AI vs. Machine Learning: What’s the Difference?

 

The terms artificial intelligence (AI) and machining learning are used interchangeably by many, and sometimes even tech professionals and enthusiasts find it hard to draw a line between the two. There is a good reason for this; Machine Learning is a subset of AI, specifically a derivative branch of study that deals with large sets of data.

Machine learning is a specialized branch of AI. If you have read about AI and machine learning in the news you may have come across the phrase, “all machine learning is AI but not all AI s machine learning.”

AI is defined as a broad aspect of computer science and engineering that aims at ultimately enabling computers to do things in a similar way as a human and even surpass them. AI is, therefore, acquisition of knowledge and using it intelligently. The main component of AI is artificial neural networks which are modeled to work similarly to a human brain.

On the other hand, machine learning is a more specific field about teaching machines how to learn instead of how to perform every possible task. In this way, machine learning can be defined as a category of computer science which takes a different philosophical direction and seeks to answer the question, ‘how can computers automatically improve with experience’ Machines are therefore exposed to vast amounts of data and learn from it and make predictions. The operative word being ‘learning’; for ML applications, it is critical for the algorithms to be able to find out if the decisions made were right or not and make improvements in their approach next time.

Classes of AI

The two main subcategories of AI are Applied AI and General AI. Applied AI, also referred to as Narrow AI, is the most common and is used in areas such as stock-trading predictions, image recognition, and automated driving. General AI is the vision of AI seen in pop culture and science fiction. Fully-autnonomous thinking robots geared towards handling all types of tasks that a human can do and more. It is more difficult to create and currently, the full capabilities of general AI have not been attained but advances in the field are growing rapidly.

Industrial applications

The main use of Applied AI today is in prediction analysis.  Control of traffic and apps that show the fastest routes to work use applied AI based on data points such as accidents incidences, traffic, and weather.

Facial Recognition is one of the largest areas of research for AI today. Already it is being used by companies such as Facebook in image classification as well as organizing the timeline. Furthermore, areas such as predictive text recognition and suggestions are being used to make recommendations, remove spam and provide more relevant information.

In terms of usage, machine learning is primarily being used in areas that require analyzing large amounts of data to make better decisions, from online search engines to manufacturing, healthcare, and banking. Direct examples of machine learning in action include processing vast amounts of data that a human being would not be able to handle. On the internet, there is a lot of data emanating from social media, e-commerce, and the millions of other motes of web detritus. Machine learning algorithms are exposed to these data sets in order to find patterns and predict human behaviors so as to help companies make better decisions for marketing and product development.

Companies such as Spotify and Netflix use it in order to help users choose better music or movies based on previous preferences, and mood, and active user input. The recommender system is also used heavily by Amazon, social media companies in enabling users to find things they want faster and enhancing the customer experience.

Industries that require automation are among the most viable candidates for machine learning and AI. Tasks that are repetitive and require to be done for long periods of time use machine learning in order to speed up the process, reduce wastage and learn and improve more efficiently. Robotics incorporates aspects of design, build and programs. When machine learning and AI are incorporated in robotics, they enhance areas such as motion control, grasping, categorizing objects and vision. Robotic learning through adaptive control or reinforcement learning is helping robotics industry understand physical and logistical data patterns and be proactive. For example, a robotic arm that is able to detect and grasp objects of varying shapes, learning about obstacles avoidance through neural image recognition, joint manipulation among others. Generally, automation and robotics without incorporating machine learning and AI is not efficient way of resource allocation. With machine learning and AI, the capabilities are enhanced much further providing new patterns and push the boundaries in making products and processes that are more intuitive and responsive to changes. It is clear that the factory of the future will be smart factory.

Machine Learning relies on exposing algorithms to large sets of data
Machine Learning relies on exposing algorithms to large sets of data

The intersection of AI and machine learning

From the above analysis, the intersection of AI and machine learning is now visible. The need for AI has led to developments of machine learning as an important pillar to the ultimate goal of smart neural networks that work like the way human brain does. In the beginning, the general consensus was that in order to achieve AI goals, humans needed to input and encode as many rules as possible into the computer. However, that turned out to be not the efficient way of achieving that. Instead, teaching computers how to learn is better approach that will ultimately generate new insights and that has been the focus on machine learning. Combining this with artificial neural networks ultimately generates breakthrough in optimal utilization of machine learning and AI without any input from human.

 

 

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