Is Machine Learning A Matter Of fact AI Part 2

Is Machine Learning A Matter Of fact AI

Is Machine Learning A Matter Of fact AI Part 2

If AI is to be a useful term to assist delineates unique technology and approaches from one another, then it needs to be meaningful. A word that means everything to everybody means nothing to anybody. The previous article on this series” Is Machine Learning Really AI? “went across the many viewpoints on what AI has to mean to be useful.

The overall belief is that systems are smart when they can feel and understand their environment, learn from previous behaviors and apply that learning to potential behaviors, and conform to new circumstances by reasoning from learning and experience and then generating new learning from these new conditions and encounters.

Machine learning is the set of technologies and approaches that give away by which computer systems can encode learning from experience and data and then apply future info to that learning to come to decisions.

This system learning is in contrast to explicit programming, where the human uses its intelligence to accomplish all the goals of cognition. Certainly, machine learning is a prerequisite for AI. But, ML is essential, but not sufficient for AI. Likewise, not all ML systems are working in the context of what we’re attempting to achieve with AI.

Which Parts of ML aren’t AI?

From the above article, we talk about what parts of AI isn’t ML, but we didn’t dive into what elements of ML aren’t AI. Since we haven’t yet achieved Artificial General Intelligence (AGI), despite some efforts to get us to shut, all present, practical implementations of AI from the field are narrow AI of one kind or another. Unfortunately, this is not especially beneficial.

It is not helpful to call a data science effort which utilizes random decision forests, which are a form of ML, which are focused on the particular task of attaining a very specific learning effect to be in the same level as efforts to build systems that can learn and adapt to new situations.

The tools utilized might be system learning, but the outcomes are not supposed to be especially smart. By way of instance, types of predictive analytics that use the methods of machine learning might indeed be ML jobs, but they’re not AI projects in themselves. Essentially, using ML techniques to learn one narrow specific application, and in that training version cannot be applied to various conditions or has some other means to evolve or adapt to new situations isn’t an AI-focused ML project. It is ML with no AI.

Data Science Perspective: ML for Predictive Analytics not for Intelligence

Part of why we are seeing a resurgence of interest within the discipline of AI isn’t only the development of algorithms that are better to do machine learning (notably Deep Learning), but also the sheer amount of information we have and better processing power to take care of it. Not only did the Big Data revolution bring about new ways of managing and dealing with large amounts of information, but it helped usher in the fields of data science and data technology to provide insight and hidden value in the information and better methods for manipulating massive data sets.

It’s no wonder that the methods and techniques of machine learning are attractive to information scientists that have before had to deal with more advanced data queries using SQL and other data access procedures. ML provides a wide selection of algorithms, techniques, and strategies to gain insight, supply predictive power, and further enhance the value of data from the business, elevating information to information, and then to understanding.

Read Also: Is Machine Learning A Matter Of fact AI?

However what distinguishes many data science-driven ML jobs from AI-driven ML projects is that the models that are being assembled for these efforts and the reach of these projects are extremely narrowly constrained to one problem domain, for example, credit card fraud. This intriguing Quora exchange between data scientists makes it crystal clear that the ML approaches being used are being used to fix narrow issues of predictive analytics, perhaps not greater challenges of AI. In this manner, these ML jobs aren’t AI projects, but rather predictive analytics projects. We could call this” ML for predictive analytics” as opposed to ML for AI.

Likewise, you will find additional programs of ML for special single-task use, such as kinds of Optical Character Recognition (OCR) and even types of Natural Language Processing (NLP) and Natural Language Generation (NLG) where ML approaches are used to extract valuable data from handwriting or speech. We have had OCR and NLP options for decades, and before this new AI summer wave, they have never known as their approaches AI. Rather, they must be enabling some greater target for these to be considered AI.

What is considered ML in the context of AI?

To generate AI operate, we need ML, however, we don’t require ML models narrowly constructed for something like credit card fraud detection to produce smart systems work. Rather, what we want our ML systems that permit the AI attempt to learn not only the particular models in which they are taught but rather a framework where these systems can learn by themselves. ML from the context of AI emphasizes not only self-learning but in addition, the idea that this learning could be applied to new conditions and circumstances that might not have been modeled, trained, or learned earlier. In various ways, this form of continuous, enlarging learning is the objective of adaptive systems.

Adaptation and self-learning are keys to not just handling the explicit problems of now, but also the unknown issues of tomorrow. ML programs that are built in this manner support these goals for AI and are fundamentally more complex and sophisticated than their thinner, single-task ML brethren. The essential insight is the fact that it is not the algorithm that decides whether ML is used in an AI context or not, but instead how it is being implemented, and the sophistication of these learning systems that surround those calculations.

Are we simply nitpicking here? Should all kinds of ML be considered AI, even though they are super narrow, data science has driven or using decades-old OCR? If we consider AI for a continuum from super feeble, really narrow forms to the ultimate AGI goal? Perhaps, and we see no fault with having that standpoint about AI. Regardless of your perspective, it is helpful to make the definition of AI meaningful so that we can achieve real advancement from the aims of AI, rather than using AI since the buzzword of today. Because if we do this, then AI will endure as it did at the winters of yesterday

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