Remove 2020 Remove Explainability Remove Explainable AI
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Beyond the Hype: Unveiling the Real Impact of Generative AI in Drug Discovery

Unite.AI

The allure of AI-driven drug discovery lies in its promise of creating novel therapies faster and cheaper, providing a solution to one of the industry's biggest challenges: the high cost and long timelines of bringing new drugs to market.

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Meet the Fellow: Umang Bhatt

NYU Center for Data Science

Motivated by applications in healthcare and criminal justice, Umang studies how to create algorithmic decision-making systems endowed with the ability to explain their behavior and adapt to a stakeholder’s expertise to improve human-machine team performance. By Meryl Phair

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Explainability in AI and Machine Learning Systems: An Overview

Heartbeat

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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GenAI: How to Synthesize Data 1000x Faster with Better Results and Lower Costs

ODSC - Open Data Science

Indeed, the whole technique epitomizes explainable AI. Figure 1: Synthetic data (left) versus real (right), Telecom dataset The main hyperparameter vector specifies the number of quantile intervals to use for each feature (one per feature). It is easy to fine-tune, allowing for auto-tuning.

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Trusted AI for Homeland Security

DataRobot Blog

DataRobot is a proud partner enabling governments and organizations to face their unique challenges and leverage AI for value they can trust. Numerous government agencies recognize the opportunity with AI. DataRobot believes trusted, explainable AI can help generate better outcomes than either humans or machines alone.

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AI for Climate Change and Weather Risk

DataRobot Blog

From 2018 to 2020, the U.S. Using either the code-centric DataRobot Core or no-code Graphical User Interface (GUI), both data scientists and non-data scientists such as risk analysts, government experts, or first responders can build, compare, explain, and deploy their own models. The scale and costs of weather disasters in the U.S.

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Financial Market Challenges and ML-Supported Asset Allocation

ODSC - Open Data Science

For example, rising interest rates and falling equities already in 2013 and again in 2020 and 2022 led to drawdowns of risk parity schemes. However, changing correlations can be a challenge for this type of portfolio allocation technique. In 2023-Q1, we even saw failing banks like SVB simply because of investments in “safe” treasury bonds.

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