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How AI is taking over the work of doctors that analyse x-rays and other kinds of tests (watch first few minutes)

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This AI Is Beating Doctors At Their Own Game

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AI to detect cancer and other diseases

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AI in Healthcare: Top  A.I. Algorithms In Healthcare - The Medical Futurist

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Medical chatbot

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How AI is being used in medicine

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A New Era of Healthcare with AI | I AM AI

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Things that AI is doing in healthcare

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AI in Medicine: Possible Applications and Potentials

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How IBM's AI (Watson) is getting involved in sports

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The impact of AI technology on the future of sports

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August marks the start of the US Open tennis tournament and the kick off of the Fantasy Football season. And while tennis and fantasy football have millions of fans, few of them likely know that behind the scenes, cutting edge technologies including artificial intelligence, data analytics and cloud computing are changing the game, revolutionizing both the digital, and in-person, fan experience and, when it comes to tennis, even starting to impact the way coaches train their players.

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Seven different ways that AI is being used in sport

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For the last two decades, coaches have been using data science in sports to help improve the performance of their players.

They've been leveraging big data to help them make split second on-the-field decisions, and relying on sports analytics to help them sign the "next big thing".

Referees, meanwhile, now use Video Assistant Technology (VAR) in football to help them make more precise judgments regarding the big decisions, such as penalties, free kicks, and red cards.

And now that AI, and specifically Deep Learning, has got involved, the sports experience is going to change even more.

In this article, we'll cover some of the most exciting use cases of artificial intelligence in sports and the technology behind them, including:

AI applications in sports

And hey, if you are new to this whole "AI thing", make sure to check out these guides:

1. What is Machine Learning? The Ultimate Beginner's Guide

2. Computer Vision: Everything You Need to Know

Now, let's dive in.

AI Referee

It was the 1986 World Cup.

Argentina was drawing 0-0 with their old enemy, England, in the sweltering Mexican heat when the pint-sized Diego Maradona rose with the England goalkeeper to meet a cross.

The keeper was the favorite to reach the ball - after all, he was able to use his arms.

And yet, somehow the impish Maradona sprung into the air faster, met the ball with his head - and nodded the ball into the net.

Of course, anyone who knows football knows that isn't exactly what happened.

From the moment Maradona connected with the ball, the England defenders were apoplectic with rage, gesturing to the referee that the Argentinian superstar had scored an illegal goal with his hand.

Today, such a goal would have been chalked off with the use of AI Referees in just a matter of minutes. All big decisions that change the outcomes of games would be judged more accurately, the margin for error would be a lot slimmer - and, arguably, controversies would be hugely reduced if AI in sports was used to help the referees.

You don't have to look far for confirmation-the Euro 2020 game between England and Denmark with the questionable penalty called when Raheem Sterling went down near the goal, is the perfect example. What would AI decide?

The AI Referee verdict, based on the model trained on V7, confirmed that: "any "contact" with Sterling happened 150ms before Denmark would have reached the ball. An AI ref would have still given the penalty to England.

However-

With more camera angles than 2 available for analysis, the outcome may have been different.

Naturally, ever since sports have existed the outcome of a game has often been influenced by the so-called fine margins - line calls in tennis handballs in football, and offensive fouls in the NBA. And for many years, thousands of incidents have been called incorrectly by the referees, leading to anger from coaches, fans, and clubs.

The situation has hardly improved over the years.

In fact, research shows shows that:

Since March 2015 to June 2018, the NBA viewed 26,822 plays from 1,476 games. In those 4,297 minutes of action, the officials have missed or incorrectly called 2,197 plays, or about 8.2% of all calls reviewed. This amounts to 1.49% wrong decisions in the final minutes of each close game.

I know, 1.49% might not sound like a lot - but in sport, it is huge.

It can be the deciding factor between a team winning or losing, especially in a game like NBA where matches often go right down to the wire. Every point matters - and every refereeing call matters.

Sport is at the point now where it's ready to embrace AI, not to change the outcome of sporting events necessarily, but to improve the decision-making process.

Offside detection from overhead cameras, for example, can assist VAR in football to ensure goals are not correctly or incorrectly given or denied, while on-field tracking systems (such as Hawkeye) can detect tennis balls so that line calls are judged more accurately.

This is especially true in clay tennis, where technology still isn't able to give a 100% accurate reading of a line call, thanks to the slower movement of the ball.

Computer Vision can also assist in identifying potential penalties across sports so as to reduce mistakes, controversies and prevent games from swaying one way or another due to a poor refereeing decision.

Goal-line technology, meanwhile, was brought into football after England was denied a clear equalizer versus Germany at the 2010 World Cup. As per FIFA's definition, it is "a technical means of instantly determining whether the whole of the ball has crossed the line."

The technology uses magnetic fields and cameras to great effect. Interestingly, there are various criteria that GLT needs to meet in order to gain FIFA's approval, and this includes being able to function accurately "under adverse conditions."

A typical GLT relies on 14 strategically positioned cameras that are streamed to a cluster of computers, who use advanced image processing algorithms to a) determine the object (ball), b) distinguish it from interferences, such as a player's boot, and c) confirm whether the whole of the ball has crossed the line.

Personalized training and diet plans

Training and diet plans have improved immeasurably over the years for professional sportsmen and women. Back in 1990s Britain, it wasn't unusual for a British footballer to hit the pub after training, sink several pints and grab a kebab on the way home. It was normal.

However, the introduction of Artificial Intelligence in sports can help to take the personalization of training and diet plans to the next level. Research has already shown promising results regarding the use of AI in weight training.

The data was based on measurements from 15 rather inexperienced participants performing 3-5 sets of 10-12 repetitions on a leg press machine. The assessment made by professional trainers (using video analysis) showed good performance and prediction outcomes. It indicated the feasibility and potency of AI techniques in assessing performance on weight training equipment automatically and providing sportsmen with prompt advice.

Essentially, an AI diet plan uses machine learning to personalize different plans to different players based on their needs and their current situation (is there a match the next day? Is today a recovery day?)

Apps like FoodVisor are already using deep learning-specifically object recognition-to identify more than 1,200 types of food, estimate the amount, and produce a quick report on the nutritional breakdown.

And that's just the tip of the iceberg!

Let's not forget about the AI-assisted fitness apps that are flooding the market.

Thanks to the computer vision technique called human pose estimation, we can now train algorithms that are able to detect human poses in real-time.

Keypoint skeleton annotation for human pose estimation in V7

This technology found applications in fields like online yoga and pilates where keypoint skeleton models can be used to identify human joints and provide the user with guidance on how to exercise the right way.

By the way, if you'd like to get hands-on experience annotating data for your Human Pose Estimation projects, make sure to check out the video below.

Player performance

Performance analysis in sport is nothing new.

But-

The way player performance is analyzed is now more sophisticated than ever before thanks to the AI. Using data and visuals, coaches are able to gain insights into their teams' strengths and weaknesses on any current day, allowing them to make changes to tactics and strategy, thereby exploiting any weaknesses of their opponent.

This is true of all sports - from football to tennis, to even handball and swimming.

Computer vision, for example, is used for human motion tracking and analysis. In handball, the development of a computer system for tracking players has caught the attention, with a study describing the process:

The cameras are collecting data and the output result will be spatio-temporal trajectories for all players. These player trajectories provide the sports expert with valuable data of the player capabilities and performance.

Using video sequences, they ran three experiments to identify the best method for the automated player tracking of:

Tracking using just motion detection

Colour tracking

Combination of colour and template tracking

The result: C - The combination of colour and template tracking proved to be the most suitable, thanks to its speed and minimum human operator intervention compared to other methods.

This is how player tracking would look like using bounding boxes for object detection.

Bounding boxes for player tracking in V7

In swimming, meanwhile, human pose estimation has been used to analyze swimmers' performance using single stationary cameras above and below the water filters. This method will eventually take over the quantitive evaluation method, which previously relied on manual annotations of body parts in each video frame (very time-consuming).

The global player tracking market is expected to grow at a CAGR of 24.9% over the next 5 years, with teams in both elite and even minor leagues able to use tracking solutions that will help them understand their players' movements.

This will not only enable them to make better tactical switches, but it will also highlight who's putting in the effort (and who isn't), and which areas of performance - both offensive and defensive - can be improved upon.

4. Scouting and recruitment

These days, teams are taking things further by adding Artificial Intelligence to their scouting and recruitment box of tricks. Everything that happens on the field of play - swings in baseball, runs in football, blocks in basketball - is tracked, with teams amassing terabytes of data.

Indeed, computer vision in sports is used to track players' movements and the orientation of their bodies during play (object tracking in videos).

Here's how it looks like on an example of soccer players. Labeling data with keypoint skeleton tool and then training the model will allow it to track and then predict players' movements.

Players' movement tracking using keypoint skeleton annotation in V7

Essentially, Machine Learning algorithms use aggregated data to "evaluate players' skills and overall potential and rank them in various categories.

For example, offensive forward in the NHL and point guard in the NBA.

Teams can also use computer vision to spot specific attributes - such as corner-taking abilities in soccer - that help determine future performance.

The insights this data gives teams allows them to make better recruitment choices going forward that ensure they build a successful team and buy untapped players for cheap, before developing them and selling them for a profit at a later date.

Brentford did exactly this with striker Ollie Watkins, who was bought for around $2,000,000 and sold for $35,000,000.

5. Match predictions

Bettors have for years been trying to process a heap of data in a bid to predict the outcome of future matches and win lots of money. They've examined first and second serve percentages in tennis, the number of aces, backhand winners, and so on in an effort at predicting sports outcomes.

Ultimately, though, a human can't process as much data as an AI-driven football algorithm prediction can, and neither can they accurately predict enough matches so as to become millionaires (ha). They're always hamstrung by their human limitations - and thus most won't become millionaires.

AI can't accurately predict the outcome of every single match either. But it can get much closer than a human can with a predicting algorithm.

For example, computer vision is used to determine ball possession time.

Researchers ran multiple configurations of the pos-N-M model on a manually labeled set of 4,000 frames and achieved an 85.5% accuracy on a test. This was despite several issues, such as the ball being small and not very visible, or the ball at times being covered by a player.

When computer vision is able to be trained properly as a ball possession model, it can help to predict future match results.

Computer vision can also be used to collect and analyze data based on:

Number of passes between teammates

A team's formation

Number of goals scored

Chances created

Key passes that led to a goalscoring opportunity

Using this data, the model can forecast whether a team will win, lose or draw a future match.

6. Ticketing

Even in 2021, elite sports teams are experiencing entry delays. Southampton FC had to refund their fans after thousands were left stranded outside after the game had begun.

Fans struggling to get inside stadiums on time for the match is nothing new at big sporting events. Indeed, it's been happening for decades. Nothing has resolved the issue - until now (potentially).

Columbus Crew, for example, is using facial recognition to allow their fans to enter the stadium without having to display their ticket.

Wicket's facial ticketing platform lets fans upload an image of their faces and the barcode number for their tickets to a game.

This makes stadium entry way more efficient and prevents bottlenecks. Moreover, it will also prove extremely useful at enticing COVID-wary supporters back inside stadiums who don't want to get stuck in lengthy queues.

Computer vision, meanwhile, is on hand to "monitor crowd density inside the stadium, with staff receiving alerts when certain areas get overcrowded."

Of course, it's rare in modern sports that overcrowding happens - especially not on a scale once seen in 1980s European football, where deadly crushes happened more than once. However, with British football clubs set to introduce standing sections for the first time since the early 1990s, the use of computer vision to monitor crowd density will provide an extra layer of safety and security that can prevent crushes.

Predictive analytics can also forecast what attendance is likely to be, as well as when supporters might be expected to turn up. This is helpful for arranging merchandise and food and keeping up with demand.

6. Automated sports journalism

Sports journalism is big business. But in many elite sports - and especially baseball, soccer, and tennis - there is so much to cover.

Some would even say there is too much to cover, especially when it comes to statistics and data in the minor leagues and tournaments that are taking place almost daily all over the world.

Such exhaustive workloads in other sectors are now being eased by AI - and this is also the case in sports journalism.

Wordsmith, for example, is an AI-driven platform that translates hard data from MiLB into narratives, using natural language. Developed by Automated Insights, it has helped AP increase its reporting capacity to cover 13 leagues and 14 MLB-affiliated teams.

Then there's the small matter of match reports. Each day, there are hundreds of soccer matches taking place - sometimes, there are thousands. To cope with the demand, AI bots are now being used to write accurate match reports that explain the key events, offer statistics and data, and keep the reader engaged by sustaining the required drama.

And while some might argue that "AI is taking all our jobs," check this out: Dutch regional media group NDC is using AI to help it cover 60,000 soccer matches in one year, which basically amounts to every single local match.

This is an astonishing feat and one that couldn't reasonably be achieved without AI.

Therefore, with the benefit of AI, local matches are now being covered which means teams, coaches, players, and fans who would normally be absent from news pages are getting their moment in the sun.

AI Sports Industry: Key takeaways

Machine learning in sports is growing at a rapid rate, with new developments and applications emerging each year. Everything barring the games themselves is set to be improved via data and technology.

Of course, it's all about balance.

Implementing AI in sports sounds good in theory, but it can't come at a cost of the fan experience. For example, while using facial recognition to allow fans into the stadium without them even needing a ticket prevents bottlenecks and gets fans into stadiums faster, but it also removes the turnstile which - for many European football fans, at least - is synonymous with a romantic vision of match day.

The future of sports technology, however, undoubtedly lies with AI. But we're certainly not at the final frontier just yet - in fact, we're probably only at the beginning. And that's exciting.
💡 Read more: Data Annotation Tutorial: Definition, Tools, Datasets 3 Signs You Are Ready to Annotate Data for Machine Learning An Introductory Guide to Quality Training Data for Machine Learning 13 Best Image Annotation Tools of 2021

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7 Game-Changing AI Applications in the Sports Industry

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Artificial Intelligence in sports is emerging all over the industry covering post-game analysis, in-game activity - and even the fan experience. Everything, from scouting new talent to optimizing an individual player's nutritional intake is destined to be dictated by AI.

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How AI is tracking the way athletes move

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How AI is being used in motor racing

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The Fast Track to AI-Powered Driving - Roborace | Season 1 Episode 5 | I AM AI Docuseries

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How AI is being used in ice hockey

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Taking the Ice with AI - ICEBERG Hockey Analytics | Season 1 Episode 7 | I AM AI Docuseries

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Strawberry picking

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Growing plants more intelligently

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Could these AI robots replace farmers and make agriculture more sustainable?

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How AI robots are being used in farming

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The Future of Farming Robots - 13 High Tech Examples (Compilation)

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Self-driving shuttles

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Self-driving transport is coming to cities. Here's how it works

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How AI is being used in transportation

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Robots are already far more widespread than many people realize. They're now essential to many industries, unlocking new opportunities and avenues for innovation. One of the sectors that stands to gain the most from robots is transportation.

Outside of manufacturing, the implementation of robotics in transportation is a relatively recent phenomenon. Despite this relative novelty, robots have already made significant strides in the transportation industry, driving innovation across multiple sub-sectors and applications. Modern transportation wouldn't be the same without robots, and the future will only serve to solidify this.

Here's how robots are pushing the transportation industry forward and where they could go from here.

Self-Driving Cars

Perhaps the most iconic example of robots driving innovation in transport is self-driving cars. While fully autonomous vehicles are not yet a reality, automated features have already brought significant improvements to cars today. Automatic braking, lane correction, and adaptive cruise control are all examples of robotic control in today's vehicles.

Automatic emergency braking has cut rear collisions by 50% in cars that have it. These systems rely on robotic technologies like machine vision to recognize and respond to obstacles. They then turn the entire vehicle into a type of robot, acting without human input.

The only things holding back fully autonomous cars today are robotic technologies that are not yet advanced enough. Their artificial intelligence (AI) systems have to respond remarkably quickly, even for a robot, and perform consistently in varying and unpredictable situations. As robots advance and these goals become possible, true self-driving cars will become a daily reality.

Autonomous Public Transport

While fully robotic passenger vehicles have proved a challenge, automating public transit may be easier. Buses, trains, and shuttles follow fixed routes, providing the predictability that robots today need to perform well. They also typically travel in dedicated spaces, reducing the risk of collision with other vehicles.

The city of Bad Birnbach, Germany, started testing an autonomous bus in 2017. Within the first year, it had completed more than 10,000 kilometers of driverless travel, carrying around 20,000 passengers. EasyMile, the company that made the autonomous shuttle, has since helped cities across the globe establish driverless bus routes.

In 2019, an autonomous train pulled 30 freight cars across 48 miles of track, showing the potential of driverless trains. As the U.S. looks to expand and modernize its rail system, autonomous trains could become commonplace. AI features like early braking could make rail travel safer, too.

Faster Production

While autonomous vehicles may be the most exciting application of robots in transportation, they're far from the only one. A more common use case for robotics in the transport industry is in the manufacturing centers that build vehicles. Automation has become a critical part of car manufacturing, enabling higher output.

The Tesla Gigafactory leads the industry in robotics use, with some sections being 90% automated, requiring almost no human input. This high level of automation lets the factory produce its technologically complex vehicles in record time to meet high demand. Considering how the Tesla Cybertruck sold more than 250,000 preorders in its first month, that speed is essential.

Faster production times also let automakers roll out new models in less time. As a result, they can produce innovative, cutting-edge designs and get them in drivers' hands while the idea is still new and exciting.

Improved Sustainability

This reliance on automation in transportation manufacturing also enables factories to become more sustainable. Robots may take energy to operate, but they also let facilities take more control over the power they consume. These robotic systems produce data about their efficiency and energy use as they work, guiding process improvements to minimize energy waste.

Newer robots can even adjust their operations automatically to use as little energy as possible. Software robots can go a step further, analyzing factory data to produce insights into how to become more sustainable. Automakers can then minimize their environmental footprint as much as possible.

Becoming more sustainable is essential for the transportation industry. The average car produces more than 2 tons of carbon dioxide every year, giving the sector much room to improve. These process improvements on the manufacturing side of the industry help it make up for its history of carbon emissions.

Efficient Logistics

Software robots may not be what comes to mind when people think of "robotics," but they're some of the most valuable robotics applications. These AI programs have proven particularly helpful in the logistics sector of transportation. By feeding data into software robots, supply chains have become far more efficient in the past few years.

Some AI logistics programs analyze more than 250 million data points across millions of shipping transactions. With this data, they produce insights into how logistics companies can optimize their loads, routes, and outsourcing to save time and money. Similar systems adjust drivers' navigation in real-time to produce the most efficient delivery routes possible.

Physical robots help improve logistics efficiency, too. Automated systems can work alongside humans to load and unload trucks in far less time than an entirely manual process. As both physical and software robots improve, freight transportation will become increasingly efficient.

Road Maintenance

Some of the ways robotics improve transportation are less direct. For example, one of the most innovative but less publicized use cases for robots in transport is road maintenance. Researchers have recently developed automated systems that can help detect and repair roads, helping governments improve infrastructure more efficiently.

In 2019, scientists designed a drone that can detect road damage, showing relevant authorities where to target their repair efforts. One U.K.-based company is developing a robot that can detect and fix potholes and cracks, automating the entire repair process. With advancements like this, roadways can become safer, faster.

Employing these robots will improve the performance of other transportation robotics, too. Self-driving vehicles will have an easier time navigating on roads that are in good condition.

Robots Are Integral to the Future of Transportation

Whether the public realizes it or not, robots have become a critical part of the transportation sector. From manufacturing to logistics to passenger transport, robotics have influenced and continue to influence every aspect of transportation. As these technologies drive further innovation, they will become inseparable from the industry.

In many ways, robots will drive the future of transportation. If current trends continue, all sub-sectors of transport will rely on various types of robotics in the near future. Before long, it will be difficult to imagine the transportation sector without robots.

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Robots Continue to Drive Innovation in Modern Transportation | RoboticsTomorrow

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Robots are already far more widespread than many people realize. They're now essential to many industries, unlocking new opportunities and avenues for innovation. One of the sectors that stands to gain the most from robots is transportation. Outside of manufacturing, the implementation of robotics in transportation is a relatively recent phenomenon.

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Waymo self-driving cars (watch first 2 minutes)

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Waymo 360° Experience: A Fully Autonomous Driving Journey

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Self-driving trucks (video is long, watch only the first few minutes)

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How Amazon Demand Drives Autonomous Truck Tech

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A robot that flips burger (watch the video)

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Miso Robotics

The next time you place an order at a fast food joint, you could see a robot behind the counter. Flippy is an AI-driven kitchen assistant that can flip burgers and place them on buns, and it debuted today at a CaliBurger restaurant in Pasadena, California.

Flippy was developed by Miso Robotics and CaliBurger's owner, Cali Group. It uses cameras, sensors and deep learning software to locate ingredients in a kitchen without needing to reconfigure existing equipment. Not only does it position and flip the patties, it tracks their temperature and cooking time too. When the burgers are done, it alerts a human cook, who applies the cheese and other toppings.

"Much like self-driving vehicles, our system continuously learns from its experiences to improve over time," said David Zito, CEO of Miso Robotics, in a statement. Eventually, Zito said Flippy can be trained to help with other kitchen tasks, like frying chicken, cutting vegetables or plating.

Self-ordering kiosks are already replacing workers at fast food chains like McDonald's and Wendys. But, Miso Robotics said Flippy is designed to work alongside human staff. Once its "probation" ends in Pasadena, it will roll out to more than 50 CaliBurger locations over the next two years. When that happens, some people could be reassigned to the dining room to engage more with customers, while others will be trained to operate their new assistant.

"Tasting food and creating recipes will always be the purview of a chef," Zito recently told TechCrunch. "And restaurants are gathering places where we go to interact with each other. Humans will always play a very critical role in the hospitality side of the business given the social aspects of food. We just don't know what the new roles will be yet in the industry."

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Burger-flipping robot has its first day on the job in California | Engadget

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The next time you place an order at a fast food joint, you could see a robot behind the counter. Flippy is an AI-driven kitchen assistant that can flip burgers and place them on buns, and it debuted today at a CaliBurger restaurant in Pasadena, California.

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A robot that can cook meals

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RoboEatz | The Future of Kitchen Robotics

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Flippy is the world’s first autonomous robotic kitchen assistant that can learn from its surroundings and acquire new skills over time.

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How AI is being used in restaurants

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When people think of AI in restaurants, their minds always seem to jump right to kitchens staffed by robots who are able to cook so well, they can replace their human counterparts.

While that's not an impossible scenario anymore (there are actual restaurants in the world that have robots doing at least some of the cooking), it's not the most common way AI is changing the restaurant industry. You might be surprised to learn about some of the ways even less-than-tech-savvy restaurant owners can implement AI to streamline their operations and improve their guests' experiences.

AI may be a little bit of a buzzword, but this technology is not out of reach for the average restaurateur. Read on to learn about AI in restaurants-and how restaurants can use artificial intelligence in small ways to make big changes.

What Is Artificial Intelligence, Really?

AI is so much more than robot kitchens. It's also not what pop culture might lead you to believe-we're not going to eventually be outsmarted by restaurant technology and end up serving it, instead of the other way around.

Artificial intelligence is pretty much what it sounds like: Intelligence that didn't happen naturally. Instead, it's created by a human to allow a computer to mimic human behavior. AI can receive information from the world around it and react to it, but it cannot store that information and use it to grow and become more complex over time. Siri and Alexa are good examples of AI-they can answer queries from their users, but they don't get to know their users over time, learning their wants and anticipating their needs.

This brings us to another topic that often comes up when we talk about AI in restaurants: Machine learning.

Machine learning takes AI to the next level. This kind of technology allows computers to not only take in information, but also to use complex algorithms to learn from it, so they can make predictions about events or human behavior based on historical patterns. Machine learning is used by platforms like Netflix and Spotify that get to know your likes over time and provide you with new content you might enjoy.

Is AI in Restaurants Really Necessary?

As technology of all kinds becomes a more integral part of running a restaurant, AI is emerging as an area of restaurant technology with virtually limitless potential.

Here are just a few of the benefits of using AI in an independent restaurant:

Cut costs: AI technology can be used to automate a lot of simple and rote tasks (like taking reservations or entering orders into the POS). This means less money spent on staffing your restaurant, and lower costs overall.

Reduce errors: Human error accounts for a lot of things that can go wrong in a restaurant. For example, a server in a busy dining room might mishear a guest's order, resulting in the guest receiving the wrong dish. When guests order via AI, there's less opportunity for human error.

Customize orders: AI gives more control to guests during the ordering process. This allows them to customize their orders (and their overall experience) to fit their needs.

Improve guest service: By embracing AI's ability to perform simple and rote tasks in your restaurant, you can free up employees to do what they're best at (and what AI can't just yet): Focus on your guests and provide them with the best possible experience. For all the talk about robots being poised to take over restaurant kitchens, there's one thing they definitely can't do yet: Mimic the human touch that can make a restaurant experience so special.

Find new customers: AI technology can supercharge your restaurant's marketing efforts, allowing you to identify what kinds of customers might love your food, reach out to them, and encourage them to pay you a visit. AI can also help with retaining guests-remarketing efforts can encourage them to make a repeat visit after coming to your restaurant.

9 Ways AI in Restaurants is Becoming More Common

In recent years, the restaurant industry has been changing rapidly, particularly when it comes to technology.

AI has been one area where technology has created changes for restaurants that look straight out of a sci-fi novel. For example, take Flippy, the burger-flipping robot at a CaliBurger franchise in Pasadena. Or the delivery drone used by Domino's to drop off a pizza order in New Zealand. These big, quick-service restaurant chains have enough capital to invest in AI technologies that come with high up-front costs, but this is the exception for the restaurant industry, not the rule.

While advances in technology have made AI more accessible than ever before, robots that do the food preparation and cooking or deliver orders to guests are still out of reach for the vast majority of restaurants. In the foodservice industry, AI is becoming more common, but in smaller ways, often behind the scenes.

AI in the Front of House

AI Phone Answering

Missed phone calls can have a disastrous effect on a restaurant business. A recent survey conducted by Popmenu found that a staggering 83 percent of customers will move on and find another restaurant if they try to call and get voicemail more than once.

That's why many restaurants are adopting technology that helps them avoid missing customers' calls. AI phone answering technology can take messages, make reservations, add a guest to the waitlist, or even answer commonly asked questions (for example, if a guest asks, "Can I bring my dog on the patio," and AI phone answering service can be programmed to pick out keywords like "dog" and "patio" and respond, "Our patio is dog-friendly.").

This technology is similar to the AI chatbots that have been common online for a few years now. They can handle many simple guest queries, which frees up your front-of-house staff for on-premise service-and ensures you no longer miss phone calls from guests.

‍

Voice Ordering

27 percent of all online people now use voice search, and nearly 40 percent prefer voice search over their smartphones when searching for nearby restaurants. As voice assistants have become more popular-Amazon Alexa, Google Home, and Siri, just to name a few-the tasks they can help users accomplish have become more complex and far-ranging.

Guests have been using virtual assistants to search for restaurants for a while now. But a new technology that's gaining popularity is voice ordering-where guests can use their smart devices to place restaurant orders on the go or while multitasking.

But this technology has even more applications than just ordering on guests' personal devices. Voice ordering could be implemented at self-service kiosks or in drive-thrus, allowing guests to place orders conversationally-and without touching anything, a great safety measure in the pandemic age.

Self-Serve Options

Self-serve technology goes hand-in-hand with voice ordering. This encompasses all the different ways guests are being given the power to control and customize their own dining experience through self-service.

Customizing menu items while ordering from kiosks. Splitting the check and paying from tableside tablets-or even their own devices. More and more guests are embracing self-service technology in a variety of ways-and from all kinds of restaurants, from fast food to sit-down dining.

Kiosks that Personalize Customer Experiences

While ordering kiosks have become fairly common in a variety of restaurants, there are some eateries that are taking that experience a step further. Take KFC, for example, which is experimenting with kiosks with facial recognition technology that can recognize repeat visitors and tailor their experience based on their past orders and preferences.

While facial recognition might not be accessible for all restaurants, there are many ways you can use AI to help personalize the dining experience for your guests. Online ordering and digital marketing are two places where restaurants are more commonly collecting guest data, and then using that data to provide personalized service, from dish recommendations to targeted marketing.

AI in the Back of House

Integrated Inventory and Purchasing

One area of restaurant operations where AI can have a major impact is by integrating inventory and purchasing systems with your point of sale (POS systems). AI-enabled software can track historical inventory and purchasing data, looking for trends and making real-time recommendations for how many ingredients and supplies to buy.

Not only does this ensure your restaurant has enough inventory on hand to avoid having the 86 a popular dish, but it can help cut down on food waste by tailoring purchasing to only meet inventory needs, without buying excess that your restaurant won't be able to use before it expires.

Smarter Staffing and Scheduling

Staffing your restaurant and scheduling employees can be one of the trickiest parts of restaurant management. That's why it's an area where AI technology is becoming more and more popular.

Using AI-enabled software, restaurants can track staffing and sales data to determine any trends or patterns in busy times (and less busy times). Then, they can staff the restaurant appropriately for the traffic and sales that are expected during each shift.

Data-Based Insights and Predictions

In 2019, McDonald's started using predictive AI technologies and forecasting orders made in its drive-thrus. By analyzing historical data about what customers ordered and when, McDonald's stores were able to anticipate rushes and even predict what dish would be most popular during a given time of day. By anticipating orders, the stores using this technology were able to reduce wait times by 30 seconds on average-quite a feat when you consider that drive-thru ordering experiences typically only last a few minutes.

This kind of predictive technology can be used by restaurants in so many ways: To manage inventory, staffing, menu pricing, and more.

Automated Marketing and Remarketing

One place where most restaurants can implement AI today is in their digital marketing plans. There are many tools available now that use automation to send out marketing emails, social media posts, and targeted ads to the right people at the right times to maximize views, click-through rates, and more.

AI marketing can also be used to retarget recent visitors to your restaurant, encouraging them to become repeat guests. It can help identify promotions that might help you upsell guests. All you need is the right automated marketing tools (which are included in every Popmenu package).

AI Content Creation

AI isn't just a great tool for sending out well timed marketing messages, it can also help restaurants create engaging content that's unique to their business. Many owners have taken advantage of AI marketing content creation that uses information from their restaurants website (like upcoming events, popular dishes, and more) to instantly generate a calendar of emails, social posts, and text messages. This means they can easily stay in touch with guests without dedicating massive amounts of time to writing content themselves.

AI in Restaurants is Here to Stay

While AI might seem like an intimidating subject to many less tech-savvy restaurateurs, the bottom line is that it's earned its place in the restaurant industry's future. AI is here to stay, and along with it, many benefits for independent restaurants that embrace the trend.

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Grocery shopping with AI

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South Korean tech conglomerates LG and Samsung both plan to unveil smart fridge software updates featuring food-detecting AI capabilities, according to The Verge.

Business Insider Intelligence

The software updates, to be disseminated later in 2020, will use interior fridge cameras to identify foods that need to be restocked, and can even suggest recipes based on available ingredients. Both companies will also offer new models sporting these camera-based AI features, in addition to the software updates set to roll out for their current generation of high-end fridges - the Instaview ThinQ from LG and the Family Hub from Samsung, each of which retails for upwards of $3,000.

The forthcoming smart fridge updates present Samsung and LG with an opportunity to become intermediaries between consumers and grocers. The South Korean companies already occupy a leading position in the US household appliance market.

Regular software updates can bring value for users who have invested considerable sums in their appliances, building brand loyalty while also unlocking new revenue segments. Adding computer vision-powered food recognition to smart fridges is a perfect example, as Samsung and LG will gain deep insight into food consumption habits, a highly valuable asset in the grocery industry.

They could leverage food consumption data, for instance, to steer consumers to food brands in the same way Spotify uses consumption habits to develop personalized AI-generated playlists. And the large touch displays featured on the companies' fridges could be used to suggest recipes that rely on sponsored ingredients, or maintain a running list of food items to order for delivery.

LG and Samsung should use fridge food recognition capabilities to become central hubs in facilitating grocery delivery services. As we suggested in our 2018 Business Insider Intelligence report on e-commerce in the smart home, smart fridges could manage the inventory for the burgeoning grocery delivery business. Smart fridge owners could opt into a subscription service, for instance, that would automatically deliver certain food items according to fridge inventory.

As AI systems advance, smart fridges could even learn and predict future user consumption patterns for foods and order accordingly. This could help make procuring groceries a frictionless experience, working in tandem with existing services such as the Walmart grocery delivery program, which allows couriers - who gain access to homes through smart locks - to drop off groceries in users' fridges.

Adoption of these reduced-friction grocery experiences may be limited as much by consumer behavior as by technology. Only 3% of grocery sales in 2019 occurred online, compared to 22% of apparel sales and 30% of electronics sales, according to The Atlantic.

The concept of an AI-powered smart fridge shopping experience will certainly appeal to a contingent of early adopters, eager to embrace this radical departure from traditional shopping. But moving beyond the vanguard of consumers will require more concerted efforts from companies like Samsung and LG to transform the ways in which consumers relate to their appliances, service providers, and food.
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Samsung and LG are set to unveil AI-equipped smart fridges with food-identification capabilities

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South Korean tech conglomerates LG and Samsung both plan to unveil smart fridge software updates featuring food-detecting AI capabilities, according to The Verge. The software updates, to be disseminated later in 2020, will use interior fridge cameras to identify foods that need to be restocked, and can even suggest recipes based on available ingredients.

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