Just a few years ago, the use of AI was limited to working with existing data or automating existing processes to identify patterns and trends or optimize company workflows. And while this was a huge help to many companies and one that was very much appreciated among team leaders worldwide, artificial intelligence’s reach was limited to what already existed. Well, until generative AI was introduced, we mean.
What is generative AI? Well, just as it sounds: it’s a form of artificial intelligence that can generate content on its own, both pulling from existing sources of information and creating completely new ideas. Through the training of generative AI models, these tools learn from data sets, just like other forms of AI.
In this article, we’ll dive into the introduction of generative AI into the tools we use daily, explore its advantages, discuss its more common uses, and give some tips on how to make the most of this powerful technology.
The Origins of Generative AI
Even though it has grown in popularity in recent years, generative AI has been around since the 1960s, but scientists lacked the machine learning knowledge required to take the step towards AI models generating their own, original content. In 2014, however, generative adversarial models were introduced, which completely transformed the use of generative AI and made it a complete reality.
Generative AI works in a very straightforward way: its prompt can take the form of text, video, audio, or an image, and based on the data that the AI tool has been trained on, it analyzes its data and the prompt to produce its output. Different techniques are used for different purposes; natural language processing techniques are used for text generation and variational autoencoders are used for image creation.
Isn’t this just chatbot technology? Well, conversational AI and generative AI are similar, yet differ in a few key ways; conversational AI focuses on achieving a natural, human-like ability to converse with the users, able to adjust for dialect or language preferences. Generative AI, on the other hand, aims to create content without direct user input, using its training data to create content instead of the input of a user.
The benefits of generative AI
As you can see, generative AI is incredibly powerful and has transformed a number of industries (in both positive and negative ways); let’s focus right now on the benefits of using generative AI:
It’s a fast and cost-efficient way to produce content: companies who don’t have the budget or time for in-house content creation can benefit from generative AI tools that can write text, create graphics, or optimize existing texts.
Companies can provide increasingly personalized experiences to their users: we’ve learned that users react positively to personalized user experiences and generative AI can be used to provide personalized and tailored experiences to clients.
Generative AI can help analyze data: the best decisions are backed by data and generative AI tools are able to analyze large data sets in seconds, making recommendations to help advance your company.
The uses of generative AI
Diving into the uses of generative AI is better explained through industry-specific examples that truly outline what generative AI can do in real-life scenarios:
In the financial industry, institutions can better their fraud detection systems by training systems to recognize unique user data, therefore providing better suggestions on how to protect accounts and identify fraud when it does occur.
In the legal industry, lawyers and other professionals can feed contracts or legal documents to a generative AI system and receive suggestions on potential problems or arguments to make.
In the media industry, companies can further spread their content and ideas by using generative AI to market their content or translate/transcribe their text into many different languages to make it more accessible.
It’s clear: generative AI has a wide reach and will continue to impact various industries. Let’s dive now into how to use generative AI
Using Generative AI
The vast majority of generative AI tools are easy to use and have a rather flat learning curve, but if you’re interested in the more technical side of things and want to be on the forefront of innovation in this growing space, learning the intricacies of generative AI is the right first step. And to do so, you can follow these steps.
Familiarize yourself with the basics of machine learning
Generative AI is based on machine learning and to understand how to create the ideal generative AI models for your specific needs, you must have at least a basic understanding of how machine learning models work.
Supervised and unsupervised learning and the process to actually train models is also knowledge that will help you once you get started.
Learn Python
Different AI tools are based on different programming languages, but the majority are backed by Python and taking the time to learn Python and its use within machine learning can help you have even more reach once you start building your own model.
There are many free online courses or videos that can help you learn, or you can choose a more structured course for more hands-on and personalized assistance.
Get your hands dirty with generative AI
The best way to learn is by practicing and trying to work on your own generative AI model. When you start, try to work with existing models to get used to the models and then once you have the confidence, try to create your own model, working slowly but surely to make sure you understand each step.
Network with generative AI professionals
The best way to learn is to interact with generative AI professionals who have experience in both the technical and industry side of things; attend conferences or talks that can help you advance your career and network with more experienced professionals, creating a nurturing relationship that will help your skills improve.
Things to keep in mind with generative AI
Although generative AI seems to be an incredible resource–and it is–there are some concerns to keep in mind as you begin your generative AI journey:
False information: generative AI models have been known to produce false, biased, or otherwise influenced information which can lead to serious problems of misinformation if left undetected.
Plagiarized content: because generative AI models use existing data and content to come up with their outputs, the risk of plagiarized content being used as original content is high.
Fake images of real people: as generative AI models become more advanced, the ability to use real people’s information or images to create fake images increases.
Cyber attacks: generative AI models are becoming increasingly better at impersonating the human voice or image and this can be used to create more realistic and effective cyber attacks.
Ensuring personal safety and copyright laws are respected, all while supporting innovation on the generative AI front, will be the priorities moving forward in both the artificial intelligence field specifically and the entire tech world.
