Artificial Intelligence (Al), Benefits

Artificial Intelligence

Overviews Artificial Intelligence

Artificial Intelligence (Al) is not limited to Science Fiction and Research Labs. It is now mainstream. According to PWC, it contributed $ 2 Trillion to America’s economy in the last year. This number will rise to $ 15.7 trillion by 2030 according to the report. Artificial Intelligence touches millions daily through Smart Phones, Personal Computers and other Smart Devices. It has immense benefits in all sectors, including Healthcare, Manufacturing, Transportation, Education, Information Technology and Marketing.

Artificial Intelligence: Key Benefits

Here are the benefits of Artificial Intelligence.

Reduce Human Intensive Labor

Smart Automation has helped reduce the human-intensive work of AI. According to the Oxford Economics Report, June 2019, there are more than 2.25million robots deployed around the world (a threefold increase over last decade). Many factories now have AI-enabled robotics that can do all the lifting, carrying and transporting. This allows for a significant reduction in human effort, which can be used to produce more productive activities.

An example of Amazon’s AI-enabled Kiva robots is the deployment of more than 100,000 in their fulfillment centre. AI-enabled robotics reduces the human effort required to move large quantities of inventory from one shelf to the next. It also increases safety at work. Cyborgs can unload and load a full trailer of stock in 30 minutes, compared to human workers who take several hours.

Pharma Industry Efficiency: Increasing Efficiency

AI has been a great boon for the Pharma and Healthcare Industry. According to MIT’s study, only 13% of drugs make it through the clinical trial stage. Further, the cost for Pharma companies to have any drug pass clinical trials is millions of dollars. Pharma companies use AI to improve the chance of their drugs passing clinical trials. Different Machine Learning algorithms assist scientists in finding the right combination of salts in drugs. They do this by analysing historical data about Genes and chemical reactions.

Example: Novartis is a leading Pharma Company that uses Machine Learning Algorithms to determine which compound is most effective at killing the diseased cells under study. This process used to require manual microscopy for each sample. It was time-consuming and susceptible to human error. They can now run machine learning-based simulations in real time and obtain more precise results faster.

Transformation of the Financial Sector

Financial Applications rely heavily on past data analysis to improve their results. It is not surprising that Artificial Intelligence, whose USP in analysing past data is a huge success in the Finance Sector, is so popular. AI is used in many areas of the Finance Industry, including Risk Assessment, Fraud Detection and Algorithm based trading, Financial Advisory, Finance Management, and Financial Advisory.

An example of a fraudulent transaction: Paypal uses advanced Deep Learning Algorithms to detect fraud. Paypal processes huge amounts of transaction data. It processed over $235 billion from more than 170,000,000 transactions. Paypal uses Deep Learning algorithm to analyze large amounts of transaction data and compare transactions with fraud pattern stored in their database. It can distinguish fraudulent transactions from regular transactions based on the pattern comparison.

AI Chat-Bots make it easier to provide customer service faster and more efficiently.

Chat-Bots interactions in an earlier version were very frustrating and time-consuming. The chat-bots could not assist in certain tasks and would often run into loops. Natural Language Processing is an AI-powered chatbot that uses Natural Language Processing. It has a greater understanding of human interactions, can learn by itself and is therefore far more capable of providing a satisfactory response to customers.

Example: Bank of America’s virtual assistant Erica, is an example of an AI-enabled chatbot. Since its launch in June 2018, it has helped more than 7 million clients. Erica used Artificial Intelligence and Predictive Analytics to help more than 50 million clients. These requests range from simple banking tasks such as Bill Payment, Bank Balance Information, and Bill Payments to more complex tasks such as Investment Planning and Budgeting Suggestions.

Road Safety Improvement

According to the World Health Organization Report, over a million people are killed in road accidents each year. Artificial Intelligence plays a significant role in reducing these fatalities. AI has been used by many companies to analyse and record every detail about the driving patterns of different drivers. This includes distance between vehicles, traffic rules, lane discipline, and speed. These details are used by AI applications for safety recommendations and to help automobile companies create safer vehicles.

Example: Microsoft is using HAMS (Harnessing Automotive-Mobiles for Safety), to improve safety on Indian roads. It considers two factors: the driver’s current state and the vehicle’s relative position to other vehicles. The system uses a Front and Rear camera located in front of the Driver’s seat. The driver’s physical condition is monitored by the front camera. It detects eye movement and yawning frequency. These are detected using Mouth Aspect Ratio. Rear cameras measure lane discipline as well as distance to other vehicles. All of this data is analysed using AI applications that use Edge-based processing. Safety-based recommendation alerts can be generated in real time.

Rapider and better response to disasters: Predicting and enabling quicker responses

Artificial Intelligence has been a silver lining in times of disaster. Artificial Intelligence applications can be used to prevent natural disasters by using different patterns recognition algorithms. It can also be used to help in disaster relief and mitigate losses following such disasters. This is where AIDR (Artificial Intelligence for Disaster Response), is used extensively.

Exemple: AiDR was used in the rescue effort after the earthquake in Nepal (2015). AIDR enabled rescue workers and volunteers to quickly reach the victims. AIDR uses Social Media analytics for categorizing all tagged tweets. These insights were invaluable in helping rescuers reach the area quickly and also to help them categorize areas based upon urgency so that they can channel the rescue efforts more efficiently.

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