Things to know about AI and Machine Learning

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Evolving technologies has a great impact on businesses as it offers many opportunities and areas for development which can change the face of your business. There is a lot of potential when it comes to adopting a certain technology and implementing it so that you can try new ways to take your business to the next level. Artificial Intelligence is a hot topic of discussion in recent times because it offers a chance to take the next best technological step.

Artificial Intelligence was coined as a term in early 1950 but only now it has come into practical use, thanks to the growing data and various fields where AI can be used with advanced algorithms and higher compute power. Artificial Intelligence is not an independent technology because it is a broad term which consists of many other terms which are related to AI and ranges from robotics to machine learning. Some refer to AI as ‘cognitive computing’ or ‘machine learning’ while others call it as ‘machine learning’. People tend to confuse these terms with one another because the end goal of AI is to build machines capable enough to perform critical tasks and cognitive functions which are actually within the scope of a human acumen.

Artificial Intelligence is about machines learning the basic and most critical programs to gain experience and respond to demands and perform human-like tasks. You can also say that machine learning is a type of AI which allows the software to predict outcomes and provide results without being programmed for specific tasks. By providing inputs and programming a machine to do specific tasks, first it processes data and certain patterns because it needs to follow a code.

In order to get the desired result, machines must be able to learn specific capabilities instead of feeling the need to programme them explicitly. We have achieved stunning progress in the field of AI in the last 10 years. Here are some of the amazing examples where companies have implemented AI into their business:

1. Google’s AI-Powered Predictions

Google Maps makes use of location data from smartphones, which analyzes the pace and movement of traffic at any given time. With the help of their Waze app which detects traffic incidents like construction and accidents, Google Maps can easily find out the traffic in reported areas. When vast amounts of data regarding traffic is received and fed into the algorithm, then Google Maps can easily provide the fastest route and areas which are less congested.

2. Ridesharing Applications

Uber is able to minimize your wait time when you hail a car or determine the price of your ride and provide you a service which is equivalent to other passenger’s location which minimizes detour. Have you ever given it a thought that how is Uber able to do it? The answer is Machine Learning. This provides a user with ETAs for rides, optimal pickup locations, drop-off points and avoiding detours for multiple customers. All this is possible due to machine learning.

3. Use on an AI-based Autopilot

AI technology in commercial airlines for autopilots dates back to 1914, which is surprisingly early for the use of an AI-based technology. A report suggests that only 7 minutes of human command is needed, which is reserved only during take-offs and landing else everything is taken care by autopilot based on AI.

Further, we can divide Machine Learning into different categories based on the different algorithms. There are 4 types of ML according to their purpose and algorithms which are as follows:

1. Supervised Learning

Supervised Learning is a concept where we need to insert an algorithm to train a machine for receiving a desired end result. We need to design a response which will best serve our query and will provide us with the exact desired solution. Many a times we are not able to create a true function which gives us the correct predictions and other reason can be assumptions made by humans which are hard for machines to understand. Here humans acts as a teacher where we feed data to a computer which contains inputs (Predictors) and also feed the correct answers from which a computer should be able to learn through patterns. Supervised learning algorithms are dependent on predicted output and code inputs so it can forecast the output based on previous data sets.

2. Unsupervised Learning

In unsupervised learning there is no labeled data which is grouped for a specific outcome. Unlike supervised learning, here there is no teacher or a supervisor who trains or inserts any kind of algorithm into the machine. When any type of data is fed into unsupervised machines, the machine processes this data on its own and produces new patterns and ideas. This too belongs to the family of machine learning algorithms which uses pattern detection and descriptive modeling and recognizes certain data patterns to provide a result even if it is not the desired one. When there is no specific algorithm to be followed by the machine, they still try to make a relationship between the actual output required based on the data fed to it.

3. Semi-Supervised Learning

The previous two types of learning either required a teacher or it didn’t, but this type of learning falls between the two of them. In supervised learning, there was labeled data which would provide the result which is expected and in unsupervised learning there was no specific label for a group of data which will provide the desired result. Here, skilled human experts are required for observations of algorithms which will be able to group labeled and unlabeled data to receive the outcome which is necessary.

4. Reinforcement Learning

Reinforcement Learning is a type of machine learning and is also a type of AI. Reinforcement learning is a type of agent which observes the previous experience in a specific environment to maximize the result and minimize risk. Here, the machine is in continuous learning phase in a particular environment in an interactive fashion. The continuous observing of events leads to extraction of complete possibilities of a data pattern. Reinforcement learning allows machines and software agents to determine ideal behavior between a specific environment to maximize the results. This provides various solutions to one’s query so that multiple options are ready to choose from.

Conclusion

We are experiencing a major shift in technology and it is up to us if we are ready to acknowledge and adopt these technologies in our lives. Artificial Intelligence is truly the future because it caters to a lot of needs through automation and continuous learning. Human efforts are reduced with the help of AI and machine learning.