Ahmed Banafa Tech Expert

Ahmed Banafa is an expert in new tech with appearances on ABC, NBC , CBS, FOX TV and radio stations. He served as a professor, academic advisor and coordinator at well-known American universities and colleges. His researches are featured on Forbes, MIT Technology Review, ComputerWorld and Techonomy. He published over 100 articles about the internet of things, blockchain, artificial intelligence, cloud computing and big data. His research papers are used in many patents, numerous thesis and conferences. He is also a guest speaker at international technology conferences. He is the recipient of several awards, including Distinguished Tenured Staff Award, Instructor of the year and Certificate of Honor from the City and County of San Francisco. Ahmed studied cyber security at Harvard University. He is the author of the book: Secure and Smart Internet of Things Using Blockchain and AI

 
Understanding the Opportunities & Concerns of AI Hallucinations

Understanding the Opportunities & Concerns of AI Hallucinations

The term "AI hallucination" in this context refers to the ability of AI models to generate content that is not based on any real-world data, but rather is a product of the model's own imagination.

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4 Pillars of Artificial Intelligence Ethics

4 Pillars of Artificial Intelligence Ethics

Artificial intelligence (AI) is transforming our world in countless ways, from healthcare to education, business to cybersecurity.

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New AI and Robotics

New AI and Robotics

Artificial Intelligence (AI) and robotics are two rapidly evolving fields that have the potential to revolutionize the way we live and work.

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Innovative Careers in the Age of Artificial Intelligence

Innovative Careers in the Age of Artificial Intelligence

Artificial Intelligence (AI) is set to revolutionize the workplace by creating new jobs.

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Generative AI: Types, Skills, Opportunities and Challenges

Generative AI: Types, Skills, Opportunities and Challenges

Generative AI refers to a class of machine learning techniques that aim to generate new data that is similar to, but not identical to, the data it was trained on.

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