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Artificial Intelligence

22 January 2024

Composite AI: a mix of techniques for better results

In a world where technology is increasingly present in various daily activities, one of the challenges is to understand how to integrate the potential of AI into one’s business strategy and facilitate the achievement of business goals.

For the development of AI solutions, today’s market also offers platforms that combine multiple AI technology services, infrastructure and tools to enable organizations to achieve tailor-made solutions, providing a quick and easy way to adopt the technology and optimizing the effort/result ratio.

Back in 2020, Gartner included Composite AI as one of its five new innovation profiles in its annual Hype Cycle for artificial intelligence. This technology enables organizations to effectively tackle complex market challenges.

What is Composite AI and how does it work?

Indeed, Composite AI represents the latest innovation in the field, raising the quality and scope of AI applications, and is expected to be a major trend in the near future.

In simple terms, composite AI platforms integrate different AI technologies, such as Machine Learning, Deep Learning and knowledge graphs. This fusion ensures the best possible results for users, leading to better performance and successful outcomes.

For example, to solve a Natural Language Processing (NLP) task, the combined use of machine learning and knowledge graph leads to faster and more relevant search and recommendation results, higher accuracy of information and reduced time-to-value. It enables customized results based on function or business domain, improving user satisfaction and proving to be an invaluable aid for organizations in data management.

Combining different techniques has proven to be a much more effective way of extracting actionable information, handling unstructured data from multiple sources and providing relevant and accurate search results. Another important advantage of composite AI is its ability to utilize existing and industry-standard knowledge graphs, which can be adapted to meet the specific needs of various industries.

Although these knowledge graphs may require some refinement, they are generally applicable to a wide range of companies.
In conclusion, the integration of various AI techniques promises to shape the future of AI applications and generate significant impact across sectors


Technology at the heart of innovation: Almawave's approach

Almawave embraces the composite AI approach ‘by design’ and integrates Machine Learning, Deep Learning (with pre-trained models), and Knowledge Graph technologies to improve performance, user experience, transparency and applicability. This combination allows our solutions to discover new possibilities, extract information and deliver customized results, improving user satisfaction.

The Almawave Group laboratories are constantly researching and experimenting with new methods and techniques to be included in the composite artificial intelligence offering, such as Large Language and Generative AI models.

Understanding human language is a challenging task for Artificial Intelligence, especially in the field of NLU.
The growth of AI has been closely linked to the rise of cloud computing in recent years. Cloud computing is a vast distributed technology system that uses the Internet to provide access to shared and customizable computational resources. These resources can be allocated and releazed rapidly with minimal management or integration efforts. The Cloud plays a central role in enabling AI platforms, making Artificial Intelligence more cost-efficient, faster and, consequently, more attractive to businesses.