It’s national coding week within the 2018 Year of Engineering, so what better time to consider how we engineers work.
Like all engineers we use tools. CAD systems, modelling systems, database handlers, languages, and debuggers exist in abundance. The choice can, at times, seem overwhelming. Every engineer will have their favourite, and just like cars, music or clothing, fashion plays a part. However, as every wise engineer or designer knows, picking the right tool for the job is what’s important. So how best can we put technology to work?
The world is changing…
We are moving inexorably into a digital world where AI is becoming ever more prolific in the operation of our daily lives. Change is underway. There has been much talk about lost jobs in the future but to counter that, one should, I think, consider the arising opportunities. Will we for example become ever more intelligent ourselves? And what will this mean for putting technology to work?
Every engineer knows that the best way to achieve a task is with the correct tool. It would seem obvious therefore that we require tools aimed at the new digital world. Coding languages are often the starting point for putting digital technology to work, so does the more recent rise in the number and types of languages available, serve to indicate just how fast the world is currently changing? I think this must be yes. The rise in languages is not just fashion based, it is to service a need. The need for financial data analysis, for example has driven a whole new tech industry with its own set of digital tools, so what is needed for AI?
What is the goal here?
Tools from the mathematical world have provided the starting point, and languages such as Python have been adapted to enable engineers to put the technology to work. But what is the goal here? If the goal is merely to implement algorithms for AI and machine learning then maybe we are already on a good track. However if the goal were also to ensure that we ourselves become more intelligent as a result, what does that mean? Should we be looking for tools which inform as well as implement.
Test engineers, or those adopting test driven approaches to development, have been advocating transparency of operation for many years. Could we use such approaches to ensure that AI does not just provide machine learning but also provides human learning? I will be watching with interest over the next years as I’m sure new tools will arise. Choosing the correct one for any job will be a challenge, but it will I’m sure, just add to the fun involved with coding and putting technology to work.
Research into the art and science of technology development is continually on-going and I am more than happy to discuss and debate this important topic.
Please do contact me to register interest.