Adaptive AI

A significant part of artificial intelligence's future is adaptive AI. The uniqueness of adaptive AI lies in the fact that it can adjust code, for example, anticipating real-world changes or information. That adaptability is possible even when devs or coders didn't know or assume the changes.

As AI becomes essential to running a business, it is no longer a differentiating factor. In fact, researchers have found that by 2025, around 97% of companies will be actively using or planning to expand their AI initiatives.

To get ahead of the competition, leaders need to:

• Advance their AI capabilities.

• Get more from their investments.

Adaptive artificial intelligence is the next generation of AI systems. It can adjust its code for real-world changes, even when the coders didn’t know or anticipate these changes when they wrote the code.

Innovations in machine learning and deep learning have advanced AI technology by leaps and bounds. AI algorithms can ingest and interpret data more accurately and effectively than ever. As a result, adaptive AI generates staggering insights and better predictions to help organizations make better decisions, become more resilient, and work more efficiently.

As businesses shift away from the fixed, historical data of traditional AI and embrace adaptive frameworks, technology will evolve even more to fit into an increasingly digital world. That is why Gartner proclaimed adaptive AI among the top strategic technology trends of 2024. Indeed, Gartner experts predict that organizations embracing adaptive AI will outperform competitors by 25% by 2026.

In a rapidly evolving digital world, adaptive AI is a critical tool to meet the needs of today’s customers and continue to create crucial innovations. But what is it, and how is adaptive AI transforming the future? Let’s dive into the basics of adaptive AI and why it’s crucial for businesses today.

What is adaptive AI?

Adaptive AI is a form of artificial intelligence that learns, adapts, and improves as it encounters changes, both in data and the environment.

While more traditional forms of AI follow set rules and algorithms and tend to fall apart when faced with obstacles, adaptive AI systems can modify their behavior based on their experiences. It adjusts its own code without input from a person, providing a level of adaptability and business resilience not seen before. This enables AI to deliver better outcomes faster. On that note, let's discuss some key characteristics of adaptive AI.

Characteristics of adaptive AI

Some key characteristics that distinguish adaptive AI systems are:

• Ability to learn. Machine learning algorithms allow the technology to process and analyze new information. As a result, adaptive AI can acquire knowledge, identify patterns, and make predictions.

• Adaptability. Adaptive AI systems adjust their algorithms and decision-making processes when they encounter changes in input data or the context in which they operate. This flexibility makes them practical and relevant even in dynamic and unpredictable situations.

• Self-improvement. Systems improve their capabilities over time by analyzing their own performance, identifying weak or inefficient areas, and refining their algorithms in response.

• Problem-solving capabilities. Adaptive AI systems develop sophisticated and nuanced approaches to problems by learning from experience and adapting to new information. These enhanced problem-solving capabilities often surpass traditional AI systems, leading to more innovative solutions to even the most challenging issues.

Traditional vs. adaptive AI

While traditional AI lets companies work faster and become more productive, the technology is limited in its real-world application.

The development of adaptive AI lets organizations become more flexible, resilient, and effective when faced with complex challenges in daily business.

How does adaptive AI work?

Adaptive artificial intelligence has critical components that help it learn, adapt, and improve over time. These are the most critical components…

Machine learning algorithms

These algorithms allow AI systems to learn from data and make predictions or decisions based on their learning. Machine learning is categorized into three main types:

• Supervised learning. This is where the system receives labeled data and learns to map input data to known outputs.

• Unsupervised learning. The system learns patterns and structures in unlabeled, often identifying hidden relationships or clustering similar data points.

• Reinforcement learning. Through trial and error, the system adjusts its actions based on feedback in the form of rewards or penalties.

Neural networks and deep learning

The structure of the human brain inspired the creation of neural networks, which have interconnected layers of nodes or neurons. Deep learning involves using large neural networks with multiple layers to learn complex patterns and representations in data.

AI systems learn hierarchical features with these techniques and perform tasks such as image and speech recognition, natural language processing, and more.

(Follow our recurrent neural network tutorial.)

Transfer learning and meta-learning

AI systems leverage the knowledge it learns from one task or domain and apply it to another related one with transfer learning. This significantly reduces the required training to speed up the learning process.

Meta-learning, sometimes called learning to learn, trains AI systems to optimize their learning algorithms, improving their ability to learn new tasks or adapt to changing environments.

(Related reading: continual learning in AI.)

Evolutionary algorithms

These algorithms use natural selection and involve optimization through successive generations of candidate solutions. Adaptive AI uses evolutionary algorithms to:

• Optimize AI models.

• Select features.

• Tune hyperparameters.

All this enhances the system’s adaptability and performance.

Working together, these components enable adaptive AI systems to learn from experience, adjust based on new information, and continually improve performance over time.

Continue Reading: Splunk