Artificial Intelligence is quite obviously a buzzword which attracts significant marketing hype – that has been the case for a decade at least. There are countless number of high-profile examples where AI is used to simply describe an automated (usually software-enabled) routine. Good examples of this can be seen in Facebook’s use of AI: filters that simply track and flag keywords, or images, that break a set of human-defined rules. The fact of the large number of false positives they ‘capture’ demonstrates that, while these programs may be artificial, they’re not always intelligent as we humans would define it. They are, more often than not, just forms of computational automation.
Don’t get me wrong, computational automation can be beneficial, it can speed things up and save significant time, hence money. But it does not add skills, nor does it bring added, intelligent value to a design team – which is what we’re trying to do for our customers. Given the wide and potentially misleading use of the term, there is no doubt that when we chose to use AI to refer to the capabilities of our AMALIA Design Enabler we really had to pause and check we were being honest with ourselves, and with our customers.
AMALIA Design Enabler passes the acid test. We threw problems at the system, asking it to find solutions to problems – looking for answers we didn’t know existed. In the video, you can see a perfect example of its application and the benefits of a truly-AI solution for supporting design problems: helping the designer to compare and assess alternative components to resolve a technical issue with the system. In this case, during a process migration, a very low current voltage regulator was taking far too long to achieve a zero temperature coefficient state… to compare the options and find a solution just in the small circuit in question could have taken the design team several weeks.
By entering the requirements into AMALIA Design Enabler, along with the options available, the Design Enabler AI algorithm was able to find its own way to the answer in relatively few steps and a very short amount of time: reaching the answer in just 40 steps, in spite of there being thousands of potential variables.
The reason we’re confident in calling the Design Enabler true-AI, is exactly that: the system is learning as it goes, changing direction based on initial findings and zeroing-in on the correct solution: it is not simply observing, calculating, and running every possible scenario before ‘happening’ upon the correct answer by brute force and computational power.
So, AI is dead. Long live AI! We believe that true-AI does have a place in design automation. Selecting the appropriate components and enabling appropriate IP reuse in process migration can be a complex, time-consuming task – but when it’s done well, it significantly improves design profitability and system performance.