Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning

Why do you think is software development process evolving nowadays?

To cater to emerging technologies such as artificial intelligence and machine learning so that they can be incorporated to improve the existing systems and develop new ones.

We all know that the two most common buzzwords going around today are Artificial intelligence and Machine Learning. While they both are indeed different, people tend to use them interchangeably because they are confused about what exactly these buzzwords entail.

Do you know what these technologies really are?

Don’t worry, if you too don’t know precise answers to what artificial intelligence and machine learning are because we have got you covered.

In this blog, we will discuss both artificial intelligence and machine learning in detail.

Let’s first start with the definitions and their applications:

Artificial Intelligence

In1956, John McCarthy came up with the term Artificial Intelligence. It has since then been used frequently, albeit not as regularly as it is used today. Various popular movies such as The Matrix, The Terminator, and Ex Machina also refer to it, highlighting the mystery and possibilities of AI.

In a nutshell, AI refers to the development of machines that can react and work just like humans. For instance, planning, understanding languages, solving problems and recognizing sounds and objects.

Gartner has predicted that the business value created by AI will be close to $3.9 trillion by 2022. Yes, you read that right. It’s trillion.

These are some of the most re-knowned applications of AI:

Self-driving Vehicles

Autonomous vehicles are equipped with numerous sensors, long-range radars, cameras, light detection, and ranging technology to collect information. AI processes the gathered information and helps take specific actions, for instance, directing that car to a re-charge or filling station when the need arises, adjusting the direction of the car to the quickest route after taking into account the real-time data, and avoiding accidents by mitigating a number of risks on the roads.

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Healthcare

Artificial intelligence is reforming the healthcare industry for the better. From improving the medical diagnosis systems to drafting effective as well as personalised treatment plans for patients, AI is making substantial contributions. What’s even more fascinating is that in order to care for the elderly, robots that use artificial intelligence can be developed and used. These robots can assist not only in repetitive jobs that are necessary but also go above and beyond for instance by entertaining elderly people, putting them in touch with their respective families, or call for help if needed.

Also Read: Benefits of AI in Healthcare

Finance

Extracting vital information from various sources and then taking real-time investment decisions are an integral part of artificial intelligence in the banking industry. Plus, algorithmic trading makes use of AI to take trading decisions at very high speeds. As far as personal finance is concerned, a number of applications that are powered by artificial intelligence are used to help individuals optimise their savings as well as spending.

Machine Learning

It was Arthur Samuel in 1959 who came up with the term Machine Learning and defined it as the capability of machines to learn without being programmed. Basically, machine learning is a way of achieving artificial intelligence. Instead of writing hundreds if not thousands of lines of complex code to get the machines to do a certain task, machine learning can be used to effectively as well as efficiently train an algorithm. The training is done by feeding heaps of data to the algorithm and allowing it to learn as well as adapt.

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Did you know that a machine learning algorithm helped Netflix save close to $1 billion by giving subscribers personalised, valuable information?

Such is the importance of ML now that SAP – a multinational software company – is now exploring it in detail to help boost their own business and add value in the lives of people.

One example of ML is evident in computer vision – the ability of the computer to recognise a particular object in a video or an image. By feeding the data for instance of thousands of pictures in which there is a dog and in which there isn’t one, the algorithm will learn to tag a picture with a dog in it, and the accuracy will increase over time.

Following are some of the most prominent applications of ML:

Virtual Assistants

Having been integrated into smart speakers, mobile phones, and apps, virtual assistant make use of machine learning to act as one’s personal assistants. They gather and refine the information they receive from an individual’s interaction with them and then use that information further to deliver results that are more personalised.

Surveillance

Training computers to do video surveillance is another application of machine learning. In order to improve security and prevent crimes, humans are replaced by machines to better monitor activity. Each and every action such as limping, throwing, walking, and standing motionless is tracked by computers and alerts are given to the relevant authorities in a timely manner to avoid mishaps. Machines continuously improve themselves to provide better surveillance services and take each experience – either it be the one in which machines helped deter crime or the ones in which they couldn’t – as a learning curve.

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Malware filtering and Spam

The systems security programs such as anti-virus or malware software utilise machine learning to detect malware. They do this by analysing the coding patterns of malware that was previously filtered and given that most of them are quite similar to an extent, they filter out malware before it can actually do any harm. Similarly, machine learning is also used to block spam emails. With an ever-increase in unsolicited emails, the need to effectively filter out the spam was high. Hence, various machine learning based spam filtering techniques such as a C 4.5 Decision Tree Induction and Multi-Layer Perceptron were developed.

Conclusion

It must be noted that while artificial intelligence and machine learning both are emerging technologies that are being continuously improved upon, they are not substitutes for humans. Yes, they can improve the existing systems and enhance productivity in various industries, but they cannot replace human beings.

It’s in the best interest of people worldwide that these technologies be embraced and continuously improved upon in order to boost efficiency and ensure convenience in both one’s professional and personal lives.

Author Bio:Zoe Kent has recently joined the Rezaid team as a Content Marketing Executive, shortly after graduating from Manchester Metropolitan University. Zoe specialises in SEO, website content creation, and social media marketing. She enjoys writing blogs on a variety of topics – from social issues, disruptive technologies, to lifestyle management and fashion. Being able to write on multi-topics is challenging, and Zoe’s work depicts the hard work and effort needed to write them well.

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