Governance: Governments and organizations have started developing guidelines and regulations for AI to ensure safety, fairness, and accountability in its deployment. Those processes remain in their infancy and do not yet offer any significant protection.
Challenges and Ethical Considerations
Fairness: AI can inherit biases intrinsic to data on which they train. Those biases can influence the outcomes selected by the AI model. Ensuring fairness in AI processes poses a significant challenge.
Privacy and Security Issues: AI systems collect and analyze vast amounts of personal data. This should and does raise concerns about privacy and data security. We need to ensure the safeguarding of such data, which may include personal information about individuals.
Automation/Job Loss: Automation of tasks through AI will likely lead to job displacement in some industries. We need to ensure that the workforce receives preparation. As a society, we need to consider what we will do about the displaced workers, their potential futures and economic security.
Existential Risks: AI’s increasing power and flexibility creates concerns about the potential for misuse or unintended consequences that could pose existential risks to our society and our population. Industry and governments have raised these concerns and the need for regulation to mitigate these risks. Doing so will require responsible AI development and implementing effective monitoring and governance.
Glossary of AI-Related Terms: We did not design this glossary as all-inclusive of every term you might encounter in connection with AI. It includes the terms we think you will most likely encounter in articles and discussions about AI.
Artificial Intelligence: Artificial Intelligence refers to the development of computer systems capable of performing typically requiring human intelligence.
Narrow (weak) AI: Also known as Weak AI, Narrow AI is designed for specific tasks, such as virtual personal assistants (Siri, Alexa), recommendation systems, and chatbots.
General (strong) AI: AI with human-level intelligence capable of performing any intellectual task a human can. True General AI remains a long-term goal.
Machine Learning (ML): A subfield of AI that focuses on developing algorithms and models enabling computers to improve performance on a task through experience, without explicit programming.
Supervised Learning: ML where the model trains on a labeled dataset, learning to make predictions or decisions based on input data.
Unsupervised Learning: ML where the model learns patterns and structures in data without labeled examples, often used for clustering and dimensionality reduction.
Reinforcement Learning: ML where agents learn to decide by interacting with an environment and receiving rewards or penalties.
Deep Learning: A subfield of machine learning using artificial neural networks, specifically deep neural networks, to model and solve complex problems, often achieving state-of-the-art performance in tasks like image and speech recognition.
Neural Networks: Computer models inspired by the structure and function of the human brain, consisting of interconnected nodes (neurons).
Natural Language Processing: A field of AI focusing on enabling computers to understand, interpret, and generate human language.
Sentiment Analysis: Analyzing text to determine the sentiment or emotional tone; often used in social media monitoring and customer feedback analysis.
Big Data: Large and complex datasets not easily managed or analyzed with traditional data processing tools. AI often leverages big data for training and decision-making.
Data Mining: Seeking and discovering patterns, trends, and insights in large datasets.
Neural networks: Layers of interconnected nodes. Different types of layers perform specific functions in deep learning models.
Algorithm: A step-by-step set of instructions or rules for solving a specific problem or performing a specific task. AI uses algorithms to train models and make predictions.
Metrics: Tools used to evaluate the performance of supervised learning models in tasks like classification and regression.
Autonomous: We describe a machine as autonomous if it can perform its task or tasks without human intervention.
Chatbot: A program designed to communicate with people through text or voice commands in a way that mimics human-to-human conversation.