Generative AI is a form of artificial intelligence that relies on natural language processing, massive training datasets, and advanced AI technologies like neural networks and deep learning to generate original content.
Tech professionals and laypeople alike are becoming familiar with content generation models like ChatGPT, but this example of generative AI only skims the surface of what this technology can do and where it’s heading.
In this guide to the generative AI landscape, we’ll explore what generative AI is capable of and how it emerged and became so popular. We’ll also examine current trends in the generative AI space and predict what consumers should expect from this technology in the near future.
Generative AI Landscape: Table of Contents
- Generative AI Landscape: Use Cases
- Why is Generative AI Emerging Now
- Leaders in the Generative AI Landscape
- Generative AI: Future Landscape
- Bottom Line: the Generative Landscape
Generative AI Landscape: Use Cases
Though generative AI technology is still fairly new, current AI models are being used to fulfill a range of personal and business use cases. These are some of the most common applications in the generative AI landscape today:
- Personal use: Recreational users of generative AI tools are frequently using them for textual content generation; specific use cases include Q&A, trip and event planning, conversation, and research.
- AI assistants, chatbots, and search: AI-powered search engines are quickly gaining steam. Several startups also offer enterprise search tools that are powered by generative AI.
- Developer tasks: Generative AI tools can be used to predict code sequences, support problem-solving, document existing code, and automate tasks.
- Creative content generation: Beyond text generation, several generative AI models can also generate audio, video, and imagery. Some of the latest trends in AI content generation include music, videogame, and podcast content creation.
- Computer vision: Synthetic data generation and 3D model creation can be applied to computer vision use cases like driver monitoring and pedestrian detection, AR/VR/XR, virtual try-on, and more.
- Cybersecurity and data security: Generative AI tools can be used for simulated attacks and environments, threat intelligence, and synthetic data digital twins of sensitive data. While these models can be used for greater security protections, they can also be manipulated by malicious actors and hackers.
- Voice and audio synthesis: AI-generated voices can be used for enterprise projects like marketing videos; AI voice options are quickly becoming available in a wide range of languages and tones.
On a related topic: What is Generative AI?
Industries and Departments That Use Generative AI
Especially with content generation models so widely accessible, many industries are dabbling in generative AI to support worker productivity. Across the generative AI landscape, there are the industries and business departments that are currently using generative AI most extensively:
- Marketing and sales
- Customer service and contact centers
- Graphic design and video production
- Biology, chemistry, and biophysics
- Legal and government
- Fashion, retail, and e-commerce
More on this topic: Generative AI Examples
Why Is Generative AI Emerging Now?
Generative AI is emerging on a massive scale right now for two primary reasons: 1) More mature neural networking techniques have emerged and 2) Models and compute power are now more accessible.
In 2017, Google laid the foundation for the generative AI we use today when the company first proposed a neural network architecture called the Transformer. With transformers, it became possible to create higher-quality language models that could be trained more efficiently and with more customizable features. At this time, tools with predictive text and simple AI chatbots began to emerge and mature sparsely.
However, even with the development of transformers and related neural networking architecture, generative AI models remained prohibitively expensive. Processing generative AI queries required power resources that most companies did not have, or even has access to.
Starting in 2022, compute power and the AI platform infrastructure layer began catching up to processing requirements for generative AI tools, making it possible for more companies to develop generative AI technologies. And more importantly, for existing generative AI developers to extend their models to other users at an affordable rate.
Around the same time, new neural networking techniques, such as diffusion models, also arrived to lower the barriers to entry for generative AI development.
With generative AI requiring less energy and financial investment, the generative AI landscape has expanded to include a number of established tech companies and generative AI startups. The landscape continues to evolve as existing models are extending to more users through APIs and open-source software, leading to new application and use case developments on a regular basis.
Emerging leaders in the generative AI landscape: Generative AI Startups
Leaders in the Generative AI Landscape
OpenAI is the clear leader in the generative AI landscape, currently valued at nearly $30 billion. With its close relationship with Microsoft, its newest GPT-4 model, its incredibly popular ChatGPT tool, and its ongoing innovations in other forms of content generation, OpenAI is poised for continued growth and third-party investments to fuel that growth.
Major tech companies like Amazon and IBM are also driving the generative AI scene, but the most prominent of the big players right now are Microsoft and Google. Microsoft currently has an edge over Google with the range of content generation, AI assistance, cybersecurity, and coding support tools that it now offers.
Google has long been an innovator in what has become the generative AI landscape. While Google may be experiencing a slower start in its actual release of generative AI tools, its commitment to thorough testing and AI ethics indicates that its upcoming solutions will be powerful and effective when they are eventually released.
Learn more: Generative AI Companies: Top 12 Leaders
Generative AI: the Future Landscape
As influential has generative AI has quickly become, the future suggests a far more all-encompassing future that affects various sectors, from education to virtual reality.
Generative AI’s Impact on Education
Students are already using tools like ChatGPT to answer homework questions or write essays, and teachers and parents are concerned.
Although these large language models don’t necessarily “know” the answers to educational assignments, their training has prepared them to accurately predict a sequence of text for various inputs, making it possible for students to use these tools to solve educational problems. Of course, this could have negative impacts on students’ education, but it could also benefit students and their teachers if education systems learn how to implement AI solutions as assistive learning tools.
Similarly to when classroom technologies have changed in the past — overhead projectors, anyone? — generative AI will require teachers to evolve their educational approach. For instance, virtual learning is an exciting area of generative AI that is quickly evolving. Generative AI games and AI storytelling solutions are being released now, offering teachers instructional support and engaging new ways to deliver educational content to students.
But there’s still the issue of AI-based plagiarism. To combat students’ tendency to rely on ChatGPT and similar tools to do their homework, teachers can use one of the many free AI content plagiarism detectors that have now emerged. Though they’re not foolproof, these tools are able to effectively estimate what percentage of content has been artificially generated.
As educational concerns grow, users can expect these plagiarism checker tools to evolve too.
Generative AI and Virtual Reality
Video and 3D models are some of the fastest-growing generative AI model formats today.
Games and entertainment media can certainly benefit from this advancement, but the impact these models will have on virtual reality (VR) and augmented reality (AR) technology — the metaverse — is what many people are most anxiously awaiting. As they’re refined, these more advanced models will use generative AI technology to create the immersive experiences that make virtual reality feel real.
Shifting Career Paths and Opportunities
Generative AI tools are already supplementing certain types of work and, in the future, may come to replace certain kinds of work. But this shouldn’t raise alarms for the average working professional, so long as they’re willing to pivot and build on their skills as job expectations change.
For example, many writers currently focus on SEO writing, a form of writing that mostly involves crafting content that ranks well in search results. This is exactly the type of content generative AI models can produce through their algorithmic training. For a writer who is concerned about their job security as large language models become more capable, it will be important to develop new strategic skills, like editorial planning and content quality assurance management, and to work with companies that value human creativity and research.
On the plus side, these tools will likely make certain types of unskilled tasks easier, like sending emails and taking notes. When employees have time freed up as these tasks are taken off their plates, they’ll be able to focus on higher value strategic work more than ever before.
Embedded AI Applications
Many big tech companies, like Microsoft, are currently experimenting with AI assistants that guide user search experiences on the web. And some of the biggest generative AI startups, such as Cohere and Glean, provide AI-powered enterprise search tools to users.
As enterprise search tools continue to expand their features and functions, many companies will begin to embed these applications into their websites and internal software to create a better self-service user experience for employees and customers alike.
Contextualized Generative AI
The majority of today’s generative AI models have time-based and linguistic limitations. As generative AI grows in demand around the world, more and more of these vendors will need to make sure their tools can accept inputs and create outputs that align with various linguistic and cultural contexts.
Additionally, generative AI models will need to offer more accurate, real-time information to users over time. Though ChatGPT is currently the most popular content generation and large language model available, it may eventually fall behind competitors like Bard that are connected to the internet and generate answers based on up-to-date information. In contrast, ChatGPT currently works with data that stops in September 2021.
Also see: Generative AI Companies: Top 12 Leaders
Bottom Line: The Generative AI Landscape
Bursting upon the scene in late 2022, within months generative AI quickly began radically reshaping the tech sector. In fact it’s no exaggeration to say that the “generative AI landscape” and the “overall tech landscape” are essentially merging into a singly entity, as generative AI technologies find their way into a growing list of tech tools and solutions.
Apart from forecasting enormous growth, the full effect of generative AI is impossible to predict. The technology is growing exponentially, driven by advances in neural networks, deep learning, machine learning, and GPU speeds. As for how business and society will shape generative AI (or by shaped by generative AI), that is a open question – and it offers a number of highly complex challenge for citizens and leaders
In any case, it appears inarguable that the generative AI landscape will enlarge at a remarkable pace, and offer great benefits even as it presents enormous challenges.
On a related topic: The AI Market: An Overview