This young man is about to make a terrible decision.

The year is 1993, he is a 25-yr-old research engineer at what is now Orange Labs in Brittany, in the Neural Networks team headed by Daniel Collobert (Ronan Collobert’s dad), deeply hidden inside the Flat Screens Department. Because, you see, neural networks at that time sounded a bit like voodoo magic (nothing has changed except that voodoo magic is now a corporate priority) to be practiced under deep cover. This young man joined the team in 1992 with a passion for neural networks and had the fortune of sharing an office with a wonderful, friendly neural networks postdoc from Québec, Samy Bengio. And the young research engineer was hearing again and again about how this Yoshua Bengio (brother of the aforementioned) was doing great work and that their friend Yann LeCun’s backpropagation thing was big. So one day, failing to see how anyone would ever muster enough compute power and enough training data to make it work, this young research engineer decided that other machine learning approaches were a safer bet. He thought that perhaps evolutionary algorithms were more promising, or perhaps even self-organizing maps or Hopfield networks if one insisted on keeping with neural networks.The young research engineer shifted his attention to other biomimetic learning algorithms. It looked like a great decision for some time. For a little less than 20 years. But boys were these guys persistent! For a little less than 20 years, algorithms improved and some important innovations emerged, but success by and large came from scale of data and compute. Once the barrier was broken, progress was exponentially fast. And enabled more structural and algorithmic innovation -attention, transformers, pretrained transformers, ...  Today training data and compute remain major sources of competitive advantage. The young man is a wee bit older. He made a 180 on his assessment around 2013. Perhaps there was enough training data and compute power to make it work after all. Tell me I haven’t changed all that much.