The future is now Machine Learning and Artificial Intelligence. Artificial Intelligence impacts the way we transact and interact more than ever before. From finance and shopping to social media and medicine, machine learning solutions power more aspects of daily life than you could imagine.

In the age of Artificial Intelligence (AI), machine learning (ML) is a popular topic that covers Natural Language Processing and computer vision. Through machine learning, inventors have achieved incredible breakthroughs in diverse fields.

From the facial recognition feature on your phone to hyper-realistic casino gaming on platforms like Mr Bett, what previously seemed science fiction is today a reality, thanks to the advancement in the machine learning technology.

With the help of machine learning, humans can live healthier, happier, and more productive lives. Experts predict that soon, computers will replace both manual and mental labor. In some industries, this is already happening.

Here are some ways algorithms have impacted everyday life-

What is Machine Learning or ML?

ML is a subset of AI in which a computer is programmed to have the ability to self-teach while continually improving its performance of a task. Basically, it involves data analysis and interpretation.

The information extracted is used to make predictions, test whether a prediction is correct, and help the machine learn how to make better decisions. If you wanted to design AI software, you should consider taking a machine learning course. Start with the basics and build your knowledge to become an expert if this is a career you consider pursuing.

Computer Vision and its Application Today

Machine learning has experienced exponential growth over the recent past, especially in the area of computer vision. As of 2016, the human error rate was only 3% in computer vision, which means computers can recognize and analyze pictorial data better than humans. This is incredible, because decades ago before we discovered how machine learning could help change lives, computers were just piles of machinery that could even fill a room. Now, people are walking with powerful ML software in their pockets.

One real-life application of machine learning is in Diabetic Retinopathy, a complication that affects the eye. During the extensive exam required to pinpoint the problem, a machine learning model built around computer vision completes the diagnosis.

The data collected from the diagnosis is then processed and interpreted. Experts can also use the model as a second opinion. The idea is that artificial intelligence models should replicate specialists’ work. So, the technology can be deployed in third-world countries in remote locations with a shortage of specialists.

1. Machine Learning in Ride Sharing Apps

To further demonstrate how ML works, it’s important to discuss more real-life examples. One such example is ride-sharing apps such as Uber and Lyft. You probably already use these apps, but do you know how they calculate the price of your ride in real-time? And how do they match you with cabs to minimize detours? The short answer to all these questions is machine learning.

In an interview, Uber Engineering Lead, Jeff Schneider, explained the company uses AI to predict demand. Also, the head of Machine Learning at Uber, Danny Lange, confirmed the company uses artificial intelligence to estimate meal delivery times, compute pickup points, and detect fraud.

2. Machine Learning for Finance – Banking

Imagine how many people use banking services every day. Or the number of people who have bank accounts. And how many credit cards are in circulation? The simple answer is many. So, how many hours would workers need to sift through all transactions completed in a day? It would be impossible to detect anomalies or even review all transactions if the bank did everything manually.

Using machine learning, banks track credit card transactions and identify fraudulent behavior in real-time. Banking servers work with ML systems with anomaly detection models that monitor transactions and verify the authenticity on the fly. Before a credit card purchase is processed, the system can verify if it’s the owner using the card.

3. Artificial Intelligence in Education

Teachers must perform many tasks: educate, analyze student behavior, mentor, counsel, and a lot more. No computer can do all those tasks, but some of these tasks could be automated through artificial intelligence and ML. There are computer programs that review individual study plans for each student.

The software can analyze data on a student’s attendance, learning disabilities, and academic history using algorithms. With this data, teachers can identify learning gaps for each student.

4. AI in the Gaming Industry – how machine learning is used in video games?

Gaming is one of the areas in which machine learning has shown great promise. In 1997, Deep Blue, a chess computer game by IBM, defeated Gary Kasparov. Just recently, in 2016, AlphaGo by Google beat the Go world champion, Lee Sedol. Considering Go is a more challenging game for computers to learn than chess, it was an incredible milestone in the development of machine learning. The algorithm analyzes the moves of players while also learning how to play and make better moves.

For improved game play that is almost realistic, casino platforms use artificial intelligence systems. This includes generating a list of recommendations based on the type of games a player enjoys while in the casino. Also, you can use AI to predict lottery numbers.

5. AI for Dangerous Jobs

Dangerous jobs like bomb disposal could also be handed over to technologies that run on artificial intelligence. Already, security teams are experimenting with drones, which require human control to dispose of dangerous material. In the future, through machine learning, it will be possible for robots to take charge of every process.

Tasks like welding are also outsourced to robots, so humans don’t have to worry about the intense heat, toxic fumes, and noise produced during welding. Advancements in machine learning have improved accuracy and flexibility, so the robots can work unsupervised.

6. Environmental Impact Protection

Computers can store millions of records. Using stored data and real-time trends, machine learning systems can identify weather trends and recommend solutions to previously untenable problems.

A good example is the Green Horizon Project by IBM, which uses ML to analyze environmental data from sensors to provide accurate weather pollution forecasts. The information helps city planners to simulate situations and design models to mitigate environmental impact. Innovations like self-adjusting thermostats are also entering the market.

7. Smart Homes and Home Security Systems

Machine learning has also been used to create improved home security systems. Technologies such as facial recognition build a catalog of frequent visitors to your home, so any uninvited guests are flagged, and a notification is sent to your mobile device instantly. People can also benefit from AI-powered smart homes that come with features like tracking, which show when you last walked your pet.

8. Personalized Digital Recommendations

Have you ever logged into your Netflix or Spotify account and found recommendations for the kind of content you love to consume? This was machine learning at work. ML algorithms collect data about the type of content you watch or listen to often. From this data, the algorithm creates a recommendation list that matches your preferences. So, you don’t need to search on different pages to find the content you love.

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