will become increasingly important to be certain of the authenticity and accuracy of content online before sharing and posting.
In addition, when you use any machine learning model, you typically will feed it some additional data or information (see prompt engineering), whether its context about your problem, an example text or image, or something else, in order for the model to generate good results. If using a third-party’s model, you need to be careful with regard to what data they are sharing, what data they are storing, and what data they may use in the future. If not, you could put yourself, your team, or your company at risk.
However, all this means that humans will inevitably remain a key part of the GenAI pipeline. Humans-in-the-loop will remain an important part of creating new data, even if that process now involves generative AI. Yes, the technology is powerful. No, it will not replace humans.
Key Takeaways
- Generative AI is a category of AI or artificial intelligence that is designed to create new data based on existing data.
- Generative AI is powered by big machine learning models, including large language models.
- GPT-4, BERT, and LaMDa are examples of large language models.
- Generative AI has many important applications, from data science and analytics to code generation, image creation & editing, and text generation.
- The landscape of many industries will change as a result of breakthroughs in GenAI. Learn about the tech so you can better support you, your team, and your company.
About
Einblick is an AI-native data science platform that provides data teams with an agile workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.