What is artificial intelligence (AI)?

2026-06-18 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Novice, long

Summary

Artificial intelligence (AI) is an umbrella term encompassing specialized fields like machine learning, deep learning, natural language processing, computer vision, robotics, and generative AI, which notably exploded into public awareness in 2022 with ChatGPT. AI has evolved significantly since the Turing test was proposed in 1950 and the field was named in 1956, with major breakthroughs including IBM's Deep Blue beating Garry Kasparov in 1997, AlexNet's deep learning leap in 2012, and the introduction of Transformers in 2017. Despite its rapid advancement and business value in areas like automation and personalization, AI presents limitations and risks such as hallucinations, bias in training data, the "black box" problem, privacy and security concerns, potential job displacement, and the critical need for robust governance and compliance.

Key takeaway

For Directors of AI/ML evaluating new deployments, recognize that modern AI systems, while powerful, demand rigorous oversight. You must prioritize robust governance, data quality, and continuous fairness monitoring from the outset. Implement retrieval-augmented generation (RAG) and explainable AI (XAI) tools to mitigate risks like hallucinations and bias, ensuring your solutions are reliable, auditable, and compliant with evolving regulations like the EU AI Act.

Key insights

The rapid evolution of AI from academic curiosity to essential infrastructure brings both transformative capabilities and critical risks requiring careful management.

Principles

• AI systems often combine multiple specialized branches.
• AI capabilities advance faster than expected since 2022.
• AI models are optimized for fluency, not truth.

Method

The article describes mitigation strategies for AI risks: verifying outputs, curating training data, using explainable AI (XAI) tools, and building in privacy/security controls and governance from the start.

In practice

• Pair generative models with RAG systems.
• Test models against benchmark question sets.
• Monitor fairness as an ongoing practice.

Topics

• Artificial Intelligence
• Machine Learning
• Generative AI
• AI Governance
• AI Risks
• Deep Learning

Best for: AI Student, Director of AI/ML, Executive

Related on AIssential

• Detailed development in Artificial Intelligence (AI) over the last year — Generative AI and improved machine learning are driving rapid AI adoption across diverse sectors.
• People need to understand why systems make decisions, not merely whether the systems appear to perform well. A map of the Explainable AI (xAI) landscape. — Explainability is now a prerequisite for trustworthy AI, demanding tailored methods for diverse contexts.
• EP202: MCP vs RAG vs AI Agents — AI development is shifting from LLM application to fundamental research, advanced prompt engineering, and AI-driven code generation.
• 😼 AI's Biggest Problem Just Changed. Nobody's Ready. — AI's primary challenge has shifted from capability to governance and control over autonomous agents.
• AI Chatbot Development Services for Enterprise Data-Sensitive Processes — Custom generative AI chatbots are becoming functional infrastructure for enterprises handling sensitive data and complex workflows.
• Weekly Top Picks #116 — AI's rapid evolution presents complex challenges across business, geopolitics, work, and societal trust.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.