There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world’s leading companies. Here are 27 amazing practical examples of AI and machine learning.
Using natural language processing, machine learning and advanced analytics, Hello Barbie listens and responds to a child. A microphone on Barbie’s necklace records what is said and transmits it to the servers at ToyTalk. There, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue. Servers transmit the correct response back to Barbie in under a second so she can respond to the child. Answers to questions such as what their favorite food is are stored so that it can be used in conversation later.
Coca-Cola’s global market and extensive product list—more than 500 drink brands sold in more than 200 countries—make it the largest beverage company in the world. Not only does the company create a lot of data, it has embraced new technology and puts that data into practice to support new product development, capitalize on artificial intelligence bots and even trialing augmented reality in bottling plants.
Even though Dutch company Heineken has been a worldwide brewing leader for the last 150 years, they are looking to catapult their success specifically in the United States by leveraging the vast amount of data they collect. From data-driven marketing to the Internet of Things to improving operations through data analytics, Heineken looks to AI augmentation and data to improve its operations, marketing, advertising and customer service.
Culinary arts require the human touch, right? Yes and no. AI-enabled Chef Watson from IBM offers a glimpse of how artificial intelligence can become a sous-chef in the kitchen to help develop recipes and advise their human counterparts on food combinations to create completely unique flavors. Working together, AI and humans can create more in the kitchen than working alone.
Another way AI and big data can augment creativity is in the world of art and design. In one example, IBM’s machine learning system, Watson, was fed hundreds of images of artist Gaudi’s work along with other complementary material to help the machine learn possible influences for his work including Barcelona, its culture, biographies, historical articles and song lyrics. Watson analyzed all the information and delivered inspiration to the human artists who were charged with the creating a sculpture “informed” by Watson and in the style of Gaudi.
Music-generating algorithms are now inspiring new songs. Given enough input—millions of conversations, newspaper headlines and speeches—insights are gleaned that can help create a theme for lyrics. There are machines such as Watson BEAT that can come up with different musical elements to inspire composers. AI helps musicians understand what their audiences want and to help determine more accurately what songs might ultimately be hits.
Global energy leader, BP is at the forefront of realizing the opportunities big data and artificial intelligence has for the energy industry. They use the technology to drive new levels of performance, improve the use of resources and safety and reliability of oil and gas production and refining. From sensors that relay the conditions at each site to using AI technology to improve operations, BP puts data at the fingertips of engineers, scientists and decision-makers to help drive high performance.
In an attempt to deliver energy into the 21st century, GE Power uses big data, machine learning and Internet of Things (IoT) technology to build an “internet of energy.” Advanced analytics and machine learning enable predictive maintenance and power, operations and business optimization to help GE Power work toward its vision of a “digital power plant.”
With approximately 3.6 petabytes of data (and growing) about individuals around the world, credit reference agency Experian gets its extraordinary amount of data from marketing databases, transactional records and public information records. They are actively embedding machine learning into their products to allow for quicker and more effective decision-making. Over time, the machines can learn to distinguish what data points are important from those that aren’t. Insight extracted from the machines will allow Experian to optimize its processes.
American Express processes $1 trillion in transaction and has 110 million AmEx cards in operation. They rely heavily on data analytics and machine learning algorithms to help detect fraud in near real time, therefore saving millions in losses. Additionally, AmEx is leveraging its data flows to develop apps that can connect a cardholder with products or services and special offers. They are also giving merchants online business trend analysis and industry peer benchmarking.
AI and deep learning is being put to use to save lives by Infervision. In China, where there aren’t enough radiologists to keep up with the demand of reviewing 1.4 billion CT scans each year to look for early signs of lung cancer. Radiologists need to review hundreds of scans each day which is not only tedious, but human fatigue can lead to errors. Infervision trained and taught algorithms to augment the work of radiologists to allow them to diagnose cancer more accurately and efficiently.
Neuroscience is the inspiration and foundation for Google’s DeepMind, creating a machine that can mimic the thought processes of our own brains. While DeepMind has successfully beaten humans at games, what’s really intriguing are the possibilities for healthcare applications such as reducing the time it takes to plan treatments and using machines to help diagnose ailments.
Cars are increasingly connected and generate data that can be used in a number of ways. Volvouses data to help predict when parts would fail or when vehicles need servicing, uphold its impressive safety record by monitoring vehicle performance during hazardous situations and to improve driver and passenger convenience. Volvo is also conducting its own research and development on autonomous vehicles.
BMW has big data-related technology at the heart of its business model and data guides decisions throughout the business from design and engineering to sales and aftercare. The company is also a leader in driverless technology and plans for its cars to deliver Level 5 autonomy—the vehicle can drive itself without any human intervention—by 2021.
The AI tech revolution has hit farming as well, and John Deere is getting data-driven analytical tools and automation into the hands of farmers. They acquired Blue River Technology for its solution to use advanced machine learning algorithms to allow robots to make decisions based on visual data about whether or not a plan is a pest to treat it with a pesticide. The company already offers automated farm vehicles to plough and sow with pinpoint-accurate GPS systems and its Farmsight system is designed to help agricultural decision-making.
The BBC project, Talking with Machines is an audio drama that allows listeners to join in and have a two-way conversation via their smart speaker. Listeners get to be a part of the story as it prompts them to answer questions and insert their own lines into the story. Created specifically for smart speakers Amazon Echo and Google Home, the BBC expects to expand to other voice-activated devices in the future.
UK news agency Press Association (PA) is hoping robots and artificial intelligence might be able to save local news. They partnered with news automation specialist Urbs Media to have robots write 30,000 local news stories each month in a project called RADAR (Reporters and Data and Robots). Fed with a variety of data from government, public services and local authorities, the machine uses natural language generation technology to write local news stories. These robots are filling a gap in news coverage that wasn’t being filled by humans.
Big data analytics is helping Netflix predict what its customers will enjoy watching. They are also increasingly a content creator, not just a distributor, and use data to drive what content it will invest in creating. Due to the confidence they have in the data findings, they are willing to buck convention and commission multiple seasons of a new show rather than just a pilot episode.
When you first think of Burberry, you likely consider its luxury fashion and not first consider them a digital business. However, they have been busy reinventing themselves and use big data and AI to combat counterfeit products and improve sales and customer relationships. The company’s strategy for increasing sales is to nurture deep, personal connections with its customers. As part of that, they have reward and loyalty programs that create data to help them personalize the shopping experience for each customer. In fact, they are making the shopping experience at their brick-and-mortar stores just as innovative as an online experience.
As the world’s second-largest retailer, Walmart is on the cutting edge of finding ways to transform retail and provide better service to its customers. They use big data, machine learning, AI and the IoT to ensure a seamless experience between the online customer experience and the in-store experience (with 11,000 brick-and-mortar stores, something rival Amazon isn’t able to do. Enhancements include using the Scan and Go feature on the app, Pick-up Towers and they are experimenting with facial recognition technology to determine if customers are happy or sad.
Central to everything Microsoft does is leveraging smart machines. Microsoft has Cortana, a virtual assistant; chatbots that run Skype and answer customer service queries or deliver info such as weather or travel updates and the company has rolled out intelligent features within its Office enterprise. Other companies can use the Microsoft AI Platform to create their own intelligent tools. In the future, Microsoft wants to see intelligent machines with generalized AI capabilities that allow them to complete any task.
When you bring together cloud computing, geo-mapping and machine learning, some really interesting things can happen. Google is using AI and satellite data to prevent illegal fishing. On any given day, 22 million data points are created that show where ships are in the world’s waterways. Google engineers found that when they applied machine learning to the data, they could identify why a vessel was at sea. They ultimately created Global Fishing Watch that shows where fishing is happening and could then identify when fishing was happening illegally.
Always at the top of delivery extraordinary service, Disney is getting even better thanks to big data. Every visitor gets their own MagicBand wristband that serves as ID, hotel room key, tickets, FastPasses and payment system. While guest enough the convenience, Disney gets a lot of data that helps them anticipate guests’ needs and deliver an amazing, personalized experience. They can resolve traffic jams, give extra services to guests who may have been inconvenienced by a closed attraction and data even allows the company to schedule staff more efficiently.
Google is one of the pioneers of deep learning from its initial foray with the Google Brain project in 2011. Google first used deep learning for image recognition and now is able to use it for image enhancement. Google has also applied deep learning to language processing and to provide better video recommendations on YouTube, because it studies viewers’ habits and preferences when they stream content. Next up, Google’s self-driving car division also leverages deep learning. Google also used machine learning to help it figure out the right configuration of hardware and coolers in their data centers to reduce the amount of energy expended to keep them operational. AI and machine learning has helped Google unlock new ways of sustainability.
From what tweets to recommend to fighting inappropriate or racist content and enhancing the user experience, Twitter has begun to use artificial intelligence behind the scenes to enhance their product. They process lots of data through deep neural networks to learn over time what users preferences are.
Deep learning is helping Facebook draw value from a larger portion of its unstructured datasets created by almost 2 billion people updating their statuses 293,000 times per minute. Most of its deep learning technology is built on the Torch platform that focuses on deep learning technologies and neural networks.
Instagram also uses big data and artificial intelligence to target advertising and fight cyberbullying and delete offensive comments. As the amount of content grows in the platform, artificial intelligence is critical to be able to show users of the platform information they might like, fight spam and enhance the user experience.