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Machine Learning and Cognitive Systems, Part 1: A Primer

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IBM’s recent announcements of three new services based in Watson technology make it clear that there is pressure in the enterprise software space to incorporate new technologies, both in hardware and software, in order to keep pace with modern business. It seems we are approaching another turning point in technology where many concepts that were previously limited to academic research or very narrow industry niches are now being considered for mainstream enterprise software applications.

Machine learning, along with many other disciplines within the field of artificial intelligence and cognitive systems, is gaining popularity, and it may in the not so distant future have a colossal impact on the software industry. This first part of my series on machine learning explores some basic concepts of the discipline and its potential for transforming the business intelligence and analytics space.

So, what is machine learning anyway?

In simple terms, machine learning is a branch of the larger discipline of Artificial Intelligence, which involves the design and construction of computer applications or systems that are able to learn based on their data inputs and/or outputs. Basically, a machine learning system learns by experience; that is, based on specific training, the system will be able to make generalizations based on its exposition to a number of cases and then be able to perform actions after new or unforeseen events.

The discipline of machine learning also incorporates other data analysis disciplines, ranging from predictive analytics and data mining to pattern recognition. And a variety of specific algorithms are used for this purpose, frequently organized in taxonomies, these algorithms can be used depending on the type of input required (a list of algorithms can be found in Wikipedia based on their type).

As a discipline, machine learning is not new. Initial documents and references can be traced back to the early fifties with the work of Alan Turing, Arthur Samuel, and Tom M. Mitchell. And the field has undergone extensive development since that time.

One of the more important applications of machine learning is to automate the acquisition of knowledge bases used by so-called expert systems, systems that aims to emulate the decision making process of human expertise in a field. But the scope of its application has been growing. In Applications of Machine Learning and Rule Induction, Langley and Simon review some major paradigms for machine learning scenarios, all based on a very important premise:

To improve performance on some task, and the general approach involves finding and exploiting regularities in training data.

The major approaches include using neural networks, case-based learning, genetic algorithms, rule induction, and analytic learning. While in the past they were applied independently, in recent times these paradigms or models are being used in a hybrid fashion, closing the boundaries between them and enabling the development of more effective models. The combination of analytic methods can ensure effective and repeatable and reliable results, a required component for practical usage in mainstream business and industry solutions.

According to A Few Useful Things to Know about Machine Learning, while the discipline by itself is far from simple, it is based on a simple (but not simplistic) principle:



  • representation means the use of a classifier element represented in a formal language that a computer can handle and interpret; 
  • evaluation consists of a function needed to distinguish or evaluate the good and bad classifiers; and
  • optimization represents the method used to search among these classifiers within the language to find the highest scoring ones.

As the paper states:

The fundamental goal of machine learning is to generalize beyond the examples in the training set.

This way the system can infer new decisions or correct answers that then serve to increase learning and optimize accuracy and performance.

Also, each component of the machine learning process comprises a good mix of mathematical techniques, algorithms, and methodologies that can be applied (Figure 1).

Figure 1. The Three components of learning algorithms. Source: A Few Useful Things to Know about Machine Learning

In this context, machine learning can be done by applying specific learning strategies, such as:

  • a Supervised strategy to map the data inputs and model them against desired outputs, and
  • an unsupervised strategy, to map the inputs and model them to find new trends.

Derivative ones that combine these for a semi-supervised approach and others are also be used. This opens the door onto a multitude of applications for which machine learning can be used, in many areas, to describe, prescribe, and discover what is going on within large volumes of diverse data.

The increasing presence of machine learning in business, especially for analytics
Thanks to the success of the application of machine learning within certain disciplines such as speech recognition, computer vision, bio-surveillance, and robot control, the interest in and adoption of machine language technologies has grown, particularly over the last decade. Interesting also is how, in many fields, machine learning is escaping its containment from science labs to reach commercial and business applications.

There are several scenarios where machine learning can have a key role: in those systems that are so complex that algorithms are very hard to design, or when an application requires the software to adapt to an operational environment, or with complex systems that need to work with extensive and complex data sets. In this way, machine learning methods play an increasing role not just in general in the field of computer science, but also in terms of enterprise software applications, especially for those types of applications that need in-depth data analysis and adaptability. These areas include analytics, business intelligence, and Big Data.

Why business intelligence and Big Data?

In 1958, H. P. Luhn wrote what perhaps is the first document on Business Intelligence. The abstract begins as:

An automatic system is being developed to disseminate information to the various sections of any industrial, scientific or government organization. This intelligence system will utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the “action points” in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points. This paper shows the flexibility of such a system in identifying known information, in finding who needs to know it and in disseminating it efficiently either in abstract form or as a complete document.

The premise of BI systems has remained pretty much the same: to collect an organization’s data from disparate sources and process it in the best possible way to produce useful information to—and perhaps this is the most important part—help decision makers to make the best informed decision for the benefit of an organization. A simple definition, not a simple task.

In this regard, Business Intelligence has been adapting and evolving with greater or lesser degrees of accuracy and failure to provide information workers with the ability to make these decisions, and has played a very important role in the decision support platform of many organizations.

This evolution has changed the role of BI systems not just to provide high-level decision support at a strategic level, but to inform an increasing number of areas involved with middle management and operations. It has also has increased the need for BI systems and initiatives to evolve so that they are able to deal with increasingly complex data analysis problems. Applications need to be boosted so that they can deal with larger and complex amounts of data and can not only provide current status results, but also predict, play with hypothetical scenarios, and finally learn to make accurate suggestions—a green field for machine learning (Figure 2).

Figure 2. Some factors triggering the need for faster, better, and improved ways for decision support, analytics, and BI systems

A good model to understand the evolution of BI systems is D. J. Power’s  history of decision support systems, which of course BI is an important part of. According to Mr. Power, decision support systems and applications have evolved in the following stages:

  1. Model Driven. Emphasizes access to and manipulation of financial, optimization and/or simulation models. Simple quantitative models provide the most elementary level of functionality. Use limited data and parameters provided by decision makers to aid decision makers in analyzing a situation, but in general large data bases are not needed for model-driven. 
  2. Data Driven. In general, a data-driven DSS emphasizes access to and manipulation of a time-series of internal company data and sometimes external and real-time data. Simple file systems accessed by query and retrieval tools provide the most elementary level of functionality. Data warehouse systems that allow the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators provide additional functionality. Data-Driven DSS with On-line Analytical Processing OLAP provide the highest level of functionality and decision support that is linked to analysis of large collections of historical data.
  3. Communications Driven. Communications-driven DSS use network and communications technologies to facilitate decision-relevant collaboration and communication. In these systems, communication technologies are the dominant architectural component. Tools used include groupware, video conferencing and computer-based bulletin boards.
  4. Document Driven. Uses computer storage and processing technologies to provide document retrieval and analysis. Large document databases may include scanned documents, hypertext documents, images, sounds and video. Examples of documents that might be accessed by a document-driven DSS are policies and procedures, product specifications, catalogs, and corporate historical documents, including minutes of meetings and correspondence. A search engine is a primary decision-aiding tool associated with a document-driven DSS. These systems have also been called text-oriented DSS.
  5. Knowledge Driven. Knowledge-driven DSS can suggest or recommend actions to managers. These DSS are person-computer systems with specialized problem-solving expertise. The "expertise" consists of knowledge about a particular domain, understanding of problems within that domain, and "skill" at solving some of these problems.

Within these descriptions there are clear elements in place to boost the adoption of technologies and methodologies such as machine learning—collaboration, intensive management, and increase of non-traditional data (relational). The need for systems to solve complexity coincides with the advent of phenomena such as Big Data and advanced analytics in business giving a natural space for machine learning to make the entrance to help crunching big sets of complex data, and to be part of the increasingly complex machinery in place for data analysis and decision making.

Along with disciplines like data mining, natural language processing, and others, machine learning is being seen in business as a tool of choice for transforming what use to be a business intelligence application approach into a wider enterprise intelligence or analytics platform or ecosystem, which goes beyond the traditional scope of BI—focused on answering “what is going on with my business?”—to give all possible answers to “why are we doing what we’re doing?” and “how can we do it better?” and even “what should we do?”.

As businesses models are becoming more complex and producing massive amounts of data to be handled with less and less latency, decision support and BI systems are required to grow in complexity and in their ability to handle those volumes of data. This demand is boosting the growth of more sophisticated solutions to address specific business and industry problems; it’s not enough to sit out a straightforward result, systems need to provide business guidance.

Some scenarios where machine learning is gaining increased popularity in the context of analytics and BI can be found in applications for risk analysis, marketing analytics, and advanced analytics for Big Data sources.

Machine learning is a reality for business

As Tim Negris states in Getting ready for machine learning:

Despite what many business people might guess, machine learning is not in its infancy. It has come to be used very effectively across a wide array of applications.

And it’s being increasingly adopted within many analytics, Big Data, and business intelligence initiatives in the form of a component laying side-to-side with other analytics solutions, or packaged within a solution that has already adopted it as part of its functional stack.

In either case, machine learning is preparing to be part of the next evolution of enterprise intelligence business offerings.

In the next part of this series on machine learning, I will address some specifics of the usage of machine learning as part of Big Data and Advanced Analytics, as well as its role in the formation of the new so-called area of cognitive systems. In the mean time, please share comments below and let me know your thoughts.

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