Data Management Glossary
Artificial Intelligence (AI)
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to perform tasks that would typically require human intelligence such as visual perception, speech recognition, decision-making, and language translation. AI involves the development of computer systems capable of performing these tasks.
AI subfields
AI subfields employ different techniques and algorithms to enable machines to learn from data, recognize patterns, make predictions, and solve complex problems. Examples include:
- Machine learning: a prominent branch of AI, focuses on enabling machines to learn from and adapt to data without explicit programming. It involves the development of algorithms that allow computers to analyze and interpret large volumes of data, identify patterns, and make informed decisions or predictions.
- Natural language processing (NLP): Deals with enabling machines to understand, interpret, and generate human language. NLP plays a crucial role in applications such as speech recognition, language translation, chatbots, and text analysis.
- Computer vision: Involves enabling machines to interpret and understand visual information from images or videos. It enables systems to perceive and analyze visual data, such as object recognition, image classification, and autonomous driving.
- Robotics, expert systems and more.
AI has a wide range of applications across various industries, including finance, healthcare, transportation, manufacturing and entertainment. It has the potential to revolutionize industries, improve efficiency, automate processes, and solve complex problems.
AI is still an evolving field, and while it has made significant advancements, it is not yet capable of replicating the full spectrum of human intelligence. Researchers and developers continue to explore and push the boundaries of AI, striving to create more advanced and sophisticated systems. There is an ongoing discussion about the important role of regulation and governance, especially as they relate to generative AI. The leaders of OpenAI have proposed an international regulatory body.
At the end of 2022, Komprise CEO Kumar Goswami wrote about the importance of unstructured data and unstructured data management to AI and machine learning. He wrote:
Enterprises need to be ready for this wave of change and it starts by getting unstructured data prepped, as this data is the critical ingredient for AI/ML. This entails new data management strategies which create automated ways to index, segment, curate, tag and move unstructured data continuously to feed AI and ML tools. Unforeseen changes to society, fueled by AI, are coming soon and you don’t want to be caught flat-footed.
In 2023 he wrote an article entitled: The AI/ML Revolution: Data Management Must Evolve.