Everybody loves an algorithm. To be more accurate, everybody loves their smartphone with its intelligent applications that run on algorithms. But not everybody appreciates the fact that the calculations going on in the background are a form of algorithmic intelligence designed to make our lives better.
As the wider non-techie awareness of algorithms expands throughout society, people are starting to become more aware of how algorithms impact the way we work and play. But what, in really simple terms, is an algorithm?
What is an algorithm?
An algorithm is a set of step-by-step instructions designed to solve a problem. An algorithm can define the relationships between different sets of data — and provide the computational steps that need to happen in order to extract the results needed. You can think an algorithm as a recipe, or a set of directions… but the data ingredients that go into it can change.
It is from this base level of computational decision making that we have built our modern approach to Artificial Intelligence (AI). Chief scientist at industry-specific Enterprise Resource Planning (ERP) cloud software company Infor is Ziad Nejmeldeen – he explains that his firm has worked with customers to build ‘matching’ algorithms that help bring business demand and supply elements together in real world working environments.
This, if you will, is ‘operationalized AI’ i.e. a means of putting algorithms to work (in this case through the Infor Coleman AI Platform) to enable business operations to drive towards more successful and profitable outcomes.
“Bringing any operationalized AI to bear inside a working business needs to be done with great care. If it’s a flight booking system algorithm, a dating application or an app to connect care-givers to those in need with care professionals, we need to build algorithms capable of eradicating AI bias,” said Nejmeldeen, who heads up Infor’s Dynamic Science Labs division.
Eradicating AI bias
In the case of the caregiver app, Nejmeldeen explains that operationalized AI needs to be smart enough to remove any bias towards a particularly prevalent group of individuals that exists simply because there are more of them by number.
“Because mass and sheer weight of numbers does not necessarily equate to skill, truly intelligent operationalized AI needs to be aware of the existence of too many 30-something white males (for example) or any other predominant group. It also needs to be able to calculate to remove the AI bias that exists because of the prevalence of any other related set of lower level sub-variables that essentially end up describing and denoting the bias grouping we are trying to level out,” said Nejmeldeen.
This level of working operationalized AI is what Infor has been striving to build into its Coleman AI platform. With IBM Watson named after the tech giant’s founder and Salesforce Einstein named after the great man himself, Infor has played an arguably stronger diversity hand by naming its AI system after Katherine Coleman Goble Johnson. An American mathematician and woman of color, Coleman’s calculations on orbital mechanics as a NASA employee were critical to the success of the first US crewed spaceflights.
AI operationalization through personalization
This month sees the arrival and general availability of the Infor Coleman AI Platform for embedded machine learning models. The platform promises to provide the speed, repeatability and personalization needed for enterprises to fully operationalize AI, a term we have hopefully provided some context for by his point. The company says that Infor Coleman AI Platform serves as a key building block for Infor’s Intelligent CloudSuite.
Infor CEO Kevin Samuelson says that the practical operationalized use of AI and machine learning in the enterprise remains low because most tools are deeply technical and developer-centric. He suggests that too many of these AI tools have been designed for experimental projects and are therefore difficult to implement complete projects with.
In an attempt to resolve this situation, Samuelson says that his firm’s Coleman AI platform provides industry-specific starter packs (templates) to accelerate the development of repeatable big data, machine learning-based AI projects. These templates are highly personalized and tailored to specific customer data and usage patterns. Further, they are designed for use by ‘citizen developers’, who don’t need extensive data modeling skills.
According to Infor, when combined with Infor OS (Operating Service), enterprises can simplify and speed up the entire AI implementation process and so roll out complete, production-ready AI projects in less than six weeks. Infor Coleman AI Platform and Infor OS come together to form the Infor Intelligent CloudSuite, a set of software designed to automate, anticipate and predict in live operational businesses.
“With other [AI software] solutions, you have to figure out how to use AI with a multitude of other technologies. We bring an enterprise AI ensemble together in a single platform — through which we can provide a complete Intelligent CloudSuite,” said Rick Rider, Infor senior director of product management, Infor OS and the Coleman AI Platform. “The Infor Coleman AI Platform is designed specifically for business users and is built upon a foundation of industry-specific data. At any given moment, it can help with executing tasks and recommending next-best sales offers, or predicting maintenance issues and adjusting production schedules accordingly.”
Infor Coleman is described as a ‘pervasive’ machine learning platform that operates below an application’s surface. It mines data and uses machine learning to help improve processes such as inventory management, transportation routing and predictive maintenance.
This technology’s modeling environment is described as ‘digestible’, in that it doesn’t require as complex a skillset as other AI tooling, nor is it designed to require an exhaustive service engagement agreement.
What to think about AI platforms
There are AI platforms and there are AI platforms and Infor is one of a number of rising celestial objects (if not stars) in this space. What makes Infor arguably somewhat different is its focus on the industry-specific operationalized digital supply-chain issues.
The company’s core technology is built for specific industry types (key verticals include healthcare, manufacturing, financial services, public sector and retail) at the front-end of the business i.e. where the operations actually happen, rather than the back-end financial systems underpinning a customer’s business.
If Infor has successfully translated its industry-specific take on ERP forward into AI with Coleman, then there may some validation in the company’s claim that ‘all-industry’ software tools (as opposed to industry-specific ones) end up being a one-tool-fits-all Swiss Army Knife approach that is blunt by comparison.
social experiment by Livio Acerbo #greengroundit #thisisnotapost #thisisart