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ARTICLE

knowledge engineering. Initiating common sense, reasoning service. These deep learning algorithms help the app
and problem-solving power in machines is a difficult and extract street names and house numbers from photos
tedious task. Robotics is also a major field related to AI. taken by Street View cars and increase the accuracy of
Robots require intelligence to handle tasks such as object search results.
manipulation and navigation, along with sub-problems of
localization, motion planning and mapping. 4. Paypal: PayPal uses machine learning algorithms to
detect and combat fraud. By implementing deep
Machine learning is another application of artificial learning techniques, PayPal can analyse vast quantities
intelligence (AI) that provides systems the ability to of customer data and evaluate risk in a far more
automatically learn and improve from experience without efficient manner. Traditionally, fraud detection
being explicitly programmed. Machine learning focuses on algorithms have dealt with very linear results: fraud
the development of computer programs that can access either has or hasn't occurred. But with machine learning
data and use it learn for themselves. The process of learning and neural networks, PayPal is able to draw upon
begins with observations or data, such as examples, direct financial, machine, and network information to provide
experience, or instruction, in order to look for patterns in a deeper understanding of a customer's activity and
data and make better decisions in the future based on the motives.
examples that we provide. The primary aim is to allow the
computers learn automatically without human intervention 5. Netflix: More than 80 percent of TV shows on Netflix
or assistance and adjust actions accordingly. are found through its recommendation engine. Machine
learning is integral to this process, as the platform caters
Examples of AI applications: to more than 100 million subscribers. While the finer
details of Netflix's machine learning algorithms are kept
1. Voice recognition Systems: Voice recognition systems
behind closed doors, Tod Yellin, the company's VP of
such as Apple's Siri, Microsoft's Cortana use machine
product innovation states there are two things that feed
learning and deep neural networks to imitate human
the neural network: user behaviour and programme
interaction. As they progress, these apps will learn to
'understand' the nuances and semantics of our content. Together, these datasets create multiple 'taste
groups', which tell the recommendation engine which
language. For example, Siri can identify the trigger
programmes to serve up.
phrase 'Hey Siri' under almost any condition through the
use of probability distributions. By selecting appropriate
6. Chatbots: Chatbots are artificial intelligence based
speech segments from a recorded database, the
automated chat systems which simulate human chats
software can then choose responses that closely
without any human interventions. They work by
resemble real-life conversation. Amazon's Alexa and
Echo and Google's Google assistant are also examples identifying the context and emotions in the text chat
of voice recognition systems.

2. Facebook: Remember when Facebook used to prompt
you to tag your friends? Nowadays, the social network's
algorithms recognise familiar faces from your contact
list, using some seriously impressive technology. 'We
closely approach human performance,' says Yaniv
Taigman, one of the masterminds behind DeepFace,
Facebook's machine learning facial recognition
software.

3. Google Maps: Google introduced machine learning to
Google Maps in 2017, improving the usability of the

40 | 2021 | DECEMBER | BANKING FINANCE