Before we dig into the topic deeper, let\u2019s have a quick look at the definitions of AI<\/strong> and machine learning<\/strong>.<\/p>\n The<\/em> term \u201cAI<\/strong>\u201d is exceptionally broad in its scope.<\/span> It can be defined as the theory and development of computer systems that are capable of performing tasks that generally require human intelligence like speech recognition, decision making, visual perception, among others.<\/p>\n Machine learning<\/strong><\/em> is a subset of AI<\/strong> in the computer science field<\/span> that uses statistical techniques to enable the computers to learn with the help of data, without being programmed explicitly. It provides the systems with the ability to learn automatically and improve from experience.<\/p>\n It\u2019s<\/em> needless to say that both AI<\/strong> and machine learning<\/strong> are relatively newer concepts. Though AI<\/strong> stretches its origin back to certain imaginative people from decades or even centuries ago, it\u2019s only recently when it has become a reality. In the past, early computers were considered by engineers as mechanical brains and logical machines since they were capable of producing memory and arithmetic. Along with the advancements in technology, the concept of AI<\/strong> has been changed. Put simply, AI<\/strong> encompasses and mimics the decision making ability of humans to perform complicated tasks in a more human-like manner. It\u2019s a much wider concept than machine learning<\/strong>.<\/a> Let\u2019s see the tasks that are performed by AI<\/strong>-enabled systems, in general.<\/p>\n There\u2019re<\/em> two key subcategories of AI<\/strong>. The first is applied AI<\/strong> while the second one is general AI<\/strong>.<\/p>\n Applied AI<\/strong><\/a> is focused on predefined tasks. It doesn\u2019t hold any consciousness and unseen tasks can\u2019t be generalized by it. It\u2019s much more common in real world and examples of its implementation can be easily observed. Some of the applied AI<\/strong> systems include Siri, Netflix recommendations etc.<\/p>\n General AI<\/strong> holds all the capabilities of applied AI together with all the characteristics of human intelligence. It\u2019s less common as it\u2019s more complicated to create. Ideally, general AI<\/strong> systems should be able to perform any intellectual task successfully that can be done by humans. It\u2019s important to note that this subcategory is what led to the rise of machine learning<\/strong>.<\/p>\n There\u2019re<\/em> certain breakthroughs in the field of AI<\/strong> that contributed heavily in the development of machine learning<\/strong>. The first one involved identifying that it\u2019s more effective to teach computers how to learn compared to teach them how to perform each possible task and provide them with the information needed to perform those tasks. Invention of the internet is the second major breakthrough that led to a huge potential of information storage. Now, machines can access massive amounts of data that they had never been able to access earlier because of storage limitations.<\/p>\n Put simply, machine learning<\/strong> can be considered as the practice of utilizing algorithms to parse data, obtain knowledge from it, and then make a prediction or determination about something.<\/span> So, instead of hand-coding software practices with a certain set of instructions to perform a specific task, the machine becomes trained by using massive amounts of data and algorithms that enable it to learn how to perform that task thanks to data science training<\/em><\/strong>.<\/a>\u00a0Immersive data science<\/strong>\u00a0<\/em><\/a>experience<\/strong>\u00a0is a comprehensive step for machine learning.<\/p>\n With machine learning<\/strong> coming from the AI<\/strong> landscape directly, several algorithmic approaches are there that are being used heavily. Some of these include Bayesian networks, reinforcement learning, decision tree learning, clustering, and inductive logic programming, among others.<\/p>\n Neural networks<\/em><\/strong><\/a> play an essential role in the process of teaching computers human-like thinking. They allow computers to mimic human brains more closely while being more accurate, faster and less biased. Accessed data is used by neural networks to make determinations. The feedback loop for learning involved in these networks enables a machine to find out whether its decisions are right or not, and to change its approach accordingly in order to perform better the next time.<\/p>\n Though<\/em> AI<\/strong> is revolutionizing the way companies do business, it comes with some real limitations too. So, it\u2019s crucial to understand them before you go ahead with the implementation of AI<\/strong>. Here\u2019re some key limitations to effective implementation of AI<\/strong>.<\/p>\n Despite all these limitations, AI is surely going to be more and more efficient over time. However, you should consider these aspects in order to stay away from unrealistic expectations.<\/p>\n Machine learning<\/strong><\/em> also comes with its own limitations. Machine learning<\/strong> models are much more complex in nature.<\/span> Though these models are capable of making predictions and recommendations by identifying patterns in a given dataset at a scale beyond the limits of humans, it\u2019s impossible to explain how and\/or why the model has made those predictions and recommendations. This lack of transparency is a key issue in some industries.<\/p>\n Here\u2019s<\/em> a brief overview of what we discussed earlier in this post in order to determine the key differences between AI<\/strong> and machine learning<\/strong>.<\/p>\n Both AI<\/strong> and machine learning<\/strong><\/em> have a lot to offer with their promises of giving creative insights as well as automating mundane tasks. Industries in every field \u2013 from healthcare to banking to manufacturing and more \u2013 are gearing up to reap the benefits if they aren\u2019t already.<\/p>\n While AI<\/strong> has been around for a prolonged time and can sometime seem like an \u201cold hat\u201d, machine learning<\/strong> can offer businesses something new. Today, we\u2019re certainly closer than ever and we\u2019re moving toward developing human-like AI<\/strong> at an increasing speed.<\/span> It\u2019s important to understand that much of the enviable progress happening in the recent years is heavily based on the fundamental changes in the way we envisage AI<\/strong> working that has been brought about by machine learning<\/strong>.<\/p>\n Both these systems already offer some great applications and we can expect to see more to come from them. However, machine learning<\/strong> has gotten more publicity lately with a huge number of companies focusing on this source of solutions. AI<\/strong> can also prove to be extremely useful for an array of simpler applications that don\u2019t need ongoing learning.<\/p>\n Despite all the differences, the huge majority of AI<\/strong> solutions are machine learning<\/strong> solutions.<\/span> You can strategically use both AI<\/strong> and machine learning<\/strong> to maximize your benefit. In the future, it might be possible for machine learning<\/strong> to lead us to something more predictable and more sophisticated. You can consider it as a distinct method of attaining the same goal as that of AI<\/strong>.<\/p>\n There\u2019re various elements that have helped in increasing the wide adoption of and excitement around artificial intelligence. Among them, advanced artificial intelligence that is being used by companies is a variant of machine learning<\/strong> to a good extent.<\/p>\n As you can see from the above, there\u2019re differences between AI<\/strong> and machine learning<\/strong> with something in common as well. When it comes to implementation, determining which one of these is best for your business depends on your field of business and your specific requirements.<\/p>\n1- What\u2019s AI?<\/em><\/strong><\/h3>\n
![\"What\u2019s](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/03\/What\u2019s-AI-300x169.jpg\")
<\/p>\n2- What\u2019s machine learning?<\/em><\/strong><\/h3>\n
![\"What\u2019s](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/03\/What\u2019s-machine-learning-300x225.jpg\")
<\/p>\n3- The rise of AI<\/em><\/strong><\/h3>\n
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Types of AI<\/em><\/strong><\/h5>\n
![\"Netflix](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/03\/Netflix-data-science-300x169.png\")
<\/p>\n![\"AI](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/03\/AI-data-scientist-300x201.jpg\")
<\/p>\n4- Rise of machine learning<\/em><\/strong><\/h3>\n
![\"Rise](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/03\/Rise-of-machine-learning-225x300.jpg\")
<\/p>\n5- Neural networks are there too<\/em><\/strong><\/h3>\n
![\"Neural](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/03\/Neural-networks-are-there-too-300x200.jpg\")
<\/p>\n6- Limitations of AI<\/em><\/strong><\/h3>\n
![\"Limitations](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/03\/Limitations-of-AI-300x265.jpg\")
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7- Limitations of machine learning<\/em><\/strong><\/h3>\n
![\"Limitations](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/03\/Limitations-of-machine-learning-300x200.jpg\")
<\/p>\n8- Differences between AI and machine learning in a nutshell<\/em><\/strong><\/h3>\n
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9- AI or machine learning \u2013 Which one should you go for?<\/em><\/strong><\/h3>\n
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