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The Evolution of AI in Asset Management

Alpha Sense BI

AI enables analysts to uncover opportunities even amidst volatility, operate with higher agility, perform data analysis at scale, and assess risk more accurately. As time goes on and more firms adopt AI tools, these capabilities will become even more essential to maintain a competitive advantage.

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What is the Cost of Hiring Data Savvy Software Developers?

Smart Data Collective

How Much Can a Software Developer with a Background in Data Science Really Earn? Nowadays competitive firms of all sizes are financing custom-made software solutions to extend effectiveness and productivity, establish new business areas and increase innovation. The majority of offshore projects go bankrupt due to: 1.

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Generative AI in Energy

Alpha Sense BI

Decision-making within the space has traditionally relied on expert knowledge, historical data analysis , and complex modeling techniques. With its ability to analyze vast amounts of data, generate accurate models, and much more, this iteration of AI is revolutionizing the way business is being conducted.

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Generative AI in Manufacturing

Alpha Sense BI

Over the past year, generative artificial intelligence ( genAI ) has rapidly accelerated digital transformation around the globe due to AI tools like ChatGPT , Jasper, and Bard. By harnessing it, organizations can gain a competitive edge, improve efficiency, and deliver innovative AI solutions to meet the evolving needs of their customers.

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Generative AI in Financial Services

Alpha Sense BI

By harnessing the power of this new technology , financial professionals can leverage advanced algorithms and deep learning capabilities to analyze vast amounts of financial data , organize unstructured data , identify patterns, and generate valuable insights in a fraction of the time it would ordinarily take.

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AI in Biopharma: Use Cases and Considerations

Alpha Sense BI

Through the use of complex algorithms , AI sifts through large datasets , identifying potential drug candidates and biomarkers much swifter than by manual means. Further, AI’s predictive modeling algorithms refine drug target validation, thus reducing the attrition rates during the expensive clinical testing phases.