You may have noticed a drop in productivity when you utilize consumer-friendly and artificial intelligence (AI) machine learning (ML) tools. This may be due to the perceived time you know you’ll be saving with the aid of these applications.
A great example of this is the service called Craiyon, an app that simulates the experience of drawing with crayons. Aside from this, there is a plethora of chatbots and AI-generated art. They abound, and you can find no surprise in them. However, what’s shocking is the sophistication that they come with and the general public’s acceptance of them.
Microsoft’s Massive Investments
The purchase of Nuance, a voice technology company, by Microsoft, shows the tech company’s massive investments in this industry. This sale has given Microsoft control over some of the most controversial tools that power Apple’s Siri. Microsoft’s research lab, Redmond, has been building its own internal tools and features quietly. These tools are expected to be the next big technology for businesses and individual use.
How Machine Learning in Video Games Will Change the Industry
Today, machine learning techniques are used in almost every facet of our daily lives. How it merges with the gaming industry has enormous implications for Microsoft as a business. The video game industry’s primary issue is the gap between expectations and investments.
Video games have become more complex to create, fund, and manage. The reason behind this is its explosion in exponential complexity and graphical fidelity. Photorealistic demos such as the ones made by Unreal Engine are one such example.
It has scenes and graphics that are insanely amazing. However, when we look deeper into its creation, we’ll find that the manual labor involved is genuinely palpable, both regarding the time spent to produce it and, of course, the expenses.
In gaming, the definition of AI isn’t what it means in a general context. Video game NPCs or non-player characters and their enemies typically run on a rules-based model. It often has to be manually crafted by a programmer. This differentiates machine learning models as they are more fluid and capable of creating their own rules within parameters.
They also respond dynamically to new data quickly and easily. This can be seen in everything: the NPC’s behavior or content generation. When you ask a GPT-3 ML text model to create a quest for you in World of Warcraft, it uses random and unfiltered information from the internet, and the results are “fun.” It recontextualized it and repackaged the date based on the request it received. In fairness, the system wasn’t designed explicitly for these requests.
Gathering its context from the internet, the AI model produced a practically infinite quest dialogue. It has drawn information about the game from websites and wiki entries to create realistic objectives, including real place names and enemy types.
How Can A Game Developer Help
Imagine the results if direct access to better data and personalized context by a skilled game developer is added. We can anticipate better and more dynamic content from these models. Take the case of DALL-E and other similar models that can produce several variations from any given prompt. Then, they will refine them with details that are short of magical.
These models are capable of remixing existing images, what more with variations of game monsters and other in-game behaviors and animations. While these developments are expected to be the future of gaming and AI, human intervention is still a requirement for future cases and situations.
In the coming years, this proves that handmade content will still be the core foundation of the best video games.
