Jonne Sjöholm
March 24 2023
To stand out in the global competition, wholesale and retail companies need to tap into process and customer data and think about the value they can extract from analytics.
For manufacturers, true digital transformation starts by envisioning where in the value chain can data be applied to make a difference.
All public sector services should be designed to serve citizens first. Digital solutions and applications must be easy to use, despite physical and cognitive disabilities.
Digital processes and data-led services help energy and utility sector companies develop a stable energy offering with transparent, customer-centric services.
Shared platforms offer fast entry to new markets, cost-efficiently and scalably. But lasting value add comes from cross-industry collaboration and linking products with complimentary services.
Digitally disrupted, the companies in the banking, finance and insurance sector must actively innovate new approaches to build omnichannel customer experiences that fully utilize data.
While medical device software is strictly regulated, there's room for innovations that make life easier for patients and caregivers. Stable and secure data flow is a must.
March 24 2023
For this blog, I’ve collected our developers’ experiences with using machine learning tools for programming. The results can be summarized with the following three questions:
There are many potential pitfalls to using machine learning tools for generating code. Machine learning does not necessarily create the kind of code that the developer originally intended. Programming skills are still needed to correct programming errors. The code may work on the surface, but there may be surprises under the hood. Code generated by machine learning tools may also be less efficient and run slower.
The code generated might also have licensing or some other legal issue if it’s derived from some unverified or unlicensed source. If possible, one should verify the sources being used to generate the code in question.
Entering sensitive code into a third-party system is not a good idea for information security reasons. The information to be entered may require anonymization before being processed by machine learning tools, which again takes time, raising the threshold for using it.
At the moment, machine learning tools like ChatGPT have to be given a lot of guidance in order to get it to take the necessary actions. This often takes many attempts to get things moving in the right direction, which takes time. It’s not necessarily worth using these tools for complex situations because it may take so much time to give the necessary instructions – meaning you could do the work yourself in less time. Certain AI tools like ChatGPT also have a limit of 4000 tokens. This is the tool’s working memory and when it fills up, the instructions given to it start to drip out of use.
Even with these drawbacks, machine learning is a promising tool for programming. It can be compared to a dishwasher – it does the mechanical work well enough. Of course, a person could do the work in question better, but using it frees up time for other more creative tasks that are more meaningful. With that in mind, now is a good time to try out and explore new ways of programming using machine learning as a tool to help the process.
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