AI has profound implications for drug discovery, ushering in a new era of innovation and efficiency. Recent milestones - such as the first AI-designed drug molecule to enter human clinical trials and the prediction of protein structures for millions of proteins - showcase the transformative power of AI in this domain.
The future of Artificial Intelligence (AI) holds the promise of a transformative era where machines evolve from mere tools to influential collaborators. As AI technologies advance, they are set to reshape industries, augment human capabilities, and unlock unprecedented insights from data.
Retail fraud and loss prevention have always been significant business concerns impacting profitability and customer trust. However, with the emergence of AI technologies, there is a newfound potential to combat these challenges more effectively.
The OECD.AI Policy Observatory is an inclusive platform that brings together resources and expertise from the OECD and its partners to facilitate dialogue and provide evidence-based policy analysis on the impact of AI. It is built upon the foundation of the OECD AI Principles, the first intergovernmental standard on AI adopted in 2019, endorsed by OECD countries and partner economies.
Implementing responsible AI in the financial sector is crucial for ethical practices, fairness, and transparency. Financial institutions must prioritize data privacy, address biases, ensure explainability, and practice ongoing monitoring. By doing so, they build trust, mitigate risks, and foster sustainable growth.
As nearly every American household has realized over the last year, large language models combined with generative AI abilities pose tremendous challenges and opportunities for enterprises of every shape and size. Just ask anyone who has heard of ChatGPT.
In today's rapidly evolving world, pursuing AI excellence has become a top priority for organizations across industries. However, the path to building exceptional AI teams is not solely paved with technical expertise and cutting-edge algorithms. The true key to unlocking the full potential of AI lies in ensuring that the teams leading the charge in enterprise digital transformations are working cohesively.
In the era of big data, companies need help navigating through an overwhelming volume of unstructured data to uncover meaningful insights. The topic search process presents unique challenges in deciphering data signals and identifying critical information before problems escalate.
AI adoption poses several challenges for organizations, including the need for specialized skills and expertise, integration with legacy systems, data quality and accessibility, and the ethical implications of AI.
As a business practice, model development aims to create a dataset, tailored through machine learning, that can accurately predict outcomes or classify data based on input variables. By following a structured approach, developers can ensure that the model development process is efficient, effective, and reproducible.
Traditionally, responsible AI management practices are not just a matter of theoretical ethics debates but a practical matter of compliance. Financial sectors are highly regulated and monitored to ensure the safety and security of consumers and the overall economy. Many of those regulations are written so that responsible management practices are meant to avoid penalties.
From work-from-home policies and digital-first communication with prospects – the COVID-19 pandemic dramatically accelerated many trends already in progress for businesses and consumers. Among sales teams, digitally enabling buyer journeys became a must-have almost overnight.
MetLife is a leading global insurance company headquartered in New York City. It provides its customers various insurance and financial services, including life insurance, health insurance, retirement plans, and investment management.
Critical to the definition of robotic process automation (RPA) is the notion that the tasks a 'robotic' software automates are repetitive by nature, with exceptions in rare instances. While RPA cannot independently learn from and adapt to new contexts and workflow problems, it can if the RPA system is imbued with the correct AI capabilities.
A paradigm shift is happening in the manufacturing industry. Advancement in big data and machine learning is changing traditional manufacturing processes into the era of intelligent manufacturing. The concept of what gets called "industry 4.0" encourages the use of smart sensors, devices, and machines – going beyond the motives of collecting data about production.