From Generating Content to Getting Things Done
Alan Turing proposes the Turing Test. The field of AI is formally born at the Dartmouth Conference.
Backpropagation is popularized, providing a method to effectively train deep neural networks, a cornerstone of modern AI.
Generative Adversarial Networks (GANs) are introduced, enabling AI to create realistic, novel images for the first time.
The "Attention Is All You Need" paper introduces the Transformer architecture, paving the way for Large Language Models.
The public release of tools like ChatGPT and Stable Diffusion makes advanced generative AI accessible to hundreds of millions worldwide.
Any technique that mimics human intelligence.
Systems that learn patterns from data.
ML with multi-layered neural networks.
AI that *creates* new, original content.
AI is the broad field, Machine Learning is a subfield that learns from data, Deep Learning uses complex networks to do so, and Generative AI is the creative output of these technologies.
The biggest shift in AI is from passive content generation to proactive, goal-oriented action. An AI Agent is a system that can understand a goal, create a plan, and use tools to autonomously achieve it.
An agent combines the Reasoning of an LLM with Memory (short and long-term knowledge) and the ability to use Tools (like web search or code execution) to complete complex tasks.
Developers use specialized frameworks to build agentic systems. The choice depends entirely on the task at hand, trading off between general flexibility, data handling, and multi-agent collaboration.
Agents design novel molecules for drug discovery and analyze millions of research papers to form new hypotheses.
Autonomous systems conduct market research, power algorithmic trading, and detect fraudulent transactions in real time.
Multi-agent teams collaborate to write, debug, test, and deploy code, drastically accelerating development cycles.
Models trained on biased internet data can amplify stereotypes and create convincing fake content.
User data can be collected and used with little transparency, creating significant security risks.
Training on copyrighted data without consent threatens creative livelihoods and raises legal challenges.