Artificial Intelligence (AI) and Machine Learning (ML) are in buzz right now. Artificial Intelligence is the ability of devices or machines to self-perform without any external intervention. Machine Learning is typically a subset of AI, which permits machines to access the data to learn from it. Machine learning algorithms constantly improve themselves by learning from data. In this way, the learning process is similar to the human learning who refine their skill sets with daily experiences. Artificial Intelligence helps in the emulation of human thinking in actions, and with ML, it helps to develop smart robots. As both terms are used interchangeably, the two becomes confusing to understand. This post maps out the differences between artificial intelligence and machine learning.
Simply, AL is the ability of a computerized or any other high-tech device to think like a human being. AI software routines and systems efficiently show what is the gift of logic, decision-making, and memory on these devices. When using them, for instance, video games can provide a lifelike virtual opponent to fight against; speech recognition systems can predict your vocalized answers to questions, and self-driving cars can speed you securely to stations. From smartphones to talking Bluetooth speakers, today, AI routines can be found in an ever-growing array of consumer electronics of every sort. This trend continues to grow in the future-devices are going to have the smartest features. Now, various brands use AL in their strategic marketing and campaigning activities. According to the 2017 report of the Warc Toolkit, created in association with Deloitte Digital, about 58 percent of worldwide CMOs believe that most companies will compete in the artificial intelligence space to succeed in the next five years. With Artificial intelligence marketing, marketers can bridge a gap between data science and marketing campaign execution. Marketers with a keen interest in AI applications can find plenty of examples of use-cases of AI in marketing. With leveraging information provided by Big Data, AI marketing helps marketers sift through that data within a short period, discovering valuable insights into their consumers that lead to better marketing campaigns and generate high ROI. To understand AI from scratch, professionals can enroll in an artificial intelligence course and get an in-depth understanding of AI concepts.
Machine Learning is usually considered a sub-category of AI, or as a way to achieve AI. Presently, ML is more beneficial to businesses than AI and has a highly promising growth in the future. Computers can learn from examples with the help of ML software applications. Furthermore, they use acquired experience and solve problems without being programmed explicitly. ML is a technique based on pattern recognition from large sets of traditional data with the help of algorithms to provide predictive output. As ML software is improving over time, it has the potential to respond to new challenges effectively. Enterprises use ML to visualize large datasets in order to generate better outcomes to ensure business success. There are various algorithms that ML underpins like logistic regression, classification or naive Bayes and linear regression; you name it. Two categories of these algorithms are: Supervised or unsupervised. Supervised learning encompasses a training dataset, requiring the datasets input and needed output variables, towards developing a model to predict that can also be used to response values of new datasets. Unsupervised learning inputs data with no associating output variables, with the goal of reshaping the underlying data structure. Unsupervised learning does not encompass the desired output specification, or right answer and algorithms are left to their devices to identify and explain data structures. Today, various real-world intelligent machines depend on semi-supervised ML: when a bulk of input data is available, and some are labeled.
The rise of machine learning instigated a revolution in the AI world around 2012. You can even consider deep learning as representation learning because a deep learning system turns a bulk of raw data into a representation that is further used to understand a task. To compare, if AI and ML model endeavor to model a world then deep learning endeavors to model a mind. The deep learning systems reflect a common feature-artificial neural network. ANN is a computational model loosely depended on the biological brain. ANNs include various artificial neurons layers. A deep machine learning system is the one which has at least one hidden layer between the input and output layers. As of 2018, neural networks may encompass thousands neural units with millions of connections. Deep learning is a machine learning type that helps devices to process a huge quantity of data accurately, appropriately, and speedily. Moreover, it trains various high-tech solutions to become efficient at spotting objects and patterns, experiencing and responding to requests while making good decisions. Machine learning is seen in a variety of places, for instance, from image recognition software packages to speech translation tools. Now, different fields have started looking to machine-learning capabilities. Therefore, ML is likely to make its place in tomorrow’s solutions. Yes, the difference between ML and AI and other related terms like cognitive computing can seem to be difficult to understand at first glance, but it’s not that much difficult to understand once you get familiarized yourself with easy to understand information. Header image credit: FLI]]>