5 Emerging AI Trends to Watch Out For

Ushering in the new wave of human achievement with the power of AI.

Artificial Intelligence (AI) is transforming the world of business in unprecedented ways at an unprecedented rate. From automating tasks and enhancing productivity to improving customer service and creating new products/services, AI is driving innovation and enabling new levels of efficiency across various industries. Its rapid growth and transformative breakthroughs hold great promise to shape the future of both technology and business in the years to come.

Here are the top five emerging AI trends to watch out for:

#1 Generative AI

Thrust into the spotlight by popular examples such as ChatGPT and Google Bard, generative AI is a branch of AI that is capable of generating new content (e.g. images, text, music, or video). This feat is achieved by analyzing the patterns and relationships within datasets to generate new data. The datasets used are therefore crucial to the resulting products. At Kenja, we promote the usage of a local AI corpus to ensure the control, privacy, and relevancy of both data and results.

From creating realistic virtual environments to generating digital replicas of real-world environments using real-time information, generative AI’s ability to generate content based off of existing data means its applications are boundless. Businesses can leverage the technology to support remote operations supervision over factories, realistic product mock-up designing, architectural design rendering, or invoice processing workflow automation, among other tasks.

#2 Explainable AI (XAI)

As AI becomes more advanced, complex, and powerful, it is also becoming harder to decipher how it works and why it makes certain choices. Explainable AI is a field of research that is focused on making AI systems more transparent and accountable through the use of tools and frameworks that allow users to understand and interpret the outputs and actions made by AIs.

This provision of rationale for AI decisions and actions helps businesses establish and increase trust, confidence, quality, compliance, fairness, and accountability among users, customers, regulators, and other stakeholders in their use of the technology for both sensitive and routine matters. For example, explainable AI can provide the reasoning behind an AI’s decision to approve or reject loans. Or, it can offer insight into the rationale behind a medical diagnosis.

#3 Reinforcement Learning (RL)

Inventory management, autonomous manufacturing operations, resource allocations, and more can be some of the most multi-layered and difficult decision-making tasks that can even stump AI. Reinforcement learning is a branch of AI that enables AI models to learn from their own actions and experiences, adapt to changing environments, and optimize performance over time.

With its ability to adapt and address complexity, reinforcement learning can be applied to various tasks such as trading strategies, logistics, dynamic pricing, and inventory management.

#4 Edge AI

A combination of edge computing and AI, edge AI is the application of AI to devices that run on the edge of the network rather than on centralized servers or clouds. This enables data to be processed locally without the need for internet connection or data transmission, enhancing data processing efficiency, privacy, and security, while also reducing latency and bandwidth costs.

Its capability for real-time and offline processing of data lends itself useful for a variety of tasks and domains, such as industrial IoT, security cameras, autonomous vehicles, and healthcare. As the field develops, more and more industries are discovering new ways to harness its power to optimize workflows, automate business processes, and unlock new innovation opportunities.

#5 Responsible AI

The potential for AI to enhance productivity, innovation, creativity, flexibility, efficiency, and much more makes it a very exciting technology to adopt. However, as AI grows more powerful, the need for ethical principles and legal frameworks to govern the development of AI also grows more pressing. And, while general best practices may be applied when designing AI, there are a number of unique factors that are only applicable to AI which must be considered.

Responsible AI is a framework that addresses that issue and serves as a guide to help ensure AI is ethical, sustainable, aligned with human goals and values, and beneficial to society. It is composed of five core principles: fairness, transparency, accountability, privacy and safety. Companies that promote and practice responsible AI include Microsoft, Google and Accenture.

From finance to healthcare to education, AI is a versatile and powerful tool that can be applied to numerous tasks and domains across all industries. Emerging trends such as reinforcement learning and edge AI indicate development towards agility and scalability, while generative AI doubles down on the strong potential of AI to revolutionize operations. Finally, trends like explainable AI and responsible AI indicate confidence in AI remaining relevant or permanent.

Reach out to Kenja here and learn more about how AI can enrich your business.