Frankly speaking ...

Since 1956 one of the central goals of AI is achieving Artificial General Intelligence (AGI). Recently we have seen amazing advances in AI, but there are still important challenges to solve before we get there. In this article, I draw on the AAAI 2025 Presidential Panel report to outline what the AI community sees as the critical gaps that must be resolved before AGI can be achieved. AI is in a strange place right now. New benchmark results can make it seem as if the field is nearly solved. Yet these systems still fail basic common-sense tasks that humans manage with little effort. That gap is why true AGI still feels a long way off. In this article, AGI means an AI that can perform as well as a human across a wide variety of tasks, not just produce fluent text. The big problem is what researchers call the "Reasoning Paradox." Today's Large Language Models (LLMs) are very good at producing language that sounds like reasoning, but that is not the same as reliable for...

As AI models become more powerful, they are also becoming much more accessible. I have been interested in what happens when you trade a little model accuracy for the ability to run everything on your own machine, without relying on paid cloud infrastructure. For developers, researchers, and hobbyists, that shift is useful for more than just cost. It also changes how private, flexible, and portable these tools can be. This week I spent some time trying two popular ways of running local AI models: Ollama and LM Studio . Both are open source and free to use, and both make it much easier to get started than I expected. What I wanted to understand was not just whether they worked, but how they felt to use in practice and where each one made more sense. What I like about running models locally is that the benefits are immediate. There is the obvious cost saving, especially if you are experimenting often or working through lots of prompts, but privacy matters just as much. When the mode...