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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.
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!
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.
The Path Forward: Balancing Innovation with Transparency To address the risks associated with large language models' reasoning beyond human understanding, we must strike a balance between advancing AI capabilities and maintaining transparency.
The models are becoming more and more complex with deeper layers leading to greater accuracy. It is very risky to apply these black-boxAI systems in real-life applications, especially in sectors like banking and healthcare. One issue with this current trend is the focus on interpretability is lost at times.
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.
Bias and Inequality AI can also introduce societal issues like exaggerating bias if corporations aren’t careful. Amazon’s scrapped hiring AImodel infamously penalized women’s resumes as the machine learning algorithm expanded on implicit biases within the training data.
The new field of MLOps offers a much stronger framework for model validation, documentation, and oversight than traditional manual efforts, while more closely aligning to the ever increasing regulatory requirements and vastly reducing “model risk.”. The post What is Model Risk and Why Does it Matter? More on this topic.
What is contained in the model is an enormous set of parameters based on all the content that has been ingested during training, that represents the probability that one word is likely to follow another. The ability to emit a sonnet that Shakespeare never wrote: that’s transformative, even if the new sonnet isn’t very good.
In our testing, we found that QA-GPT can cover over 85% of scorecard questions out of the box without any extra configuration. Say goodbye to black-boxAImodels where you’re never quite sure if the AI got it right. We’re also improving the transparency of evaluations.
Principles of Explainable AI( Source ) Imagine a world where artificial intelligence (AI) not only makes decisions but also explains them as clearly as a human expert. This isn’t a scene from a sci-fi movie; it’s the emerging reality of Explainable AI (XAI). Present the model’s predictions to stakeholders.
By Tushar-Aggarwal.com (Author) The Dilemma of Open Science and Data Frontiers in Forum, a platform dedicated to open science and information, present an intriguing challenge when it comes to regulating AI. Most AImodels rely on publicly available data, including patents, books, and scriptures, to train their algorithms.
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