Top Artificial Intelligence (AI) Terms You Should Know and Understand
Top AI Terms to Know and Understand:
Artificial Intelligence (AI) is no longer a futuristic concept confined to sci-fi novels and movies; it has become an integral part of our daily lives.
From virtual assistants like Siri and Alexa to personalized product recommendations, AI is transforming the way we interact with technology. However, understanding the vast and ever-evolving world of AI can be daunting.
Here are the definitions of top AI terms you need to know and understand:
Artificial Intelligence (AI)
At its core, AI refers to the simulation of human intelligence in machines that are programmed to think and act like humans. It encompasses a wide range of techniques, algorithms, and technologies that enable machines to perform tasks that typically require human intelligence, such as problem-solving, learning, speech recognition, and decision-making.
AI is a discipline that attempts to create systems capable of thought or behavior based on natural processes. These natural processes include vision, language processing, reasoning, and learning. AI applies research from computer science, psychology, mathematics, linguistics, and neuroscience to develop problem-solving applications that supplant or augment human intellectual performance.
Generative AI refers to to a new suite of tools that can generate a wide variety of content and digital assets. By leveraging state of the art AI, these tools can support the enterprise by producing valuable material ranging from software code to images, video, and text.
Generative AI can create original media content, synthetic data and models of physical objects to provide breakthrough innovation opportunities. Enterprise architecture and technology innovation leaders must evaluate the various use cases for applying generative AI in their organizations.
Conversational AI—what we used to refer to as AI chatbots—involves the technologies behind application interfaces that accept human input in natural language as text or voice and produce context-appropriate responses.
Conversational AI uses natural language processing (NLP) technologies and machine learning (ML) to provide an engaging interface for applications. Conversational interfaces are able to interpret inputs using natural language understanding (NLU) technology and respond with a context-appropriate output or action.
Machine Learning (ML)
Machine Learning is a subset of AI that enables machines to learn from data and improve their performance over time without explicit programming. ML algorithms use patterns and insights from the data to make predictions, classify information, or automate tasks. Supervised, unsupervised, and reinforcement learning are some common types of ML approaches.
Deep Learning is a specialized branch of ML that involves artificial neural networks inspired by the structure and function of the human brain. These networks consist of multiple layers of interconnected nodes (neurons) that process data and extract hierarchical representations. Deep Learning has proven particularly powerful in tasks like image and speech recognition.
Natural Language Processing (NLP)
Natural language processing (NLP) is the capacity for a machine to take spoken or written natural language as input instructions or data, and produce responses or reports in natural language as output. NLP has been one of the defining goals of artificial intelligence (AI) since the inception of the discipline over 60 years ago. Conversational AI uses Natural Language Processing technologies and Machine Learning to provide an engaging interface for applications.
Computer Vision is an AI field that empowers machines to interpret and understand visual information from images and videos. It enables applications like facial recognition, object detection, and autonomous vehicles.
Robotics integrates AI and other technologies to design, build, and program machines (robots) capable of performing tasks autonomously or semi-autonomously. Robotic systems range from industrial robots used in manufacturing to social robots designed to interact with humans.
Reinforcement Learning is a type of ML where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, helping it learn the most optimal strategies to achieve its goals.
Algorithm Bias refers to the unfair or prejudiced outcomes that can arise from AI algorithms due to biased training data or flawed design. It is essential to address bias in AI systems to ensure fairness and equity in their applications.
Data Privacy and Ethics
AI applications often rely on vast amounts of data, raising concerns about data privacy and ethical considerations. Understanding the ethical implications of AI is crucial to safeguarding individual rights and avoiding misuse of personal information.
Embracing AI technology requires more than just buzzword knowledge.
Understanding these essential AI terms lays the groundwork for informed discussions and informed decision-making in an increasingly AI-driven world. Stay curious and keep exploring as AI continues to evolve, shaping our future in ways we couldn’t have imagined before.
Cybercriminals are aggressively weaponizing artificial intelligence (AI) to launch increasingly effective cyberattacks against organizations.
These cybercriminals are using AI to launch sophisticated and stealthy cyberattacks, such as creating realistic deep fakes, generating malware that can evade detection systems, creating convincing phishing emails, or identifying and exploiting vulnerabilities in real-time.
In this webinar, you will learn how AI can help you fight fire with fire to combat and survive these AI-powered cybersecurity attacks.
Some key areas we’ll cover:
- How cybercriminals are changing the game with AI?
- What solutions are available to combat these threats?
- How emerging AI technologies will transform cybersecurity?