Overviews Artificial Intelligence
Artificial Intelligence (Al) does not have to be limited to Science Fiction or Research Labs. Its mainstream adoption is already beginning to reap its benefits. It has contributed more than $ 2 Trillion in economic output last year. According to the PWC report, this figure is expected to rise to $ 15.7 Trillion by 2030. Artificial Intelligence touches millions every day through Smart Phones, Personal Computers, and other Smart Devices. This technology yields enormous benefits across all sectors including Healthcare, Manufacturing Transportation Education Technology Marketing.
Artificial Intelligence’s Key Benefits
The benefits of Artificial Intelligence are listed below:
Reducing human intensive labour
Smart Automation and AI have been key to reducing the labour-intensive nature of human labor. The Oxford Economics Report of June 2019 revealed that more than 2.25 Million Robots have been deployed in the world. This is a threefold increase on the previous decade. In many factories, robots are now capable of carrying heavy loads, lifting and transporting. This saves time and allows workers to focus on more productive tasks.
Exemple: Amazon has more than 100,000 AI-based Kiva robotics in its fulfilment center. AI-enabled robots can reduce human effort in physically demanding tasks like moving large inventory quantities between shelves. They also increase safety at work. Cyborgs can load and unload a full trailer of stock in under 30 minutes. This is a significant improvement on the time taken by human workers, who could take up to an hour.
Pharma Industry: Increase Efficiency
AI has been a boon for both the Pharma and Healthcare Industry. According to MIT, only 13% pass the clinical trial stages. Furthermore, pharmaceutical companies spend millions of dollars to ensure that their drugs clear the clinical trials. Pharma companies employ AI to increase their chances of drugs passing the clinical trials. This is to make their R&D budgets more efficient. Different Machine Learning algorithms help scientists find the right mixture of different salts within a drug by analysing historical information related to Genes, chemical reaction, and other attributes.
An example of a machine learning algorithm used by Novartis, a top Pharma Company, to identify the compound that is most effective in fighting diseased cells. The manual microscopic examination of each sample was a time-consuming process that was prone to human mistakes. Machine Learning-based algorithms allow them to run real-time simulations, which allows for more accurate results.
Transforming the financial sector
Financial Applications are based on analysing historical data to achieve better results. Artificial Intelligence is enjoying huge success in the Finance Sector because its USP is to analyse past data. AI can be used in many different areas of finance, such as Risk Assessment, Fraud Detection (Algorithm based Trading), Financial Advisory and Finance Management.
Example: Paypal used advanced Deep Learning Algorithms (DLA) to detect fraudulent transactions. Paypal processes an enormous amount of transaction data. More than $235 Billion in payments was processed from transactions that were made by over 170 million users. Paypal uses Deep learning algorithm for analysing large volumes of data to compare transactions with fraudulent transaction patterns stored in their databases. This pattern comparison allows it to distinguish fraudulent transactions and normal transactions.
AI Chat-Bots Make Customer Service Faster and Easier
Chat-Bots interaction in its earlier versions was slow and frustrating. The bots were slow and could only assist with pre-defined tasks. Natural Language Processing-powered AI chat-bots have better understanding of human interactions. They can also learn on their own, and are therefore more adept at providing the right response to customers.
Example Erica is an AI-enabled chatbot from Bank of America. Since June 2018, 7 million people have used it. Erica relies on Artificial Intelligence, Predictive Analytics, and Artificial Neural Network for more than 50,000,000 client requests. This includes simple tasks such as billing information and bank balances, but also more complicated tasks such budgeting and investment planning.
Road safety – Enhancing Safety
World Health Organization Report states that more than one million people are killed each year in road accidents. Artificial Intelligence has played a key role in decreasing these deaths. AI is being used by many companies to track and analyse the driving habits of drivers. These details include distance, speed, traffic rules, and lane discipline. AI applications use the information to make safety recommendations for drivers and assist automobile companies in designing safer vehicles.
Example Microsoft has been using HAMS (Harnessing Automobile-Mobiles to Safety) to increase safety on Indian roads. It takes into consideration two factors, the driver’s condition and the position of his/her vehicle relative to other cars. It uses the Front and Rear cameras mounted in front. The driver’s state of health is measured by the front camera. This monitors eye movement, yawning frequency and fatigue. These are detected using Mouth Aspect Ratio. Rear camera measures lane discipline and distance from other vehicles. All data is processed using AI apps using Edge-based Processing and safety-based recommendation alarms are generated in realtime.
Faster response to disasters can be achieved by anticipating them and making it easier for them to be able to plan ahead.
Artificial Intelligence is proving to be a silver-lining for those who are faced with calamity. Artificial Intelligence applications have been deployed to help prevent natural disasters. They use a variety of pattern recognition algorithms. It is also used to aid in disaster relief efforts and reduce the damage caused by such natural disasters. AIDR (Artificial Intelligence for Disaster Response), can be widely used for this purpose.
Example: The rescue effort in Nepal after the earthquake of 2015 saw the deployment of AIDR. AIDR helped rescue workers and volunteers quickly reach victims. AIDR uses Social Media analytics (Social Media analytics) to classify all tags tweets. These tweets provided valuable insights that allowed rescuers to quickly reach affected areas and helped them to categorize the areas based on their urgency in order to better channel their rescue efforts.