Towards Data Science

2 days ago | towardsdatascience.com | James O'Brien

an interesting moment in AI development. AI systems are getting memory, reasoning chains, self-critiques, and long-context recall. These capabilities are exactly some of the things that I’ve previously written would be prerequisites for an AI system to be conscious. Just to be clear, I don’t believe today’s AI systems are self-aware, but I no longer find that position as firmly supported as I once did.

2 weeks ago | towardsdatascience.com | Clara Chong

coding is here to stay. Tools like Cursor, V0, and Lovable have dramatically lowered the barrier to entry — building dashboards, pipelines, or entire apps can now be done in a fraction of the time. I use these tools daily, and they’ve definitely made me faster. But as the codebase gets more complex, the tradeoffs become clear: cryptic bugs, tangled logic, and hours lost debugging code I didn’t truly understand. AI tools are great — especially for beginners — but they come with a quiet cost.

3 weeks ago | towardsdatascience.com | Kenneth Leung

of the DeepSeek-R1 model sent ripples across the global AI community. It delivered breakthroughs on par with the reasoning models from Meta and OpenAI, achieving this in a fraction of the time and at a significantly lower cost. Beyond the headlines and online buzz, how can we assess the model’s reasoning abilities using recognized benchmarks?

1 month ago | towardsdatascience.com | Sudheer Singh

Over the years, Transformer-based large language models (LLMs) have made substantial progress across a wide range of tasks evolving from simple information retrieval systems to sophisticated agents capable of coding, writing, conducting research, and much more. But despite their capabilities, these models are still largely black boxes. Given an input, they accomplish the task but we lack intuitive ways to understand how the task was actually accomplished.

1 month ago | towardsdatascience.com | Florent Poux

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