> Writing code in research is quite different from writing code in industry
Hah, not always.
A short while back I was tech lead for an AI project, the goal of which was to reduce the weight (and, ergo, cost) of large steel structures.
The megacorp consultancy I work for decided to staff this project only with AI people, about half of which were fairly fresh graduates. Now, they all had a great grasp of AI, both old-skool (neural networks, genetic algorithms, particle swarm optimisation etc) and more recent innovations. But these were not developers.
The customer was a Microsoft shop, and mandated that we use C# (which was cool, it's my favourite language!), but it was immediately apparent that the "devs" had only basic training in Java. The code was a fcking mess, and we had numerous issues around software engineering concerns such as source control, DevOps and processes - the customer eventually canned the project due to the bugginess of the platform. They really did have some brilliant minds on the project, but those were not the minds of software engineers.
On a more recent AI project, unusually I was consulted first on staffing, and I insisted on a mix of AI research types and* software engineers - unsurprisingly, this project was far more successful.
Seems like your formatting was messed up by putting an asterisk in "f*cking", which caused everything to be rendered in italics between that word and the "and" which you actually wanted to be in italics.
Hah, not always.
A short while back I was tech lead for an AI project, the goal of which was to reduce the weight (and, ergo, cost) of large steel structures.
The megacorp consultancy I work for decided to staff this project only with AI people, about half of which were fairly fresh graduates. Now, they all had a great grasp of AI, both old-skool (neural networks, genetic algorithms, particle swarm optimisation etc) and more recent innovations. But these were not developers.
The customer was a Microsoft shop, and mandated that we use C# (which was cool, it's my favourite language!), but it was immediately apparent that the "devs" had only basic training in Java. The code was a fcking mess, and we had numerous issues around software engineering concerns such as source control, DevOps and processes - the customer eventually canned the project due to the bugginess of the platform. They really did have some brilliant minds on the project, but those were not the minds of software engineers.
On a more recent AI project, unusually I was consulted first on staffing, and I insisted on a mix of AI research types and* software engineers - unsurprisingly, this project was far more successful.