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AI can change the Future |

There is no place left in the world which hasn’t heard about or experienced Artificial Intelligence. The era of AI has dawned upon us, it has started changing how we live.

Earlier, when the computers were introduced there was an idea that one day humans will become free of the repetitive and mundane tasks, that idea is now taking shape.

All the data generated in the past 20 years is now being put to use. We are seeing the uses of AI in every field. Businesses are trying to put their data to use and increase the efficiency of their systems. 


AI in Healthcare

There are many devices on the market which claim to help develop a healthy lifestyle. Those devices track your movements, sleeping habits, and analyze them to suggest better routines.

The American Hospital Association estimated that $39 billion goes into meeting the various requirements of healthcare-related laws. Around 25% of the time of nurses and doctors goes into meeting these requirements. After entering into the hospitals, AI is helping shift that time towards patient care. It helps diagnose diseases with more accuracy.

Around 25% of health care expenditure goes towards billing in the United States, normally for the purpose of insurance. Moreover, in the countries with general public health insurance, for instance Canada, the expenditure associated with medical billing can be as high as 12% of the total budget allocated for health care.

AI introduced in the healthcare sector for billing applications utilizes algorithms to analyze the costs and assign them. These algorithms also help to correctly structure various invoice requests and even go ahead to negotiate with some insurers.

A popular application is 1Desk which can automate and coordinate the entire workflow starting from patients to insurance companies and hospitals, specialists and banks to reduce human intervention to a bare minimum in the entire billing process.

The popularity of robot-assisted surgeries have gone through the roof, these types of assisted surgeries have resulted in less pain and faster recovery time.

Deep learning algorithms are trained on specific medical data so that the algorithms can classify between the medical data of an infected person and person who hasn’t tested positive for the disease. 

AI for Autonomous Driving

In 2010, many people had the opinion that we would have flying cars in 2020. Well, we aren’t exactly there, yet but we have come a long way. We are still hesitant to let AI take the wheel, but AI assisting the driver is something we are comfortable with.

The AI can use the sensors to alert the driver of possible dangers, monitor the blind spots, take control of the brakes in an emergency. Self-driving cars can prevent traffic jams, traffic jams are caused due to lane change and braking that creates a ripple effect and creates congestion, but AI can prevent these jams from happening by advanced calculation and maintaining sufficient buffers between vehicles.

Lesser accidents will happen and fuel emission rates would also improve. It would also save people time and make the last mile travel easier. Automotive maker companies like Tesla and Waymo are investing heavily in autonomous cars. Waymo’s AI algorithms consume real-time data from a lot of modes such as sensors, radar, lidar, GPS, cameras, and cloud services. This data then produces signals which control and operate the car.

Automotive AI: Utilizing artificial intelligence in cars - Visage  Technologies


AI to deliver quality Education

Most of the time a teacher goes into the administrative tasks like grading tests, providing feedback, making a curriculum, these tasks can be easily automated by the AI and will become less error-prone.

We know that every person has his/her own learning abilities, but a teacher cannot adjust with every student’s grasping time. This has paved the way for personalized learning. The student gets a personalized learning experience, they will get guidance as per their abilities and can take their own time to learn a concept.

AI can also predict a student’s performance in the future, by assessing his/her learning patterns and how much time he/she takes to grasp a specific topic. The education industry is transforming at a very fast pace due to AI.

AI to automate media

AI can write quality articles, a human’s ability to create content is based on the knowledge that he/she possesses, but an AI has access to everything hence it can filter more facts and opinions.

AI cannot create its own opinions; it can only merge or filter data and create something factful. As the world is spiraling into the fake news dilemma, AI models can be used to fact check news and make the environment safer and more accurate for the readers.

Many media outlets use AI to make the Audio/Video noise-free, AI models can also suggest the right scene to make cuts and merge. Netflix uses a state of the art recommendation system for its users and many other media outlets are also trying to mimic it to provide its customers with more relevant content. The time is not far when AI will start to anchor the shows.

Scraping data from all the public platforms categorizing it and then matching it with the user’s activity helps companies recommend better choices. Same goes with identifying fake news, scraping through various responsible and authentic platforms and trying to match keywords to make sense of the news and rating it on the basis of authenticity is one way platforms filter news.

AI to disrupting Finance

The finance industry is not free of fraud. Incidentally, the finance industry lends someone else’s money to someone else. AI can spot fraudulent transactions quickly if a credit card has been used in a place where it has never been used. It red flags that transaction, in similar ways it assesses a transaction to predict if it is authentic or not.

In finance, it is second nature to maintain records. An AI learns from records. It can easily predict the credit of an individual and assess if he/she should be given a loan or should be suggested to buy insurance. Trading firms have also started using ML algorithms to forecast a stock’s future and help the trader make more informed and accurate decisions.

AI in Manufacturing

Using historical data AI can predict when a machine will need maintenance, and this decreases the manufacturing cost for the company, as sudden failures decrease.

Computer Vision can see microscopic quality defects that human eyes cannot see, and that helps in maintaining a certain quality of the product. Smart software can learn and predict customer behaviors and help industries make supply and demand decisions accurately. Thus, it lessens the logistics time of the delivery, and the customer also feels valued.

Caterpillar’s Marine Division saves around $400K per ship per year after AI algorithms analyzed its data on how often to clean the hulls to achieve maximum efficiency.

Also, 29% of implementations in AI in the field of manufacturing deal with production assets and maintaining machinery. General Motors studies images produced by cameras on assembly robots, to find signs of failing robotic components.

A pilot test of the previous system detected 72 such instances of component failures with the aid of 7,000 robots, identifying the problem before it could result in unplanned outages.

AI to reinventing technology & cybersecurity

In the modern world where data is fuel, data thefts have become very common. AI tools analyze the pattern of malicious software and notify the users before a data theft can occur. AI has helped improve face and voice recognition; hence authentication has become full proof, any effort to bypass authentication can be detected easily. AI does the delegation of menial tasks like spam categorizing, and human interference is not needed.

Mobile phones have become more interactive, you can schedule an appointment using your voice, or just command the application to do something for you and it does. AI chatbots are replacing human efforts and helping customers with more natural queries that do not need human interference. This method is saving a lot of time and cost for the users and organisations.

Historical data captured during the hacks in the past help ML algorithms identify the patterns, and then in the future, if they see a pattern resembling the prior data, they flag it.


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