How to build an AI model step-by-step
How to build an AI model step-by-step September 14, 2023 Authored: By Julian Horsey Published: Geeky Gadgets Summary: Creating AI models involves five interconnected stages: data preparation, model training, model validation, model tuning, and model deployment. Data is crucial, and preparation includes categorization and filtering. Model training involves choosing the right foundational model and tokenizing […]
A test of artificial intelligence
A test of artificial intelligence September 14, 2023 Authored: By Michael Eisenstein Published: Nature Summary: A study on OpenAI’s GPT-4 AI model suggests it displays rudimentary reasoning abilities. Researchers subjected GPT-4 to diverse challenges, including revising code to draw a unicorn. While some argue this indicates reasoning, others claim AI’s responses are probabilistic and lack […]
Generative AI: Are we Seeing the Future of Corporate L&D?
Diane Nowell – Headspring Executive Development
Generative AI, represented by ChatGPT from OpenAI, is rapidly gaining popularity and has the potential to transform corporate Learning & Development (L&D) programs. Its applications include creating personalized training content, improving access and inclusion for learners, scaling cross-organizational provision, delivering continuous learning at scale, and accelerating skills-based learning. However, human oversight and planning will remain crucial, as AI’s accuracy depends on the quality of training data and the human understanding of learning methodologies. Integrating generative AI into L&D culture will require careful consideration and may increase the workload for L&D departments.
7 Common Myths About AI
Sebastian Schaal – Medium
This article debunks several myths about artificial intelligence (AI), machine learning (ML), and deep learning (DL). It clarifies the distinctions between these terms, the role of data in ML, and the limitations of neural networks compared to the human brain. It also emphasizes that superintelligence is still a distant concept and that open-source initiatives have democratized ML research beyond major tech companies.
Can Machines Think?
Rockwell Anyoha – Harvard University
The article traces the history of Artificial Intelligence ( from its inception in science fiction to its development in the 20th century. It highlights the early challenges faced by AI due to computational limitations and the subsequent breakthroughs in algorithmic techniques and increased computer power. The article discusses AI’s achievements, including chess-playing computers and speech recognition software. The current age of “big data” and AI’s applications in various industries are explored, and the future prospects of AI, particularly in language processing and driverless cars, are considered. The ultimate goal of achieving general intelligence is mentioned, with ethical concerns surrounding it.