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The constant stream of model releases, new tools, and cutting-edge research can make it difficult to stop for a few minutes to reflect on the bigger picture of ai. What are the questions that practitioners are trying to answer (or at least need to consider)? What does all this innovation really mean for those working in data science and machine learning, and for the communities and societies that these evolving technologies will shape in the years to come?
Our feature article series this week tackles these questions from multiple angles: from the business models that support (and sometimes drive) the excitement around ai to the core goals that models can and cannot achieve. Ready for some thought-provoking discussions? Let’s dig deeper.
- The economics of generative ai
“What should we expect and what is just hype? What is the difference between the promise of this technology and practical reality?” Stephanie KirmerThe latest article takes a straightforward and uncompromising look at the business case for ai products, a timely exploration given growing pessimism (at least in some circles) about the industry’s near-future prospects. - LLM Triangle Principles for Designing Trustworthy ai Applications
Even if we put aside the economics of ai-powered products, we still have to deal with the process of actually building them. Almog BakuRecent articles by aim to add structure and clarity to an ecosystem that can often seem chaotic; taking a cue from software developers, their latest contribution focuses on the basic product design principles that practitioners should respect when building ai applications.
- What does transformer architecture tell us?
Conversations about ai tend to revolve around utility, efficiency, and scale. Stephanie ShenThe latest paper focuses on the inner workings of the Transformer architecture to open up a very different line of research: the insights we might gain about human cognition and the human brain by better understanding the complex mathematical operations within ai systems. - Why machine learning is not designed for causal estimation
With the advent of any innovative technology, it is critical to understand not only what it can achieve, but also what it cannot. Dr. Quentin Gallea He highlights the importance of this distinction in his introduction to predictive and causal inference, where he discusses the reasons why models have become so good at the former while still struggling with the latter.
