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For this article, AI News caught up with some of the worlds leading minds to see what they envision for the year ahead. Smaller, purpose-driven models Grant Shipley, Senior Director of AI at Red Hat , predicts a shift away from valuing AImodels by their sizeable parameter counts. The solutions?
Across industries, the exponential growth of technologies such as hybrid cloud, data and analytics, AI and IoT have reshaped the way businesses operate and heightened customer expectations. Businesses are now entering an even greater digital era marked by broader applications of AI, including generative AImodels.
Data products come in many forms including datasets, programs and AImodels. Take the example of a client who integrated a set of disparate company ESG data into a new dataset. Their data services were a full dataset download plus an API wrap around the data, which could be queried for ESG data based on a company ticker symbol.
Formerly Chief Strategy Officer at ACA, Raj oversaw corporate development and M&A, also serving as Interim Co-CEO, Chief Innovation Officer, and Head of RegTech and ESG. Thank you for the great interview, readers who wish to learn more should visit BlueFlame AI. to complete deals.
Supplier visibility and traceability is growing in importance to help achieve environmental, social and governance (ESG) targets. Siloed processes can become integrated by using intelligent workflows, which help enable seamless and automated exchange of financial, informational and physical supply chain data in one distributed network.
Organizations are facing ever-increasing requirements for sustainability goals alongside environmental, social, and governance (ESG) practices. This post serves as a starting point for any executive seeking to navigate the intersection of generative artificial intelligence (generative AI) and sustainability. A Gartner, Inc.
But simultaneously, generative AI has the power to transform the process of application modernization through code reverse engineering, code generation, code conversion from one language to another, defining modernization workflow and other automated processes. Much more can be said about IT operations as a foundation of modernization.
And while use cases for generative AI in supply chains are expansive – including increased automation, demand forecasting, order processing and tracking, predictive maintenance of machinery, risk management, supplier management, and more – many also apply to predictive AI and have already been adopted and deployed at scale.
Scienaptic AI : Also based in New York, Scienaptic AI introduces a new era of credit underwriting. Using non-tradeline data and adaptive AImodels, Scienaptic AI equips banks and credit institutions with predictive intelligence, thereby innovating the traditional underwriting process.
Figure 1: Sources of competitive advantage in an AI system (cf. Lets take the example of an airline business to illustrate some opportunities across these categories: Figure 2: Mapping AI opportunities for an airline Of course, the first branch productivity and automation looks like the low-hanging fruit.
In such cases, companies are training AImodels on their own, historical data and what they can glean from partners. According to EY, one area in which supply chain companies are exploring the use of GenAI is regulatory and ESG reporting. Then they’re asking GenAI to find ways to boost efficiency.
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