How Industrial IoT is Influenced by Cognitive Anomaly Detection

How Industrial IoT is Influenced by Cognitive Anomaly Detection

There are about 6,000 sensors on an A350 airplane.

The average Airbus flight generates 2.5 petabytes per flight with over 100,000 flights per day!

Industrial Internet of Things, or IIoT, is a massive market.

It includes airplane and car manufacturers, power plants, oil rigs, and assembly lines, all of which contain sensors measuring thousands of different attributes.

But most IIoT companies let 80% of their data go unused. And this is a big challenge for businesses.

But there are other challenges too, like latency issues that affect the results from real time data, the failure to predict when parts will breakdown, and the expense of hiring data scientists.

A Cognitive approach to Anomaly Detection, powered by Machine Learning and excellent data and analytics, is providing IIoT businesses with solutions, and helping them to overcome the limitations of traditional statistical approaches.

Machine Learning is becoming a commonplace tool for businesses, accelerating root cause analysis.

Anomaly detection refers to the problem of finding patterns in data that don’t conform to expected behavior.

There are many different types of anomalies, and determining which is a good and bad anomaly is challenging.

In Industrial IoT, one main objectives is the automatic monitoring and detection of these abnormal events, or changes and shifts in the collected data, including all the techniques aimed at identifying data patterns that deviate from the expected behavior.

With the help of Data Scientist Taj Darra from DataRPM, we can understand the importance of a bottom up approach to anomaly detection, which you can see here:

When Machine Learning is enhanced with a cognitive IoT framework, it enables IIoT businesses to detect anomalies from the initial ingestion of sensor data to outputting predictions and determining whether or not something is an anomaly in just 2 days.

With cognitive predictive maintenance powered by Machine Learning, all of the sensors can be measured in parallel.

Let’s break down the phases of anomaly detection:

Cognition is giving businesses the means to gain control over enormous quantities of sensor data generating from every machine.

This means augmented asset failure management, reduction of unplanned downtime, improved failure prediction, and enhanced asset life.

As the IIoT industry moves into the future, there is an urgency for change because of the limitations of traditional machine learning approaches.

There are opportunities for businesses to take advantage of Cognitive Anomaly Detection now.


How Industrial IoT is Influenced by Cognitive Anomaly Detection

The Future of IoT and Machine to Machine Payments

The Future of IoT and Machine to Machine Payments

Companies like Amazon, and Facebook are setting the standard for customer expectations and customer experience.

This includes everything from understanding the whole customer journey, defining the context and personalizing it, to ensuring the payment experience is seamless and frictionless without compromising security.

Personalization and contextuality in the mobile payment domain is evolving with Machine to Machine payments. As the popularity of in-app experiences grow, like those used by Uber, there’s a corresponding need for a streamlined IoT enabled payment system. Billions of IoT devices are connected all over the world, and it won’t be long before almost all of our devices and technologies are connected through IoT.

Our technology is communicating with each other, or Machines are exchanging data with other Machines without the help of people. IoT improves this Machine to Machine interaction significantly, changing the experiences that we’re having as consumers.

The Machine to Machine, or M2M, connections market is predicted to reach $27 Billion by 2023, so we’re going to see an increase of IoT and M2M payment solutions. M2M is a term that is sometimes used interchangeably with IoT. But they’re actually different concepts. IoT technology is how devices communicate between diverse systems, and M2M refers to isolated systems that don’t communicate with each other.

When applied to M2M, Artificial Intelligence and Machine Learning enables systems to communicate with each other and make their own autonomous choices. So M2M payments can include a multitude of scenarios, like transactions based on customer behavior without our knowledge. Regulations, ethics, and business rules can be included in intelligent machines through smart contracts, which are stored on blockchain technology. This increases the security of M2M transactions and enforces contract performance. Device agnostic solutions, like automatic SIM activation for telecom, also helps to support M2M capabilities and communication, and optimizes network resources. Furthermore, contextualizing payments with data and analytics helps facilitate fraud detection and terminal tracking, defines customer profiles, and blocks stolen devices.

The M2M payment system is going to continue to significantly disrupt the payments industry, simplifying transactions in emerging markets. The combination of IoT, AI and Machine Learning, and smart contracts are creating opportunities for new, different purchasing behaviors. And integrating the user experience with apps like mobile wallets, will cause M2M financial activities to be even more commonplace in the future.

I’d like to thank Mahindra Comviva and Srinivas Nidugondi for their insight.


The Future of IoT and Machine to Machine Payments

Blockchain Potential to Transform Artificial Intelligence

Blockchain Potential to Transform Artificial Intelligence

The research on improving Artificial Intelligence (A.I.) has been ongoing for decades. However, it wasn’t until recently that developers were finally able to create smart systems that closely resemble the A.I. capabilities of humans.

The main reason for this breakthrough in technology is advancements in Big Data. Recent developments in Big Data have allowed us the capability to organize a very large amount of information into structured components that can be very quickly processed by computers.

Another technology that has the potential for rapidly advancing and transforming Artificial Intelligence is the Blockchain. While some of the applications that have been developed on Blockchain are nothing more than ledger records of transactions, others are so incredibly smart that they almost appear like AI.

Here, we will look more closely at the opportunities for A.I. advancement through the Blockchain protocol.

Blockchain Technology

Supporters of Blockchain believe that it can offer benefits in a large number of industries. The technology has already proved its usefulness in the financial and money exchange markets. 

The mortgage lending industry can benefit from Blockchain application for loan origination, payment, and trading. Smart contracts allow automated contingencies that will be executed when stakeholders meet their respective obligations of the contract. 

Major retail corporations such as Wal-Mart are working with IBM to apply Blockchain in their processes. They aim to improve inventory control and reduce wastage. A Blockchain-based supply chain can help retailers keep track of product batches and maintain a steady supply in stores.

Blockchain can also be useful in the healthcare industry, as it allows patients to create medical history records that are completely secure, yet easily accessible from the Blockchain network.

Some even believe that the technology will be used to hold elections in the near future.

Improvements in Artificial Intelligence through Blockchain

Researchers have also looked at ways to utilize Blockchain for improving Artificial Intelligence. Blockchain developers make a good case on why the distributed ledger system is the perfect platform for testing the next generation of developments in A.I.

The existing A.I. testing databases are operating in what can be called the red ocean. There is a lot of competition. Similar technologies and methods are being tested with many businesses competing for the same incremental gains.

A Blockchain-based database for A.I. represents the blue ocean of uncontested markets. This is because the technology is still new, secure, and transparent. It has the potential to achieve great things in the future.

Some of the characteristics that make Blockchain a good contender for testing and building Artificial Intelligence are outlined here.

Decentralized Control and Data Sharing

The Blockchain works on a decentralized network of nodes, working together to solve complex algorithms. The mining node on the network which finds the best solution first adds the entry to the blockchain ledger.

Artificial Intelligence works on a similar model. When a decision must be taken by an A.I. system, it tests the possible solutions and alternating branches of possibilities that emerge as a result of taking the first decision. Evaluations of all possible alternatives are tested to the end result before the A.I. chooses the best option.

What makes Blockchain exceptionally good is that instead of a single, central system testing all possible hypotheses, the task is divided among hundreds of nodes spread around the world, which makes the process much faster.

Additional Security

An A.I system being run on a single, central processor is prone to hacking, as any malcontent only needs to break into a single system to manipulate the instructions.

Entries to the blockchain platform must be authenticated by the majority of nodes on the network before they are accepted and processed into the ledger. The higher the number of nodes that are operating on the network, the more difficult it is to hack the system.

While a Blockchain-based A.I. platform would not be impossible to hack, it is still far more difficult to manipulate and break such a system.

Greater Trust

In order to be reliable, a system must be trusted by the public in general. Blockchain allows far greater transparency than a closed A.I. system. Records maintained on a Blockchain ledger can be reviewed and audited at any time by authorized people with access to the system. At the same time, users who have not been granted access would not be able to view anything, as the database is encrypted.

Take the case of Blockchain application in the healthcare industry. People with medical complications may not want their medical records to be accessed by unauthorized people. Keeping the medical history in an encrypted format instead of plain English ensures that their records could not be accessed by any individual.

On the other hand, keeping the record on a Blockchain also ensures that medical practitioners would be able to provide quick medical aid in case of emergency by accessing the files.

How Blockchain will Transform Artificial Intelligence

Developments in A.I. technology rely on the availability of data from a large number of sources. Organizations such as Google, Facebook, and telecommunication companies have access to large sources of data which can be useful for testing many A.I. processes. The problem is, this data is not accessible on the market.

This problem can be solved by Blockchain’s P2P connection. The Blockchain ledger is an openly distributed registry. The database becomes available to all the nodes on the network. The Blockchain may be the best thing to end the control on data from a few major corporations by allowing it to be freely available.

Modern A.I. & Data

The development of A.I. depends on access to data in much the same way that the construction of a building depends on materials, stone, and steel. This is because data is consistently needed to test and retest alternative solutions for A.I. 

As an A.I. system continuously tests these hypotheses, rejects the wrong answers, and builds upon the right solution, it improves its capability to make sense of things. This is what we commonly refer to as Machine Learning.

Machines do not have the same sense of intuition that humans developed over millions of years. In order for A.I to one day reach a similar level of intelligence as humans, it would need to test the data of millions of transactions in a matter of years.

Control Over the Use of Data

This is perhaps the most important and limiting factor in the development of A.I., and the reason why Blockchain would work where centralized databases have not. 

Think of Facebook or Google. When a user logs into their Facebook account, they don’t have the right to any content uploaded on their platform. The content on the platform belongs to the website.

What makes Blockchain different is that data on the Blockchain is not owned by the operators but the individual wallet holder. This gives each user the ability to share their data on the platform without requiring permission for the network operators.

The future of A.I. development lies on a network that allows free flow of information between connected users and operators. The decentralized nature of Blockchain technology means that this could be the platform where we see the most breakthroughs on A.I.

About The Author 

If you would like to read more from Ronald van Loon on the possibilities of Artificial Intelligence, Big Data, and the Internet of Things (IoT), please click “Follow” and connect on LinkedInTwitter and YouTube.


Blockchain Potential to Transform Artificial Intelligence

Machine Learning Explained: Understanding Supervised, Unsupervised & Reinforcement Learning

Machine Learning Explained: Understanding Supervised, Unsupervised & Reinforcement Learning

Machine Learning is guiding Artificial Intelligence capabilities.

Image Classification, Recommendation Systems, and AI in Gaming, are popular uses of Machine Learning capabilities in our everyday lives. If we breakdown machine learning further, we find that these 3 Machine Learning examples are powered by different types of machine learning:

  • Image classification comes from Supervised Learning.
  • Recommendation systems comes from Unsupervised Learning.
  • Gaming AI comes from Reinforcement Learning.

How can we better understand Supervised, Unsupervised, and Reinforcement Learning?

Let’s start with Supervised Learning, which makes up most of the uses for Machine Learning today. In Supervised Learning, the machine already knows the output of the algorithm before it starts working on it. The algorithm is taught through a training data set that guides the machine, and the machine works out the steps from input to output. Supervised learning is used for image classification or identity fraud detection, and for weather forecasting. But how is Unsupervised Learning different?

Well first off, with Unsupervised Learning, the system does not have any concrete data sets, and the outcomes are also mostly unknown. Unsupervised Learning has the ability to interpret and find solutions to a limitless amount of data. Now when you log onto Hulu or Netflix, you have personalized recommendations because of Unsupervised Learning.

Lastly, there is Reinforcement Learning. Reinforcement Learning is different, because it gives a high degree of control to software agents and machines, which are determining what the behavior within a context should be. People are helping the machine to grow by maximizing performance, providing feedback to the machine, helping it to learn its behavior.

Reinforcement Learning requires the use of tons of different algorithms, giving control to the agent as they decide the best action based on the current results. When you are gaming on PC, Xbox, Playstation, or Nintendo, and you witness AI in Gaming, this is because of Reinforcement Learning.


Machine Learning Explained: Understanding Supervised, Unsupervised & Reinforcement Learning

Cognitive computing: Moving From Hype to Deployment

Cognitive computing: Moving From Hype to Deployment

Although cognitive computing, which is many a times referred to as AI or Artificial Intelligence, is not a new concept, the hype surrounding it and the level of interest pertaining to it is definitely new. The combination of hype surrounding robot overlords, vendor marketing and concerns regarding job losses has fueled the hype into where we stand now.

But, behind the cloud of hype that is surrounding the technology currently, there lies a potential for increased productivity, the ability to solve problems deemed too complex for the average human brains and better knowledge based transactions and interactions with consumers. I recently got a chance to catch up with Dmitri Tcherevik, who is the CTO of Progress, about this disruption and we had a healthy discussion which led to the following insights. 

Cognitive computing is considered a marketing jargon by many, but in layman terms it is used to define the ability of computers to replicate or stimulate human thought processes. The processes behind cognitive computing may make use of the same principles as AI, including neural networks, machine learning, contextual awareness, sentimental analysis, and natural language processing. However, there is a minute difference between both of them.

Difference between Cognitive Computing and AI

Both AI and Cognitive Computing may look extremely alike, but like we mentioned above there is a small difference between both methods.

 

Firstly, artificial intelligence does not work at mimicking human thought processes. The concept behind AI is to not mimic human thought and processes, but to solve a problem through the use of the best possible algorithm. This can be illustrated through an example of a car, which stays on course and avoids a collision. The processes in AI are not looking to process data in the same way as it would be processed by humans, but they’re looking to process it through the best known algorithm present. Processing data the way humans do it is a far more fault-prone and complex algorithm. And, we all know that a self-driven car isn’t giving suggestions to the driver, it’s responsible for all the decisions in driving.

Secondly, cognitive computing is not responsible for making decisions for humans, instead it is responsible for complementing or supplementing our own cognitive abilities of decision making. AI in medicine would be all about making the right decisions pertaining to a patient or the preferred mode of treatment, and minimizing the role of the doctor. Cognitive computing, on the contrary, would be more focused on achieving evidence that could supplement the human expert into making more flawless medical diagnoses.

Emerging Use of Cognitive Computing in Industries

We can gauge the success of cognitive computing and the development through the opportunities it has across industries. Cognitive computing is currently in a research phase, where research is going into properly implementing the technology in the fields deemed appropriate for its use. One can assess the opportunities for cognitive computing by looking at industries and industry specific scenarios where cognitive computing could make a big difference.

Customer services

Companies offering customer services deal with a lot of data which they have to accommodate with large processing requirements and are required to be efficient and flawless in advising customers to the right outcome. With so much happening, one can think about the opportunities for cognitive computing in this specific industry. At a consumer level, we can take the aid of robo-advisors that assist staff in advising new customers about what they can do and how they can go about creating a new account. There is also the concept of automated document processing that will limit human involvement and the flaws that come with it to a large extent. According to Dmitri: ‘Customer services are up for disruption, and the use of chatbots while booking airplane tickets or checking your insurance claim will go a long way in the future.’


Healthcare

Whenever we talk about Big Data, Machine Learning, AI or Cognitive Computing, the services that will be rendered through these technologies in healthcare always spring to mind. Human healthcare is certainly not at 100 per cent efficiency nowadays, which is because of the fact that there are certain flaws in the process. These flaws can be eradicated by giving machines the cognitive abilities required for going through a report and forming a basic judgment regarding the condition of any patient. The results can then be communicated to humans through a virtual display.

Industrial IoT

Most of the Industrial IoT giants that we have in industries such as car manufacturing, transportation, etc., have implemented exemplary data collection methods. These data collection methods do their job well, and hand over the necessary input to their patron organizations. Now, when the data is collected and stored off, the real challenge of anomaly analytics arises. Despite having stringent data collection and storage facilities, these firms don’t know what to do with their data and how to find actionable results.

The biggest problem facing businesses in today’s myopia is that only 20 percent of all problems or anomalies that occur are predicted and understood beforehand. This means that around 80 percent of the problems that businesses face are unpredicted, and the business is not prepared to handle them because of below par anomaly detection.

The Cognitive Anomaly detection is different from the traditional method, as it is a machine and data-first solution.  The future for cognitive anomaly detection is seemingly bright, and it is now the time to move from a research phase to deployment.

How to Move to Deployment

The deployment of cognitive computing requires adhering to a certain set of levels for achieving the desired aims. The levels that should be used for proper deployment of the technique include:

  1. Scale and Automate: It is necessary that you determine the scale of the deployment and then automate the process towards achieving the necessary scale. By knowing the scale of the move and the automation that is required, you can seamlessly incorporate cognitive computing in your setup.
  2. Start Using APIs: The next level in the deployment of cognitive computing includes the creation of APIs or Application Programming Interfaces. Chatbots and natural language processing are added to the interface to make it effective.
  3. Automation Middleware: The stage of automation middleware already provides 75 per cent of the entire share that is going into achieving the solution. Application developers need to put together applications quickly here, according to Dmitri Tcherevik. The fast processing of applications at this middle stage defines the success of the levels.
  4. App Blueprints per Domain: Despite the thoroughness of the steps mentioned above, there is still a need for an application blueprint for each domain. Application blueprints are created by Progress for different domains. Dmitri mentioned that they have created several blueprints for domains in healthcare. The applications required are complex, which is why there is a need for blueprints. The applications can then be personalized based on clients.
  5. DevOps: Once you have deployed the cognitive applications, there is the need to look out and monitor. The monitoring is done to look out for possible updates that can be incorporated. Cognitive applications need to be updated on a continuous basis to remain smart and up to date with what is expected from them.

With cognitive computing gaining center stage, it is expected that the concept will develop over time and will be implemented over numerous industries. Industrial IoT is expected to benefit a lot from cognitive computing as it can be used for deriving meaning out of the data they work with. In short, cognitive computing is currently leading the wave of the future as it holds the key to not only making healthcare, AI and Industrial IoT better, but also providing human thought processing and behavior that was needed here.

 

About The Author 

If you would like to read more from Ronald van Loon on the possibilities of Artificial IntelligenceBig Data, and the Internet of Things (IoT), please click “Follow” and connect on LinkedInTwitter and YouTube.


Cognitive computing: Moving From Hype to Deployment

AI’s Impact on Retail: Examples of Walmart and Amazon

AI’s Impact on Retail: Examples of Walmart and Amazon

Artificial Intelligence or AI is expected to be in major demand by retail consumers due to its ability to make interactions in retail as flawless and seamless as possible. Many of us do realize the potential of AI and all that it is capable of, along with the support of Machine Learning or ML, but don’t realize that the implementation of AI in certain segments has already begun. 

AI in Retail 

The future for AI and the complicated computer processes involved behind it is really bright in the field of retail. AI currently has numerous data sets working along with computer visualization methods to ensure that the users get the most seamless experience when it comes to AI in the workplace. There are some interesting facts that pertain to the use of AI in retail. Here we have some of them to build the insight into what you can expect during the feature; 

  • It is expected that customers will manage 85% of their relationship with the enterprise without interacting with a human. 
  • According to a report by Business Insider it is said that customers who interact in online opinions and reviews with retailers are 97 percent more likely to convert along with the retailer during this phase of change. 

With such promising figures on the card, one cannot help but notice the wave of change that has already started in the field of retail. With work already in progress, major retailers such as Amazon and Walmart have made advances that are expected to dictate this transition to AI in retail. We will be looking at these advancements, and will see how they can work out in the future. 

Walmart’s Shelf Scanning Robots 

Source YouTube/Walmart

You might have heard of shelf-scanning robots being tested by retailers, but we’re just about to witness one of the most interesting advances in the deployment of these robots. Walmart, which is one of the biggest physical retail chains across the world, is planning to extend the tests for its shelf-scanning robots across 50 additional stores, including some from its native land of Arkansas. 

The machines, which have been deemed to be the future of shelf scanning, will roam around the aisles to check all factors including pricing, misplaced items, and stock levels, to assess the level of stocks within the store. This would not only save human staff all the hassle of checking these trivial details by themselves, but would also mean that they can focus on other more important details. The machines will require technicians to be present on site to handle the situation in case of a technological impairment, but the robots are currently fully autonomous to handle their tasks themselves. These robots will be using the concepts of 3D imaging to roam around aisles, dodge obstacles, and to make notes about the blockages in their pathway. 

Amazon Go 

Source YouTube/Amazon

Amazon Go is the latest wave of technology in retail that is expected to lead the way to the future of AI in retail. The basic concept behind Amazon Go is that it is a new kind of store that flourishes on the concept of no checkout requirements. Consumers who walk into a store can take whatever they want without having to go through the hassle of lines and waiting for checkout. 

The checkout free shopping experience in Amazon Go is only made possible through the use of the same technology that is currently in place behind computer vision, sensor fusion, and self-driving cars. The technology automatically detects all that is being taken and keeps track of them in a virtual cart. Shortly after the consumer leaves, they will be sent a receipt and charged through their Amazon account. 

About The Author 

If you would like to read more from Ronald van Loon on the possibilities of Artificial IntelligenceBig Data, and the Internet of Things (IoT), please click “Follow” and connect on LinkedInTwitter and YouTube.


AI’s Impact on Retail: Examples of Walmart and Amazon