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Impact and Example of Artificial Intelligence
10 Lectures 1 hours
15 Lectures 54 mins
Artificial intelligence deals with programming computers to detect patterns in new data, make decisions based on user input, and give output based on the user input. All the rules are not explicitly defined and it is expected by the developer that the machine learns these rules by experience, using a reward-punishment mechanism.
Applications of Machine Learning
Artificial intelligence has come a long way with self-driving cars, bots, object and facial recognition, and much more.
In recent times, algorithms have been developed with a better accuracy that can help provide outputs which have better accuracies.
Due to the improvements in the field of Artificial intelligence, the number of trivial human interactions (customers communicating with service agents) have reduced due to chatbots, improved and better-quality healthcare, object recognition and more.
Since patterns can be detected from data, the resources required can be planned, and can be utilised efficiently. This way, machine learning makes businesses cost effective too.
Predictions based on sales in businesses can be more accurate leading to better outcomes. It can also help manage resources during difficult times.
Machine learning can help detect fraud before it happens, and prevent fraudulent transactions thereby saving businesses from heavy losses. There threats can be of a wide variety depending on the nature of the business.
An important aspect of business is to manage the ever-flowing data in a better way, use it well and extract insights from it so that it can be used for the benefit of the organisation. Unsupervised machine learning algorithms do just that. They don’t need to be explicitly told to extract specific patterns, since they work well on unstructured data.
When data is put to good use and meaningful insights are extracted from it, it can help take better decisions that would eventually improve the revenue.
Patterns in data can be recognized that would help improve the revenue of the business.
Like data security, machine learning algorithms can help differentiate between useful and useless data (in the form of emails, and more). This ensures that businesses don’t waste resources on filtering data, rather than putting the relevant data to good use.
Healthcare organisations can use sophisticated tools to yield accurate results in a shorter span of time depending on the medical history of the person.
Examples and Enhancements to Machine Learning
Let us now see some examples and enhancements
Enhanced User Experience − Instead of talking to agents about trivial attributes, the human resources can be used efficiently. These have been replaced by chatbots.
Home Assistants − Applications like Alexa are built which use voice-based inputs to produce outputs.
Home Automation − Using Internet of Things, and Machine Learning, based on the user’s movement, objects inside the home can be controlled.
Object Recognition − In automated traffic detection, objects can be identified with better accuracy in real-time.
Self-driving cars − Companies like Tesla are milking with the developments in Machine Learning by automating car driving.
Robots − Robots can be used for dangerous tasks instead of humans.
Reduction in errors − Since computers are better with calculation, the number of errors has reduced.
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