All about AI, ML and DL

Humans’ obsession with technology has been leaping. In one way or another, automation has infiltrated in every aspect of our lives and will continue to do so. The gadgets and devices we use have a number of interrelated technologies ingrained in them and today we will be discussing some of these in this blog. Artificial intelligence is an umbrella term that has become very popular in the last few decades. It has made machines way smarter and efficient than before. Not just this, researchers and experts are also working upon the concept of Machine Learning and Deep Learning too. 

Through Artificial Intelligence, machines are given the power to interpret, think and perform tasks just like humans but without their intervention. All this is done with the help of algorithms or by subsuming human intelligence in machines. So in simple words, it’s the mechanism through which machines are trained to perform activities which the human brain can only perform. There are three crucial aspects on which AI focuses to achieve maximum output. Learning, reasoning and self-correcting. Learning is a process in which data is gathered, a set of rules are made to convert this information in an actionable state. In reasoning, you select the best algorithms to get the efficacious results. Lastly, in the self-corrective process you keep on making adjustments in the algorithms in order to get precise results. In many recurring tasks that require detail, Artificial Intelligence is better than humans in terms of performance. For example AL tools help in analyzing and assessing professional, legal, or technical documents to check they are free from errors. Al also eases business operations by providing valuable insights to them. There are countless examples of AI powered applications such as the ride sharing app Uber. Some airplanes also have AI autopilot features.

Machine Learning is one of the subgroups of Artificial Intelligence in which machines (systems or computers) have the ability to automatically learn and improve its functions according to the prior experience it has. Smart systems are built without the need for explicit programming or human intervention. There is a further classification of machine learning algorithms i.e. supervised, unsupervised and reinforcement learning. The most common example of ML is Siri and Alexa which serve as your virtual personal assistants.

Another component of Artificial Intelligence or a sub discipline of Machine Learning is Deep Learning. Here the data is processed, filtered and classified just like the human brain. The neural network imitates the behavior and functions of the human brain and organizes the information. Its emphasis is on information processing patterns and it works on large data sets. Image analysis is one major example in which meaningful information and some visual attributes are extracted from the images.

Artificial Intelligence, Machine Learning and Deep learning are all new and interconnected terms that are subliminally and positively affecting our lives.

 

Top 5 Applications of Machine Learning

Machine Learning is a very interesting technology and a subcategory of Artificial Intelligence which is everywhere around us today. With the help of machine learning, computer programs learn, adapt and improve themselves on the basis of past data and experiences. It also allows systems to identify patterns, perform tasks and make predictions, accordingly. This technology requires very less human interference as machines autonomously access data. As more data is fed into machines, the algorithms teach the systems, which ultimately presents and delivers better results. For example, if you request Alexa to play your favorite song, she will go through the list of your most played songs and then pick one to play. You have to give her commands if you want to make changes such as adjust volume or skip a song. ML applications do not require explicit programming. As they are fed with new data, they learn, change, and develop on their own. Through an iterative process computers are able to find insightful information independently.

Applications of Machine Learning

All the industries immensely benefit from machine learning technology, especially those that have massive amounts of data to handle. Many of them have realized its significance and value and are taking maximum advantage to stay ahead of their competitors. Let's closely see the various sectors that are making the most out of this technology.

1. Retail Sector

ML is being implemented by many shopping and retail companies who prioritize their customers. From capturing and analyzing data to running marketing campaigns, gaining customer insights, and price optimization, it is present everywhere. Based on the purchase history of customers, retail websites suggest products using machine learning technology. They also use conversational Chatbots to engage with them. To satisfy their customers and provide them a personalized shopping experience, sellers adopt various ML techniques. Netflix and YouTube are the perfect examples of companies that heavily depend on ML. They recommend movies, shows and videos to their viewers or users based on their past searches and viewing history.

2. Healthcare sector

A number of devices and wearables have sensors inside them for example fitness trackers, sleep trackers or smart health watches. Such devices are used to check, assess and monitor the overall health and fitness of patients or users in real time. ML algorithms are very useful for medical practitioners and experts to make accurate predictions about the lifespan of a patient dealing with a deadly disease. Making or discovering a new drug is very costly and a long process, but through ML it is easy to analyze large volumes of data.

3. Travel sector

All the ride sharing apps such as Uber, Careem and InDrive incorporate machine learning, especially for the dynamic pricing. Companies also use it to analyze the reviews of users and also for the brand, compliance or campaign monitoring. The demand and supply as well as the traffic patterns are taken into account. Also, real time predictive modeling is used for the fare and ride booking. 

4. Finance sector

Banks and financial institutions make use of machine learning technology in order to analyze large volumes of data. Cyber security teams can also get information and warnings regarding fraudulent activities so that they can deal with them in a timely manner. ML also provides valuable insights to the investors related to investment opportunities.

5. Social Media

Machine learning has played a crucial role in showing user specific ads through the personalization of news feed. A plethora of users can connect and engage with each other through different social media platforms and networks. Through ML and image recognition, Facebook recognizes your friend’s face so that you can automatically tag them on posts.

Machine learning has influenced all industries around the globe and will continue to do so, since computer algorithms are becoming more efficient with the passage of time. Overall, an upward trajectory is expected for machine learning in the coming years.