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What Is Generative AI and Why Are Companies Spending Billions on It?

what is generative ai

What Is Generative AI and Why Are Companies Spending Billions on It?

As the endless march of tech innovation continues, new trends can disappear almost as quickly as they were created. Generative AI, like ChatGPT, is one of the latest products. But if the huge investments in this technology are anything to go by, it’s got staying power. How exactly does it work, and why is there so much hype? Find out the answers below.

What Is Generative AI?

When talking about topics like natural language processing (NLP) and large language models (LLMs), the term “Generative AI” will appear often. It’s a common misconception that generative AI and LLMs are the same, but this isn’t entirely true.

While all LLMs are a kind of generative AI model, not all generative AI models are classed as LLMs. This is because LLMs are used to generate natural text, whereas generative AI can create audio or images, too. The concepts are similar, however, since all generative AI models and LLMs are trained on huge amounts of data.

ai art
Generative AI art is created by AI models that are trained on billions of images found across the internet.

A lot of the time, this AI is used creatively, i.e. to create new text, music, or photos. This is possible because the AI has been trained on data while self-supervised. This means it’s able to learn about data patterns and how they’re related to each other.

Rather than simply copying and recombining elements from its training dataset, the AI uses these learned relationships to create new outputs based on a prompt, which is usually in text form. Often, you’ll encounter these AI models in the form of software, where you can enter a text prompt and watch in awe as the AI generates a response.

How Can Generative AI Be Used?

The uses of generative AI are almost as vast as the data it’s been trained on. Whether you need music, text, or photos, there’s probably a way to use AI to assist you. Some of the most common uses are covered next.

Creative Industries

As well as content creation, generative AI can be used to develop ideas for advertising and marketing materials. You can even use AI to create the materials themselves. AI can also personalize content such as targeted advertising, helping to make customer acquisition more profitable.

The gaming industry is also benefitting greatly from generative AI, which can be used to create music, characters, and environments, and even simulate physics to make the game more realistic.

Code Generation

Developing code is almost creating content of sorts, and is another area where generative AI is causing significant changes. While the current generative AI models are nowhere near flawless, they can generate and explain blocks of code much faster than most humans can. That’s why they’re not only invaluable for educational purposes, but also for tech businesses looking to save money when generating code.

Customer Service

Generative AI can be extremely useful in making chatbots more knowledgeable and human-like in their communication, offering big benefits to companies that want to streamline their customer support.

ai chatbot
A customer service chatbot uses machine learning and natural language understanding to mimic human speech.

While chatbots aren’t entirely new, not many consumers have particularly pleasant experiences with them. Being able to make this process more efficient and useful for the consumer will help massively with many companies’ reputations and profits.

Education

At the moment, there’s a big focus in the news on students using generative AI to skimp on writing their essays, but this isn’t the only application of AI for education. Being able to personalize educational content for each student and their abilities is a potential use that could save a lot of time.

Speeding up content creation in general means that educators will be able to free up a large portion of their time to focus on other aspects of teaching.

Healthcare

The health sector might not seem like the most obvious place where generative AI has a purpose. However, there are many ways AI can improve the efficacy of healthcare. As well as personalizing treatment plans, AI can be used to simulate molecular properties.

This can help cut down drug development times substantially. AI is also great for analyzing data. To this end, it could be used to interpret medical images and scans, as well as identify patterns within health records.

Why Are Companies Investing in Generative AI?

After taking a look at the plethora of uses of generative AI, it’s not too hard to imagine why so many companies are investing in it. There is untapped potential within generative AI that could drastically transform both the way we work and what we work on.

And, as with anything that promises a boost in productivity, comes the possibility of increasing profits by cutting down on labor costs and resource consumption. Even the ability to simulate expensive processes and experiments can lead to massive savings in itself.

what is generative ai
The Guardian notes that AI productivity is tied to the notion that, for robots, “the hard problems are easy and the easy problems hard.”

Furthermore, investing in new technologies and work processes is essential for maintaining a competitive — and, therefore, profitable — edge. Keeping ahead of the curve in terms of productivity and personalization is seen as a must-do for many companies.

There is a possibility that some investment has been driven by hype, which, in turn, has driven more hype. It’s hard to detect when investment is misguided, especially with emerging technologies. However, the huge investment in AI looks hopeful. We should eventually see a leveling of enthusiasm, followed by a clearer picture of generative AI’s success.

Wrapping Up

Although still in its relative infancy, generative AI has made big waves across the globe. It’s being adopted by and invested in by global companies in equal measure. While it’s impossible to predict the future, the sheer usefulness and accessibility of generative AI definitely stack the odds in its favor.

Almost every industry can benefit from the task automation, content creation, and personalization that this AI makes possible. If the limitations of this technology can be overcome, radical changes to our relationship with work and technology, on the whole, are to be expected.

Frequently Asked Questions

What is generative AI?

Generative AI is a type of AI that has been trained on huge sets of data, and can use learned patterns to produce new content in the form of text, music, images, or video from a prompt.

What can generative AI be used for?

There are many uses for generative AI, but some of the most common are content creation, personalization of customer experience, education and medicine, drug discovery, and code generation.

Why are companies investing so much in generative AI?

There is huge potential for generative AI to transform industries, mainly by automating tasks, creating content, and creating products and services that would otherwise be virtually impossible. Since a huge benefit of generative AI is the improvement of productivity and reduction of costs, many companies are eager to invest and remain competitive.

What are the limitations of generative AI?

Generative AI can reproduce biases and harmful content due to the data it’s trained on. There are also some ethical and copyright concerns, mostly relating to training data, as well as concerns about data privacy.

When was generative AI invented?

The idea of generative AI has been around since the 1990s, but it’s only recently that there’s been widespread interest and adoption of the technology. As it’s currently known, generative AI came into being in 2018.

How will generative AI be used in the future?

It’s hard to say, but with generative AI being refined and improved every day, it’s likely it will become a lot more commonplace in our working lives. Datasets will increase in size and algorithms will grow more complex, so the applications of AI will become even more abundant. The real stopgap when it comes to generative AI will be around ethics, and any legislation introduced that may hinder its development and rollout.

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