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Tech Leaders Highlighting the Risks of AI & the Urgency of Robust AI Regulation

Unite.AI

Statista reports that by 2024, the global AI market will generate a staggering revenue of around $3000 billion, compared to $126 billion in 2015. However, tech leaders are now warning us about the various risks of AI. These AI-backed developments are vulnerable due to many AI shortcomings that malicious agents can expose.

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UK commits £13M to cutting-edge AI healthcare research

AI News

Dr Antonio Espingardeiro, IEEE member and software and robotics expert, comments: “As it becomes more sophisticated, AI can efficiently conduct tasks traditionally undertaken by humans. The University of Oxford’s project, bolstered by £640,000, seeks to expedite research into a foundational AI model for clinical risk prediction.

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What Is Trustworthy AI?

NVIDIA

Trustworthy AI initiatives recognize the real-world effects that AI can have on people and society, and aim to channel that power responsibly for positive change. What Is Trustworthy AI? Trustworthy AI is an approach to AI development that prioritizes safety and transparency for those who interact with it.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

It encompasses risk management and regulatory compliance and guides how AI is managed within an organization. Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data.

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What is Google’s DeepMind Known For? A Look Into Their Major Breakthroughs

ODSC - Open Data Science

EVENT — ODSC East 2024 In-Person and Virtual Conference April 23rd to 25th, 2024 Join us for a deep dive into the latest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI. AlphaGo AlpahGo is the program that turned the game of Go on its head.

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Synthetic Data: A Model Training Solution

Viso.ai

Organizations can easily source data to promote the development, deployment, and scaling of their computer vision applications. An example is a privacy-preserving solution for developing healthcare AI models. This allows for: Developing Robust and Generalizable AI Models. Rapid AI Development.

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Breaking down the advantages and disadvantages of artificial intelligence

IBM Journey to AI blog

But even with the myriad benefits of AI, it does have noteworthy disadvantages when compared to traditional programming methods. AI development and deployment can come with data privacy concerns, job displacements and cybersecurity risks, not to mention the massive technical undertaking of ensuring AI systems behave as intended.