Gary Ackerman @garyackerman@qoto.org

The reason "artificial intelligence" is so hard to measure has to do with the noun, not the adjective.

Data Security helps maintain data integrity and keep data safe. Know about access requirements (who sees data), data encryption (making data unreadable), data transmission protection, and data masking. #DataSecurity #DataIntegrity

Cybersecurity programs aim to protect the confidentiality, integrity, and availability of information and systems, often referred to as the CIA Triad.

Effective curriculum design should consider the zone of proximal development for each student and provide a range of support mechanisms.

With Zone of Proximal Development, think of learning difficulty on a graph: skills below the ZPD are ones students can do independently. Skills above the ZPD are currently beyond their capacity.

Standard deviation measures data dispersion around the mean, indicating how spread out the values are. A higher standard deviation implies greater volatility or variability in the data.

Measures of frequency, like counts and percentages, help understand how often specific values or categories occur within a dataset. This provides insight into the distribution and commonality of data points.

Importantly, generative AI and traditional AI are not mutually exclusive. Generative AI can complement existing traditional AI investments, like using generative models for free conversation and predictive models for sentiment analysis in a chatbot. #AIIntegration #AICoexistence

Adopting Generative AI requires a strategic approach for enterprises. Considerations include risks like generating incorrect or biased content (hallucinations) and needing robust governance & responsible AI policies. #AIStrategy #ResponsibleAI

Large Language Models (LLMs) are central to the recent excitement around generative AI. Trained on immense text, they understand & generate human-like text for diverse tasks like answering questions or writing code. #LLMs #AIModels

Traditional AI models are generally less complex and require fewer resources, running on various hardware. Generative AI models, especially LLMs, are large, complex, and often require large cloud compute nodes. #AIComplexity #AIResources

Training differs greatly: Traditional AI uses smaller datasets of labeled data. Generative AI trains on massive datasets of existing content, like millions of images or vast amounts of text. #AIData #AITraining

Traditional AI follows a deterministic, rule-based approach, resulting in predictable outcomes. Generative AI uses a probabilistic approach, leading to varied, non-deterministic outcomes not explicitly programmed.

The most fundamental difference? Traditional AI predicts or classifies (like identifying spam). Generative AI is designed to create entirely new content, such as realistic text, images, code, or music. #AICreation #AIDifference

Traditional AI, also known as narrow AI, operates using classical data science and a systematic approach. It's focused on prediction or classification tasks based on existing data within predefined boundaries. #TraditionalAI #NarrowAI

Generative AI is capturing public interest, driving discussions and seen as a key driver for the next wave of digital transformation. It's fundamentally different from traditional AI. #GenerativeAI #DigitalTransformation

Can machines truly think or merely simulate thought?

Technologies are non-neutral. They influence how we act, interact, and especially how we think. Information itself may be treated neutrally by systems, but its impact on humans is not.

Problems that are complex, irreducible, and have a social dimension, like education, are recognized as wicked problems. Unlike tame problems, their causes aren't clear, they're hard to understand, and solutions are tentative.

Educational planning has historically focused on setting goals & measuring outcomes, often based on standardized tests. This approach, common for tame problems, is applied despite contentious debates about validity.