AI is prevalent and gaining popularity. Generative tools already appear in most marketing routines, filling a number of roles. With the power to create content from scratch, these tools speed up creative efforts, expand contexts, and empower creatives.
In short, it has taken over, and marketing is just the first example that comes to mind. This stuff is everywhere, and as you learn more about it, you will see that no one can afford to ignore it.
Understanding Generative AI
What is generative AI, and how does it work?
As a type of artificial intelligence, generative AI is software that can create brand-new content from user inputs. Whether you type in prompts, provide a photo, or upload a video, the AI can analyze that input and then create content accordingly.
For comparison, discriminative AI takes inputs and categorizes them accordingly. This would include things like email spam filters and facial recognition software.
For the most part, generative AI functions through one of three models: GANs, VAEs, and transformers.
Generative adversarial networks (GANs) pit a content generator against a discriminator to refine outputs. The best example of this can be seen with Duolingo.
Variational autoencoders (VAEs) analyze the structure of content to make new stuff by sampling from a learned space. You can see this with Stable Diffusion.
Transformers break content along contextual lines to predict the best possible output. This is a simplification of how ChatGPT works.
With those basics covered, we can look at how generative AI fits into the modern technical world to understand how rapidly everything is changing.
Generative AI vs. General AI
To compare generative and general AI, we should first define general AI. This concept describes an AI that can perform a wide range of tasks. There is no real-world example to cite, as no attempt at general AI quite lives up to this definition, but we can look at current attempts.
Perhaps the best example is Google services. In one ecosystem, you have access to speech recognition, real-time transcriptions, generative tools, analytical tools, information and fact finding, and more.
Clearly, this suite offers more than any single generative AI tool, but there are still many, many tasks that elude the Google package. A true general AI would be able to take on any new task and still succeed.
Training methods and model architectures
The approaches to building these two types of AI look substantially different. Most generative models utilize statistical training. With very large sources of training data and complex analytical models, these AIs break down many examples of the type of content they create. This creates a baseline that can then be used to predict outputs and generate new content.
This is how a picture generator learns to make new images. It’s also how chatbots talk.
General AIs take a number of different approaches. While many train statistically, general AIs currently try to combine the skill sets of multiple specialized AIs. The general AI, in this sense, is really just a series of bridges between other AIs to enable them to work together.
Real-world applications
Applications for content generators are easy to understand. Want to create a brand logo but you don’t have graphic design skills? Image generators are your friend. Need help creating original written content? Chatbots are like adding a writing assistant and an editor to your team.
As for general AI, theoretical use cases would include a virtual assistant. A powerful AI could help you book trips, organize your work schedule, consolidate emails, remember birthdays, and maybe even drive your car.
In current iterations, general AI models can offer a wide range of services that can offload much busy work, but as of yet, they do not match the power of a human assistant. Additionally, general AI attempts right now usually fall behind specialized AI performance in each category.
As an example, a general AI that includes text generation is typically not as capable as a dedicated chatbot.
The Most Famous Generative AI Models and Applications
By looking at some of the most notable examples, you can really see what AI is doing and how people are using it.
OpenAI and GPT
OpenAI stunned the world in November of 2022 when the company fully launched ChatGpt. It proved capable of text generation on a level before unseen. ChatGPT can keep up with conversations, generate realistic text, understand points of view, and more, all while providing perfect grammar and syntax.
While it is no longer the only powerful chatbot in the business, it is remembered as the revolutionary technology that changed how everyone thinks about AI.
Stable Diffusion
Also in 2022, Stable Diffusion became widely available. It is essentially the ChatGPT of the image generation world. With just a few prompts, Stable Diffusion proved able to generate brand-new, contextualized, highly detailed images.
It formed the second prong of the 2022 AI revolution, and like ChatGPT, it has enjoyed many upgrades since.
StyleGAN
StyleGAN predates both ChatGPT and Stable Diffusion. First released in 2018, it surprised people by producing photo-realistic faces. While less generalized than other image generators, several iterative upgrades made StyleGAN one of the most powerful and convincing face generators in the business.
Music and video generation
Today, you can find plenty of different music and video generators, varying in function and capability. Among the most renowned music generators are Soundraw, Mubert, and Loudly, with plenty of additional choices available. They can create brand-new songs in a matter of seconds.
Similarly, you can find a number of powerful video generators. At the time of this writing, Synthesia and Lumen5 got the most attention, with many viable alternatives on the market.
Generative AI in Action: Real-World Use Cases
Each of those AI examples shows you the types of tasks available, but some specific applications that exist today can broaden the context.
Healthcare
The applications in healthcare can be staggering. You can train chatbots on medical texts, enabling them to rapidly search through information to help with diagnostics and treatment prescriptions.
Video generation can help medical experts explain situations and train new providers.
AI assistants can help medical staff stay ahead of paperwork while reducing the risk of medical errors.
Marketing and advertising
The value of AI here is easy to understand. Music, video, and image generators can help you create new advertisements and marketing tools while avoiding costly royalties.
Text generators can help you map out new slogans and campaigns. They can also help you edit all of your written content to avoid typos and similar mistakes.
Entertainment
Entertainment benefits from the same tools as marketing and advertising. A social media content creator can gain access to brand-new songs for their video content. They can create unique images that help tell a story or engage an audience.
And, chatbots can assist with script writing, tags, and any other written content.
Gaming
Generative AI holds a fascinating place in gaming. Generative models can create unique virtual environments for games. As an example, games already use procedural generation to make vast areas. In cases like No Man’s Sky, the game is so big you could never explore it all in your lifetime.
Generative AI can also create more realistic characters who can interact with anything you say to them and grow and change over time. These ideas only scratch the tip of the iceberg.
Finance
Chatbots in finance can fill a role similar to that of healthcare. Bots trained specifically on financial resources can help advisors and traders sift through information quickly and accurately.
On a different foot, chatbots can help automate many accounting functions. Creating audits and capturing invoices, they handle much of the busy work in finance.
The Future of Generative AI: Challenges, Opportunities, and Ethical Considerations
Generative AI is changing AI itself. The future will be interesting, to say the least. AI is changing fast, and it’s taking the whole world with it. Let’s explore a few future expectations to see what you can expect in the coming years.
Advancements and innovations
Researchers are certainly chasing the notion of general AI. Over the next several years, you can expect advances in this area. Whether or not a true general AI will emerge, AI suites will take on more and more tasks, offering you better-generalized help across a wide range of functions.
At the same time, specialized AIs will continue to benefit from iterative improvements. Your favorite AIs today will only get better with time, and you can expect them to become everyday partners in work and business.
Addressing challenges
Of course, these advances come with a number of challenges. Most notably, diminishing returns make each iteration more difficult and often more expensive. As an example, GPT-4 utilizes 500 times as many parameters as GPT-3. To make that same level of improvement to GPT-5, you might expect the parameters to increase by another 500 times.
Yet, GPT-4 already uses 100 trillion parameters. Increasing that by multiple orders of magnitude pushes the limits of modern computational processing, thus the challenge of diminishing returns.
There’s also an issue with convergence. Statistical training and neural networks use math to optimize decision-making. That math can only solve some types of problems, known as convergent systems. Divergent systems also exist, and thus far, they elude AI and automation attempts.
As developers conquer more and more convergent problems, the remaining challenges will live in the divergent realm. Without a major breakthrough, the gulf between what AI can and cannot do will become more apparent and more limiting (at least in those areas).
Ethical concerns
AI faces more than just developmental challenges. Many people are concerned with the ethical implications.
One major concern relates to jobs. Research published by Fortune estimates that more than 4,000 workers lost their jobs to AI in 2023 alone. That number won’t break the economy, but it could grow to a dangerous point.
That’s not all.
AI also increases computer usage. This translates to more energy consumption for daily tasks as we offload them to AIs. That energy consumption has economic and environmental implications, leading many to wonder if AI is ultimately going to cause more harm than good.
It’s still too early to tell just how far these ethical concerns will reach, but we certainly need to keep an eye on AI and how it impacts economies and environments.
Collaboration
On a brighter note, the immediate future of AI can be found in human-computer collaboration. The best way to use the tools we have right now is with human oversight.
Chatbots can do a lot in terms of writing, but they still lack human insight, and they are notorious for projecting wrong information.
Image and video creators again provide value and save time, but they can’t always produce exactly what you want. In many creator spaces, AI video editing helps save time when it comes to polishing the videos, but it still benefits from the human inputs that created the video in the first place.
Right now, you can even find professional services that use AI to make content creation cheaper, but they marry the AI with human creators to guarantee accuracy and quality. You can expect to see an explosion of AI collaboration over the next few years.
Regulations and policy
This is the hardest part to predict. Governments and regulatory bodies are discussing AI every day, but there are many directions that oversight can take. What you can expect is that regulators will want to minimize job losses to AI. Other regulators might focus on environmental impacts.
There are also security concerns, privacy issues, and arguments around creativity and originality — all of which still need solutions.
You can expect to see new regulations arise in terms of AI, but how it will actually impact AI and its usage is impossible to predict.
Conclusion
The bottom line is that AI is here, it’s making waves, and it behooves all of us to learn more, gain familiarity, and figure out how to make it work in our own lives. What you see here is just an introduction.
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