Top 5 IoT Applications for Retailers

From smart shelves to smart coffee machine, the internet of things (IoT) has become a game changer across every industry. In fact, according to Statista, the number of connected devices worldwide will hit 30.73 billion by 2020. And with 88% of adopters saying that IoT is critical to the success of their company, IoT devices are expected to top 75 billion by 2025.

Whether connecting new or existing devices, retailers are finding plenty of reasons to embrace IoT. Microsoft recently commissioned the IoT Signals reports which provides insights into IoT adoption rates across retail and other industries. It turns out that 92% of retail decision makers view IoT as critical to business success—and 96% say they are satisfied with the value that IoT has added to their company.

So, how are retailers using IoT to drive business results? Below, we’ll count down to the top IoT usage scenario for retailers. If you’d like to learn more, be sure to register for the upcoming IoT in action in New York in January.

Number 5: Reducing energy usage

Energy consumption can be a major expense for retailers and as businesses look to trim costs and shrink their carbon footprint, they’re turning to IoT. Forty percent of retailers cite energy optimization as a top reason for adopting IoT.

They’re turning to solutions such as ICONICS . This Azure-based, smart building solution centralizes disparate building management systems that control heating, cooling, lighting, and more. By applying advanced analytics, the solution helps deliver energy savings in multiple areas.

Number 4: Preventing product loss

In 2019, retailers reported an average inventory shrink of 1.38 percent. It’s not surprising that loss prevention comprises 44% of IoT use cases. IoT can prevent product loss by detecting missed items at checkout and preventing unpurchased products from walking out the door.

Number 3: Keeping shoppers and staff safe

Keeping customers and staff safe is another priority for retailers. Surveillance and security accounts for 48% of IoT use cases in the retail industry. IoT surveillance and security solutions monitor for suspicious behaviors, persons of interest, and potential safety hazards, sending alerts when issues are detected.

Number 2: Managing inventory

Tracking inventory levels and keeping products in stock can be difficult, expensive, and time-consuming. Which is why retailers are turning to IoT to optimize inventory, accounting for 59% of IoT use cases.

IoT can identify products and their location in real time and monitor and send alerts for inventory building up in dressing rooms. IoT can also scan display shelves for gaps and product positions and even send alerts for misplaced or mislabeled products.

Number 1: Optimizing the supply chain

The ability to track products throughout the supply chain has been a huge differentiator in every industry, and accounts for 64% of all retail IoT use cases. Real-time transparency into shipping status, location, delays, conditions, and more helps retailers to keep products on the shelves and keep customers happy.

Bosch has a massive OEM ecosystem that produces approximately half a billion parts daily, which means an efficient supply chain is critical. To increase visibility into the supply chain, Bosch created Trac360. Benefits of this IoT solution include shipping transparency and trace-ability, real-time insights, and automated warehouse operations such as GPS tracking, notifications, and invoice processing. The solution has increased customer satisfaction and warehouse productivity while also reducing inventory costs.

World’s first IoT ‘device-to-cloud’ solution announced

  • Gemalto and Eseye launch ‘Intelligent Cloud Connect’, the only AWS Qualified IoT solution that vastly simplifies IoT device design, connectivity management, security and billing
  • New solution will transform global IoT deployment by delivering out of the box, zero-touch global IoT connectivity directly into AWS IoT Core, reducing time to market by more than 75%

The world’s first fully automated, direct IoT ‘Device-to-Cloud’ solution to simplify the process of onboarding an IoT device into AWS IoT Core securely, was today announced by Eseye, a leader in ubiquitous global IoT connectivity.

Intelligent Cloud Connect, the new joint solution developed in partnership with Gemalto, a Thales company, will be formally launched and demonstrated at AWS re:Invent in Las Vegas from 2-6 December.

To realise the benefits of IoT, organisations need to navigate an enormously complex ecosystem and a fragmented value chain. With many development hurdles to cross, typically it can take an average of two years to launch a new IoT solution, while many projects are paralysed by complexity and even struggle to make it to market.

With the new Intelligent Cloud Connect solution, Eseye and Gemalto are fundamentally disrupting the IoT ecosystem with a collaborative IoT Connectivity Platform, which cuts through the complexity of IoT and enables new product development timelines to be reduced from 2 years to less than six months.

The foundation of this first solution developed in partnership is Gemalto’s ground-breaking Cinterion® PLS62-W Global IoT Module which comes pre-installed with Eseye’s market leading intelligent AnyNet Secure® SIM, also provided by Gemalto. Each AnyNet Secure® SIM comes pre-programmed to leverage Eseye’s unique network switching as a service platform, delivering near 100% global cellular connectivity. As each Intelligent Cloud Connect device is powered-on a dedicated embedded application automatically and securely connects directly to AWS IoT Core, delivering ‘plug and play’ global IoT connectivity.

Anand Gandhi, VP of Worldwide Channels & Alliances at Eseye, comments:
“This partnership will change the way IoT devices are developed and deployed in the future. Intelligent Cloud Connect vastly reduces the complexities of creating an IoT device and then directly connecting it to the cloud, saving customers significant resources and time, whilst giving them a distinct competitive advantage.”

Andreas Haegele, VP IoT at Gemalto, a Thales company, comments:

“Our customers can now follow a quick and easy process to deliver IoT data securely to the cloud with confidence. It paves the way for massive innovation and marks a watershed moment for the IoT industry, which can now accelerate the deployment of secure IoT solutions at previously unachievable speeds.”

Intelligent Cloud Connect allows customers to develop a single IoT product SKU for any application that connects out-of-the-box on power-up to any mobile network in the world, while offering seamless and secure data provisioning to the AWS IoT Core. This means it is now possible to have an IoT device automatically activated and fully connected to AWS in less than 10 minutes.

The platform handles zero-touch IoT security certification with AWS IoT Core, as well as lifecycle device management, allowing customers to manage global device estates through a single pane of glass. With this solution the complexity of balancing bandwidth, data plans and negotiating multiple Mobile Network Operator (MNO) contracts is completely removed, providing customers with only one single bill for consumed MQTT messages, which can be conveniently purchased via the AWS marketplace.

AI in CPG

In an era of AI (artificial intelligence) and the IoT (Internet of Things), enterprise applications are continuing to evolve, and CPG (consumer packaged goods) companies are taking note and moving to new applications. The result will be greater efficiency, improved visibility, and businesses that are embracing the latest emerging technologies.

According to IDC, the use of artificial intelligence and ML (machine learning) is occurring in a wide range of applications from ERP (enterprise resource planning) and manufacturing software to content management, collaboration, and user productivity. Artificial intelligence and machine learning are being considered by most organizations today. IDC expects that AI will be the disrupting influence changing entire industries throughout the next decade.

Many of the CPG companies are beginning to identify how to best leverage AI today—which is still the early stage for many. The Hershey Co., about how to use the technology to predict and formulate product to meet the consumer needs.

In other recent news, Nielsen announced it is growing its analytic relationship with General Mills, meaning General Mills will use Nielsen’s new Connect platform, which can help expand the view into the consumer marketplace to provide a deeper understanding of customer needs, while also enabling faster decision making.

Role of AI in CPG

Another example of innovation for the enterprise in general comes from Oracle OpenWorld, which took place in September. It announced availability of its AI-trained voice with Oracle Digital Assistant, which means enterprise customers can use voice commands to communicate with their enterprise applications.

One company leaping into Industry 4.0 is Titan Intl., which is a manufacturer of off-highway wheels, tires, assemblies, and undercarriage products. The company has moved its core business processes to Oracle Cloud applications for finance, supply chain, and manufacturing.

The objective? To build a smart factory. It is automating manual processes and using data from sensors to improve insights into inventory. A second phase will introduce this automation to more production areas and the final phase will include machine monitoring on the shop floor so Titan can better understand the health of its machines and do predictive maintenance.

While using AI in a factory environment is just one example, the trend toward leveraging more data throughout a large enterprise continues. In many cases, the objective is to enhance communication and bring transparency to a business, all while improving efficiencies.

AI-Driven Fleet Management

Artificial intelligence (AI) has slowly woven its way into many aspects of our modern lives. Just ten years ago, most of us relied on paper maps for directions, and we performed laborious internet searches to find a store or restaurant that had what we were looking for.

AI (artificial intelligence) will help improve many aspects of fleet management and maintenance, from reducing unplanned downtime to increasing efficiency throughout the maintenance and repair process and improving fuel economy. Machine learning and AI can provide fleet operators with critical data that can be used to optimize operations, as well as predictive analytics to enable better decision making in the future based on the analysis of past fleet activities.

Drivers too can benefit from IoT (Internet of Things)-enabled data. For instance, realtime data about what’s going on outside on the roadways, such as weather conditions, road conditions, and traffic, can help fleet drivers get where they’re going faster and with fewer hiccups along the way. Fleet managers can similarly use this data to track vehicles and make informed decisions about dispatching, eliminating much of the guesswork involved in fleet logistics. When data makes fleet operations more predictable, operations can become more streamlined and efficient.

Fleet Management System

Innovative technologies are also helping to keep fleet drivers safe behind the wheel. Lytx , a provider of machine vision and AI-powered video telematics solutions for fleets, recently shared a use case for AI in reducing drowsy driving. Lytx’s customer, transportation service provider Hogan Transportation , says it leverages the company’s Driver Safety Program to gather the data it needs to gain visibility into fleet operations and to better understand what is happening across its fleet of vehicles. With this knowledge, Hogan is taking steps to reduce driver fatigue.

The Driver Safety Program combines video-based coaching, comprehensive reporting, and backend support to help drivers be as safe as possible. A DriveCam Event Recorder, a small LTE (long-term evolution)-enabled device mounted beneath a fleet vehicle’s rear-view mirror, records driving behaviors that may be risky and sends this data to the cloud, where it’s analyzed, prioritized, and sent back to the fleet manager to open doors for analysis, reporting, and coaching. Behind the solution are AI and machine learning algorithms and insights derived from Lytx’s database of 100 billion miles of driving data.

Lytx Fleet Management

Thanks to realtime, in-cab alerts from the Lytx device, drivers can get instant feedback on their driving behaviors, prompting immediate self-correction. Unsafe driving behaviors include things like using a handheld device, speeding, cornering, and not wearing a seat belt. It also includes drowsy driving. The company’s data reflects a 39% reduction in drowsy driving events among clients between June 2018 and June 2019, as well as 66% reduction in drivers falling asleep behind the wheel. Given the rapid adoption of smart fleet management solutions powered by AI, Grand View Research estimates that this industry will be worth $565.1 billion by 2025.

Advantages of Fleet Management

When it comes to identifying and preventing distracted and drowsy driving, technology can help curb unsafe behaviors in the short-term (i.e., via realtime visible or audible alerts) and the long-term (i.e., via analytics, reports, and coaching). For fleet managers, whose businesses depend on safe driving, the use of AI to recognize patterns that enable data-driven decision making can be extremely valuable. With a window into the cab and fleets as a whole, management becomes a lot easier. With the motivation to do well always, fleet drivers become a lot safer.

AI Bias Will Explode!!

We all are hearing a lot about AI (artificial intelligence) these days. It’s no wonder that Google’s latest smartphone announcement, the Pixel 4, with AI-enhanced recording and transcribing app received so much attention.

As we march toward a world enhanced by AI and automation, we need to make sure we’re not marching forward blindly. We need to think about what problems these technologies may create as they revolutionize industries.

And, while we won’t be able to anticipate all problems or even come up with answers to all of the problems, we can anticipate, we can start talking about AI from every angle to help prevent us from being blindsided. One discussion we need to keep having is AI and ethics and more specifically, bias in AI.

Biasing in AI

There is no question that artificial intelligence is fundamentally changing our society. While most you reading this blog already might know how great AI is, and how great it can be, perhaps we also need to shed a little caution and check our enthusiasm and talk about the ethical concerns that it raises.

If we begin to look at AI with a broader lens it opens the door to many questions about the use of AI and ethics. Some of the things we need to be thinking about in designing AI systems is transparency, fairness, accountability, safety, and bias.

We know that when looking at AI there are many tradeoffs that have to be taken into consideration during the development in the way the algorithms behind these automated systems operate and make decisions.

These tradeoffs explain how bias ends up in AI. Humans are the ones building the algorithms and training the AI. Humans must teach the AI how to make decisions, and they’re going to do so based on their own personal worldviews.

If we accept this thinking then it’s just inevitable and inescapable.

But on further examination, MIT makes the case for bias entering the AI equation before the algorithms even exist—as far back as before the data that will inform those algorithms are even created.

Consider how people go about creating deep-learning models in the first place. Say their goal is to make a good business decision. The AI is going to end up prioritizing whatever makes the most business sense over other factors like fairness or discrimination.

Another sticky point to consider is this: Do we know exactly what “fairness” means in the context of machine-learning outcomes?

A research paper argued the point to the contrary and that we can’t really do that. And yet, even as I write this, do we have an obligation to try?

We have to keep working at this. The stakes are too high. AI is already being used or will soon be used to make decisions regarding loans, insurance rates, college admissions, job placement and candidacy, and so on. Bias is not just a problem when AI is making big decisions, either.

How about when voice assistants can’t understand a person’s dialect? Is that fair?

When there’s a racial imbalance in the teams creating AI solutions like voice assistants, it can mean those AI systems have a hard time understanding people who speak differently than the people who are predominantly training these algorithms.

AI gets a lot right, though. And we need to remember that even a biased AI system has the potential to be less biased than a human, who doesn’t necessarily try to be unfair, but makes decisions based on factors that even that person can’t always understand or explain.

The scary thing about bias in AI versus bias in humans is the potential for AI to scale—and the bias along with it.

Biasing in AI by Mckinsey

As Mckinsey & Co. puts it: “AI can help reduce bias, but it can also bake in and scale bias.”

The Algorithmic justice League is a collective that aims to highlight algorithmic bias and increase awareness about it. It provides a space for people to voice their experiences with and concerns about bias in AI, and it helps develop practices for accountability during the design, development, and deployment of coded systems.

On its website, users can request a bias check to get help testing their tech with diverse users. They can report bias in someone else’s tech. And it offers a lot of resources that help raise awareness about bias in AI through media, art, and science outlets.

The future will be riddled with biased AI. The only question left to ask is how will companies address the fairness and biases we see to make it work? Or will they even care enough to address the issues that are going to explode in the years ahead?

IOT decision-making tree: Choosing the right IOT protocol

Many business decision-makers have moved beyond the point where IOT is just a buzzword. They are seeing the tangible benefits of IOT and what it can do for business productivity, profits and future growth.

Typical questions that every decision-maker faces during the planning phase of adopting an IOT strategy into their business operations is:

Where do I start? And which IOT solution will best fit my business needs?

There are various factors to consider.

For example, when considering the advantages and disadvantages of the type of IOT connectivity to use, it should be based completely on the use case of the IOT product you are developing, which is, in turn, based on stakeholders’ needs. In addition, you have to consider which communication protocols (such as MQTT, HTTPS or COAP) best serve the purpose of your IOT product.

To guide you through this complex process, we have drafted a decision-making tree premised on various questions/statements with basic yes/no answers and which route to take in each case.

We will unpack each branch of the tree in successive articles. This article focuses on branch one: your device is IP-enabled with custom firmware.IoT decision-making tree, first branch highlighted.

IoT decision-making tree

Definitions

However, before we explore these questions, a few definitions:

  • COAP (Constrained Application Protocol): COAP is a specialised Web transfer protocol for use with constrained nodes and constrained networks in the IOT.
  • HTTP(S) (HyperText Transfer Protocol): HTTP is the World Wide Web’s underlying protocol that determines how messages are formatted and transmitted as well as what actions Web servers and Web browsers should take in response to commands. HTTPS is simply an encrypted version of HTTP.
  • IP (Internet Protocol): The Internet Protocol (IP) is the method or protocol by which data is sent from one computer to another on the Internet. Each computer (known as a host) on the Internet has at least one IP address that uniquely identifies it from all other computers on the Internet.
  • LoRaWAN: LoRaWAN is a low speed, but long-range and low power communication protocol. It is an open specification, so anyone is free to implement the protocol themselves on their own equipment. The RF spectrum used is also unlicensed, so anyone is free to roll out their own LORAWAN network.
  • MQTT (Message Queuing Telemetry Transport): MQTT is an M2M/IOT connectivity protocol designed as a lightweight publish/subscribe messaging transport.
  • NB-IOT: NB-IOT runs in the mobile telephone radio spectrum, and piggybacks on old, unused GSM channels, or free space between LTE channels.
  • Sigfox: Sigfox is a proprietary network and protocol. It is typically used for remote meter reading but can be used for any remote data uplink. It is low speed and low power, but also long range.
  • TCP (Transmission Control Protocol): TCP defines how to establish and maintain network communication through application programs that exchange data. TCP works with IP and defines how computers transfer packets of data to each other. It establishes and controls a connection on top of IP.
  • TCP/IP (Transmission Control Protocol/Internet Protocol): TCP/IP is the protocol a device uses to access the Internet. It comprises protocols designed to establish a network of networks to grant Internet access to a host. It is also two layers of the most common four-layer stack used on the Internet.
  • UDP (User Datagram Protocol): UDP is an alternative to TCP and is used mainly to establish low-latency and loss tolerating connections between applications on the Internet.

Explaining the IOT decision-making tree

Decision Tree

The overarching question when commencing the development of your IOT product is whether your device is or will be IP-enabled. In this regard, the predominant networking stack is an IP stack which includes an application library to open or close connections to remote devices and can send and receive data between the remote devices. The four-layer IP stack is discussed in more detail below.

A typical use case is, whether your device needs to be connected and enabled to send or receive data via the Internet? This requires a connection via an IP-enabled device, either with its own IP protocol or via a secondary IP-enabled communication device which is discussed later.

1. Scenario 1: Your device is IP-enabled

1.1 The next question to resolve is can you implement and control the firmware yourself? Having a device that has an IP-enabled stack with full control over the firmware gives you a lot of flexibility to develop and build exactly what you want with full control down to chip-level, that is, software compiled onto the device and updating the firmware etc.

1.1.1 If you can implement and control the firmware yourself, the next consideration is whether you require frequent communication to and from the device such as sending commands and firmware updates. The details of this decision is discussed below.

1.2 If you cannot implement and control the firmware you have to ascertain if your firmware is already connected to a backend.

1.2.1 If yes, this would mean the device pushes data to a backend that the hardware manufacturer controls. You would need to pull the data from their backend using an API they provide. This could also be possible through a provided MQTT broker.

1.2.2 If the device is not connected to a backend, typically the device will publish data to a message broker online that you can integrate with to receive the data. In this case the protocol that is used is usually MQTT. Sometimes the device can, however, be set up to push data to an HTTP end point that you can configure on the device. The answer of which protocol to use here is therefore dictated by the hardware manufacturer, and you have to integrate with what is provided.

2. Scenario 2: Your device is not IP-enabled

If your device is not IP-enabled your options include LORA, Sigfox and NBIOT. With these protocols, your focus is on connecting things over fairly long distances and you are able to pull data into your own data pipeline via a bridge between your backend and the provider’s backend.

Unpacking branch one of the IOT decision-making tree

For ease of reference, branch one of the tree is colour-coded in yellow and, as mentioned above, concerns an IP-enabled device with firmware you can implement and control yourself. The next consideration of this branch is whether you require frequent communication with the ability to send firmware updates?

a) If yes:

Do you require reciprocal communication and real-time feedback? This approach runs on a publish/subscribe mechanism which means that nodes in this network can communicate with each other. MQTT that runs on TCP/IP is an example of this. This does not include a central server with clients; communication is directed at the broker that in turn transmits the data to the correct node. This means the frontend can communicate with and send commands to any of the devices. It’s an always-on connection that allows frequent communication with the ability to send commands and firmware updates to the device.

An example of this approach is smart irrigation to manage irrigation schedules and decrease water overflows, where commands such as when to switch on/off are sent to an irrigation and real-time data is returned for analysis.

b) If no:

Do you only require data to be sent to the cloud at regular intervals without any/minimal return communication (such as running a firmware update) with the end goal being to store large chunks of data? A practical example is a weather station that only needs to capture data and log it logically on an IOT service; in other words, send the data only one way. For this type of architecture, you will look at HTTP or COAP. COAP is semantically aligned with HTTP to allow one-to-one mapping to and from HTTP.

Evolution of Work and the AI Ecosystem

AI ECOSYSTEM

Automation and AI (artificial intelligence) will have tremendous impacts on the future workplace and future workers, as well as the future of work in general. Mckinsey Global Institute “Jobs lost, jobs gained: Workforce transitions in a time of automation” report found that about half of all current work activities are automatable using technology that exists and has already been demonstrated. Meanwhile, six of 10 current occupations could automate more than 30% of activities, according to McKinsey’s research. Clearly, this transition will be an impactful one.

Evolution of AI

Some jobs will be displaced as automation grows. Workers will either need to up-skill, re-skill, or change occupational categories altogether. One skill set that will undoubtedly become more and more important as the workforce changes is RPA (robotic process automation). A program called AAU (Automation Anywhere University) from RPA provider Automation Anywhere aims to help workers prepare for a future in which AI and automation are prominent components in the workplace. Automation Anywhere’s enterprise-grade platform uses software bots that work side-by-side with people to automate repetitive work in industries like financial services, insurance, healthcare, manufacturing, and logistics, among others.

Technology research firm ISG says enterprises are going to need AI skills if they’re going to maximize value from automation tech. At its ISG U.S. Automation Summit, summit session attendees identified AI/cognitive technology skills and RPA technology skills as the top two automation skillsets that will be most in demand this year. Within the next five years, Automation Anywhere anticipates certifying more than 1 million individuals for the future of work, and it has partnered with many different organizations to provide RPA training for business and IT professionals looking to up-skill or re-skill.

RPA Evolution

Meanwhile, the AI ecosystem itself will change, forcing AI professionals to continue to evolve alongside it. Recurrent Dynamics is a Canada-based AI startup that aims to advance the capabilities of artificial intelligence. The company’s novel method of training AI could create some ripples in the industry. Recurrent Dynamics says it has achieved a computational breakthrough that makes it viable to train AI continuously and at the edge.

The current paradigm for AI training can be complex and costly, but perhaps this won’t always be the case. Rather than training on a centralized cloud infrastructure, Recurrent Dynamics says its new method of training AI can be done on small cheap devices like a smartphone or tablet or any other connected device, including an autonomous vehicle. The company hopes its offering will bring ubiquitous, human like AI closer to the present by a decade.

Cloud computing and AI : changing business landscape

Traditionally, the benefits of AI training in the cloud have included scalability and accessible capabilities and options that don’t require specialized skills in AI and machine learning and/or a dedicated team of data scientists. However, as training methods evolve and advance, the ecosystem will evolve too. Similarly, as more industries adopt automation, the entire work dynamic will change. Those organizations—and the professionals within those organizations—that can find a way to keep up with this change will reap the rewards when the future of work is upon us.

AI in Healthcare sector

Artificial intelligence and healthcare transformation

The use of AI (artificial intelligence) and ML (machine learning) will be the disrupting influence changing entire industries throughout the next decade. The use of it is occurring in a wide range of applications from ERP (enterprise resource planning) and manufacturing software to content management, collaboration, and user productivity. Predictions show artificial intelligence and machine learning are being considered by many organizations today.

According to the recently updated IDC (Intl. Data Corp.) Worldwide Artificial intelligence system spending guide, spending on AI systems will reach $97.9 billion in 2023, more than two and one half times the $37.5 billion that will be spent in 2019.

IDC finds strategic decision makers across all industries are now dealing with the question of how to effectively proceed with AI. Some have been more successful than others, as evidenced by banking, retail, manufacturing, healthcare, and professional services firms, making up more than half of the AI spend.

Simone healthcare firm development

Healthcare owners recognize this tech trend is coming and are adapting. As one example, Simone Healthcare Development , a hospital construction company, says AI has redefined the healthcare field with developments in the areas of artificial voice technology and artificial intelligence medical assisting. The primary goal is bridging the gap between artificial intelligence understanding treatment techniques and then applying them to patients in order to achieve improved case outcomes. Artificial intelligence aims to mimic normal human cognitive functions in order to produce quality care that doesn’t rely on physical physicians.

The company sees three additional trends in healthcare that will have an impact on hospitals and their construction.

Healthcare AI market share

Telehealth, as well as telecommunications, has taken the medical world by storm. Telehealth improves healthcare by transforming in-person care into a personal service that can be accessed remotely. Health-related services can be obtained through electronic information allowing for long distance health management, which replaces the need for in-office visits.

Tele-health care with AI

Wearable apps now allow patients to proactively manage their own health and prevent disease by collecting and reviewing relevant health data. This innovative technology will allow for extreme accuracy for medical prevention, diagnosis, and treatment. Wearable apps will bring on a series of transformations to healthcare.

Wayfinding can simply mean just physical navigation, but wayfinding technologies refers to the mobile applications that offer directional instructions and guide individuals through a physical place. Recent wayfinding trends in healthcare environments include implementing mobile apps, digital adaptive signage, and interactive kiosks equipped with touchscreens. These developments incorporate smart wayfinding technology in a professional medical setting with ease and accessibility.

AI is already improving, changing, and redeveloping how industries function, including healthcare. In the case of healthcare, it is helping understand treatment techniques and applying them to patients in order to get the best results.

AI in the world of Automotive

AI in automotive Industry

In the world of AI (artificial intelligence) there are a lot of exciting companies making their mark in a pretty impressive way in the automotive industry. Some of these disruptive startups are truly giving us all a glimpse of where the AI market is headed.

Ford and its Ford city insights program, which uses its AI and data from traffic cameras, parking garages, and other sources to share mobility-related information is a perfect example. This AI-powered database is able to predict where collisions are more likely to happen or where microtransit solutions will be most valuable within city limits.

Future of AI in automobile

The company piloted its platform in Ann Arbor, Mich., and it’s expanding the use of the City Insights platform toolset to six more cities. Three of the six new cities are Austin, Indianapolis, and Detroit, and reports suggest the other three will be Miami, Pittsburgh, and Grand Rapids. By leveraging parking, transit, traffic, safety, and census data, Ford says the platform allows city planners to visualize their entire mobility ecosystem.

Features of AI in automotive

This helps them explore various solutions before implementing them in the real world. One of Ann Arbor’s goals during the pilot was to evaluate its existing parking infrastructure. People were saying parking is a problem, and the city wanted to know: do we have enough spaces? What’s the nature of the problem, exactly? That way, they can go about fixing the problem.

Thanks to the data-derived insights from the pilot, the city determined it actually does have enough parking. It just needs to invest in solutions that will help direct people to those open spots.

AI impact in automotive industry

With this platform, cities can access not only police reports of traffic collisions, but also near misses—like when someone slams on the breaks but avoids a crash—thanks to connected vehicle data. With this additional data, city planners have a more complete picture of where in a city they need to focus their efforts to improve traffic safety.

Also, this month in the automotive space, Kia Motors contributed a big chunk toward a $25 million funding round for Code42.ai, an AI startup that’s developing an autonomous transportation-as-a-service platform.

Code42.ai’s platform is called UMOS (urban mobility operating system), and it’s working to integrate future mobility options including shopping, fast delivery, and transportation. UMOS integrates self-driving cars, drones, and automated delivery robots to provide mobility services.

Hyundai Motor develops world’s first ML(machine learning) based smart cruise control

Hyundai Motor is also an investor for this exciting startup. It will be interesting to see what they come up with in this partnership. Ultimately, if all goes well, these strategic partnerships will help companies to collaborate on developing fleet and mobility systems, connected services, and more efficient self-driving carss.

Pony.ai launched self driving car

Poni.ai is another transportation and AI-related company that I have my eye on. This Chinese company has been testing PonyPilot, a RoboTaxi pilot service, in China. This summer, Pony.ai and Toyota teamed up on an autonomous driving pilot, which began in September on public roads in Beijing and Shanghai.

What’s going to be interesting to watch is how this company stacks up against its Google competitor in the U.S., Waymo. It appears to be planning a pilot in the U.S., because it got permits for RoboTaxi operation in California this summer.

AI in the automotive space has been in the headlines a lot, as new startups pop up to prove they can increase efficiency and offer greater value add and more optimization. Without a doubt, these are certainly interesting times for AI and automotive.

Predicting the future isn’t magic , it’s a artificial intelligence – Dave Waters

iDrink IoT Machine

Intelligent vending Machine

iDrink and SUIC have completed installation of 100 units of iDrink IoT smart beverage vending machines in bars and restaurants in four smart cities – Taipei, Hsinchu,Taichung and Kaohsiung.

Future of iDrink technology

In Cities that are leading in both technological capabilities and local implementation, as Taiwan is highly ranked in taking the lead in “smart city” development.

SUIC and iDrink are taking advantage of the up-trend in the global demand for intelligent dispensing solutions in several industrial segments including public entertainment, theaters, fun zones, restaurants and retail outlets, pubs, KTV’s, hotels, supermarkets, corporate offices, hospitals, airports, cafeterias, etc. driven by modern urbanization and digitization in virtually everywhere in the Asia Pacific. iDrink IoT smart beverage vending machines adopt smart technologies, including its own Drink Coin crypto and iCloud cloud computing management system, that help these enterprises with iDrink IoT machine safety, augment for easy vending of their beverages, enhance customer engagement, and thereby drive sales and profitability.

Recent market studies show that the intelligent vending machine market is expected to exceed USD30 billion by 2024, and the food and beverages segment comprises the highest demand.

iDrink Fridge

iDrink IoT machines sales are at an average of 20 to 40 cups daily per machine, each machine generating sales revenues of TWD 60,000 to TWD 120,000 per month, and these 100 machines are expected to provide gross profits of TWD 36 million to TWD 72 million each year.

iDrink Self-Service Food Ordering Machines, POS Systems !

SUIC and iDrink teams have developed a digital payment dedicated mobile app that transmits information using ultrasound as a technology protocol with multi-factor authorization and unique identifiers for faster-tokenized payments of iDrink IoT smart machines for seamless O2O user experience. Ultrasonic communication uses sound to transfer data and works with ~19 kHz frequencies.

At the same time, SUIC and iDrink own over twenty technologies and patents in 20 countries and regions. Thus, the iDrink IoT smart machines can be equipped for use as self-service food and drinks ordering machines in restaurants, bars and other places of business. This self-service app receives ultrasonic waves via customers’ mobile devices, phones, tablets, etc. The businesses, restaurants, shops, etc. do not have to purchase additional equipment to operate this iDrink Self-Service system app as this function can be programmed directly into the iDrink IoT smart machines. These enterprises could save on labor costs in the process, and at the same time, obtain customer data more efficiently and safely, eliminating security concerns and risks of data compromise.

The self-service market is valued at more than USD 24 billion in 2019 and is expected to reach USD 58.41 billion by the end of 2024, registering a CAGR of 16.43% during the forecast period 2019-2024.

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