Bank of Ireland is positive in its outlook for further growth of the Indigenous Tech Sector in 2019
Bank of Ireland has seen a marked uplift in activity across various sub sectors such as Education Technology (EdTech), Telecoms and Media/Entertainment, testament to the buoyancy of the sector nationally.
Dedicated Technology Sector Team
In January, Bank of Ireland announced the launch of a dedicated Technology Sector, Business Banking team. Paul Swift, Head of Technology Sector, leads this newly established relationship team, dedicated to working with technology customers nationwide, while also further developing and driving the technology sector strategy for the bank. This dedicated resource is a differentiator for the bank and over recent weeks we have seen a marked uplift in activity across various sub sectors such as EdTech, Telecoms and Media/Entertainment, pointing to not only awareness of the team among our new and established customers, but also to the buoyancy of the sector nationally.
“Every company is a technology company”
It was autumn 2013 when Peter Sondergaard (formerly EVP at Gartner) first uttered those words pointing to how we were entering a new digital industry economy where everyone would be a technology company. At the core of this statement were the convergence of four interdependent trends; social, mobile, cloud and big data; this meant digital transformation of every business and sector. Today we are continuing to see these trends now forming the bedrock of disruption and convergence, enabling digital first business creation across all industries. More recently we are seeing more evidence of this convergence across some of our sectors such as Healthcare and Manufacturing and this trend is likely to continue across all sectors over time as companies seek to deploy technology platforms and solutions to improve efficiency but most of all to improve the experience for their customers and clients.
The year of everything as a service.
Deloitte’s global technology, media and telecommunications industry leader and US global technology sector leader, Paul Sallomi says that 2019 will be the year of “everything as a service” (XaaS), enabling companies to speed up innovation and experimentation. XaaS now makes it cheaper and easier for users to access technologies and cutting-edge services they otherwise would not have access to acquire. Cloud-based solutions remove the need for traditional investment in servers, networking and storage and instead can enable companies to leverage the investments and expertise of some of the world’s biggest technology companies, without the associated risks and costs of acquiring scarce expertise.
Jargon Buster: So what is Artificial Intelligence vs Machine Learning vs Deep Learning.
Despite the fact that each of the above terms have become part of everyday language , there is still some confusion as to the understanding and differences between the three. So here goes…
Artificial Intelligence (AI): is a division of computer science that stimulates the creation of intelligent technologies to work and react like humans, with use cases such as speech recognition, learning and problem solving.
Machine Learning: is a subset of AI, focused on the technological development of human knowledge by providing systems with the ability to automatically learn and improve from experience without being explicitly programmed to do so, relying on patterns and inference instead. Some use cases include data security (malware), private security (screening at airports) and healthcare (computer assisted diagnosis).
Deep Learning: is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similar to how humans learn by experience, the deployment of deep learning techniques repeatedly perform tasks and continuously tweak it a little to improve outcomes. Problems that require thought in order to be solved fit the profile for using deep learning. The process works on massive data sets and given the amount of data now being generated, makes deep learning possible. Use cases include autonomous vehicles, facial recognition and medicine.