Interaction of Machine Learning with Artificial Intelligence:

Machine learning and Artificial intelligence are two evolving worlds in the field of technology. Both are frequently used to describe the advancement of humans in creating technologies that can match human abilities in executing tasks. Artificial Intelligence can be described as the intelligence exhibited by machines. Machine learning and Artificial Intelligence, both go along just like science and technology. Machine learning is one of the sciences behind Artificial Intelligence. What is Machine learning?

It is the science of designing and implementing algorithms that learns from data in order to perform specific tasks. The result of the machine performing the task successfully can be described as Artificial Intelligence.

An algorithm can be described as a series of procedures or principles designed that a machine or software follows to perform its tasks. Machine learning is based on teaching a machine how to learn based on the algorithm, In this situation a machine is able to learn on its own, learn gradually on how to perform a task efficiently, until it is able to master the task. Artificial Intelligence is the product of a machine mastering how to perform a specific task.

Branches of Machine Learning and its Applications:

Supervised Learning:
It is a form of Machine Learning that depends greatly on labelled data. Computer vision is one of the the most important areas of application of Supervised Learning. It teaches computer systems to recognize patterns in an image and interpret what they mean. It involves training a model with labelled data from which it can learn from. For example, a computer model that is to perform the task of accurately detecting a dog in an image. To achieve this, the model is trained with images of different breeds of dogs labelled as dog. The ability of the model to detect a dog accurately can be described as Artificial intelligence. Computer Vision is an integral part of self driving cars(autonomous vehicles) that provides an efficient vision system in cars.

The accuracy of a model performing efficiently depends on the neural networks used. There are different kinds of neural networks available depending on the complexity of the layers involved in the model. Some of them include; Recurrent Neural Networks, Feed Forward Networks, Convolutional Neural Networks, Residual Networks.

Neural Networks: They are networks of interconnected neurons similar to the connection of neurons or nerve cells in the human brain. Each neuron acting as a mathematical function that takes in an input and gives an output.

Unsupervised Learning: 
In unsupervised learning the data involved are unlabeled. It is widely used in solving and analysing many real life problems. Some of the common uses are in clustering and market segmentation, analysing customers' buying habit and market situations, helping businesses in making better decisions. Unsupervised Learning helps to organize data together, grouping similar data together. It is useful in making better decisions in solving practical problems.

A good deal of companies make use of unsupervised learning to achieving better customer experience. Facebook make use of unsupervised Learning to recommend relevant contents, friends, pages and groups to people. E-commerce sites like Amazon make use of unsupervised learning in making product suggestions to their customers based on their regular purchases.

Reinforcement Learning:
It can be regarded as the most advanced aspect of Machine Learning. It is mostly applied in the aspect of robotics. Where by a software agent or a machine is able to act on its own, take decisions on what to do based on its experience in the environment it finds itself.

The future tech:

Data is the key to achieving AI. One of the fundamentals of machine learning is to feed a software or machine with the necessary data. Any algorithm in Machine Learning is meant to update the machine with the information that will make it achieve its goal. The fundamental basis is learning.

Why is the term Intelligence used to describe the output of Machine Learning?

Consider this illustration in natural intelligence; when a baby is born, the baby is with little prior knowledge. As babies grow up, they become aware of the environment they found themselves by what they learn from their environment. Knowledge acquisition is a gradual process in every living creature. Similar principle is applied to machine learning, It is not programmed to perform every task, a machine or software is trained to learn gradually. Intelligence comes in here, when it is able to learn properly and perform its task efficiently.

There are skepticisms concerning the advancement of AI, the behavior of an AI actually depends on the type of data it learns from. As I earlier stated that a baby is born with limited prior knowledge. Kids are known to be innocent and pure in heart, what they learn as they grow up will determine their behaviour. Children who learn negative actions will exhibit negative attitudes. Likewise children that learn positive actions will exhibit positive attitudes. This also applies to machine learning, negative data will produce bad AI, positive data will produce good AI. There is a common belief that AI systems will go rogue. This depends on we humans ourselves but not the AI, what we expose it to will determine what it will become.

There are so many positive uses of Artificial Intelligence. It is the future of technology. It is the key to solving intractable problems. It has helped a lot in augmenting human tasks and is applied in different fields. In the field of healthcare, AI is used to assist doctors in identifying the right treatment for diseases such as cancer. AI is used in banking industry in organizing financial operations and has helped to reduce fraud and financial crimes by efficiently monitoring the users transactions to detect any abnormal changes. It is used to achieve effective advertising by studying the behavior of customers and targeting them with the appropriate ads. It is used in search engines such as Google search, to achieve an efficient search mechanism.

Intelligent AI assistants is becoming part of our every day life, such as Cortana, Google Assistant, Siri, Amazon Alexa. They are integrated in smart phones and they help in various ways, such as going online to search for an answer to a user’s question, showing weather forecast, setting alarms and creating calendar entries. Amazon Alexa, Google Home and Apple HomePod are used in homes to perform a wide range of roles such as making calls, providing real time information and controlling smart devices using home automation systems. The contribution of Artificial Intelligence to life is incalculable. It is an evolving technology that will pave way for a lot of future possibilities. The key to achieving a healthy world with Artificial Intelligence is positive attitude towards innovation, machine learning should be based on creating productive algorithms that will produce beneficial AI systems.