Machine Learning And Artificial Intelligence In Apps

It’s hardly possible to have a conversation about tech nowadays without mentioning machine learning (ML) and artificial intelligence (AI). In a way, tech companies and the media have been responsible for pushing AI & ML into the mainstream consciousness for at least a decade. And one could say that they have been overwhelmingly successful in achieving this goal.

But why should developers care about machine learning and artificial intelligence? After all, we’ve done fine with apps and games that had no or only limited implementations of AI for several decades. So what gives — why the sudden interest in AI & ML? And how could rudimentary apps benefit from AI & ML — wouldn’t this drive up development costs and utilize additional compute power? 

Let’s give you the short answer first? No, most simple apps won’t need an AI or ML implementation. While your to-do list app or ‘Frogger’ clone will benefit from an AI implementation, you’ll likely roll out a basic logic system or finite state machine (FSM). And if necessary, you could integrate AI agents into your FSM if you’re building a more complex game, simulation, or AR/VR app. 

But as a general rule, you’ll want to rely on simpler systems for most of your apps. However, if you’re creating something like a photo editing app that autocorrects imagery, you’ll want to tap into the power AI & ML brings to the table. Below, we go into greater detail about what this means for your app development endeavors!

What Is Machine Learning?

Before we begin, we need to explain what machine learning is and how it differs from artificial intelligence. Machine learning attempts to emulate the way humans learn. And similarly to humans, learning is an iterative process where accuracy improves gradually. 

But how would ML help improve an app? Consider a game where enemies and non-player characters (NPCs) need to respond like real humans. Or self-driving vehicles that need to respond to varying traffic conditions in real-time. 

A solution based only on AI would rely heavily on pre-determined behaviors, which isn’t ideal. We’d effectively have a situation where some enemy characters respond the same when trying to dodge the player’s bullets. And self-driving vehicles would run into problems when encountering any non-predefined obstacles.   

Ideally, we’d want the AI to mimic human behavior as closely as possible. That means we don’t only want pattern recognition but also the ability to distinguish the slightest nuances in those patterns. Something humans are very good at doing, as our brains can analyze all surrounding visible objects accurately and rapidly. We can quickly determine the distance, color, shape, size, and texture of one object when compared to another.

However, computer systems are only good at rudimentary pattern recognition. At least, that was the case until the introduction of machine learning. And that’s why it’s making inroads in the fields of data science, customer service, and even stock trading.

How Does Machine Learning Work?

Machine learning relies on the following processes to work effectively:

• The first phase consists of the decision-making process that ingests input data. This data could either be labeled or unlabeled, and the ML algorithm will compute an estimation of the patterns detected in the data. 
• Then, an error function will compare and evaluate any predictive models and assess their accuracy. 
• Once done, the model optimization process will kick off. These predictive models will undergo weight adjustments to fit with existing data sets. These data sets come from the training sets, which contain the most accurate example of what the ML algorithm should achieve. Furthermore, the algorithm will repeat this process and update the weights automatically until it reaches the desired accuracy threshold.

What Types Of Apps Benefit From Machine Learning? 

As we mentioned earlier, machine learning isn’t necessary for most apps. But given that more users and businesses require features that automate specific processes, AI & ML implementations have become indispensable. And that’s certainly true for the following kinds of apps:

• Automatic Speech Recognition (ASR): Most mobile devices come with apps that recognize human speech, such as Google Assistant and Siri. And the most common uses include voice search and speech-to-text functionality. But even third-party apps can benefit from natural language processing (NLP) to deliver optimal speech recognition.
• Chatbots & virtual assistants: There’s a greater need for chatbots in customer service. And that’s because they can serve customers around the clock and in multiple zones concurrently. Moreover, they’re a low-cost solution to employing human agents. 
• Imaging technologies: AI & ML implementations work great for applications that use or manipulate images. Many modern applications need to collect and analyze data from digital photos, videos, and visual inputs. Furthermore, they can enhance these photos and videos with limited or no input from the user. 
• Recommendation engines: Many e-commerce and mobile apps feature recommendation engines to help users make the right choices. AI algorithms and past behavior data help the app place the most adequate information in front of the user.
• Crypto & stock trading: More investors and day traders utilize their mobile phones to trade cryptocurrencies and other financial instruments. But AI-powered platforms allow users to engage in high-frequency trading while on the go.

Implementing Artificial Intelligence In Mobile Games 

It’s not necessary to implement machine learning in mobile games. And the reason for this is that older mobile devices don’t have CPUs and GPUs powerful enough to handle intense ML workloads. And that’s not taking into account the GPU processing budget for the graphics and additional overhead the underlying game engine brings forth. 

But it’s still possible to implement AI algorithms that make NPCs and enemy characters convincing enough. And since most mobile games tend to be simpler experiences than PC and console games, it’s unnecessary to implement complex AI algorithms that may cause high CPU usage. 

And the good news is that it’s a relatively simple process to implement algorithms such as A* pathfinding, Alpha Beta search, Minimax, and Monte Carlo tree search, without stressing the CPU. These work remarkably well in action, arcade, puzzle, strategy, and mobile board games. 

If you’re developing a role-playing game (RPG), you’ll rely heavily on a custom-made database to store character and enemy variables. And to manipulate any of these variables, you’ll rely mostly on formulas. You’ll find that these formulas resemble those found in Excel spreadsheets.

However, you’ll need to implement AI for your NPCs and battle scenes. But how complex your AI will end up largely depends on the realism of NPC behavior and the intricacy of your battle systems. And if you’re planning on adding epic boss fights, implementing convincing AI will prove challenging. Your team members will need to put their engineering caps on to deal with convoluted behavior trees.

In Conclusion

Machine learning and artificial intelligence help make apps a lot smarter. And these apps often feel like they have human-like intelligence, even though it’s a bunch of cleverly-crafted algorithms running in the background. 

As mobile app development matures, AI & ML implementations will be standard practice. And that’s because tools and frameworks are getting better, and system-on-a-chip (SoC) vendors are implementing neural processing for ML applications. Contact NS804 today to learn how we’ll help you develop apps powered by advanced AI & ML.