As AI continues to garner column inches by the mile, Brian Runciman MBCS looks back at how AI seeped into the business arena from the perspective of BCS.
The natural starting point to look at how AI has developed is the BCS Specialist Group on Artificial Intelligence (SGAI). Another example of BCS’ deep roots in computing, the SGAI was founded in June 1980 and has organised an annual international conference since 1981. And its purpose goes beyond the academic, as they mention: ‘A substantial proportion of the group's membership is from industry. It provides a valuable forum in which the academic and industrial AI communities can meet.’
Each year, in December, the SGAI has its international conference. Whilst it has a strong technical bent, of course, it also runs an application stream. Almost at random, the 2006 edition caught my eye, as it explicitly mentions business applications. The subjects of application stream papers from that year make for an interesting snapshot of the time, but also fascinating precursors to current AI:
- Managing restaurant tables using constraints
- Use of data mining techniques to model crime scene investigator performance
- Bringing chatbots into education: towards natural language negotiation of open learner models
- Domain dependent distributed models for railway scheduling
- Automatic species identification of live moths: a case study in practical data mining
- Sass applied to optimum work roll profile selection in the hot-rolling of wide steel
Hospitality, education, scheduling, natural sciences, heavy industry… quite the spread of domains. With lots of relevance for now. And that trend continues as you look through SGAI’s website.
In 2022 there were application papers on areas where the general public may now consider AI to have an influence. For example, health:
For you
Be part of something bigger, join BCS, The Chartered Institute for IT.
- Patients forecasting in emergency services by using machine learning and exogenous variables
- Context-aware support for cardiac health monitoring using federated machine learning
And as we get ever more whiffs of Musk, transport:
- Modelling satellite data for automobile insurance risk
- Evolving large scale prediction models for vehicle volume forecasting in service stations
- Adaptive manoeuvre planning for autonomous vehicles using behaviour tree on Apollo platform
And maybe a watch-this-space example: towards a brain controller interface for generating simple Berlin school style music with interactive genetic algorithms.
From the academic to the application, BCS has resources worth visiting. And for a recent example, check out the September 2024 event BCS was involved with ‘The secrets of delivering AI at scale.’
AI is now in the business — and scaling is the next big issue.
