article thumbnail

With Generative AI Advances, The Time to Tackle Responsible AI Is Now

Unite.AI

Today, seven in 10 companies are experimenting with generative AI, meaning that the number of AI models in production will skyrocket over the coming years. As a result, industry discussions around responsible AI have taken on greater urgency.

article thumbnail

Advancing AI trust with new responsible AI tools, capabilities, and resources

AWS Machine Learning Blog

As generative AI continues to drive innovation across industries and our daily lives, the need for responsible AI has become increasingly important. At AWS, we believe the long-term success of AI depends on the ability to inspire trust among users, customers, and society.

professionals

Sign Up for our Newsletter

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

article thumbnail

Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning Blog

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

article thumbnail

Using AI technologies for future asset management

AI News

Solution: The company was able to quickly evaluate large datasets by implementing an AI-powered predictive analytics system. The AI algorithms examined market patterns, assessed risk factors, and dynamically altered the portfolio.

article thumbnail

Navigating AI Bias: A Guide for Responsible Development

Unite.AI

Businesses relying on AI must address these risks to ensure fairness, transparency, and compliance with evolving regulations. The following are risks that companies often face regarding AI bias. Algorithmic Bias in Decision-Making AI-powered recruitment tools can reinforce biases, impacting hiring decisions and creating legal risks.

Algorithm 159
article thumbnail

DeepMind Introduces JEST Algorithm: Making AI Model Training Faster, Cheaper, Greener

Unite.AI

The learning algorithms need significant computational power to train generative AI models with large datasets, which leads to high energy consumption and a notable carbon footprint. In this article, we explore the challenges of AI training and how JEST tackles these issues.

Algorithm 193
article thumbnail

Daniel Cane, Co-CEO and Co-Founder of ModMed – Interview Series

Unite.AI

However, poor data sourcing and ill-trained AI tools could have the opposite effect, leaving providers to instead spend an inordinate amount of time fixing errors and re-writing notes. Additionally, bias is a significant risk associated with AI algorithms, and quality data can play a key role in mitigating healthcare disparities.