Artificial Intelligence (AI) simulates human thinking and behavior through intelligent machines. In essence, AI enables computers to learn from their experiences and carry out activities similar to those performed by humans. However, machine learning is the component of AI that allows machines to learn from the massive amounts of data they receive without being explicitly programmed. For instance, ML can carry out tasks that weren’t specifically coded for it, such as employing statistical techniques to generate predictions.
A wide range of businesses and applications are impacted and helped by machine learning in their day-to-day operations. You may see examples of it in use below:
- Google’s Predictions Powered by AI
Google Maps (Maps) can assess traffic velocity at any given moment using anonymized location data from cell phones. Maps can shorten commutes by recommending the quickest routes to and from work thanks to access to large amounts of data given to its unique algorithms. Additionally, Maps can more readily include user-reported traffic issues like construction and accidents since it bought the crowdsourced traffic software Waze in 2013.
- Smartening Up Self-Driving Vehicles
Self-driving cars utilize sensors to gather massive amounts of data, which machine learning algorithms then analyze to help the car decide how to react in various circumstances, such as a red light or a pedestrian crossing the street.
- Customer Notification Timing
Machine learning is a component of the technology used by the free language-learning program Duolingo. Duolingo’s statistical algorithm analyzes the information you provided in your responses to determine how long you will be able to recall a particular word before you require a refresher course. As a result, Duolingo is aware of when to ping you and advises that you retake the course.
- Ridesharing services such as Uber and Lyft
How is the cost of your ride determined? When you call for a car, how do they reduce the wait time? How do these services pair you up with other passengers in the best possible way to minimize detours? ML is the response to each of these inquiries. In an interview with NPR, Uber ATC’s Engineering Lead Jeff Schneider explained how the firm utilizes machine learning (ML) to estimate rider demand to make sure that “surge pricing”—short bursts of abrupt price rises to reduce rider demand and enhance driver supply—will soon become obsolete. Danny Lange, the head of machine learning at Uber, revealed that the company uses machine learning to compute optimum pickup locations, predict meal delivery times on UberEATS, and detect fraud.
- Improved Cloud Services
To assist programmers and data scientists in creating, honing, and deploying machine learning models, Amazon’s cloud service AWS provides free machine learning services and tools like its Amazon SageMaker. Amazon Rekognition, another service provided by AWS, employs machine learning to recognize objects, people, text, and activities in still images and to move pictures.
- An AI Autopilot is used on Commercial Aircraft
Depending on how broadly you define autopilot, AI autopilots in commercial airlines are a relatively old application of AI technology that goes as far back as 1914. According to The New York Times, the average flight time of a Boeing aircraft is only seven minutes, with the majority of that time being used for takeoff and landing.
- Recommending Products and Services
Netflix uses machine learning to analyze the viewing habits of its millions of customers to make predictions on which streaming video shows you may likely enjoy the most and make recommendations based on those predictions.
- Grading and Assessment
Many high school and college students are familiar with services like Turnitin and popular tools instructors use to analyze students’ writing for plagiarism. While Turnitin doesn’t reveal precisely how it detects plagiarism, research demonstrates how ML can be used to develop a plagiarism detector.
- Stock Market Trading
Machine learning is used by Trading Technologies, a futures trading platform, to recognize trading activity that can prompt regulatory inquiry.
- Calculating a customer’s lifetime value
Asos, a fashion company, employs artificial intelligence to calculate client lifetime value (CLTV). This indicator predicts the net profit a company will make from a particular client over time. Asos uses machine learning to identify customers likely to repurchase its products and those more likely to have low CLTV, which could affect whether it offers them free shipping or other promotions.
- Validation Of Credit Worth
Machine learning is used by fintech business Deserve to analyze creditworthiness. Many first-time credit card applicants, including students, lack a credit history. The machine learning algorithm Deserve also considers additional aspects, such as the applicant’s financial situation and current spending patterns.
- Personal Finance and Banking
Customers no longer need to physically deliver checks to the bank because most big banks now allow check deposits through smartphone apps. According to a 2014 SEC filing, the majority of central banks rely on Mitek technology, which uses AI and ML to decode and translate handwriting on checks into text through OCR.
- Online purchasing
Amazon searches for items like “ironing board,” pizza stone,” Android charger,” and other terms rapidly produce a list of the most suitable products. Although Amazon doesn’t specify how it accomplishes this, it does state that its algorithms “automatically learn to combine several relevancy features” in a description of its product search engine. We have many of these relevant features thanks to the structured data in our catalog, and we also learn from previous search trends and adjust to what matters to our clients.
- Assessing Plants
Agtech startup Blue River Technology combines machine learning and computer vision to distinguish between crops and weeds and to achieve the right amount of space between plants. The company’s See & Spray rig sprays herbicide or fertilizer on targeted plants.
- Selling And Marketing Efficiency Improvement
HubSpot, a provider of business software for marketing, sales, and services, employs machine learning in various applications. In order to award predictive lead scores for sales teams to utilize when determining whether clients are prepared to buy their products, it provides content marketers with information about what search engineers correlate their material with.
Today’s smartphones come with voice-to-text functionality as standard. You can start speaking, and your phone will turn the audio into text by hitting a button or pronouncing a specific phrase (like “Ok, Google”). This is a very routine operation, but even the most sophisticated computers could not accurately automate transcription for many years—artificial neural networks power Google’sGoogle’s voice search.
We’ve only begun to scratch the surface of real-world applications for AI and ML. Beyond what is covered in this article, certain professions and pastimes also regularly engage with AI.
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Amreen Bawa is a consulting intern at MarktechPost. Along with pursuing BA Hons in Social Sciences from Panjab University, Chandigarh, she is also a keen learner and writer, having special interest in the application and scope of artificial intelligence in various facets of life.