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Researchers from the Tokyo University of Science (TUS) have developed a method to enable large-scale AImodels to selectively “forget” specific classes of data. Progress in AI has provided tools capable of revolutionising various domains, from healthcare to autonomous driving.
This simplicity opens the door for people from all kinds of backgrounds to interact with AI and see how it works. By making explainable AI more approachable, LLMs can help people understand the workings of AImodels and build trust in using them in their work and daily lives.
At the root of AI mistakes like these is the nature of AImodels themselves. Most AI today use “blackbox” logic, meaning no one can see how the algorithm makes decisions. BlackboxAI lack transparency, leading to risks like logic bias , discrimination and inaccurate results.
This week, we are diving into some very interesting resources on the AI ‘blackbox problem’, interpretability, and AI decision-making. Parallely, we also dive into Anthropic’s new framework for assessing the risk of AImodels sabotaging human efforts to control and evaluate them. Enjoy the read!
The primary appeal of the model is its ability to handle complex reasoning tasks with high efficiency at a lower cost. It excels in performing logic-based problems, processing multiple steps of information, and offering solutions that are typically difficult for traditional models to manage.
The adoption of Artificial Intelligence (AI) has increased rapidly across domains such as healthcare, finance, and legal systems. However, this surge in AI usage has raised concerns about transparency and accountability. Composite AI is a cutting-edge approach to holistically tackling complex business problems.
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