Generative AI is transforming mundane tasks into creative opportunities. How is it reshaping the landscape of personal and professional creativity?
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Generative AI refers to artificial intelligence technologies that create new content, ranging from text and images to code, by learning patterns from existing data.
In the ever-evolving landscape of technological advancements, few topics have garnered as much attention and anticipation as generative artificial intelligence (AI). But what exactly is generative AI, why is it important and what can it do for a modern enterprise that is so transformative?
These are the burning questions that a lot of people are asking, so to begin with we need to have a better understanding of what exactly generative AI is and what it brings to the table. What are the tools needed to enable applications and application developers to build solutions with native generative AI functionality and how does data, specifically contextual data, play a vital role in bringing generative AI to the application?
So before we go and dive into the deep end of all things AI and machine learning, let’s wade into the shallow end first and look at what generative AI really is and some of the basic concepts that power and drive generative AI applications.
So how is generative AI different from traditional predictive AI? Probably the biggest difference between generative AI and traditional AI is that where traditional AI is designed and built around operating within a set of defined rules, generative AI creates new content from existing data by associating patterns with that existing data, and learns from those patterns to “generate” or create new content based on trained data as well as new data as it is created and added to the process.
Let’s look at an example, traditional AI models have been defined around predicting an outcome. Traditional AI is deterministic; it has a set of rules that are defined and it will tell you the outcome of something based on those rules. Traditional AI has been typically used in predictive analytics scenarios. When there is a bunch of data that can be used to define the rules under which decisions can be made. Things like predicting weather patterns and systems, when these conditions are met, these outcomes typically follow.
Generative AI on the other hand is designed to use data to create new outputs. It is designed to take data into models and use that data to grow experience and use that experience to provide outputs that match what is being asked. Things like natural language processing chatbots that when asked a question can respond with not only the appropriate answer but can use the “experience” of other conversations to know that the question has context, and that other people who have also asked the same question have follow up questions on other topics. A generative AI system leverages this learning to augment the answers it provides by adding more or less context based on experience, generating new outputs and outcomes.
So if you look at the different applications, traditional predictive AI can read a restaurant review and can tell you the sentiment of it, generative AI on the other hand can write the review for you. Predictive AI can tell you if the image supplied is that of a cat or a dog, whereas generative AI can create a completely new image of a cat or dog for you.
It is this ability to provide cognitive thought to a question and to generate and discriminate (General Adversarial Network) multiple different answers in the learning process that opens up so much potential for generative AI. Generative AI can take examples from all the great renaissance artists and create a picture based on that style. Generative AI can take an office memo for an upcoming company picnic and rewrite it in the style of a swashbuckling pirate. Or more importantly, generative AI can take samples of data across large volumes of medical trials and create synthetic data for research and development purposes, proposing new alternatives to solving medical problems.
What generative AI brings to the table is the ability to simulate, test, and validate ideas and concepts too rapidly generate new content. With generative AI we are seeing the beginnings of the amazing science fiction we have grown up with on shows like Star Trek where an engineer could run 100s of simulations to test new ideas before ever putting somebody else in harm's way.
But how does all of this work, how does a generative AI model recognize patterns, analyze those patterns and ultimately generate new content based on those patterns?
The first layer that has to be addressed is data collection, without data AI models don’t have any experience. As Julius Caesar said “Experience is the teacher of all things”, but with AI there is no ability to gain experience organically so the only way it can gain more than just learning, but gain experience, is through data. It is this data that is at the foundation of generative AI’s experience building process.
The second layer to gaining experience is through the modeling process and in generative AI there are multiple machine learning models used, and one of the more common processes is through a generative adversarial network, or GAN. The models used for machine learning are described in more detail below, however the goal of all of the machine learning models is to allow for the process to learn. In a GAN there are two processes that are pitted against each other that allow for the model to learn and grow. A generator that takes the input and uses that input to create new content based on the trained data available, and the discriminator that evaluates the output from the generator and compares it in closeness similarity to the real data used to generate the new output.
It is through these learning processes and models that generative AI is able to create new content and new ideas around things like image generation or natural language processing. Now there is a lot of complexity involved in how these systems learn and the models used, just like teaching different subjects in school requires different techniques. Teaching and art class for example is very different compared to teaching a physics class. Just like training an generative AI to create images requires different approaches compared to training it to generate the shortest possible path for delivering packages.
While Generative adversarial networks are one of the ways generative AI models learn, it isn’t the only way. There are multiple approaches to building models for AI and each has different benefits and applications.
A Generative adversarial network is probably one of the easiest approaches to visualize and it is all built around the concept of outputs and criticism. The primary goal of a GAN is to provide a process for output, feedback, and refinement that can be done 100s/1000s/1000000s of times in quick succession, allowing for the AI generator to hone in on exactly what the expected output should be. A GAN has to take into account trade-offs, what outputs are generated with an overzealous generator that is adding or removing critical details. Or what is the impact on the output results and performance from a highly critical discriminator.
A good discriminator in our example GAN setup needs to be able to quickly and easily be able to determine which of these images are images is of a Chihuahua, and if our generator is design to output images of a Chihuahua then it needs to produce images of Chihuahua’s that are not ambiguous enough to be mistaken for blueberry muffins.
The fundamental goal of a GAN is to train the generator to get so good that the content it generates “tricks” the discriminator into thinking that the content it created is the original content used by the discriminator to train itself with. So in our example above we would train our discriminator with millions of known images of Chihuahua’s so that it could determine which of the images in our above picture is a blueberry muffin and which is a Chihuahua.
The generator is asked then to create images of a Chihuahua and these images are fed into the discriminator. The discriminator then makes a decision on if it looks like a Chihuahua or more like a blueberry muffin. If it looks like a Chihuahua the generator passes if it looks more like a blueberry muffin then generator fails and the discriminator tells it to refine the generation to be nearer to a Chihuahua.
Within a GAN the generator and the discriminator work together to constantly refine the content the generator is creating. The ultimate goal for the generator is to create content that the discriminator cannot determine is any different from the original content used to train the discriminator with, ultimately the goal is for the generator to create content that a human discriminator can not determine is any different than content created by a human creator, can you guess which image was generated by AI?
The first image is the AI Image, the second is real and is attributed to Image by wirestock on Freepik.
To explain variational autoencoders let's look at a similar but slightly different example and implementation. Let’s say that instead of trying to determine the difference between Chihuahuas versus blueberry muffins we want an AI model to scan a manufacturing line and determine that each component is made to specification. For this example we are comparing the output of the manufacturing line to a known example of a given component, we want to validate that every component produced matches, within a given set of variance, the constant or baseline. Well, that is where the variational autoencoder approach comes into play.
Variational autoencoders are like a GAN in that they are made up of two distinct sides, a neural network for encoding and a neural network for decoding. The encoder network is designed for efficient representation of data while the decoder network is built and tuned for efficient recreation of the original data set. Typically the encoder reduces the data into what is known as a vector or vector embedding. With this vector embedding decoders can compare new content, and generated content for anomalies or data inconsistencies.
So in our example above, inputting our baseline component into a VAE with samples from all the other components being created our VAE can take all the inputs, represent them as vectorized data, and compare those data points quickly for anomalies, like if the component is missing piece or has a sub-component out of alignment. With a VAE you leverage data pipelines to encode data in real-time and compare that data against any existing vectors, typically stored in a vector database, to determine if the output/result is close or close enough to the original. VAEs are really good at anomaly detection, predictive maintenance, fraud detection, and any application where you are looking at some type of signal processing. The other thing VAEs are specifically designed for is the ability to fill in gaps within generated content. For example, if you want to remove a portion of an image, like somebody photobombing your family photo, and have AI generate fill in the portion of the image to match the rest of the background. It is the ability of VAEs to do gap analysis, anomaly detection, style transfers, and data augmentation where they offer a unique approach to machine learning.
Probably more familiar than the models used for generative AI are some of the tools that are being mainstreamed for general purpose use. Tools like ChatGPT, Dall-E and Copilot have become extremely popular for creating content like memos, images and videos, and even code.
Whether you know it or not, generative AI tools are starting to be used to improve productivity or for doing otherwise tedious tasks in our daily lives. Take for example an executive at a hospital who was asked to put together a memo at the last minute for a building dedication that was happening. Instead of sitting down at her computer to write out the detailed memo she can feed the relevant information into ChatGPT and ask it to write the memo for her. Within seconds she has her memo ready to distribute with a few minor changes.
Or how about a developer that needs to write a piece of Python code to parse email addresses out of a registration form to send a thank you email to? With Github Copilot he is able to simply use natural language to describe the functionality he needs and gets back a fully functional code block.
Or what if a crazy fan club president needs to create some artistic content for his Arnold Schwarzenegger/Vincent Van Gogh fanclub website? Within seconds he can ask Dall-E 2 to create me an image of Arnold Schwarzenegger in the style of Vincent Van Gogh and have a fully believable recreation of his favorite action hero in the style of Vincent Van Gogh.
More and more tools are being developed every day for tasks that generative AI is perfectly suited to help offload tasks that typically required special skills or took tedious amounts of time.
As you can see from some of the examples above, one of the interesting things generative AI brings to the table is to grant abilities and talents to individuals that might not necessarily have had them. I can tell you that when it comes to drawing a picture of Arnold Schwarzenegger in the style of Vincent Van Gogh, I have not been blessed with the talent to do this, but Dall-E 2 can step in and bring my vision into reality and allow me to show my artistic expression even though I don’t possess any artistic talent.
It is this ability to create and augment content on demand and in real-time that generative AI is ideally suited. Things like writing a memo for an upcoming retirement celebration, and creating email fliers promoting your annual community garage sale, to running simulations on the outcomes a new heart medication has on lowering blood pressure and the side effects that come along with it.
The potential and power of generative AI is just starting to be seen in applications like natural language chatbots to interact with customers and provide real-time natural responses to questions that improve customer service and direct customers to the right information with ease. But the opportunity is endless. Think of an electrician that needs to wire a house and needs to create the blueprint for all the wire mapping for all the electrical outlets, with the right information generative AI can create the map in seconds compared to hours or days it would have taken to draw these by hand.
With the right data, generative AI has the power to create new content, learn, grow and change. When generative AI is paired with predictive AI we unlock further benefits. For example, we can detect that a customer is not satisfied and generate unique personalized offers that are known to ingratiate the customer, ultimately subduing their dissatisfaction and keeping them happy and engaged.
In our examples above we can see some of the many benefits of using generative AI. It is these benefits that are extremely compelling for both personal and enterprise utilization.
That is truly the power of generative AI, it allows us to do things that we have always wanted to do but just couldn’t because of limited time, limited creativity, or limited scale. With the advent and adoption of generative AI, we can now remove so many of the constraints under which we operate.
Generative AI is revolutionizing the way we work on a daily basis. Tools like ChatGPT, Dall-E, and CoPilot are already being incorporated into daily tasks for things like writing, creating images, and writing code. The real question we must ask is what and where are the limits of using generative AI. Tools like ChatGPT have been made for general consumption but the real power of machine learning and AI is the ability to focus the learning and usage for specific purposes. Think of an architect that can feed in designs and have generative AI modify those designs based on structural analysis. Think of the pharmaceutical company that can run millions of simulations of a drug trial and have generative AI create new treatments based on those findings.
This is the real power of what generative AI can bring to the world, but for this specialized focus, we need tools that open up your enterprise data, in a secure way, for generative AI applications to use, learn and grow from. While this may sound complex, Vector Search on AstraDB takes care of all of this for you with a fully integrated solution that provides all of the pieces you need for contextual data built for AI. From the nervous system built on data pipelines to embeddings all the way to core memory storage and retrieval, access, and processing, via the most scalable vector database on the market today, in an easy-to-use cloud platform. Try for free today.
Generative AI is a facet of artificial intelligence that focuses on the creation of new content from existing data by identifying patterns within that data. Unlike traditional AI which operates within a predefined set of rules, generative AI learns from patterns to generate or create new content based on both trained data and new data as it's created and added to the process.
Traditional AI operates within set rules and is often used in predictive analytics scenarios, providing outcomes based on predefined conditions. On the contrary, generative AI uses data to create new outputs, growing experience from the data to provide outputs that match the given query. It can leverage the learning from past interactions to enhance the responses it provides, thereby generating new outputs and outcomes.
In the domain of text, generative AI can write a restaurant review based on certain parameters or sentiment analysis, unlike predictive AI which can only analyze the sentiment of a given review. In image recognition, while predictive AI can identify whether a given image is of a cat or a dog, generative AI can create a completely new image of a cat or dog. Generative AI can also rewrite an office memo in a whimsical style or create synthetic data for medical research.
The transformative potential of generative AI lies in its ability to create new, valuable content, which can be a game-changer in many fields including art, text generation, and medical research. Its promise of rapid content generation and idea validation is akin to the futuristic scenarios depicted in science fiction, bringing a slice of that future closer to reality.
Tools that facilitate data collection, modeling, and application development are essential for enabling generative AI functionality. These tools help in harnessing the power of data, which is fundamental for building experience in generative AI models, and providing a platform for the development and deployment of generative AI applications.
The learning in generative AI models is an iterative process involving feedback and refinement. For instance, in a GAN, the generator creates content which is evaluated by the discriminator. Feedback from the discriminator helps the generator to refine its output, gradually improving the quality of generated content. This continuous cycle of generation, evaluation, and refinement underpins the learning and improvement in generative AI models.
