Artificial Intelligence is a fast-growing sector. You can see its impact in many fields, including healthcare, transport, finance, and more. What’s fascinating is its results are both small and large. In this article, we’ll take a look at some of these AI examples and understand how influential and essential this technology has become.
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Artificial Intelligence Examples
In the transport sector, you’ll find plenty of AI examples. From taxi service apps to Google, multiple areas are using the power of AI to solve their complex problems. A great example of AI in transport is the development of self-driving cars.
These cars can reduce the total number of vehicles on the road by 75% and reduce traffic accidents to about 90%. They all are under development and can arrive in the market in the next few years. AI autopilots have been in use for decades, and they are an essential part of the aviation sector.
Google Maps uses AI to analyze the speed of traffic and recommend the best possible route from one location to another. It had acquired Waze, a traffic app, in 2013. That acquisition helped Maps to incorporate users’ reports of accidents and constructions.
It uses an extensive database that gets constant input from various users and devices. It’s one of the most popular AI use cases as many people use this app for their daily commute. Google Maps can tell you how long it will take for you to reach a specific destination according to various factors. Its algorithms help it in determining an accurate ETA for different transport methods and routes.
One of the biggest challenges for ridesharing services such as Uber and Ola was pricing. How can they determine prices for various scenarios? To tackle this problem, they use dynamic pricing, which, as you would’ve guessed, is based on Machine Learning and AI.
Dynamic pricing allows them to determine prices for their services according to ride distance, demand, and availability. They use ML and AI to solve other problems too. These technologies help them in determining ETAs, finding pickup locations, and detecting frauds.
Email might seem like a small area, but it has seen many advancements due to AI use cases and applications. You might’ve used Gmail’s automated reply suggestions multiple times. Google released that feature in 2015, and since then, it has been a popular feature.
Another result of AI in the email is Google’s autocomplete. It gives you suggestions to complete your sentences with just a press of a button. You don’t need to write those long emails if you have that feature available. Here are some other impacts of Artificial Intelligence and Machine Learning in emails:
You must’ve seen Gmail’s categorization of emails in ‘Primary,’ ‘Social,’ and ‘Promotion’ inboxes. Have you ever wondered how Gmail categorizes those emails?
It uses Machine Learning and AI for this purpose. Google pointed out how this works in a research paper as they mentioned that whenever you mark an email as necessary. Gmail learns from it and categorizes emails of that sort accordingly.
Just how Gmail can categorize your emails, it can also recognize spam. Spam emails are a significant problem for many people. Around 14.5 billion messages every day are spam. And there are many types of spam emails.
Identity theft, phishing, fraud, are just some of the many threats spam emails pose. To help you avoid spam, Gmail uses AI and ML to recognize such emails. It needs AI and ML, as simple filters aren’t very useful in this situation.
For example, if you filter away emails that contain the term – “Nigerian prince,” it would only be a temporary solution. Spammers will start using new names for this purpose. That’s why the filters have to always learn to ensure that it identifies spam.
Another issue with spam filtering is personalization. A marketing email might be spam for someone else, but maybe it’s not spam for you.
Read: AI Project Ideas
Artificial Intelligence has many applications in the field of economics. For example, companies like Betterment and Wealthfront are using AI to give customers investing advice that’s based on the best practices of expert investors. The advantage of this solution would be that people could get highly valuable guidance at a low cost.
Robo-advisors are gaining popularity in many spheres. Many young people use these advisors to make financial decisions. Banks and other major institutions of this sector are also looking for different ways to use AI to make more progress. Learn more about AI in banking. Apart from that, some other AI examples in finance are as follows:
Prevention of Fraud
FICO, a credit-rating determining company, uses AI to make predictions about fraudulent transactions. Analyzing the transactions taking place in a financial organization such as a bank is nearly impossible for simple human minds.
The transaction volumes of banks and major financial institutions are quite high. That’s why AI can help in this regard. FICO uses a neural network for this purpose. It checks multiple factors such as the size of transactions and their frequency to determine the trustworthiness.
Check Deposit through Mobile
Many major banks in the US have started providing the facility of depositing checks through several smartphone apps. It is one of the most interesting AI examples as the customers don’t need to visit the bank physically just to deposit their checks.
They can simply take out their phone, open the app, scan the check, and make the deposit. In these AI use cases, the software examines the writing on the checks and converts them into text by using OCR.
4. Social Media
Social media platforms have become an integral part of our day-to-day lives. And they haven’t been out of touch. All major social media platforms use Artificial Intelligence and Machine Learning in one way or another.
Snapchat’s facial filters are a great example of AI in social media. Their filters were called Lenses, and they arrived in 2015. Since then, they have become the main highlight of Snapchat. It tracks the movement of faces and applies filters accordingly.
We’ve discussed other AI use cases in social media in the following points:
Have you ever wondered how Facebook suggests you friends that you can tag when you upload a photo of them?
Facebook uses AI for this purpose. It identifies the people present in the photo through facial recognition software and gives you suggestions accordingly. Facebook is capable of such sophisticated facial recognition through significant investments in AI.
Facebook had acquired multiple companies due to their facial recognition technologies. They had acquired Faciometrics and Masquerade in 2016, and Face.com in 2012. They all were multi-million acquisitions.
Facial-recognition isn’t the only place where Facebook uses AI. It also uses AI to personalize its users’ feeds. AI also helps Facebook in improving its targeted ads. The better accuracy of target ads, the higher its click-rate. Facebook makes money from its ads, and so, it focuses a lot on improving their targeting.
Learn more: Expert System in Artificial Intelligence
Instagram and Pinterest
Instagram rose fast in the social media industry. Its fast growth was many of the various reasons why Facebook acquired this platform in 2012.
Instagram also uses AI. It uses Artificial Intelligence to understand the context of emojis. By understanding the meaning of emojis, it has built a recommendation system that suggests emojis to people. For example, a shocked emoji might be a replacement for ‘OMG.’
While it might seem like a wasteful application of AI, Instagram has seen a considerable rise in emoji use. And this feature has helped them enhance user engagement. It also helps them understand how people use their platform.
Similarly, Pinterest uses AI to find the objects present in an image. After identifying the objects present in an image, it recommends similar images (or ‘pins’) to the user. Preventing spam and optimizing ad performance are some other areas where Pinterest uses Machine Learning.
You must’ve seen chatbots on multiple platforms. They are also a product of Artificial Intelligence. Facebook had acquired Wit.ai in 2015. Wit.ai is an engine that helps developers in creating chatbots. These bots can integrate NLP (natural language processing).
After Facebook acquired Wit.ai, it released its messenger to developers so they could build chatbots that are more conversational and advanced as they used Wit.ai’s capabilities for this purpose.
Slack is another example of such platforms. It allows developers to incorporate chatbots. Apart from social media, many websites also utilize this AI-based technology to enhance user experience. Learn more about how to make a chatbot in Python.
AI is the Future
After reading the various AI examples we’ve shared here, you must’ve understood how impactful this technology has become. And it’s still on the rise. Many organizations are using AI to enhance their user experience, performance, or efficiency.
That’s why there’s a huge demand for AI professionals. If you want to become an AI expert, we recommend taking a course on Artificial Intelligence. You can also head to our blog and take a look at our articles and guides on this topic.
If you’re interested to learn more about artificial intelligence examples, machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.
What are the top career choices in artificial intelligence?
Artificial intelligence has unlocked a whole new world of employment opportunities that nobody had ever thought existed before. And as artificial intelligence applications continue to gain additional momentum, it generates an increasing number of possibilities of different prospects for individuals who wish to pursue their career in this field of technology. Candidates with the right skillset can aim for the top jobs in AI, starting from application developer, NLP engineer, and AI researcher to AI engineer, AI user experience specialists, and data analytics, among others. Studies indicate that by the latter part of 2022, 58 million AI jobs are likely to be there worldwide.
What should you learn first in artificial intelligence?
AI is one of the hottest career options in the technological field today and holds immense potential for generating endless employment opportunities in the future too. Considering this, it is not a surprise that aspirants wish to start early when it comes to learning artificial intelligence. However, there are some vital concepts that they must understand before even beginning to learn AI. They should have a sound knowledge of algorithms, programming languages like Python and R, and strong mathematics fundamentals, especially probability, statistics, calculus, linear algebra, etc. A basic understanding of machine learning will also be helpful for learning AI.
How many programming languages do you need to know for AI?
It is crucial to have sound knowledge of programming languages, to understand or build artificial intelligence systems. Some of the most recommended programming languages for artificial intelligence are – firstly, Python. Python is the most widely used language for AI and specialized fields like machine learning, NLP, deep learning, neural networks, etc. Next comes R, which can be extensively used in data visualization, data science, machine learning, neural networks, etc. Apart from these, knowledge of C++, Java, Prolog, and LISP is also helpful for learning AI.