“Internet of Things” and “Machine Learning” can seem like buzzwords. They are both in the “Peak of Inflated Expectations” stage on Gartner’s emerging technologies hype cycle, but together they will change the world in various capacities in the next couple of years.
Internet of Things (IoT) refers to the network of devices and data shared from them. You know that Fitbit that you wear? That’s an IoT device. Machine Learning (ML) finds patterns in data and does something based on those patterns without being explicitly programmed. The data collected from IoT devices overtime is enormous and would be difficult for one person or even a team to uncover all insights. That’s where machine learning comes in – it can scale and simplify IoT data analysis. Internet of Things and Machine Learning complement each other. Here are some use cases to illustrate how IoT and ML can work together..
IoT and ML use cases
Analyze traffic patterns for city planning
Use machine learning to forecast traffic and peak demand within smart cities to make recommendations on alternative transportation or travel times. You would need to collect hourly or daily traffic data so city officials can make predictions to identify bottlenecks and make city planning recommendations.
Predictive maintenance for wind turbines
Predict cooling system usage on wind turbines and schedule for preventative maintenance optimization. You would need to collect hourly or daily usage data. A manager can then dispatch a maintenance crew when the predicted aggregate usage exceeds known maintenance thresholds.
Optimize device / systems efficiency
By gathering device and systems data you can optimize efficiency by linking usage forecast to supply chain and device operations. Developers or analysts can use machine learning to send alerts when predicted usage has exceeded a known threshold.
Collaboration is key
The list goes on and on of possible use cases for IoT and ML to achieve greater insights together, but collaboration will yield the best results. By collaboration we mean minimizing siloes that separate departments, companies, or industry verticals. A good example of this is a grocery store chain’s data on customer food purchases by customer ID collaborating with a healthcare company who has data on health history to uncover relationships between food and health overtime. This type of partnership enables the dynamic duo of machine learning and internet of things to work together effectively.
We need to start thinking of internet of things and machine learning as a dynamic duo that together will create positive social impact, especially with collaboration.
The statistics around how the Internet of Things (IoT) will affect different industries are fascinating. According to MarketsandMarkets, the market for IoT in healthcare will hit $ 158 billion by 2022; intelligent transportation, $ 143.93bn by 2020; and manufacturing, $ 20.59bn by 2021.
The potential therefore is high in individual industries. But looking at the supply chain, it’s not impossible to connect the dots and increase the potential.
At the very end of August, software giant Oracle issued a major update to its IoT Cloud, featuring three primary trends; digital twin, applied machine learning and artificial intelligence (AI), and what Oracle calls ‘digital thread’.
At its heart, these help expedite the delivery process for organisations and can provide a one-stop shop with industry solutions built on Oracle’s IoT Cloud applications. Take fleet management as an example; by combining two products – in this case IoT Fleet Management Cloud and Oracle Logistics Cloud – companies can track shipments in real time, assess risk management, and synchronise logistics.
This is part of digital thread, as Atul Mahamuni, VP IoT at Oracle, explains. “If you look at multiple operations in the supply chain, from product design, to procurement, to manufacturing, to transportation, to logistics, to warehousing, to service, all of these today are siloed operations,” he says. “Through IoT we can interconnect all of these together.”
Lionel Chocron, Oracle VP industry and IoT cloud solutions, who will be speaking at the IoT Tech Expo North America in Santa Clara in November, puts it this way. Manufacturing companies and logistics companies started coming to conferences and asking questions several years ago; now they have gone far beyond initial implementations.
“People call it Industry 4.0, specific to manufacturing, but it’s the full supply chain digital thread topic,” he says. “From the point of designing your product, to basically getting it to the market…but more importantly, the management, the supply chain, the logistics…all that chain is absolutely critical. We see manufacturing companies, logistics companies, all embracing the topic.”
A little bit further back, but certainly on the radar for Chocron, is healthcare, which the Oracle VP claims is ‘picking up the ball big time’. “I believe healthcare is at that stage now, having tried and having dived into the security and privacy issues which are critical – because you have to be certified and conform to some specific regulation – but now they understand the value of it and how they can use it for many kinds of applications,” he says.
“The value they get out of it is astronomical, both in terms of operating the healthcare facilities and personnel and so forth, but from the creation of the drug, to the testing of the drug, to the follow-up on how the drug is being used,” adds Chocron. “Using IoT technology to bring data live from the people using that medication as an example is something they see as a major boost in information, which mean they get to go way faster in getting the drugs to market and getting them certified.
“That information’s going to save them billions of dollars because in the past they had to go through a pretty cumbersome process.”
Alongside this is Oracle’s belief that the line of business is the most influential when it comes to making these decisions. “The value proposition is a business value proposition when you talk about connecting a factory, when you talk about enabling predictive maintenance on equipment, and driving optimised field service management when you talk about connecting your workforce,” says Chocron. “At the end of the day, the metric is a business metric.”
“There’s another subtle aspect though,” adds Mahamuni. “Once you make the buying decision…your business wants to get some more data, or some more insight. A typical example we’ve seen is they want to create a new KPI. Typically that involves not just your IT department, but you have to bring in a system integrator, do changes to your implementation, and it is an expensive and time-consuming process.
“The kind of things that we have done here are, like the KPI, a business user, literally a plant manager or a fleet operator, can change and create new KPIs,” says Mahamuni. “So that’s a new paradigm where not only the decisions are moving to OT (operational technology) but even the changes. Because they know how to run their business better, they can do things and experiment, and see what they like right through their desktop or mobile application.”
Oracle is both speaking and exhibiting at the IoT Tech Expo in Santa Clara, where the message, alongside some visual cues in the form of live demos, will be around helping businesses make the right decisions wherever they are in the IIoT journey.
“Our role is to deliver a story that shows the business outcome to our customers,” says Chocron. “We don’t want to talk about a platform per se – we want to talk about the integration of our application with our IoT play to deliver a business outcome for different kinds of industries.”
In my previous blog, I outlined the major components of the Internet of Things (IoT), giving the current state of IoT technology a grade of B-minus. Why the minus? Today, I’ll dive deeper into two major issues slowing IoT adoption: complexity and security.
Complexity Fragments Markets and Hampers Interoperability
There is no such thing as the “IoT market.” The typical vertical markets associated with industrial IoT applications range from manufacturing, transportation, oil and gas, and mining to agriculture, retail, insurance, healthcare, education, and smart cities. Each of these huge markets has many submarkets, and even within each submarket there are many overlapping, often long-standing ecosystems. Car manufacturers in Europe, for example, work within a completely different supply chain from those in the United States; each has its own vocabulary, technologies, and challenges. Adding to that complexity is the fact that, with few exceptions, IoT deployments are in “brownfield” environments, where innovations have to coexist with a plethora of incompatible legacy technologies.
Then factor in access technologies. The wide range of IoT use cases drives an equally wide range of technologies that vary according to bandwidth, reach, power, and cost. Connected vending machines may need to send a few packets whenever a brand of soda needs to be restocked. On the other extreme, the sensors deployed around an oil rig may generate terabytes of data each day. These sensors are connected within the rig using a combination of Ethernet and wireless technologies. In some cases, the data can be sent back to the central data repository using a fiber cable; but when this isn’t possible for remote sites, the data is processed locally in real-time, and just the exceptions or alerts are sent back via satellite. In other cases, you might piggyback on a municipal Wi-Fi system, or use Low Power Wide Area Network (LPWAN) technologies to connect battery-powered devices. Payment apps such as Apple Pay use near-field communications (NFC), which (thankfully) won’t work more than a few inches away. Indeed, these special needs demand specialized technology—but the result is a complex tangle of often incompatible and disparate access methods.
The IoT industry has tried to bring order to all of this with horizontal and vertical standards bodies and consortia—IEEE, IETF, ODVA, ISA, IIC, OCF, and OPC, to name a few (and to get lost in alphabet soup!). Ironically, there are so many industry organizations that it’s hard to bring them all together into a cohesive set of standards that ensure interoperability across an entire IoT deployment. The various sensors in a single production facility may run on different semi-proprietary standards that limit the free flow of information. Limited access to IoT data limits the value of your IoT deployment. For example, IoT applications such as preventive maintenance can work only if they can gather, process, and analyze all the data generated by heat, pressure, and vibration sensors on a piece of heavy equipment. Standardization and interoperability are the gateway to IoT value.
Companies considering IoT deployments also have to navigate rapidly changing organizational structures. For most of the 20th century, vertically integrated vendors strived to deliver end-to-end solutions. Today, markets move too fast for any one company to develop or deliver a single, complete solution on its own. The 21st century model is the emergence of symbiotic ecosystems of partners who complement each other in developing IoT solutions together. You might picture a big square dance, where partners come together for a time, then move off to dance with someone else. For many companies, this is unknown territory, but the sooner you embrace this model, the sooner you’ll be able to benefit from the IoT economy.
Security Concerns Can Kill an IoT Deployment
Worries about security may cause decision-makers to hesitate before investing in an IoT deployment—and last year’s IoT distributed denial of service (DDoS) attacks didn’t helped matters. IoT security is in many ways unique: It is more distributed, more heterogeneous, and more dynamic than traditional IT security environments. It also introduces new scenarios that require brand new approaches to security (think connected cars, sensor swarms and consumer-class devices in the workplace).
Back in the day when industrial enterprises ran self-contained, proprietary systems, “security by obscurity” was standard practice—if you’re not connected to anything, no one can break in. That approach no longer works in today’s connected IoT environment (if it ever did), so businesses must rely on a policy-based architectural approach that builds security into every aspect of a deployment—not just defending the perimeter.
After years of under-investment, the security industry is finally addressing the special requirements of IoT in a way that is reminiscent of how it responded to the challenges of Wi-Fi 15 years ago—accelerating work in standards, interoperability and certifications.
On the Other Hand, Adoption Accelerators Can Help Realize IoT Value
While complexity and security remain obstacles to widespread IoT implementation, here are two technology trends that promise to accelerate adoption and multiply the value of IoT solutions:
Analytics: When we put sensors on things and then connect them, we begin collecting vast amounts of data in motion about those things. Analytics sifts through that data real-time or near-real-time to find what is important and delivers insights and recommended actions for business impact. Two of the four fast paths to IoT payback I’ve identified—predictive analytics and preventive maintenance—depend on analytics to create IoT value.
Blockchain: I mentioned in my last blog that the ability to have a trusted means of transferring and tracking value online is enabling a whole new class of IoT capabilities, such as authenticating interactions among autonomous vehicles or managing and reporting mining site data. The “Internet of value” created by IoT plus blockchain will transform online processes. The industry is moving swiftly to capitalize on these capabilities starting with the formation of consortia to ensure interoperability.
So while obstacles remain, I am optimistic about the trajectory of IoT. An active community of IoT innovators is working tirelessly to reduce complexity and improve security. They know that IoT value depends on it.
Placing the customer first by using a powerful customer relationship management (CRM) tool is key to the success of any business. Artificial Intelligence (AI) and its subfields such as machine learning and deep learning are emerging as key technologies to power next-generation CRM platforms. AI can sift through volumes of data from the web, social media, mobile phones, and Internet of Things (IoT) devices.
Innovating customer experience is essential to a client-based and consumer-oriented business. Customer experience is the key competitive differentiator for 85% of senior executives, Salesforce reports. AI solutions are beginning to transform customer experiences in significant ways, paving way for widespread adoption of AI-based solutions. Global revenue is expected to grow by $ 1.1 trillion in 2021, thanks to AI-based efficiencies. By 2018, 40 percent of companies are likely to adopt AI in 2018. Enterprises are also looking to utilize AI in CRM functions such as lead generation, targeted marketing campaigns, and increasing sales cycles.
Smart, sustainable, and profitable customer service isn’t just about speed and seven-figure sales. Research supports that future-proof customer service is also consistent, personalized, real-time, and omnichannel. AI technologies can ensure all these qualities and more with its predictive capabilities, data volume capacities and analysis, as well as through its automation and recommendation engines.
Driving Smarter Customer Relationship Management
Every company requires constant customer retention and acquisition to increase its customer base and revenues. Businesses, big and small, are always on the lookout to capture new markets; this always begins with correctly understanding the consumer. AI can generate more specific leads in this aspect, going beyond basic information such as demographics, location, and buying power. An AI app could provide valuable details such as specific shopping patterns, a target market’s lifestyle habits, and family-specific data.
Amazon was among the first in using technology-driven data, with 35% of sales coming from data-generated recommendations given to consumers. Salesforce Einstein—the built-in AI in the Salesforce platform—is taking data gathering a notch higher as it analyzes information from emails, social media, sales, IoT-generated statistics, and e-commerce. Jason Pontin, Editor in Chief and Publisher of MIT Technology Review predicts that Einstein will help executives predict business performance way in advance, almost acting like a new management team member.
CRM applications’ data can lead to an informed sales profile, generated through the AI’s analysis of client patterns. Apart from demographics, the customer relationship technology can sift through volumes of transactions. It can then analyze how to develop products for specific customers. A smart sales team can utilize the data to help them sustain their current market and engaging new prospects. The team has a higher chance of closing sales thanks to AI-based CRM’s data analysis.
Enabling Operational Efficiency
Companies can also enable higher skill growth among employees by transferring automated tasks and direct customer engagement to AI chatbots. Customer support can spend more time on complex issues and serve more customers while chatbots deal with answering simple questions. The Amelia chatbot has a 55 percent success rate and addresses this need by providing answers based on previous interactions. If the bot is unable to answer or identifies a hostile customer, it immediately transfers the concern to an individual.
Even Taco Bell and Sephora have improved their services using chatbots. The TacoBot provides more organized orders for customers using Slack Chat, while Kik’s computer-driven bot responds to makeup inquiries.
AI is also improving small-but-important details that enhance a customer’s experience. Mya Systems’ AI is streamlining recruitment processes such as resume approval, interview scheduling, and gathering candidate information. This technology isn’t replacing HR employees but improving the hiring process. Using AI, recruitment companies can provide better-selected candidates without going through the time-consuming processes.
A Customer-Centric Future
AI is expected to make an impact on all kinds of industries and businesses. Twenty percent of companies are expected to employ workers in 2020 for the monitoring of neural networks. CIOs are recommended to look into departments that require large data sets yet don’t have the proper platforms for analytics.
Contrary to popular belief, AI will further personalize customer service and even customer relationships. “AI is becoming the new user interface, underpinning the way we transact and interact with systems,” according to Accenture’s 2017 Technology Vision Report. With AI becoming part of the user experience, its capabilities and purposes will go beyond the intelligent interface. Each customer interaction will eventually become more personalized and natural as it deals with more data and executing specific services.
Business leaders continue to agree that AI’s biggest potential is still in connecting with customers.
Leveraging AI capabilities, companies are looking forward to taking customer relationship and customer service to a different level altogether. Artificial intelligence and its constituent technologies such as machine learning, deep learning, and NLP are making a powerful impact; they are disrupting businesses to increase brand loyalty, revenue, and duly improve customer service.
Even service delivery can expect better efficiencies, as technologies strive to limit waiting times and increase human agent availability. All these innovations won’t be replacing primary human roles but will augment their capabilities. With AI to aid, sales teams, marketing managers, and other customer relation related employees can provide targeted and strategic services. Top managers can focus on building strategy based on most current and relevant data.
Going further, businesses are going to be even bigger, more complex, and more competitive. And AI is unleashing capabilities to help companies build a robust, agile, intelligent CRM and customer service platforms to power ahead of the competition.
Businesses across the world are rapidly leveraging the Internet-of-Things (IoT) to create new products and services that are opening up new business opportunities and creating new business models. The resulting transformation is ushering in a new era of how companies run their operations and engage with customers. However, tapping into the IoT is only part of the story .
For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (#AI) technologies, which enable ‘smart machines’ to simulate intelligent behavior and make well-informed decisions with little or no human intervention .
Artificial Intelligence (AI) and the Internet of Things (IoT) are terms that project futuristic, sci-fi, imagery; both have been identified as drivers of business disruption in 2017. But, what do these terms really mean and what is their relation? Let’s start by defining both terms first:
IoT is defined as a system of interrelated Physical Objects, Sensors, Actuators, Virtual Objects, People, Services, Platforms, and Networks  that have separate identifiers and an ability to transfer data independently. Practical examples of #IoT application today include precision agriculture, remote patient monitoring, and driverless cars. Simply put, IoT is the network of “things” that collects and exchanges information from the environment .
IoT is sometimes referred to as the driver of the fourth Industrial Revolution (Industry 4.0) by industry insiders and has triggered technological changes that span a wide range of fields. Gartner forecasted there would be 20.8 billion connected things in use worldwide by 2020, but more recent predictions put the 2020 figure at over 50 billion devices . Various other reports have predicted huge growth in a variety of industries, such as estimating healthcare IoT to be worth $ 117 billion by 2020 and forecasting 250 million connected vehicles on the road by the same year. IoT developments bring exciting opportunities to make our personal lives easier as well as improving efficiency, productivity, and safety for many businesses .
AI, on the other hand, is the engine or the “brain” that will enable analytics and decision making from the data collected by IoT. In other words, IoT collects the data and AI processes this data in order to make sense of it. You can see these systems working together at a personal level in devices like fitness trackers and Google Home, Amazon’s Alexa, and Apple’s Siri .
With more connected devices comes more data that has the potential to provide amazing insights for businesses but presents a new challenge for how to analyze it all. Collecting this data benefits no one unless there is a way to understand it all. This is where AI comes in. Making sense of huge amounts of data is a perfect application for pure AI.
By applying the analytic capabilities of AI to data collected by IoT, companies can identify and understand patterns and make more informed decisions. This leads to a variety of benefits for both consumers and companies such as proactive intervention, intelligent automation, and highly personalized experiences. It also enables us to find ways for connected devices to work better together and make these systems easier to use.
This, in turn, leads to even higher adoption rates. That’s exactly why; we need to improve the speed and accuracy of data analysis with AI in order to see IoT live up to its promise. Collecting data is one thing, but sorting, analyzing, and making sense of that data is a completely different thing. That’s why it’s essential to develop faster and more accurate AIs in order to keep up with the sheer volume of data being collected as IoT starts to penetrate almost all aspects of our lives.
Examples of IoT data :
-Data that helps cities predict accidents and crimes
-Data that gives doctors real-time insight into information from pacemakers or biochips
-Data that optimize productivity across industries through predictive maintenance on equipment and machinery
-Data that creates truly smart homes with connected appliances
-Data that provides critical communication between self-driving cars
It’s simply impossible for humans to review and understand all of this data with traditional methods, even if they cut down the sample size, simply takes too much time. The big problem will be finding ways to analyze the deluge of performance data and information that all these devices create. Finding insights in terabytes of machine data is a real challenge, just ask a data scientist.
But in order for us to harvest the full benefits of IoT data, we need to improve:
There are six types of IoT Data Analysis where AI can help :
1. Data Preparation: Defining pools of data and clean them which will take us to concepts like Dark Data, Data Lakes.
2. Data Discovery: Finding useful data in the defined pools of data
3. Visualization of Streaming Data: On the fly dealing with streaming data by defining, discovering data, and visualizing it in smart ways to make it easy for the decision-making process to take place without delay.
4. Time Series Accuracy of Data: Keeping the level of confidence in data collected high with high accuracy and integrity of data
5. Predictive and Advance Analytics: a Very important step where decisions can be made based on data collected, discovered and analyzed.
6. Real-Time Geospatial and Location (logistical Data): Maintaining the flow of data smooth and under control.
AI in IoT Applications :
-Visual big data, for example – will allow computers to gain a deeper understanding of images on the screen, with new AI applications that understand the context of images.
-Cognitive systems will create new recipes that appeal to the user’s sense of taste, creating optimized menus for each individual, and automatically adapting to local ingredients.
-Newer sensors will allow computers to “hear” gathering sonic information about the user’s environment.
-Connected and Remote Operations- With smart and connected warehouse operations, workers no longer have to roam the warehouse picking goods off the shelves to fulfill an order. Instead, shelves whisk down the aisles, guided by small robotic platforms that deliver the right inventory to the right place, avoiding collisions along the way. Order fulfillment is faster, safer, and more efficient.
-Prevented/Predictive Maintenance: Saving companies millions before any breakdown or leaks by predicting and preventing locations and time of such events.
These are just a few promising applications of Artificial Intelligence in IoT. The potential for highly individualized services are endless and will dramatically change the way people lives.
Challenges facing AI in IoT 
1. Compatibility: IoT is a collection of many parts and systems they are fundamentally different in time and space.
2. Complexity: IoT is a complicated system with many moving parts and non –stop stream of data making it a very complicated ecosystem
3. Privacy/Security/Safety (PSS): PSS is always an issue with every new technology or concept, how far IA can help without compromising PSS? One of the new solutions for such problem is using Blockchain technology.
4. Ethical and legal Issues: It’s a new world for many companies with no precedents, untested territory with new laws and cases emerging rapidly.
5. Artificial Stupidity: Back to the very simple concept of GIGO (Garbage In Garbage Out), AI still needs “training” to understand human reactions/emotions so the decisions will make sense.
While IoT is quite impressive, it really doesn’t amount to much without a good AI system. Both technologies need to reach the same level of development in order to function as perfectly as we believe they should and would. Scientists are trying to find ways to make more intelligent data analysis software and devices in order to make safe and effective IoT a reality. It may take some time before this happens because AI development is lagging behind IoT, but the possibility is, nevertheless, there.
Integrating AI into IoT networks is becoming a prerequisite for success in today’s IoT-based digital ecosystems. So businesses must move rapidly to identify how they’ll drive value from combining AI and IoT—or face playing catch-up in years to come.
The only way to keep up with this IoT-generated data and gain the hidden insights it holds is using AI as the catalyst of IoT.
Originally published here. (c) istockphoto.com/ franckreporter