AI integration modernizes factory operations and enables manufacturers to achieve greater business results. Manufacturing is getting a major system upgrade. As AI amplifies existing technologies--like digital twins, the cloud, edge computing, and the industrial internet of things (IIoT)--it is enabling factory operations teams to shift from reactive, isolated problem-solving to proactive, systemwide optimization. Digital twins--physically accurate virtual representations of a piece of equipment, a production line, a process, or even an entire factory--allow workers to test, optimize, and contextualize complex, real-world environments. Manufacturers are using digital twins to simulate factory environments with pinpoint detail. "AI-powered digital twins mark a major evolution in the future of manufacturing, enabling real-time visualization of the entire production line, not just individual machines," says Indranil Sircar, global chief technology officer for the manufacturing and mobility industry at Microsoft.

The rapid growth of artificial intelligence (AI), particularly Large Language Models (LLMs), has raised concerns regarding its global environmental impact that extends beyond greenhouse gas emissions to include consideration of hardware fabrication and end-of-life processes. The opacity from major providers hinders companies' abilities to evaluate their AI-related environmental impacts and achieve net-zero targets. In this paper, we propose a methodology to estimate the environmental impact of a company's AI portfolio, providing actionable insights without necessitating extensive AI and Life-Cycle Assessment (LCA) expertise. Results confirm that large generative AI models consume up to 4600x more energy than traditional models. Our modelling approach, which accounts for increased AI usage, hardware computing efficiency, and changes in electricity mix in line with IPCC scenarios, forecasts AI electricity use up to 2030. Under a high adoption scenario, driven by widespread Generative AI and agents adoption associated to increasingly complex models and frameworks, AI electricity use is projected to rise by a factor of 24.4. Mitigating the environmental impact of Generative AI by 2030 requires coordinated efforts across the AI value chain. Isolated measures in hardware efficiency, model efficiency, or grid improvements alone are insufficient. We advocate for standardized environmental assessment frameworks, greater transparency from the all actors of the value chain and the introduction of a "Return on Environment" metric to align AI development with net-zero goals.

Artificial Intelligence (AI) is a widely developed and adopted technology across entire industry sectors. Integrating environmental, social, and governance (ESG) considerations with AI investments is crucial for ensuring ethical and sustainable technological advancement. Particularly from an investor perspective, this integration not only mitigates risks but also enhances long-term value creation by aligning AI initiatives with broader societal goals. Yet, this area has been less explored in both academia and industry. To bridge the gap, we introduce a novel ESG-AI framework, which is developed based on insights from engagements with 28 companies and comprises three key components. The framework provides a structured approach to this integration, developed in collaboration with industry practitioners. The ESG-AI framework provides an overview of the environmental and social impacts of AI applications, helping users such as investors assess the materiality of AI use. Moreover, it enables investors to evaluate a company's commitment to responsible AI through structured engagements and thorough assessment of specific risk areas. We have publicly released the framework and toolkit in April 2024, which has received significant attention and positive feedback from the investment community. This paper details each component of the framework, demonstrating its applicability in real-world contexts and its potential to guide ethical AI investments.

Few technological advances have generated as much excitement as AI. In particular, generative AI seems to have taken business discourse to a fever pitch. Many manufacturing leaders express optimism: Research conducted by MIT Technology Review Insights found ambitions for AI development to be stronger in manufacturing than in most other sectors. They see AI's utility in enhancing product and process innovation, reducing cycle time, wringing ever more efficiency from operations and assets, improving maintenance, and strengthening security, while reducing carbon emissions. Some manufacturers that have invested to develop AI capabilities are still striving to achieve their objectives.

This also feels like the end of an era for me personally: I was the first reporter to get the scoop on an early draft of the AI Act in 2021, and have followed the ensuing lobbying circus closely ever since. But the reality is that the hard work starts now. The law will enter into force in May, and people living in the EU will start seeing changes by the end of the year. Regulators will need to get set up in order to enforce the law properly, and companies will have between up to three years to comply with the law. The Act places restrictions on AI use cases that pose a high risk to people's fundamental rights, such as in healthcare, education, and policing.

I recently had a scare during the holidays when my octogenarian father, visiting from out of town, fell in the kitchen during our Christmas Eve gathering. My heart skipped a beat at that moment and 10 things ran through my mind about what to do next, like wishing we had a doctor in the family! So going to the ER was not the answer. However, we had concerns for him, and I needed some peace of mind. Could I give him Tylenol, or would that interfere with his current medicines?

If you're unfamiliar with this combination, chances are you are missing out on a lot. The main goals of financial institutions – banks, hedge funds, and insurance companies – are minimizing risks, reducing costs, and providing high-end customer services to clients using AI. With vast amounts of data in the financial sector, it becomes increasingly important to use AI for data analysis, risk management, personalized service, and managing portfolios. In this blog, we will learn about AI use cases in finance, its benefits, and the challenges financial institutions face while employing AI. AI is a combination of data, computational power, and technology.

Two-thirds of the respondents (67%) said they were still learning how AI works and exploring use cases and technologies. Just 15% of the respondents reported that they had achieved wide-scale implementation of AI. When asked how they would classify their understanding of AI terminology and capabilities, 45% of the respondents rated their level of understanding as beginner, 43% said intermediate, and only 12% said advanced. In addition, only 29% of the respondents said they are highly confident or very highly confident in their ability to evaluate AI-powered marketing technologies. The research found that marketers recognize the importance of AI and expect its use to grow significantly in the near future.

Organisations across the board are looking to artificial intelligence (AI) to find ways to more accurately manage risk, enhance efficiencies to reduce operating costs, and improve experiences for clients and customers. Nvidia has conducted a survey with some of the world's leading financial institutions to find out what's on the top of their minds. Below are the top four findings gleaned from the "State of AI in Financial Services: 2023 Trends" survey taken by nearly 500 global financial services professionals. Financial services firms, like other enterprises, are looking to optimise spending for AI training and inference -- with the knowledge that sensitive data can't be migrated to the cloud. To do so cost-effectively, they're moving many of their compute-intensive workloads to the hybrid cloud.

This White Paper assesses the progress of three pillars of U.S. leadership in AI innovation and trustworthy AI that carry the force of law: (i) the AI in Government Act of 2020; (ii) the Executive Order on "AI Leadership"; and (iii) the Executive Order on "AI in Government." Collectively, these Executive Orders and the AI in Government Act have been critical to defining the U.S. national strategy on AI and envisioning an ecosystem where the U.S. government leads in AI and promotes trustworthy AI. We systematically examined the implementation status of each requirement and performed a comprehensive search across 200 federal agencies to assess implementation of key requirements to identify regulatory authorities pertaining to AI and to enumerate AI use cases. While much progress has been made, our findings are sobering. America's AI innovation ecosystem is threatened by weak and inconsistent implementation of these legal requirements.