Ai continues to excite the world from apocalyptic forecasts of job losses and social unrest to utopian predictions of freeing people's potential to achieve Maslov's "Self-actualisation".
It does not help when there are so many definitions of AI.
"But it’s also riding the hype around artificial intelligence, and more importantly, people’s uncertainty around what constitutes artificial intelligence, what can feasibly be done with it, and how close various milestones may be."
The Singularity Hub "Why We Need to Fine-Tune Our Definition of Artificial Intelligence" June 30 2018
In the short term enterprises and public sector organisations are bombarded with messages that AI is a vital priority in all business and technology strategies. Digital transformation without AI is like "The Emperor's New Clothes" i.e. NAKED.
Yet this ignores the fact that AI is just a component of any digital transformation. It requires a complete technology stack and range of capabilities and resources to exploit to the full. That is not even considering the question as to WHY AI will help achieve the organisational goals you have set.
There is a temptation at the C-Suite Level to look to AI to address many issues. Typical is "straight through processing" of applications, enquiries, insurance claims to speed up processes and reduce costs. But invariably such a project takes longer and costs more than envisaged. WHY?
See "Why companies end up spending more on digital technologies than anticipated".
Ai is not a short-term fix but organisations under disruptive threat are pressured to apply a long-term solution in a short-term lead time. The result often disappoints.
Better, once you have a viable strategy and prioritised the digital transformation key tasks, to deliver practical outcomes this year and next that are also the stepping stones to the 5 year out full potential of AI. Take note: -
Example: Artificial Intelligence technology
"Perhaps your company is like others that believe Artificial Intelligence (AI) can contribute to their business. But you’ll find that as soon as you start to think about AI, you start to think about data and data sources. That unleashes a substantial amount of work in building data warehouses. You may encounter a hurdle that many companies often find: data sources are less reliable and less precise than you had hoped. As a result, your company will need to build new data sources or improve the existing data sources. That effort will likely move your company to implement cloud technologies, along with the analytics software and data-management software that comes with cloud.
So, what appears to be a commitment to exploring just one digital technology leads to implementing a whole pack of other new technologies. The problem is that each technology requires a learning curve of its own and often sets up a cascading effect of its own. It’s like the “dominoes effect” – one thing leads to another, leads to another and leads to another."
By Peter Bendor-Samuel, star Advisor, Contributor, CIO AUG 6, 2018 9:45 AM PT
AI is not a panacea and you have to get your digital ducks in line.
"Analogue Fools rush in where Digital Angels fear to tread".
Mike Daly September 4th, 2018
SOFTWARE that can learn is changing the world, but it needs supervision. Humans provide such oversight in two ways. The first is to show machine-learning algorithms large sets of data that describe the task at hand. Labelled pictures of cats and dogs, for instance, allow an algorithm to learn to discriminate between the two. The other form of supervision is to set a specific goal within a highly structured environment, such as achieving a high score in a video game, and then let the algorithm try out lots of possibilities until it finds one that achieves the objective.
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