If you are a business owner, then you must have heard about machine learning. It is the latest topic of discussion for businesses, quite similar to the “cloud” back in the early 2000's. Machine learning allows a system to use a combination of algorithms and statistics to analyze and process information without explicit human interference. It can transform your business in the following ways.

1. Identify Hidden Patterns

Your existing technology stock is limited in its capability to identify hidden patterns from the data and activities of your customer. This technology unlocks a greater degree of potential for you to look for hidden patterns (things you fail to notice earlier). It uses an algorithm known as clustering. Clustering is a technique that groups a similar set of data by matching common attributes or features. This means that you segment your customers more efficiently and offer them a higher quality of personalization.

2. Provide Better Recommendations

Perhaps, one of the most popular applications of machine learning is the recommender systems. They are primarily used by e-commerce websites. Recommender systems uses algorithms to offer product recommendations. For example, if you run an e-commerce website, then you might have a customer who mainly shops products that lie in the category of gaming. It can quickly understand this habit and help you deliver gaming-related recommendations to the customer so they are encouraged to purchase. Similarly, they are shown gaming-related apps so instead of being annoyed by irrelevant ads, they can relish the user experience. If you have made up your mind to harness the power of machine learning to increase your revenues, then there are multiple platforms for you to choose from. IT giants like Amazon, IBM, and Microsoft offer their own machine learning platforms. Examine your existing IT infrastructure and pick a suitable machine learning platform accordingly.

Top 3 Applications of Machine Learning

1. Virtual Personal Assistants

Some famous example of virtual personal assistance today includes Siri, Alexa, and Google Now. They assist users to find information via voice command. Machine learning is a vital part of personal assistant devices as they play a key role in collecting and refining information based on your previous experience with them.  Later, the devices use the collected data to deliver results tailored to meet your needs. Virtual personal assistants are linked to several platforms, including:
  • Smartphones and Smart TV:  Samsung S8 and Samsung Bixby
  • Mobile Apps: Google Allo, escorts gh
  • Smart Speaker:  Google Home and Amazon Echo
These machine learning virtual assistant devices are making the lives of many people easier and entertaining. For example, Amazon Alexa that runs on smart speakers will automatically turn on your room light and play your favorite playlist, whenever you say “good morning”. Siri found in iPhones will automatically open your navigation direction and send text messages to your family, whenever you say “Siri, I am going home”. Virtual personal assistant devices can also search for information, recall-related request, or send a command to other resources to gain information and answers to your questions.  It’s no secret! Machine Learning is part of our everyday lives.

2. Maintaining Engine Health

A few years ago, the maintenance of the bus or train and its components depended on a scheduled maintenance approach. The approach was based on detailed inspections and small checkups, either preventative maintenance fixated on the weakest link strategy or leads to corrective repairs.  Although this approach still works, it’s highly infective since it depends on guesswork. However, today, the modern bus or train is merged with technology components that collect and deliver continuous streams of data. These data streams help in the building of continuous-based maintenance or failure predictive models using machine learning. With the help of continuous-based maintenance, transits companies can improve equipment life and lower production downtime.

3. Video Surveillance

One small video file contains more data compared to any other media files like images or audio. That’s why extracting information from surveillance footage has steered a lot of controversies.  With this regard, video surveillance is one of the most advanced machine learning applications. The existence of a person in a different frame of a video is very common. However, in security-based applications, the identification of the person in the video is crucial. The surveillance team often use face pattern as a parameter to identify a person. Machine learning algorithms use several methods to track the movement of humans and identify them. For instance, Machine Learning trained cameras easily track people and detect crimes before they happen.  These cameras are programmed to keep track of the public and notice any suspicious activities.  For example, if a person often visits a spot to inspect it or stand somewhere for an extended period. The ML camera informs the surveillance team so they can take action.

What are the 4 types of AI?

 1. Reactive Machines

Reactive machines are the basic form of AI Systems. They can’t form memories or use past experiences to influence the present of future decisions.   They can only react to presently existing situations.  The very first version of the reactive machine is IBM’s Deep Blue, a supercomputer designed to play chess in the mid-1980s. IBM designed Deep blue to play chess and defeat a human competitor. It defeated Garry Kasparov, an international chess grandmaster in the 1990s. The machine can identify the chess pieces on the board and knows every move.   It can predict both its move and the opponent and select the most optimal move among the possibilities. However, it doesn’t have the concept of the past or any memory of what happened before.

2. Limited Memory

A limited memory machine can retain some information learned from previous data or events. Using the memory and pre-programmed data, it can build knowledge. A good example is self-driving cars which store pre-programmed data, such as map and lane marking, as well as observing surrounding variables like the speed and direction of neighboring vehicles. These cars assess the surrounding environment and adjust their driving when the need arises. However, these small and simple pieces of information about the past are transient.  They’re not stored as part of the vehicle’s library of experience that it can learn from.  Not like the way human drives gather experience over the year behind the wheel.

3. Theory of Mind

Theory of Mind AI represents an advanced class of machines which can intercept their word, plus the people in them.  The machines should understand human emotions, beliefs, and able to interact socially like humans. This concept means that robots would be able to evaluate things with their own worlds and acknowledge that the people within the environments have their own unique emotions, learned experiences, and more. These machines would be able to collect data on people's intentions and predict how they will behave as well. Although this Artificial Intelligence machine not yet developed, researches putting in a lot of effort into designing them.  Kismet is one real-world example of a theory of mind AI. It’s a robot head created in the 1990s in Massachusetts Institute of Technology. The machine can mimic human emotions and recognize them.  These abilities are key advancements in the theory of mind AI front. However, Kismet can’t convey attention to people. Some scientists believe that if the theory of mind AI advances enough, it can play a key role in providing caregiving roles like assisting disable people or the elderly with their everyday tasks.

4. Self–Awareness

Self–Awareness AI considered the future of AI. It involves machines that have their own consciousness, self- awareness, and sentiment. This machine will be smarter than the human mind. However, scientists still have a long way before they can build something like this, it’s just a theoretical concept. But if it happens, the gadget should show a desire for certain things and recognize its own feelings. Now that you know about Machine Learning and Artificial Intelligence, it would be much easier to identify them in society.  Also, you can start getting excited about what can be possible as AI technology continues to evolve.