The Subfield Of Machine Learning In Artificial Intelligence

Artificial intelligence or AI is in simple terms, the ability for a computer to learn on its own, without any input from the user, and it has the ability to think on its own and make its own decisions and is also able to understand human intelligence. This is done by using tools in many fields. These fields include, but are not limited to computer science, natural language and speech, machine learning and patter recognition, and computer vision (Luke Muehlhauser, 2014). This also includes psychology, cognitive science, neuroscience, economics, probability, and optimization and logic. These are only the beginnings of what is required to meet what is needed to even to begin to scratch the topic of

Dr. Ahrendt noted the huge advancements that have been made over the last decade, but made sure to note that the math behind AI and machine learning is quite old mathematics. “Now that we can compute things so quickly… we can see the bloom of AI and machine learning.”

They used facts and arguments from various sources, such as studies and authors. As they are introducing the topic, they use the ideas of Lev Manovich to justify their argument that the name “Big Data” can be misleading. Manovich observed that Big Data has been used to refer to data sets large enough to require supercomputers, yet large amounts of data can now be analyzed on much simpler computers. Boyd and Crawford contend that the value of the industry does not simply come from the large data sets, but the “patterns that can be derived by making connections between pieces of data…” By relating Manovich’s idea, their argument made more sense. As computers become more advanced, bigger data sets look much simpler. But the connections Big Data makes are still valuable, no matter how advanced computers

The authors of [1] aim to dispel some of the current hype surrounding big data, mainly the misnomer that it is all about technology and the process is automated. In fact there are three critical elements requiring human expertise 1) the data must be the right kind, of sufficient quantity, and clean 2) a specific process must be followed for success starting with the identification of the objective 3) expert humans who know how to use the technology, execute the big data process, and perform the mining tasks which require significant mathematical calculations.

Abstract Learning: understanding of different ideas and phenomena and how they work together; learning by looking at patterns and similarities between different things. Focus on deeper meaning rather than individual thoughts.

Artificial intelligence is the development of a computer system that is able to perform tasks of human intelligence like visual perception, speech recognition, and decision-making. Computer scientists have made a substantial advancement in the

The author points out that although there are existing algorithms and tools available to handle Big Data, they are not sufficient as the volume of data is exponentially increasing every day. To show the usefulness of Big Data mining, the author highlighted the work done by United Nations. In order to further enhance the reader’s perspective, the author provided research work of various professionals to educate its readers about the most recent updates in Big Data mining field. The author further describes the controversies surrounding Big Data. The author has first provided the context and exigence by elaborating on why we need new algorithm and tools to explore the Big Data. The author used the strategy of highlighting the logos by mentioning the research work of different industry professionals, workshops conducted on Big Data and was able to appeal to connect to the reader’s ethos. The author also used pathos by urging the budding Big Data researchers to further dig deep into the topic and explore this area

Complexity Learning Theory describes how situations unfold unpredictably from the components that are in play around them.

AI needs RL ideas (i.e., ideas regarding actions, long-term predictions, and decision-making), perhaps much more than it needed deep learning ideas (i.e., ideas regarding recognition but disregarding agent 's behavior and its long-term consequences). There is a serious shortage of RL experts. We should take advantage of that by utilizing as much RL expertise as possible from our in-house experts and outside advisors.

AI may be divided in half: weak AI & strong AI. Weak AI is a term for a “machine” that can “act as if they were intelligent”, and simulate very limited capabilities (Russell, & Norvig, p.1020). Our current level of technology, and weak Ai expands across numerous platforms, and ranges numerous examples such as AARON, Deep Blue, Ubers Automated Vehicles, and Watson (Weaver, p.7). In contrast, strong AI is the hypothetical “foundation” for an intelligent agent with cognitive abilities similar to that of a human being (Russell & Norvig, pg. 1020). Accordingly, the aspect that separates weak AI from strong AI is the level of algorithmic programming; generally, AI’s are extremely complex machines capable of acting

The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization.

Business thrive when they have the most accurate, up-to-date, and relevant information at their disposal. This information can be used for a plethora of pertinent markers in small and large businesses, relating to accounting, investments, consumer activity, and much more. Big data is a term used to describe the extremely large amounts of data that floods a business every day. For decades, big data has been a growing field, facing controversy on many levels, but as of late, it has been a major innovator in the challenge of making businesses more sustainable. Big data is often scrutinized for its over-generalization and inability to display meaningful results at times. When applied correctly, data analysis can bring earth-altering information to the table.

Artificial intelligence, or AI, is a field of computer science that attempts to simulate characteristics of human intelligence or senses. These include learning, reasoning, and adapting. This field studies the designs of intelligent

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

The evolution of technology and the importance of information management systems have led to the evolution of new experts in the industry known as “Data Scientists” who work on data science. Despite the hype and confusion surrounding data science, the need for people who can interpret data to help organizations make informed business decisions is very real. Data scientists look at the data sets in nature or complex systems and extract information from it. In today’s age businesses have started to gain tons of data and science already knew how to deal with such large volumes trough physics, applied mathematics and computer science.