MACHINE LEARNING is the science of educating and empowering computers to reason and behave autonomously in modern society. If traditional programming renders a static set of instructions, then machine learning involves deviating from these, such that any computer can interact with humans and improvise without encountering runtime and other program errors.
The aggregation of world knowledge is just one facet to this field of study and, left alone, is perhaps its most ineffective. This is because knowledge remains powerless unless it can be applied in real life scenarios. If computers can indeed learn from the world around them and use this knowledge to achieve a goal, then success is no longer the byproduct of human effort. Our economy and daily routines must give way to the power of automation.
A few areas of interests or problem domains worth mentioning are:
The Banking and Finance industries. These are likely among the oldest use cases in which machine learning has been applied, even though its moving parts have not always been centralized. Predictably, these institutions know who to send Discover and American Express credit card offers to, as well as who to bait for their high-risk products.
Space Flight is also among the oldest use cases and arguably the most riskiest of them all. Although many countries have experienced a number of successes with unmanned probes and satellites, software bugs have also contributed to disasters and human casualties.
Modern Robotics, in many ways, is an offshoot of space exploration. Many products that are just beginning to occupy our households have been long used in space flight and similar projects. Domestic robotics is a growing field, ranging from drones, unmanned ground vehicles and household machines like Temi Robot.
Search Engine Algorithms. For professionals who make their living by optimizing web pages for first page results, a big deal is made whenever search giant Google changes its algorithm. Googlebot, as it is known, is deployed to index the Web, with the capability of detecting duplicate content and discerning between what’s useful and what isn’t.
There are other factions of computer science that also make use of machine learning. E-commerce sites, for example, use learning algorithms to suggest recommended products and detect when transactions are fraudulent. Integrated Development Environments (IDEs) help streamline the software development and debugging processes. Finally, antivirus software makes use of heuristics to keep computers safe.
Learning Models and Machine Learning Basics
Computing and biology are indeed sister sciences. The way we learn and evolve as human beings is much like the way computers do. In fact, the concepts of this field of study are modeled on constructivism, in which the learner is a constructor of real information. This just so happens to be a computer’s intrinsic value, which could explain the alarming rate of development in this field, as well as the apprehension of the common man to embrace its inevitability.
The four (4) learning models of machine learning—supervised, semi-supervised, unsupervised and reinforcement—are powered by special algorithms to assist in the gathering and operation of knowledge. Each of these algorithms are said to have three (3) important functions:
Representation, also known as the “hypothesis space,” involves the conversion of data models into signals for communicating with machines. It is impossible for a computer to interact with these probabilistic assets, which is why descriptions should be created in their stead. Representation gives way to feature engineering, exchanging the probability of “unseen data” for predictive problem solving.
Evaluation. Also known as “scoring,” this component is responsible for the evaluation of hypotheses. This objective-driven function is more or less how the preference of one learning model over another is made.
Optimization is the last function of machine learning in which the most optimal learning models are sought out. The process is continuous and cyclical until the most favorable candidate is found.
It should be noted that in practice, the aforementioned concepts all meld together. Programmers and/or ranking stakeholders of a project have complete jurisdiction over this paradigm. The ultimate goal is for the learning algorithm to enable machines to interpret data they have yet to encounter, and to execute them judiciously through a process of logic.
There are thousands of learning algorithms to help achieve this purpose. Depending on the project and scope of data, it is even possible to create your own learning algorithm (which is likely more work than necessary and step in the direction of research and development). The most basic of these learning algorithms is the decision tree. This structure is a listing of all possible scenarios and outcomes.
Importance of Machine Learning
The machines that are used to execute learning algorithms will typically have access to greater data sets as well as high processing power. This enables them to parse through what appears to be random data while quickly (and accurately) identifying patterns and creating predictions. This is significant to the enhancement of human ability and the solving of critical issues of our time, including terminal diseases and global warming.
Machine Learning vs Deep Learning vs Neural Network vs Artificial Intelligence (AI)
There are many factions that intertwine and are sometimes used interchangeably with the concept of machine learning. Each is described as a subset of Artificial Intelligence (AI), and a step toward the realization of cognitive machine behavior. Neural Networks, Deep Learning, and Artificial Intelligence are beyond the scope of this topic, but there are clear similarities and differences that can be described in a simple hierarchy:
Artificial Intelligence (AI) was derived from the study of Computer Science. Personal computers, the Internet, smart devices, and countless other amenities we enjoy today are the byproducts of this influential field. But because technology is constantly evolving, there will come a time in the not-so-distant future where computers gain autonomy and will no longer require human operation. This is the overarching theme of AI and its various subsets.
To achieve anything close to human cognizance will require the successful prototyping of the human brain. This is the most powerful computer there is, though it remains to be seen if this will always be the case. The algorithms which power Neural Networks are constantly improving in pattern recognition and auditory skills.
Machine Learning, as documented thus far, assumes a computer is capable of memorizing beyond the storage and retrieval of useful information, and…
Deep Learning is one of its many processes of erudition. This, too, is modeled from neural science and enables a computer to process random data in an unsupervised manner.
Challenges and Limitations
The biggest obstacle in applied machine learning is overfitting. This is when a learning algorithm favors training data but fails upon the introduction of new datasets. In other words, the algorithm does not replicate the same findings when generalized or encountering the unseen.
Overfitting stems from the inadequacy of data and its inability to feed the algorithm. This is because learning models don’t have the capability of “creating something from nothing,” pointing to its origins in real-world scenarios. For example, history is made when noteworthy events occur, often in direct opposition of data which predicted otherwise. A statement which suggests that “a woman could never become President of the United States” is an example of overfitting—particularly when a woman becomes President of the United States.
Other challenges of machine learning involve its scripts for making and evaluating predictions. Like traditional programming, these, too, can be compromised by malicious code. The irony (and paradox) here is that vendors have begun using machine learning techniques to detect and classify malware in advanced security products. Finally, there’s the looming question of what happens when machines get so intelligent that they begin replacing humans in the global ecosystem. Martin Ford’s Rise of the Robots is an excellent read that addresses this subject.
Regardless of these challenges, machine learning is an integral part of artificial intelligence and will continue to gain ground in theory and practice. This is especially true with the onset of quantum computing which makes it possible to analyze different data sets with simpler versions of feature engineering. No one really knows what the future holds, but predictive modeling, if you will, suggests that machines will take part in it.