DeAngelisReview

This article critically examines the term "artificial intelligence" as a misleading buzzword, advocating for the more precise "machine learning" terminology. It discusses the benefits of machine learning, provides guidance on getting started with it, highlights its business applications, and emphasizes the importance of having a clear strategy and relevant data.

This article explores the philosophical debate around the simulation hypothesis - the idea that our reality is an advanced computer simulation. It discusses arguments for and against this proposition, drawing from physics, mathematics, and the fine-tuned nature of the universe's constants.

This article explains cognitive computing as an "adolescent" form of AI, operating on large datasets to handle ambiguous, complex problems between narrow AI and AGI. It discusses when to use cognitive computing and provides guidelines from the Cognitive Computing Consortium.

This article discusses key trends driving artificial intelligence and machine learning in the coming years, including ModelOps, cloud computing, federated learning, edge computing, natural language processing, generative AI, and the pursuit of better-advanced analytics to enable more complex "System 2" thinking capabilities.

This article discusses the role of cognitive computing in augmenting human decision-making capabilities, particularly in complex and ambiguous situations. It highlights cognitive computing's ability to synthesize diverse information, weigh conflicting evidence, and provide context-aware insights to support better decisions.

This article discusses emerging trends in machine learning for 2021, including its integration with IoT, edge computing, hyperautomation, augmented intelligence, cybersecurity, and the increasing focus on ethical AI practices.

This article discusses how companies are increasingly focused on making their supply chains more sustainable to combat climate change, with artificial intelligence playing a key role in helping achieve sustainability goals.

This article argues that AI systems need ethical considerations. AI can be biased by data or algorithms. The article explores ways to make AI development ethical, including human oversight and new companies specializing in responsible AI.