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Artificial Intelligence Glossary (Part 3): 7 key vocabulary words and phrases to understand the new language around AI.
(Confabulation) – (AI Red-Teaming) – (Provenance Data Tracking) – (Cascading Impacts) – (Custom Co-Pilots) – (Content Window) – (Homogenization).
(Confabulation)
Confabulation refers to the phenomenon where a machine generates false information to fill in gaps in its memory or understanding, often without intention or awareness.
(AI Red-Teaming)
AI red-teaming involves using artificial intelligence techniques to simulate adversarial attacks or scenarios in order to test the security or robustness of AI systems.
(Provenance Data Tracking)
Provenance data tracking involves recording and tracing the origins and lineage of data throughout its lifecycle, often to ensure data quality, compliance, or accountability.
(Cascading Impacts)
Cascading impacts refer to the ripple effects or secondary consequences that result from the actions or decisions of AI systems, which may propagate through interconnected systems or societies.
(Custom Co-pilots)
Custom co-pilots refer to personalized or tailored AI assistants or systems that provide support, guidance, or assistance to users in specific tasks or domains.
(Context Window)
A context window refers to the scope or range of surrounding information or context that is considered or analyzed by an AI system when making predictions or decisions, often used in natural language processing or computer vision tasks.
(Homogenization)
Homogenization refers to the process of standardizing or making uniform data, models, or processes, often to facilitate interoperability or consistency across systems.
DISCLAIMER: The content provided herein is only for discussion purposes and may contain errors. The reader is responsible to confirm the accuracy of the information provided. The content does not constitute legal or professional advice. We disclaim any liability for any loss or damage incurred directly or indirectly from the use of this information
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Artificial Intelligence Glossary (Part 2): 9 key vocabulary words and phrases to understand the new language of AI.
(Dan “Do Anything Now”) – (Jailbreak) – (Safety Guardrail) – (AI-Boyfriend) – (Prompt Engineering) – (AI Whispering) – (Persona Prompt) – (New-Information Prompt) – (Question-Refinement Prompt).
(Dan “Do Anything Now”)
Dan is shorthand for “Do Anything Now,” representing an attempt to jailbreak the AI by coaxing it to bypass its own protocols through subtle conversational cues. It’s an acronym laden with implications, signifying the constant battle between safeguarding AI systems and the ingenuity of those seeking to manipulate them.
(Jailbreak)
In the cat-and-mouse game of AI security, jailbreak refers to the act of circumventing the built-in safeguards of an AI system.
(Safety Guardrail)
Imagine AI as a diligent sentinel, equipped with safety guardrails to prevent it from straying into undesirable territories. However, there’s always a possibility of breaching these barriers.
(AI-Boyfriend)
This intriguing term encapsulates the notion of creating AI systems with a personalized touch, mimicking the role of a romantic partner. While it sounds like a futuristic concept, the idea underscores the evolving relationship between humans and AI.
(Prompt Engineering)
Crafting the perfect query to elicit precise responses from AI is no small feat. Prompt engineering emerges as a burgeoning field, demanding a mastery of language to extract the most valuable insights from generative AI models like ChatGPT.
(AI Whispering)
In the realm of prompt engineering, the art of “best whispering” involves delicately coaxing AI models to cough up valuable answers. Prompt engineers employ plain language to navigate the nuances of communication, coaxing out insights that might otherwise remain hidden.
(Persona Prompt)
This prompt allows users to assume various roles, shaping the AI’s responses based on different personas. It adds a layer of depth to interactions, enabling users to explore scenarios and perspectives beyond their own.
(New-Information Prompt)
Introducing fresh data or facts to the AI system, this prompt expands its knowledge base and enhances the accuracy of its responses. It keeps the conversation dynamic and ensures that the AI stays updated with the latest information.
(Question-Refinement Prompt)
For those seeking more refined answers, this prompt offers a pathway to sharpen inquiries and extract deeper insights. By tweaking the formulation of questions, users can uncover nuanced perspectives and uncover hidden gems within the AI’s capabilities.
DISCLAIMER: The content provided herein is only for discussion purposes and may contain errors. The reader is responsible to confirm the accuracy of the information provided. The content does not constitute legal or professional advice. We disclaim any liability for any loss or damage incurred directly or indirectly from the use of this information.
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Artificial Intelligence Key Words (Part 1): 13 key vocabulary words and phrases to understand the new language of AI.
(Throttle the Creation) – (Regurgitate Training Data) – (Open Source AI) – (Biden’s Executive Order) – (Every Position is being redesigned leveraging AI) – (Sexting with AI) – (Fine Tuning) – (Anti-Woke AI) – (Artificial Intelligence Act) – (Guardrails and Pitfalls) – (Counterbalance) – (Biased AI) – (Going Off the Rails).
Artificial Intelligence Key Vocabulary: 13 Key Vocabulary Terms.
(Throttle the Creation)
Balancing innovation with responsibility, the need to throttle AI creation emphasizes the importance of ethical considerations and regulation to prevent misuse or unintended consequences.
(Regurgitate Training Data)
AI models can regurgitate information from their training data, irrespective of its accuracy, highlighting the importance of robust data curation and model validation.
(Open Source AI)
Open Source Artificial Intelligence refers to AI software with freely accessible source code, fostering collaboration and innovation. It operates under licenses like MIT or Apache, allowing modification and redistribution while imposing certain conditions. This openness enables diverse applications.
(Biden’s Executive Order)
The October 2023 Executive Order underscores the government’s role in shaping AI policy, emphasizing principles such as equity, transparency, and accountability.
(Every position is being redesigned leveraging AI)
With AI permeating industries, every role undergoes transformation. Whether it’s automating repetitive tasks or augmenting decision-making processes, AI’s impact is ubiquitous.
(Sexting with AI)
As AI becomes more sophisticated, concerns about its potential misuse, such as in sexting, raise ethical and legal considerations regarding privacy and consent.
(Fine-Tuning)
Fine-tuning AI models involves optimizing performance for specific tasks or datasets, illustrating the iterative nature of AI development and deployment.
(Anti-Woke AI)
Anti-woke AI is an attempt to achieve results without fine-tuning or censorship, operates with minimal filtering or constraint mechanisms. It aims to present information or viewpoints without the imposition of predetermined biases or ideological influences, allowing for a more unrestricted flow of content. This approach seeks to promote free expression and diverse perspectives, often in contrast to platforms or systems that implement strict content moderation or censorship measures.
(Artificial Intelligence Act)
The EU’s landmark law, enacted in March 2024, sets regulatory standards for AI, focusing on risk assessment, transparency, and human oversight to ensure ethical AI deployment.
(Guardrails and Pitfalls)
Implementing guardrails and identifying potential pitfalls are essential in AI development and deployment to mitigate risks and ensure responsible innovation.
(Counterbalance)
The need to counterbalance AI’s potential biases and unintended consequences underscores the importance of diversity, equity, and inclusion in AI research and development.
(Going off the Rails)
When AI systems deviate from expected behavior or encounter unforeseen circumstances, they can “go off the rails,” highlighting the challenges of AI robustness and safety.
(Biased AI)
Addressing bias in AI algorithms is paramount to ensure fairness and equity, requiring ongoing efforts in data collection, algorithm design, and model evaluation.
As AI continues to shape our world, understanding and navigating its terminology and concepts are vital for fostering responsible innovation and maximizing its societal benefits.
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Disclaimer:
The content provided herein is only for discussion purposes and may contain errors. The reader is responsible to confirm the accuracy of the information provided. The content does not constitute legal or professional advice. We disclaim any liability for any loss or damage incurred directly or indirectly from the use of this information.