AI in science and research Artificial intelligence (AI) has emerged as a revolutionary force in science and research, transforming how we approach, analyze, and interpret data. The integration of AI technologies into scientific research has opened up new avenues for discovery, significantly improving efficiency, accuracy, and scope of research activities. In this blog, we will explore the various ways AI is being utilized in science and research, along with some comparative analytics to highlight its growing impact.
Above: video of Thomas Wood presenting Harmony at the Pydata on 27 March 2024
Generative AI Introduction Generative AI, a subset of AI, is fundamentally transforming industries and shaping the future. Leveraging advanced algorithms, generative AI can create content, designs, and solutions that were previously unimaginable. By using machine learning models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), generative AI systems can produce new data that mimics the patterns and structures of the training data.
Big Data The emergence of big data has revolutionized industries, transforming traditional business models and decision-making processes. In this comprehensive exploration, we delve into what big data is, its significant impacts on business strategy, and how companies can leverage vast datasets to drive innovation and competitive advantage.
AI in Finance The integration of artificial intelligence (AI) into the finance sector has revolutionized how institutions operate, from automating operations to enhancing customer engagement and improving risk management. This comprehensive exploration will delve into several key areas where AI is making significant inroads within the industry.
On 29 May, Thomas Wood presented a webinar on how AI and Natural Language Processing (NLP) can transform clinical trials in the pharmaceutical industry.
AI in business and industry Artificial intelligence (AI) is a hot topic in business, but many companies are unsure how to leverage it effectively. I will discuss some practical applications of AI, focusing on areas where it can deliver real impact.
Modelling risk and cost in clinical trials with NLP Fast Data Science’s Clinical Trial Risk Tool Clinical trials are a vital part of bringing new drugs to market, but planning and running them can be a complex and expensive process. A key part of this planning is accurately estimating the cost and risk of a trial. Traditionally, this has involved a team of experts manually sifting through lengthy clinical trial protocols, often hundreds of pages long.
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