article thumbnail

Machine unlearning: Researchers make AI models ‘forget’ data

AI News

Compounding these issues is that generalist tendencies may hinder the efficiency of AI models when applied to specific tasks. For instance, in practical applications, the classification of all kinds of object classes is rarely required, explains Associate Professor Go Irie, who led the research.

article thumbnail

How Large Language Models Are Unveiling the Mystery of ‘Blackbox’ AI

Unite.AI

Thats why explainability is such a key issue. People want to know how AI systems work, why they make certain decisions, and what data they use. The more we can explain AI, the easier it is to trust and use it. Large Language Models (LLMs) are changing how we interact with AI. Imagine an AI predicting home prices.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

AI for Money Managers: Avoid the Black Box – And Do This Instead

Unite.AI

The opportunities afforded by AI are truly significant – but can we trust black box AI to produce the right results? Instead of utilizing AI systems that they cannot explainblack box AI systems – they could utilize AI platforms that use transparent techniques , explaining how they arrive at their conclusions.

article thumbnail

The Hidden Risks of DeepSeek R1: How Large Language Models Are Evolving to Reason Beyond Human Understanding

Unite.AI

This success, however, has come at a cost, one that could have serious implications for the future of AI development. The Language Challenge DeepSeek R1 has introduced a novel training method which instead of explaining its reasoning in a way humans can understand, reward the models solely for providing correct answers.

article thumbnail

Who Is Responsible If Healthcare AI Fails?

Unite.AI

At the root of AI mistakes like these is the nature of AI models themselves. Most AI today use “black box” logic, meaning no one can see how the algorithm makes decisions. Black box AI lack transparency, leading to risks like logic bias , discrimination and inaccurate results.

article thumbnail

#48 Interpretability Might Not Be What Society Is Looking for in AI

Towards AI

Building Trustworthy AI: Interpretability in Vision and Linguistic Models By Rohan Vij This article explores the challenges of the AI black box problem and the need for interpretable machine learning in computer vision and large language models.

article thumbnail

Enhancing AI Transparency and Trust with Composite AI

Unite.AI

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.