The Pros And Cons Of Machine Learning

Computers will soon know us better than we know ourselves. A recent study made by Quartz Media published findings that revealed the average American stares at a screen for about 7.5 hours a day. The more we interact with someone or something for that matter, the more they will know of us. As we head into an age of technological advancement, artificial intelligent is facilitating the devices to become our best friends. In Jeremy Howard’s Ted Talk, The wonderful and terrifying implications of computers that can learn, he explains the complicated algorithm that technology has with tracking and remembering your every click. We have the misconception that we can do everything that computers can, however, with software’s like “deep learning that

It has widely been applied in our daily lives – when we go to Amazon.com, we see recommended items selected by the machine learning algorithms; online music streaming services, such as Spotify and Pandora, use machine learning to guess songs that we like; search engines, Google for example, use machine learning to learn our searching habits and suggest websites that we most likely want to visit. Indeed, machine learning has made our lives much easier to some extent. However, machine learning might also bias our views. Personal Assistant services, such as Google Now and Cortana, study your habits and use that data to filter and select what you prefer to see and present them to you; Facebook pushes news to your timeline, but only those the machine learning algorithm thinks you are interested in. In general, machine learning algorithms take in users’ habits and interpret them into their preferences, and therefore provide more information that matches their

The concept of artificial intelligence was first labeled by a man named Alan Turing in 1950, he believed that the future would hold the possibility for man to communicate with computers and sustain a conversation (Atkinson, Solar 1). Although, we have reached the point where it is possible to hold a simple preprogrammed conversation with a computer and give them the ability to learn, there is still a long way to go in making computers fully artificially intelligent. Atkinson and Solar continue to describe some real world applications of artificial intelligence such as, “Data mining technologies, fraud detection, and industrial-strength optimization” (8). In these examples, forms of artificial intelligence like cognitive reasoning abilities are already being used making the demand for them higher.

To progress as a society, we must first look back at all the hardships faced throughout the years. We must look at the world leaders who were able to convince entire countries to eliminate millions of people different than themselves. We must understand the groups of extremists spreading terror and fear across the globe, and we must control people in power abusing their positions to benefit themselves and their agendas. Instead of ignoring these human mistakes, we must break them down and figure out how they could happen. Artificial Intelligence relies on our abilities to learn from our mistakes and mold our future in a way that will be beneficial and equal to all people. This paper will present the many benefits that A.I. will give

In a world where computers are becoming as essential to daily life as the cars we drive or the telephones we use to communicate, it is difficult to find a person who doesn’t have some particular use for computers. Computers have become the information stores of the world. If you take a moment to think about all the kinds of information a person can and does hold on their computer it is staggering. I myself have all the passwords to my email and bank accounts, the history of every web page I’ve visited in the last 3 weeks, my credit card numbers, the complete history of all my banking transactions for the last three years stored on my computer. Additionally, think about all the

Having better, more accurate data opens the way for improved decision support based upon machine learning – as we will see later.

World changing, revolutionary, and mind-blowing is what this world demands and rotates around by an item called technology. Technology is flourishing and is altering the world as time passes by. Articles support this by using evidence and scientific reasons. Scientific advancements have many purposes to it such as helping the world, finding out new things or socializing with others. Although technology is a great thing, it has many cons to it. Technology is a dangerous resource in the world that has evolved into many forms of everyday life that should be stopped because of the dangerous advancements like gene drives that can go wrong and harm people, robots that see humans as their enemies, and even robots that can outsmart humans.

Data mining is the process of releasing concealed information from a large set of database and it can help researchers gain both narrative and deep insights of exceptional understanding of large biomedical datasets. Data mining can exhibit new biomedical and healthcare knowledge for clinical decision making. Medical assessment is very important but complicated problem that should be performed efficiently and accurately. The goal of this paper is to discuss the research contributions of data mining to solve the complex problem of Medical diagnosis prediction. This paper also reviews the various techniques along with their pros and cons. Among various data mining techniques, evaluation of classification is widely adopted for supporting medical diagnostic decisions.

The Information Technology industry is the fastest growing field in the nation, and according to the Bureau of Labor Statistics, between 2012-2022 projected 18% of the increase. The heist demand is set for Information Security Analysist, which expected to grow to 37%. (see table below)

While in the second case, once data is gathered and algorithm applied, it is used to gain fresh insights into the data which could not have been obtained without having an algorithm that is powerful enough to process such a large and complex chunk of data. An example of this in computer security will be understanding of a user’s high CPU usage when compared to others without terming it bad, based on the algorithmic output obtained about the user from the audit logs. Together with data science, machine learning can be used to gain hidden insights into data and to build predictive models to process new data.

Machine-learning fascinated me ever since I discovered the field because, throughout my mathematical education, I often thought about the idea of fitting functions on pre-existing data to create generalized solutions. There is a variety of potential applications within machine-learning that interest me; such as the automation of various tasks performed typically by doctors or the ML applications to computer software systems. Extraction of information from human-written text sources (NLP) also interests me because it enables a researcher to quantify and qualify(?)information

With more resources being poured into the development and advancement of Artificial Intelligence (A.I), the debut of Virtual Personal Assistants (VPAs) redefines the tech industry for revolutionising the interaction paradigms between users and the internet. The result opens up new possibilities for the users of next generation to gather data and communicate via new portals of the web. Keyword based inputs into search engines providing lists of potential sources would no longer be relevant, instead the future consists of individuals simply interacting via the channels of VPA. Similar to how two people have a normal conversation, users can tell their assistants what they want to find using natural languages, the VPA then proceeds to gather and analyses multiple relevant sources of information and provides the corresponding services to aid in accomplishing a wide range of tasks. Mirroring the functions and actions of a real assistant, a VPA is tailored towards each individual user, where they utilise stored information via daily interactions to analyse an individual’s preferences and draw upon their past interaction records in order to determine the next step. In addition to bringing about self-learning mechanism, further improvements to their functionalities can accumulate experiences via daily usage and interactions. The features of VPA , its implementation and the benefits it delivers is crucial in judging between success & failure. This essay will aim to further develop the

In a dynamic environment, neural networks are flexible tools and have the capacity to learn rapidly and change quickly. As the data values and outcomes change, the model quickly learns and adapts itself. Rule based systems

To further elaborate on how it actually works; in machine learning instead of programming of the computer to solve a problem, the programmer actually writes a series of rigid codes to make the computer lean to solve a problem from various examples. As you know that computers can solve complex problems like predicting the pattern of movement of galaxies and so on but it can’t perform easy tasks like identifying objects like a tree or a house, although now a days there are a couple of search engines and applications that are able to do that like Google Reverse Image Search and Apple Images, but they still fail when the image is overshadowed by some other image. So machine learning is basically making the computer think in a way how humans would in this

Deep learning is also called as machine learning it is a technique where the computers do naturally likewise humans. If consider the driverless car deep learning and machine learning is a reason behind it. Deep learning is also a reason behind the recognition of stop sign, voice control over the stop sign and hands free speakers etc. deep learning success was seen later it was impossible without the pervious strengths that adapted deep learning. Before the deep learning, machine learning came into existence and was a part of machine learning. Deep learning is just a part of machine learning algorithms it used many layers and processing of nonlinear to units its feature for transformation and extension. These algorithms have been an important supervision of applications that includes of pattern and classification and it involves multiple layers of data that helps in representation of certain features. These definitions are one of the common layers that is used in non-liner processing over the generative models that includes of hidden layers