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You may be not the first CIO flummoxed by what seem to be interchangeably used words. Just recently the head of an industry vertical for a Top 6 consulting firm send me some thoughts he had penned about what he thought was Artificial Intelligence (AI) for a quick second look before he published them. I had to tell him, I guess to his chagrin, that the use case he was describing more aptly fit Machine Learning (ML) rather than AI. This because it primarily focused on predictions based on data.
The use of data (or big data) to drive predictive analytics is the domain of Machine Learning (ML). MIT Technology Review says: ML is beginning to deliver on the potential created by big data and analytics by turning raw data into useful, predictive tools for business. Innovation-minded business leaders are embracing ML as “the next big thing” and have already crafted ML strategies and initiatives that promise real benefits and return on investment (ROI).
AI would be something more dynamic and real-time –In my own industry - a Multi-function printer which is able to predict its own failure based on past data and current conditions would be using ML. But if it then uses that to perform a set of actions - initiate shut down or self-healing, notify users, order parts for break-fix would be more like AI (my take on this, not any official position).
“CIOs need to sift through the maelstrom of acronyms and buzzwords accompanying technologies such as Artificial Intelligence, Machine Learning, Deep Learning , Data Sciences et al to get a laser focus on their distinctions and commonalities so that they can make appropriate choices to address business needs”
Extending the thinking to say the chemical industry, AI could be a system which based on certain demand or environmental parameters dynamically and in real time changes the settings of reaction vessels to produce a different product mix more suited to the new conditions.
AI is something more akin to human intelligence, AI is what sparks questions like “what it really means to be human”
What are the common attributes that all modern Artificial Intelligence systems share? Former NASA Deputy Chief of AI at the Ames Research Center, Monte Zweben tells us 5 key attributes of modern AI systems:
1.) Data Ingestion - AI systems deal with voluminous amounts of data, often in excess of billions of records, coming in at high velocity.
2.) Adaptive - AI systems adapt to their environment with machine learning. They observe their results and learn to do better.
3.) Reactive - AI systems react to the changing conditions around them. Unlike traditional applications that are more batch-oriented (you schedule them, they run, store their results, and are then shut down), AI applications continuously monitor their inputs, often from streaming data platforms, and when certain conditions apply, they invoke procedures, rules, and behaviors, or compute scores and make decisions.
4.) Forward-Looking - AI systems don’t just react they often search through a space of possible scenarios to reach an effective goal. To do this, they are projecting multiple steps into the future.
5.) Concurrent - AI systems, just like traditional applications, must handle multiple people or systems interacting simultaneously. They use techniques adopted by those developing distributed systems in the fields of operating systems and databases.
Here’s another quick take on the distinction which I like from Barnard Marr : What Is The Difference Between Artificial Intelligence And Machine Learning?
The conversation becomes even more interesting when we add Deep Learning (DL) and Data Sciences to the mix. The best distinction of the boundaries and overlaps between all 4 comes from this pictorial representation which came to me from Prof. Ajay Anand, Deputy Director, Goergen Institute of Data Science at the University of Rochester:
These distinctions become relevant as increasingly CIOs get tasked with building the teams and infrastructure to deploy these capabilities within organizations. Vendors and technology partners may often position themselves as having overarching competence over all four. However, there are many niche players with core competence in one or the other.
Why is this important?
As Forrester describes: We find that, to stay ahead, CIOs, CTOs, CDOs, and other executives integrating leading-edge technologies into their companies’ operations and business models must turn their attention to automation technologies, including intelligent machines, robotic process automation (RPA) bots, artificial intelligence, and physical robotics.
By raising the importance of automation as a discipline within the organization, CIOs can turn automation into a strategic competency — and a source of business model differentiation.
And this “automation” will be built on the pillars of Artificial Intelligence, Machine Learning, Deep Learning , Data Sciences et al.
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