Data AI (19)

In the era of digitalization, IoT is fostering the upcoming revolution in mobile apps. The ways companies used to provide mobile app development are changing because of IoT. After helping thousands of corporates to deliver extraordinary user experiences, IoT is all set with some new and advanced mobile app development trends. 

The tech world is the one that is continuously evolving. Every year and each day, innovations come to light. Each of them is revolutionizing our lives in one or the other ways. From the first wheel to smart cities, humans have come a long way.

The evolution and foundation of smart cities is the result of IoT or the Internet of Things. IoT has definitely stirred quite an uproar in the digital world with the mass potential it has. It can bring everything and everyone online. 

As per the latest mobile app development stats, IoT will become a more significant player in the mobile app development industry. The market share of IoT is going to increase more than double in 2021 with a staggering amount of 520 billion USD. While four years back in 2017, this number was 235 billion USD. 

Soon the IoT mobile app development will face new trends in the coming year and beyond.

Let us take a look at the top IoT mobile app development trends.

IoT App Trend #1: Cybersecurity for IoT

With an increase in the number of devices online, cybersecurity is the top priority for all businesses as IoT gains popularity. The network is expected to expand in the coming years, and so the data volume will also increase. All this draws attention to more information to protect.

IoT security will see an exponential rise as more users will store their data over the cloud. From banking details to home security, everything is easily breached if the security firewall is weak in IoT applications. 

Therefore mobile app development companies need to work upon the up-gradation of their IoT enabled mobile apps. 

IoT App Trend #2: Roaring Popularity of Smart Home Devices

When smart home devices were launched, many mocked them by calling them unrealistic toys for lazy youngsters. Now, the same people are finding it increasingly difficult to resist the charm of IoT devices. 

IoT devices are expected to be very popular in 2021 and the years to come. The reason behind their growing popularity is that the IoT devices are becoming highly intuitive and innovative. They are extended not only to the comfort of home automation but also to home security and the safety of your family.

Another great advantage of implementing smart IoT development adoption is the need to save energy. The intelligent lights or intelligent thermostats help in conserving energy, reducing bills. These reasons will lead to more and more people to adopt smart home devices.

IoT App Trend #3: Backed by AI and ML

Artificial Intelligence and Machine Learning both are thriving technologies. Both of these are the facilitators of automation. We all know how Artificial Intelligence has touched millions of lives around the globe. 

Together with IoT, AI and ML are unique data-driven technologies shaping the future of human-machine interactions. The developers set up a combination of IoT and Artificial Intelligence that helps automate the routine tasks, simplifies work, and gets the most accurate information.

IoT App Trend #4: IoT and Healthcare

With the revolution in the health-tech industry, healthcare companies are turning towards mobile platforms. IoT enabled apps to open up new opportunities to improve the medical sector.

IoT has immense applications that are already running in the healthcare field and is expected to increase by 26.2% 

Healthcare apps featuring IoT technology are expected to reform the world of medical sciences. These IoT mobile apps can even help doctors and medical professionals treat their patients even from a distance.

Smart wearables and implants will be able to record diverse parameters to keep the patient’s health in check. By integrating sensors, portable devices, and all kinds of medical equipment, real-time updates of a patient’s health can be recorded and sent to the concerned person. 

IoT App Trend #5: Edge Computing to Overtake Cloud Computing

This is a change where we have to be careful. For the past many years, IoT devices have been storing their data on cloud storage. However, the IoT developers, development services, and manufacturers have started thinking about the utility of storing, calculating, and analyzing data to the limit.

So basically this means, in place of sending the entire data from IoT devices to the cloud, the data is first transmitted to a local or nearer storage device located close to the IoT device or on the edge of the network. 

This local storage device then analyzes, sorts, filters and calculates the data and then sends all or only a part of the data to the cloud, reducing the traffic on the network avoiding any bottleneck situation.

Known as “edge computing”, this approach has several advantages if used correctly. Firstly, it helps in the better management of the large amount of data that each device sends. Second, the reduced dependency on cloud storage allows devices and applications to perform faster and also reduce latency.

Being able to collect and process data locally, the IoT application is expected to consume lesser bandwidth and work even when connectivity to the cloud is affected. After seeing these positive aspects, state-of-the-art computing is looking forward to better innovation and broad adoption in IoT, both consumer and industrial.

Reduced connectivity to the cloud will also result in fewer security costs and facilitate better security practices. 2021 will see better state-of-the-art IT in IoT.

IoT App Trend #6: Are You Excited About Smart Cities?

Well, all of us are super excited to witness smart cities. Smart cities are one of the significant accomplishments of IoT and modernization. Integrated with IoT-powered devices, smart cities promise improved efficiency and security for the common folk on the streets and inside their homes.

With superfast data transfer supported by 5G, public transportation will also see a massive change in the way they work. 

By now, we know that IoT will focus on developing smart parking lots, street lights, and traffic controls. To add up to this, with IoT and fast internet, we will live inside a world where our refrigerators will be aware of what food we have inside.

IoT will impact traffic congestion and security. It will also help in the development of sustainable cities leading us to a green future.

IoT App Trend #7: Blockchain for IoT Security

Many financial and governmental institutions, entrepreneurs, consumers as well as industrialists will be decentralized, self-governing, and be quite smart. Most of the new companies are seen building their territory on the entanglement of IOTA to develop modules and other components for firms without the cost of SaaS and Cloud.

IOTA is a distributed ledger especially designed to record and execute transactions between devices in the IoT ecosystem.

If you are in this industry, then you should prepare to see the centralized and monolithic computer models that are separated in the jobs and microservices. All this will be distributed to decentralized machines and devices. 

In the coming future, IoT will penetrate the disciplines of health, government, transactions, and others that we cannot think of right now. Such types of IoT technology trends will create significant effective differences.

IoT App Trend #8: IoT for Retail Apps

The eCommerce industry will also get benefited from IoT integration. Retail supply change will be more efficient after the incorporation of IoT mobile apps. It is expected to improve the online shopping experience for individuals across the globe.

Also, IoT will make the retail experience more personalized for each customer with in-app advertisements based on the user’s shopping history. We already get notifications once we purchase a product from a particular eStore. With IoT enabled mobile apps, the app will guide us to our favorite store using in-site maps.

IoT App Trend #9- Will IoT Boost Predictive Maintenance?

Yes, it will. In 2021 and beyond, the smart home system will notify the owner about plumbing leaks, appliance failures, or any other problem so that the house owner can avoid any disaster. Soon these intelligent sensors will enter our houses.

In response to these predictive skills of IoT, we can expect to see home care offers as a contractor service. If there will be a need for any emergency action, your presence in the house will not be necessary. 

IoT App Trend #10: Easy and Better Commuting

IoT mobile applications are expected to make commuting easier for students, the elderly, the business person, and many more. Today, due to heavy traffic, commuting is a significant issue for most of us. With major innovations in technology and integration of IoT, mobile applications will make traveling a breeze for everyone.

Here are some of the conventional ways that commuting will change:

  • Smart street lights will make walking on the road safe for pedestrians
  • Finding parking spaces will be a lot easier and seamless with data-driven parking apps. 
  • In-app navigation and public transportation will definitely make public transit more reliable 
  • IoT powered mobile apps will also improve routing between different modes of transfer.

With so many innovative ideas and benefits for iOS and android based IoT mobile apps, the mobile app development market will see an influx of transportation apps in the years to come.

IoT App Trend #11: Sustainable-as-a-Service Becomes the Norm.

While talking about the IoT trends, SaaS or Sustainable-as-a-Service is considered as one of the hot topics for the estimated market. Because of the low cost of entry, SaaS is quickly getting to the top list for being the favorite firm in the IT gaming sector. 

Out of these emerging technological IoT trends, Software-as-a-service will make the lives of people better than ever.

IoT App Trend #12- Energy and Resource Management 

Do you know what affects energy management the most? Well, energy management majorly depends on the acquisition of a better understanding of how to consume resources. IoT mobile app-based electronics are expected to play a significant role in the conservation of energy. 

All of these IoT trends can be integrated into resource management, making lives more accessible, more comfortable, and responsible.

Automatic notifications can also be added to the mobile app in order to send information to the owner in case the power threshold exceeds. Various other fancy features can also be added to these IoT mobile apps such as sprinkler control, in-house temperature management, etc.

Conclusion

We all know that IoT has great potential to bring revolutionary changes in the present mobile app development industry trends. It is expected to open up immense possibilities for every business or individual related to this field. Directly or indirectly, IoT will drive the future of almost every industry.

The above mentioned are some of the trends that will dominate the IoT app development ecosystem in the years to come. Amid all these predictions and trends, the future is promising and worth the wait. 

 

 

 

 

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By: Kelly McNelis

We have faced unprecedented disruption from the many challenges of COVID-19, and PTC’s LiveWorx was no exception. The definitive digital transformation event went virtual this year, and despite the transition from physical to digital, LiveWorx delivered.

Of the many insightful virtual keynotes, one that caught everyone’s attention was ‘Digital Transformation: The Technology & Support You Need to Succeed,’ presented by PTC’s Executive Vice President (EVP) of Products, Kevin Wrenn, and PTC’s EVP and Chief Customer Officer, Eduarda Camacho.

Their keynote focused on how companies should be prioritizing the use of best-in-class technology that will meet their changing needs during times of disruption and accelerated digital transformation. Wrenn and Camacho highlighted five of our customers through interactive case studies on how they are using PTC technology to capitalize on digital transformation to thrive in an era of disruption.

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Below is a summary of the five customers and their stories that were highlighted during the keynote.

1. Royal Enfield (Mass Customization)

Royal Enfield is an Indian motorcycle company that has been manufacturing motor bikes since 1901. They have British roots, and their main customer base is located in India and Europe. Riders of Royal Enfield wants their bikes to be particular to their brand, so they worked to better manage the complexities of mass customization and respond to market demands.

Royal Enfield is a long time PTC customer, but they were on old versions of PTC technology. They first upgraded Creo and Windchill to the latest releases so they could leverage the new capabilities. They then moved on to transform their processes for platform and variant designs, introduced simulation much earlier by using Creo Simulation Live, and leveraged generative design by bringing AI into engineering and applying it to engine and chassis complex custom forged components. Finally, they retrained and retooled their engineering staff to fully leverage the power of new processes and technologies.

The entire Royal Enfield team now has digital capabilities that accelerate new product designs, variants, and accessories for personalization; as a result, they are able to deliver a much-shortened design cycle. Royal Enfield is continuing their digital transformation trend, and will invest in new ways to create value while leveraging augmented reality with PTC's Vuforia suite.

2. VCST (Manufacturing Efficiency, Quality, and Innovation)

VCST is part of the BMT Group and are a world-class automotive supplier of precision-machined power train and brake components. Their problem was that they had high costs for their production facility in Belgium. They either needed to improve their cost efficiency in their plant or face the potential of needing to shut down the facility and relocate it to another region. VCST decided to implement ThingWorx so that anyone can have instant visibility to asset status and performance. VCST is also creating the ability to digitize maintenance requests and the ability to acquire about spare parts to improve the overall efficiency in support of their costs reduction goals.

Additionally, VCST has a goal to reach zero complaints for their customers and, if any quality problems appear to their customers, they can be required to do a 100% inspection until the problem is solved. Moreover, as cars have gotten quieter with electrification, the noise from the gears has become an issue, and puts pressure on VCST to innovate and reduce gear noise.

VCST has again relied on ThingWorx and Windchill to collect and share data for joint collaborative analysis to innovate and reduce gear noise. VCST also plans to use Vuforia Expert Capture and Vuforia Chalk to train maintenance workers to further improve their efficiency and cost effectiveness. The company is not done with their digital transformation, and they have plans to implement Creo and Windchill to enable end-to-end digital thread connectivity to the factory.

3. BID Group Holdings (Connected Product)

BID Group Holdings operates in the wood processing industry. It is one of the largest integrated suppliers and North American leader in the field. The purpose of BID Group is to deliver a complete range of innovative equipment, digital technologies, turnkey installations, and aftermarket services to their customers. BID Group decided to focus on their areas of expertise, an rely on PTC, Microsoft, and Rockwell Automation’s combined capabilities and scale to deliver SaaS type solutions to their own industry.

Leveraging this combined power, the BID Group developed a digital strategy for service to improve mill efficiency and profitability. The solution is named OPER8 and was built on the ThingWorx platform. This allowed BID Group to provide their customers an out of the box solution with efficient time-to-value and low costs of ownership. BID Group is continuing to work with PTC and Rockwell Automation, to develop additional solutions that will reduce downtime of OPER8 with a predictive analytics module by using ThingWorx Analytics and LogixAI.

4. Hitachi (Service Optimization)

Hitachi operates an extensive service decision that ensures its customers’ data systems remain up and running. Their challenge was not to only meet their customers uptime Service Level Agreements, but to do it without killing their cost structure. Hitachi decided to implement PTC’s Servigistics Service Parts Management software to ensure the right parts are available when and where they are needed for service. With Servigistics, Hitachi was able to accomplish their needs while staying cost effective and delighting their customers.

Hitachi runs on the cloud, which allows them to upgrade to current releases more often, take advantage of new functionality, and avoid unexpected costs.

PTC has driven engagement and support for Hitachi through the PTC Community, and encourages all customers to utilize this platform. The network of collaborative spaces in a gathering place for PTC customers and partners to showcase their work, inspire each other, and share ideas or best practices in order to expand the value of their PTC solutions and services.

5. COVID-19 Response 

COVID-19 has put significant strain on the world’s hospitals and healthcare infrastructure, and hospitalization rates for COVID brought into question the capacity of being able to handle cases. Many countries began thinking of the value field hospitals could bring to safely care for patients and ease the admissions numbers of ‘regular’ hospitals. However, the complication is that field hospitals have essentially no isolation or air filtration capability that is required for treating COVID patients or healthcare workers.

As a result, the US Army Corp of Engineers has put out specifications to create self-contained isolation units, which are fully functioning hospital rooms that can be transported or built onsite. But, the assembly needed to happen fast, and a group of companies (including PTC) led by The Innovation Machine rallied to help design and define the SCIU’s.

With buy-in from numerous companies, a common platform was needed for companies to collaborate. PTC felt compelled to react, and many PTC customers and partners joined in to help create a collaboration platform, with cloud-based Windchill as the foundation. But, PTC didn’t just provide software to this collaboration; PTC also contributed with digital thread and design advice to help the group solve some of the major challenges. This design is a result of the many companies coming together to create deployments across various US state governments, agencies, and FEMA.

Final Thoughts

All of the above customers approached digital transformation as a business imperative. They all had sizeable challenges that needed to be solved and took leadership positions to implement plans that leveraged digital transformation technologies combined with new processes.

PTC will continue to innovate across the digital transformation portfolio and is committed to ensuring that customer success offerings capture value faster and provide the best outcomes.

Original Post Link: https://www.ptc.com/en/product-lifecycle-report/liveworx-digital-transformation–technology-and-support-you-need-to-succeed

Author Bio: Kelly is a corporate communications specialist at PTC. Her responsibilities include drafting and approving content for PTC’s external and social media presence and supporting communications for the Chief Strategy Officer. Kelly has previous experience as a communications specialist working to create and implement materials for the Executive Vice President of the Products Organization and senior management team members.

 

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The tinyML Foundation is excited to be offering a new activity to our community: tinyML Talks webcast series. A strong line-up of speakers making 30-minute presentations will take place twice a month on Tuesdays at 8 am Pacific time to make sure that tinyML enthusiasts worldwide will have an opportunity to watch them live. Presentations and videos will be available online the day afterwards for those that were not able to join live.

View Schedule of Upcoming Talks

If you want to re-watch all talks starting March 31 or were unable to join us live, the slides and links to our YouTube Channel of the talks are posted at our tinyML Forums. Many questions were asked during the presentations but not all could be answered in the allotted time frame. The answers to some of those can be found on the tinyML Forums as well.

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Helium Expands to Europe

Helium, the company behind one of the world’s first peer-to-peer wireless networks, is announcing the introduction of Helium Tabs, its first branded IoT tracking device that runs on The People’s Network. In addition, after launching its network in 1,000 cities in North America within one year, the company is expanding to Europe to address growing market demand with Helium Hotspots shipping to the region starting July 2020. 

Since its launch in June 2019, Helium quickly grew its footprint with Hotspots covering more than 700,000 square miles across North America. Helium is now expanding to Europe to allow for seamless use of connected devices across borders. Powered by entrepreneurs looking to own a piece of the people-powered network, Helium’s open-source blockchain technology incentivizes individuals to deploy Hotspots and earn Helium (HNT), a new cryptocurrency, for simultaneously building the network and enabling IoT devices to send data to the Internet. When connected with other nearby Hotspots, this acts as the backbone of the network. 

“We’re excited to launch Helium Tabs at a time where we’ve seen incredible growth of The People’s Network across North America,” said Amir Haleem, Helium’s CEO and co-founder. “We could not have accomplished what we have done, in such a short amount of time, without the support of our partners and our incredible community. We look forward to launching The People’s Network in Europe and eventually bringing Helium Tabs and other third-party IoT devices to consumers there.”  

Introducing Helium Tabs that Run on The People’s Network
Unlike other tracking devices,Tabs uses LongFi technology, which combines the LoRaWAN wireless protocol with the Helium blockchain, and provides network coverage up to 10 miles away from a single Hotspot. This is a game-changer compared to WiFi and Bluetooth enabled tracking devices which only work up to 100 feet from a network source. What’s more, due to Helium’s unique blockchain-based rewards system, Hotspot owners will be rewarded with Helium (HNT) each time a Tab connects to its network. 

In addition to its increased growth with partners and customers, Helium has also seen accelerated expansion of its Helium Patrons program, which was introduced in late 2019. All three combined have helped to strengthen its network. 

Patrons are entrepreneurial customers who purchase 15 or more Hotspots to help blanket their cities with coverage and enable customers, who use the network. In return, they receive discounts, priority shipping, network tools, and Helium support. Currently, the program has more than 70 Patrons throughout North America and is expanding to Europe. 

Key brands that use the Helium Network include: 

  • Nestle, ReadyRefresh, a beverage delivery service company
  • Agulus, an agricultural tech company
  • Conserv, a collections-focused environmental monitoring platform

Helium Tabs will initially be available to existing Hotspot owners for $49. The Helium Hotspot is now available for purchase online in Europe for €450.

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This blog is the second part of a series covering the insights I uncovered at the 2020 Embedded Online Conference. 

Last week, I wrote about the fascinating intersection of the embedded and IoT world with data science and machine learning, and the deeper co-operation I am experiencing between software and hardware developers. This intersection is driving a new wave of intelligence on small and cost-sensitive devices.

Today, I’d like to share with you my excitement around how far we have come in the FPGA world, what used to be something only a few individuals in the world used to be able to do, is at the verge of becoming more accessible.

I’m a hardware guy and I started my career writing in VHDL at university. I then started working on designing digital circuits with Verilog and C and used Python only as a way of automating some of the most tedious daily tasks. More recently, I have started to appreciate the power of abstraction and simplicity that is achievable through the use of higher-level languages, such as Python, Go, and Java. And I dream of a reality in which I’m able to use these languages to program even the most constrained embedded platforms.

At the Embedded Online Conference, Clive Maxfield talked about FPGAs, he mentions “in a world of 22 million software developers, there are only around a million core embedded programmers and even fewer FPGA engineers.” But, things are changing. As an industry, we are moving towards a world in which taking advantage of the capabilities of a reconfigurable hardware device, such as an FPGA, is becoming easier.

  • What the FAQ is an FPGA, by Max the Magnificent, starts with what an FPGA is and the beauties of parallelism in hardware – something that took me quite some time to grasp when I first started writing in HDL (hardware description languages). This is not only the case for an FPGA, but it also holds true in any digital circuit. The cool thing about an FPGA is the fact that at any point you can just reprogram the whole board to operate in a different hardware configuration, allowing you to accelerate a completely new set of software functions. What I find extremely interesting is the new tendency to abstract away even further, by creating HLS (high-level synthesis) representations that allow a wider set of software developers to start experimenting with programmable logic.
  • The concept of extending the way FPGAs can be programmed to an even wider audience is taken to the next level by Adam Taylor. He talks about PYNQ, an open-source project that allows you to program Xilinx boards in Python. This is extremely interesting as it opens up the world of FPGAs to even more software engineers. Adam demonstrates how you can program an FPGA to accelerate machine learning operations using the PYNQ framework, from creating and training a neural network model to running it on Arm-based Xilinx FPGA with custom hardware accelerator blocks in the FPGA fabric.

FPGAs always had the stigma of being hard and difficult to work on. The idea of programming an FPGA in Python, was something that no one had even imagined a few years ago. But, today, thanks to the many efforts all around our industry, embedded technologies, including FPGAs, are being made more accessible, allowing more developers to participate, experiment, and drive innovation.

I’m excited that more computing technologies are being put in the hands of more developers, improving development standards, driving innovation, and transforming our industry for the better.

If you missed the conference and would like to catch the talks mentioned above*, visit www.embeddedonlineconference.com

Part 3 of my review can be viewed by clicking here

In case you missed the previous post in this blog series, here it is:

*This blog only features a small collection of all the amazing speakers and talks delivered at the Conference! 

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I recently joined the Embedded Online Conference thinking I was going to gain new insights on embedded and IoT techniques. But I was pleasantly surprised to see a huge variety of sessions with a focus on modern software development practices. It is becoming more and more important to gain familiarity with a more modern software approach, even when you’re programming a constrained microcontroller or an FPGA.

Historically, there has been a large separation between application developers and those writing code for constrained embedded devices. But, things are now changing. The embedded world intersecting with the world of IoT, data science, and ML, and the deeper co-operation between software and hardware communities is driving innovation. The Embedded Online Conference, artfully organised by Jacob Beningo, represented exactly this cross-section, projecting light on some of the most interesting areas in the embedded world - machine learning on microcontrollers, using test-driven development to reduce bugs and programming an FPGA in Python are all things that a few years ago, had little to do with the IoT and embedded industry.

This blog is the first part of a series discussing these new and exciting changes in the embedded industry. In this article, we will focus on machine learning techniques for low-power and cost-sensitive IoT and embedded Arm-based devices.

Think like a machine learning developer

Considered for many year's an academic dead end of limited practical use, machine learning has gained a lot of renewed traction in recent years and it has now become one of the most interesting trends in the IoT space. TinyML is the buzzword of the moment. And this was a hot topic at the Embedded Online Conference. However, for embedded developers, this buzzword can sometimes add an element of uncertainty.

The thought of developing IoT applications with the addition of machine learning can seem quite daunting. During Pete Warden’s session about the past, present and future of embedded ML, he described the embedded and machine learning worlds to be very fragmented; there are so many hardware variants, RTOS’s, toolchains and sensors meaning the ability to compile and run a simple ‘hello world’ program can take developers a long time. In the new world of machine learning, there’s a constant churn of new models, which often use different types of mathematical operations. Plus, exporting ML models to a development board or other targets is often more difficult than it should be.

Despite some of these challenges, change is coming. Machine learning on constrained IoT and embedded devices is being made easier by new development platforms, models that work out-of-the-box with these platforms, plus the expertise and increased resources from organisations like Arm and communities like tinyML. Here are a few must-watch talks to help in your embedded ML development: 

  • New to the tinyML space is Edge Impulse, a start-up that provides a solution for collecting device data, building a model based around it and deploying it to make sense of the data directly on the device. CTO at Edge Impulse, Jan Jongboom talks about how to use a traditional signal processing pipeline to detect anomalies with a machine learning model to detect different gestures. All of this has now been made even easier by the announced collaboration with Arduino, which simplifies even further the journey to train a neural network and deploy it on your device.
  • Arm recently announced new machine learning IP that not only has the capabilities to deliver a huge uplift in performance for low-power ML applications, but will also help solve many issues developers are facing today in terms of fragmented toolchains. The new Cortex-M55 processor and Ethos-U55 microNPU will be supported by a unified development flow for DSP and ML workloads, integrating optimizations for machine learning frameworks. Watch this talk to learn how to get started writing optimized code for these new processors.
  • An early adopter implementing object detection with ML on a Cortex-M is the OpenMV camera - a low-cost module for machine vision algorithms. During the conference, embedded software engineer, Lorenzo Rizzello walks you through how to get started with ML models and deploying them to the OpenMV camera to detect objects and the environment around the device.

Putting these machine learning technologies in the hands of embedded developers opens up new opportunities. I’m excited to see and hear what will come of all this amazing work and how it will improve development standards and transform embedded devices of the future.

If you missed the conference and would like to catch the talks mentioned above*, visit www.embeddedonlineconference.com

*This blog only features a small collection of all the amazing speakers and talks delivered at the Conference!

Part 2 of my review can be viewed by clicking here

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Artificial Intelligence is a popular term currently evolving around software industries. Many app development companies were developing their requirement process to recommend AI-bases workers and also many institutes were trained to learn the concept of AI. It clearly says that in the future, most of the tasks will drive through AI. Hence it is good to know how AI will help the professions to run their routine work effectively. It’s not only about learning the skill but also depends upon the interest you have to learn. AI does not only deals with a particular requirement.

It also deals with sectors like data analytics, machine learning, data mining, etc. By combining these factors will help to maintain the AI to train the job for the long term. Hence make sure to know the areas that follow to build the profession in profit. This blog will help you to know the information for the profession that helps to operate effectively.

Software Development

Software development is one of the topmost demanding jobs in every country. By getting into the job as a software developer will help to promote the career faster than the other industries. In the future, AI will help the programmers to think less as machine learning will take place to eliminate the code and introduce the algorithm to build the application. By allowing the Ai to adopt the section to work will help to play the complete function in an easy mode. IT reduces the effort of using the coding and also helps to build the apps easier and effectively. For example, software testing is one of the important roles in development, by placing AI to automate the testing will help to replace the employee and reduce the effort of them.

Machine Industry

The machine industry is a vital part to run society. Workers were working for the long term and creating a great effort to complete the work. AI will help to respond to the function to operate smarter and effectively. By utilizing the data analytics, the result to maintain the chain process will become easier and more effective. Hence complete machine industry will get a hike to improve their quality. By focusing on furthermore internets of things get communicated with the sources from industry and get huge control. Hence by using these technologies will bury the effort of workers and also increase the quality of time from the manufacturing side. It improves the quality and helps to maintain the product to get qualified.

Education System

Education is the right of every citizen of the country but most of the time students get frustrated due to the load that is given by the system. Hence it collapses the mind easily. Thus to prevent it Ai can implement it. It helps the system to decrease the load of education and improves understanding. It allows the student to get interact easier and helps to improve the concept to understand.

For example, if a student wants to learn the practical session, its easy to make it live virtually. It helps them to improve their creativity and also increase their interest to observe. Hence by implementing AI will tend to improve the whole concept of education.

Healthcare

The healthcare industry is always an important part to get noticed. Every person was looking for improving their health but most of the time they were lazy to build the habit to take care. Thus by using Ai, the cost of spending bucks for health will reduce and the improvement of the human cycle will get increases. Already Ai apps were built to support the human by analyzing their symptom. Hence in the future, apps get increased and also the technology will get improved. By acknowledging the human about their problem with the symptom will help to improve their health without any support and also help to save their money. Even data analytics will help the patient’s improvement of humans in terms of analyzing the error. Thus by using data science as it is a part of Ai will help to maintain the record of patient safer and also reliable to the human.

Supply Chain

The supply chain is one of the toughest parts of the profession as it requires used by many companies to service for them. By using the data analyst, the usage level of getting information will be much good rather than depending on humans. It helps the business person to prevent the time shortage and also increases the quality of response. By ensuring the time is important to supply chain business as it matters a lot to the supply chain profession. Hence applying Ai to the supply chain will be prettier and help the process to work properly and reliability for the system.

Wearable Gadgets

Technology is much reliable to society to help the human and increase the concentration on their work. Wearable devices are one of the popular devices that have been getting merge with human routines. Especially devices are used for health reasons. Hence by using the gadgets will allow the user to acknowledge the ratings of health. By using the gadgets, the usage will be finer to track. Also, the apps related to wearable devices were much high and also demand is also getting a hike. Many top app companies were working for wearable apps. Hence the usage of these kind apps will bring great attention to the software industry. It is related to IOT. Thus automatically Ai will get into the game to manage the data.

Business Models

Maintaining the business as per the requirement of the client is the major responsibility of every person. The important part is the data that has to be analyzed well. It should not get criticized. Hence analyzing the data with the help of AI models will improve the business requirement and also the client requirement. Thus in the future, many companies will seek data analytics to improve their business.

Final Words

Artificial intelligence is one of the topmost sectors in today’s world. And driving the field via these techniques will help to improve the complete session.

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                                                                                             Source - Salesforce

 

“By 2020, 85% of customer interactions will be managed without a human” – Gartner
Prior to jump into depth to sharing about ‘customer experience optimization through AI technology’ let’s first understand ‘what is customer experience?’. The perception or the value created about the brand to the customer, is called customer experience. The process to increase the awareness with positive intent towards the brand, is called customer experience optimization.

 

To optimize the customer experience, Business needs to first of all understand all moves to be taken by customer while landing initially onto portal to either purchasing the product or services without facing any hassle. Nowadays, AI technology has been implemented across all industries due to have its immense capability to drive strategic data as well as to respond customers quickly that cause business growth.

 

Problems that can be solved using AI:

 

Nowadays, Customers have multiple options when it comes to choose any product or service so Business should be proactive indeed on planning for futuristic action to engage the customer in order to compete with their potential competitors in the market. AI captures the existing data of current business and suggest the next step to be taken by implementing machine learning algorithm.

 

How AI plays a key role in making customer experience optimized

 

AI uses the machine learning algorithm to deliver human sense to customer. AI boost up the business process in such a way that the same service can be delivered in a quick and effective way. Being a customer, we face challenge in accessing support service such as long waiting time to connect with support agent, immediate actions to be taken on account, accessing personal details securely in no time etc. While accessing service or product solution, customer stuck at some point and they look for urgent help by contacting Phone/chat support team through number of channels such as Phone, email, web chat that usually consumes more than expected which may cause customer interest toward the brand.

 

Rather than getting help from support team, Customer always shows their positive intent to resolve the issue by self that enables customer to find the answer quickly, perform the required actions within no time, no additional charge with high secure, share less their sentiment to other human in very secure way. AI helps the customer to access key information based on their keywords they input to the application. Rich collection of content is prepared that allows computer system to sense the customer need and when required, customer access the custom fit content in no time.

 

Impact/Benefit of Artificial Intelligence on Business:

 

AI helps the business to automate and improve complex analytical tasks, to look at strategic data in real-time, adjusting its behavior with minimal need for supervision as well as to Increase efficiency and accuracy.

Below are the following benefits that are accessed by business on AI implementation:

• Increase operational performance that results less effort & time
• Helps stakeholder to take business decision quickly
• Eliminate human error with AI based algorithm
• Prediction analysis through analytics
• Data extraction through solid algorithm
• Increase Profitability
• Intelligent advice suggestion

 

One of the interesting part about AI, it follows pre-trained machine learning models and out-of-the box services that integrates with key customer experience process and solutions. AI can be used in almost unimaginable number of ways including sentiment analysis to understand the next move of a prospect/customer and recommend products based on customer interest.

 

The benefit is not only limited to customer; it is very useful for support process where agent access the knowledge base which contains the information that need to be shared with customer. Each of the little information can’t be memorized by human henceforth AI comes into the picture that helps the support team to guide the customer in right way quickly. AI provides the prediction analysis, and data plays an important role to succeed. AI utilize the captured data into computer system and apply the algorithm to make custom fit solution for end users. With consideration of customer intent, AI is solving many business problems on continuous rate that makes the AI as disruptive solution.

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In 2016 in my article “ The future of “The Internet of Olympic Games”, I considered Rio as the first Internet of Things (IoT) Olympic games. In Rio we could see how athletes, coaches, judges, fans, stadiums and cities benefited from IoT technology and IoT solutions and somehow changed the way we see and experience sports. Next year we will have opportunity to verify if my predictions for Tokyo 2020 will become a reality and we will name Tokyo as the first Artificial Intelligent (AI) Olympic Games.

During my presentation in Dubai, I explained the audience the incredible way IoT and AI technologies are impacting sports. I dedicated some time explaining how IoT and AI are playing an increasingly significant role in boosting talent, managing health and improving coaching and training. Today these technologies are already enabling athletes to improve performance, coaches to better prepare games, judges to fail less, fans enjoyed with new excited experiences. I also remarked the importance that teams clubs and cities collaborate to make the stadiums more secure and more exciting for fans.

I emphasized how we are creating smart things, the importance of use AI and IoT to make every thousandth of a second count for athletes and coaches and how AI and IoT are used to predict the future of a race, a match or a bet.

I introduced different examples how all sports are using IoT and AI, and of course I share my vision in 10-15 years from now. Can you imagine integrated virtual and real world for sports? Can you imagine mixed teams of robots and humans or super-humans playing new games?

I did not forget to talk about the challenges involved in building machine learning models in sports and the challenges that IoT and AI still have.

I used my speech to raise awareness to the attendants that there is also a dark side in these technologies. We cannot forget that Sport is also a business and therefore enterprises, Governments and individuals can make a wrong use of these technologies.

In summary, it was a great session in which I shared my point of view about:

  • How we want IoT and AI transform coaches, athletes, judges and fans.
  • How we want IoT and AI continue attracting people to the stadiums
  • How we want IoT and AI transform Sport Business.
  • How AI is changing the future of sport betting?

How we want IoT and AI transform athletes, coaches, judges and fans?

Athletes

While the true essence of sport still lies in the talent and perseverance of athletes, it is often no longer enough. Therefore athletes will continue demanding increasingly sophisticated technologies and more advanced training techniques to improve performance. For instance, biomechanical machine learning models of players will predict and prevent potential career-threatening physical and mental injuries or can even detect early signs of fatigue or stress-induced injuries. It can also be used to estimate players’ market values to make the right offers while acquiring new talent.

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Coaches are using AI to identify patterns in opponents’ tactics, strengths and weaknesses while preparing for games. This helps coaches to devise detailed gamelans based on their assessment of the opposition and maximize the likelihood of victory. In many leading teams, AI systems are used to constantly analyze the stream of data collected by wearables to identify the signs that are indicative of players developing musculoskeletal or cardiovascular problems. This will enable sports teams to maintain their most valuable assets in prime condition through long competitive seasons.

Judges

We tend to think that technology helps make the sport more just when we are victims or witnessed of unjust decision. That´s why we approve inventions like Paul Hawkins - creator of Hawk-Eye, a technology that is now an integral part of the spectator experience when watching sport live or more recently VAR in soccer.

The use of technology allows watch in real time multiple cameras, with aggregated info from sensors (stadiums, things and athletes) to make their decisions more accurate and objective.

We as spectators or fans need more transparency about the exercise’s difficulty, degree of compliance and final score. And we have the technology to do it.

The IoT and AI technology doesn't claim to be infallible - just very, very reliable and Judges also need to be adapted to new technologies.

Fans

Without fans, sports would find it difficult to exist. It is understandable companies are also targeting fans with IoT and artificial intelligence to keep them engaged whether in the stadium or at home.

How we want IoT and AI continue attracting people to the stadiums?

Within the stadiums, sports clubs and many leagues across the globe are incorporating inside and outside the stadium technologies to boost fans unique experiences for fans and not only the 90 minutes.

The challenge is how to combine what the oldest and newest supporters are looking to attend to the stadiums?

How will the stadiums of the future be? I read numerous initiatives of big clubs and leagues, but I am exciting about the future stadium of Real Madrid. I wish the club would allow me to advise them how to create a smart intelligent Global environment to provide each fan with an individual experience, know who is in the crowd, learn fan behaviors to anticipate their needs

How we want IoT and AI transform Sport Business.

“As long as sports remain a fascination for the masses, businesses will always have the opportunity to profit from it. As long as there is profiting to be gained from the world of sports, the investment in and incorporation of technology for sports will continue.”

I read an article warning about the new entirely new world order that is being formed right now. The author explained how 9 companies are responsible for the future of AI. Three of the companies are Chinese (Baidu, Alibaba and Tencent, often collectively referred to as BAT), while the other six are American (Google, Amazon, IBM, Facebook, Apple and Microsoft, often referred as the G.Mafia). The reason is obvious, as far as AI is about optimization using the data that’s available, these 9 companies will manage more of the sport data generated in the world.

Collaboration is needed now to stop this danger and to address the democratization of AI in sports. It is urgent companies and governments around the globe to work together to create guiding principles for the development and use of AI and not only in Sports. This mean we need regulating it but in a different way. We do not want AI becomes in the hands of a group of lawmakers, who are very well read and very smart people but overwhelmingly lack degrees in AI and IoT.

Will AI change the future of sport betting?

The impact of technology on sports cannot be specifically measured, but some technological innovations do raise questions about fairness. Are we still comparing apples with apples? Is it right to compare the speed of an athlete wearing high-tech running shoes to one without?

Whether we like it or not, technology will continue to enhance athlete performance. And at some point we will have to put specific rules and regulations in place about which tech enhancements are allowed.

There is a downside to advanced technology being introduced to sports. Machine Learning models are now used routinely to predict the results of games. Sport betting is a competitive sport itself among fans, but AI can substantially tilt that playing field.

I analyzed many IoT and AI companies for Sports in order to prepare my session. I am scare about the game result predictions capabilities but more scare about the manipulation of competition using AI algorithms with the Terabytes of data collected daily from IoT devices and other sources like social media networks, without the permission of the users.

The sport business market is generating billions of US$ every year but without control and education we could find future generation of ludopaths and a small number of Sports Service Providers controlling the Sports.

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We are in the dawn of a new cyber society. A society where organizations shall design plans to utilize the unique skillsets of both AI Systems and humans. A society where Humans and AI systems shall work and live together and without fear. A society where humans shall use newfound time and freedom to advance strategic skills and individual talents.
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As mobile devices become smaller and smarter, artificial intelligence (AI) is steadily gaining significant popularity among users and developers alike. Every now and then mobile developers around the world are working assiduously to develop and employ the emerging technology in mobile app development which is aimed at improving the way users interact with apps. Already, there are several signs, indications, and signals revealing that the AI will dominate the future of mobile apps.

In the tech world, AI is believed to hold immense potential and Indian app developers are gradually embracing and integrating this relatively new technology into their mobile app development seeing that it presents the best bet for the future. Already, the current mobile app market is consequently being flooded with new mobile applications and models leading to the creation of new and improved mobile app development services.

Even if you don’t notice it, AI is already around you and it has come to stay. In the past, this technology was only regarded as a futuristic concept for movies but today it has become a reality. And there is no better time to get involved with the trend than now. Interestingly, many Indian app developers are beginning to discover that mobile development and AI share common features and can make a perfect match. Obviously, there are lots of possibilities that can be accrued from the advancement of AI.

Combining artificial intelligence (AI) with mobile development will result in the creation of intelligent apps. Basically, this is concerned with the design and development of mobile apps that have the ability to learn, think logically, and solve problems. In a bid to effectively engage users, transform customer experience, and ultimately retain them, many app developers and top app development companies in India alike are already working to integrate the technology into their mobile applications.

The impact of AI on mobile development

Many tech and industry experts are suggesting that AI will be a major trend in various sectors, particularly in the mobile application development. To this end, everyone in the industry including, startups, growing businesses, and top app development companies are investing in artificial intelligence (AI) with the aim of providing efficient customer services and bring about a positive change. While some are incorporating the technology in the form of chatbots, others are looking to embed it into the infrastructure of their mobile app development as assistants to create smart apps.

Already, some tech giants like Uber, Amazon, eBay and the rest are making use of AI and judging from the look of things, it is a meaningful realization. With this new technology, Indian app developers are helping businesses support their customers with relevant, seamless, and personalized services. With time AI in mobile apps will understand customer behavior, thanks to its ability to effectively gather massive amounts of data from previous customer interactions and learn them. Apart from helping to bring customers closer to the business, AI-enabled apps are also helping to enhance customer interaction thereby boosting customer retention rate.

Basically, Indian app developers are finding ways to make use of the data that businesses are getting via mobile devices, online traffic, and point-of-sale machines to impact both business and consumer experience with AI’s influence. As more artificial and machine learning-driven apps make their way into app stores, things will change in the way and manner people communicate and interact. In a bid to create more insightful, context-rich experiences, the algorithms will be able to sift through the obtained data, find correlations and trends and get the apps adjusted to suit the personal needs of the user.

Obviously, there is much to achieve with these artificial intelligence algorithms in mobile app development. There is a wide range of AI-based mobile app development projects undertaken by Indian app developers. With the development of personal assistants, chatbots, and other artificial intelligence features, many big companies are already reinventing their user experience (UX) strategies. And in order to remain ahead of the competition, other businesses are following suit.

The future of AI-driven apps

Now that the entire ecosystem has been enhanced with regular and active access of data management and delivery, many Indian app developers will be employing AI which will become an essential necessity for robust mobile app development in the near future. Basically, there is every need for systems featuring data governance, data security, and metadata management to be fast and robust in indexing and cataloging.

Here are other ways through which AI development will impact the industry

Cloud services

It’s no longer news that businesses are adopting cloud computing technology to improve their services. It may interest you to know that Indian app developers will not only be adopting this technology to enhance development but will also be using it to troubleshoot errors in AI-driven apps.

Business apps

As already mentioned, many businesses are already seeking to enhance customer interaction by investing mobile app development. However, integrating AI will help to boost convenience for customers and also help businesses reach a wider target audience. Businesses will not only be using AI-driven apps to observe internal communications, but these will help to simplify business activities in several ways.

Location-based applications

Today, people are using location-based apps to search and find virtually anything they need in any location. AI-enabled apps will be synchronizing users’ interest, as well as their frequent searches to create results. Basically, these apps will be using obtained data to provide more desirable suggestions. Already, Google users can easily search for promotion offers, nearby restaurants and department stores with their smartphone via Google Assistant or Siri.

Internet of Things (IoT)

In recent times, there has been an increase in a range of new technologies due to the desire to further increase the mobility of users. IoT is one of such recent developments making waves in the industry. No doubt, AI will be enhancing the development of IoT helping smartphone users manage real-life events in the near future.

AR and VR

Together with AI, Augmented Reality (AR) and Virtual Reality (VR) is taking both the gaming and entertainment industry by storm. The release of Oculus Rift, Google Cardboard, Samsung Gear VR and other numerous models of VR devices are already influencing the industry.

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The intelligence in AI is computational intelligence, and a better word could be Automated Intelligence. But when it comes to good judgment, AI is not smarter than the human brain that designed it. Many automated systems perform poorly, to the point that you are wondering if AI is an abbreviation for Artificial Innumeracy.

Critical systems - automated piloting, running a power plant - usually do well with AI and automation, as considerable testing is done before deploying these systems. But for many mundane tasks, such as spam detection, chatbots, spell checking, detecting duplicate or fake accounts on social networks, detecting fake reviews or hate speech in social networks, search engine technology (Google) or AI-based advertising, a lot of progress must be made. It works just like in the video below, featuring a drunken robot.

 

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Why can driverless cars recognize a street sign, but Facebook algorithms can not recognize if a picture contains text or not ? Why can't the Alexa robot understand the command "Close the lights" but understands "Turn off the lights"? Sometimes the limitation of AI just reflects the lack of knowledge of the people who implement these solutions: they might not know much about the business operations and products, and are sometimes glorified coders. In some cases, the systems are so poorly designed that they can be used in unintended, harmful ways. For instance, some Google algorithms automatically detect bad websites using tricks to be listed at the top on search results pages. These algorithms will block you if you use such tricks, but indeed you can use these tricks against your competitors to get them blocked, defeating the purpose of the algorithm.

Why is AI still failing on mundane tasks?

I don't have an answer. But I think that tasks that are not critical for the survival of a business (such as spam detection) receive little attention from executives, and even employees working on these tasks might be tempted to not do anything revolutionary, and show a low profile. Imagination is not encouraged, beyond some limited level. Is is as "if it ain't broken, don't fix it."

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For instance, if advertising dollars are misused by some poorly designed AI system (assuming the advertising budget is fixed) the negative impact on the business is limited. If, to the contrary it is done well, the upside could be great. The fact is, for non-critical tasks, businesses are not willing to significantly change the routine, especially for projects where ROI is deemed impossible to measure accurately.. For tiny companies where the CEO is also a data scientist, things are very different, and the incentive to have performing AI (to beat competition or reduce workload) is high. 

Originally posted here. Follow the author on Twitter, at @GranvilleDSC

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Guest blog post by Ajit Jaokar

Introduction 

According to Deloitte: by the “end of 2016 more than 80 of the world’s 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products”. Gartner also predicts that 40 percent of the new investment made by enterprises will be in predictive analytics by 2020. AI is moving fast into the Enterprise and AI developments can create value for the Enterprise. This value can be captured/visualized by considering an ‘Enterprise AI layer’. This AI layer is focussed on solving relatively mundane problems which are domain specific.  While this is not as ‘sexy’ as the original vision of AI, it provides tangible benefits to companies.

 

In this brief article, we proposed a logical concept called the AI layer for the Enterprise.  We could see such a layer as an extension to the Data Warehouse or the ERP system. This has tangible and practical benefits for the Enterprise with a clear business model. The AI layer could also incorporate the IoT datasets and unite the disparate ecosystem. The Enterprise AI layer theme is a key part of the Data Science for Internet of Things course. Only a last few places remain for this course!.

 

Enterprise AI – an Intelligent Data Warehouse/ERP system?

AI enables computers to do some things better than humans especially when it comes to finding insights from large amounts of Unstructured or semi-structured data. Technologies like Machine learning , Natural language processing (NLP) , Speech recognition, and computer vision drive the AI layer. More specifically, AI applies to an algorithm which is learning on its own.

 

To understand this, we have to ask ourselves: How do we train a Big Data algorithm?  

There are two ways:

  • Start with the Rules and apply them to Data (Top down) OR
  • Start with the data and find the rules from the Data (Bottom up)

 

The Top-down approach involved writing enough rules for all possible circumstances.  But this approach is obviously limited by the number of rules and by its finite rules base. The Bottom-up approach applies for two cases. Firstly, when rules can be derived from instances of positive and negative examples(SPAM /NO SPAN). This is traditional machine learning when the Algorithm can  be trained.  But, the more extreme case is : Where there are no examples to train the algorithm.

 

What do we mean by ‘no examples’?

 

a)      There is no schema

b)      Linearity(sequence) and hierarchy is not known

c)      The  output is not known(non-deterministic)

d)     Problem domain is not finite

 

Hence, this is not an easy problem to solve. However, there is a payoff in the enterprise if AI algorithms can be created to learn and self-train manual, repetitive tasks – especially when the tasks involve both structured and unstructured data.

 

How can we visualize the AI layer?

One simple way is to think of it as an ‘Intelligent Data warehouse’ i.e. an extension to either the Data warehouse or the ERP system

 

For instance,  an organization would transcribe call centre agents’ interactions with customers create a more intelligent workflow, bot etc using Deep learning algorithms.

Enterprise AI layer – What it mean to the Enterprise

So, if we imagine such a conceptual AI layer for the enterprise, what does it mean in terms of new services that can be offered?  Here are some examples

  • Bots : Bots are a great example of the use of AI to automate repetitive tasks like scheduling meetings. Bots are often the starting point of engagement for AI especially in Retail and Financial services
  • Inferring from textual/voice narrative:  Security applications to detect suspicious behaviour, Algorithms that  can draw connections between how patients describe their symptoms etc
  • Detecting patterns from vast amounts of data: Using log files to predict future failures, predicting cyberseurity attacks etc
  • Creating a knowledge base from large datasets: for example an AI program that can read all of Wikipedia or Github.
  • Creating content on scale: Using Robots to replace Writers or even to compose Pop songs
  • Predicting future workflows: Using existing patterns to predict future workflows
  • Mass personalization:  in advertising
  • Video and image analytics: Collision Avoidance for Drones, Autonomous vehicles, Agricultural Crop Health Analysis etc

 

These  applications provide competitive advantage, Differentiation, Customer loyalty and  mass personalization. They have simple business models (such as deployed as premium features /new products /cost reduction )

 

The Enterprise AI layer and IoT

 

So, the final question is: What does the Enterprise layer mean for IoT?

 

IoT has tremendous potential but faces an inherent problem. Currently, IoT is implemented in verticals/ silos and these silos do not talk to each other. To realize the full potential of IoT, an over-arching layer above individual verticals could ‘connect the dots’. Coming from the Telco industry, these ideas are not new i.e. the winners of the mobile/Telco ecosystem were iPhone and Android – which succeeded in doing exactly that.

 

Firstly, the AI layer could help in deriving actionable insights from billions of data points which come from IoT devices across verticals. This is the obvious benefit as IoT data from various verticals can act as an input to the AI layer.  Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms to learn on their own. This concept of machines learning on their own can be extended to ‘machines teaching other machines’. This idea is not so far-fetched and is already happening, A Fanuc robot teaches itself to perform a task overnight by observation and through reinforcement learning. Fanuc’s robot uses reinforcement learning to train itself. After eight hours or so it gets to 90 percent accuracy or above, which is almost the same as if an expert were to program it. The process can be accelerated if several robots work in parallel and then share what they have learned. This form of distributed learning is called cloud robotics

 

We can extend the idea of ‘machines teaching other machines’ more generically within the Enterprise. Any entity in an enterprise can train other ‘peer’ entities in the Enterprise. That could be buildings learning from other buildings – or planes or oil rigs.  We see early examples of this approach in Salesforce.com and Einstein. Longer term, Reinforcement learning is the key technology that drives IoT and AI layer for the Enterprise – but initially any technologies that implement self learning algorithms would help for this task

Conclusion

In this brief article, we proposed a logical concept called the AI layer for the Enterprise.  We could see such a layer as an extension to the Data Warehouse or the ERP system. This has tangible and practical benefits for the Enterprise with a clear business model. The AI layer could also incorporate the IoT datasets and unite the disparate ecosystem.  This will not be easy. But it is worth it because the payoffs for creating such an AI layer around the Enterprise are huge! The Enterprise AI layer theme is a key part of the Data Science for Internet of Things course. Only a last few places remain for this course!.

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Guest blog post by Vincent Granville

AI was very popular 30 years ago, then disappeared, and is now making a big come back because of  new robotic technologies: driver-less cars, automated diagnostic, IoT (including vacuum cleaning and other household robots), automated companies with zero employee, soldier robots, and much more.

Will AI replace data scientists? I think so, though data scientists will be initially replaced by "low intelligence" yet extremely stable and robust systems. There has been a lot of discussions about the automated statistician. I am myself developing data science techniques such as  Jackknife regression  that are simple, robust, suitable for black-box, machine-to-machine communications or other automated use, and easy to understand and pilot by the layman, just like a Google driver-less car can be "driven" by an 8 years old kid. 

My approach to automating data science and data cleaning / EDA (exploratory data analysis) is not really AI: it's just a starting point, but not a permanent solution. In the long term, it is possible that AI will handle complex regression models, far more complex than my Jackknife regression: after all, all the steps of linear or logistics regression modeling, currently handled by human beings spending several days or weeks on the problem, involve extremely repetitive, boring, predictable tasks, and thus it is a good candidate for an AI implementation entirely managed by robots.  

As machine learning (ML) more and more involves AI, and the blending of ML and AI is referred to as deep learning, I can see data science evolving to deep data science (DDS) or automated data science (ADS), where AI, robots, or automation at large, take a more prominent role. 

 

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True AI systems can even predict travel time in real time based on expected traffic bottlenecks and road closures

Which jobs are threatened by AI?

Just like data science will take years to get a high level automation, where as much as 50% of human tasks are replaced by robots, I believe that these professions are at risk, but the erosion will be modest and slow, taking a lot of time to materialize:

  • Teachers: some topics such as mathematics or computer science can be taught by robots, at least for the 10% of students that are self-learners. Generally speaking, topics that are currently taught by robots include flying a plane, training on an AI-powered simulator. Ironically, planes can be flew without human pilots, but studies have shown that passengers would be very scared to board a pilot-less plane. The biggest threat for teachers is not AI though, it is online training.
  • Grading student papers, detect plagiarism. But students / authors are getting more sophisticated, using article-generating software powered by AI, to avoid detection. This could lead to an interesting war: AI robots designed for fraud detection fighting against AI robots designed to cheat.
  • For publishers, automatically writing high-quality, curated articles in a short amount of time. An article such as this one is a good candidate for automated, AI-powered production. The first step is to identify articles that are good candidates (for curation)  for a specific audience; this is also accomplished using AI. 
  • Can AI writes AI algorithms, or in short, can AI automate AI? I believe so; after all, I was one of the pioneers who wrote programs that write programs (software code compilers or interpreters also fit in this category). I guess this is just an extension of this concept.
  • Automated diagnostic (or automated doctor, but also automated lawyer). I guess this will eliminate a small proportion of these practitioners. But what about a robot performing a brain surgery with higher efficiency than a human surgeon? Or a robot manufacturing an ad-hoc, customized client-specific drug for maximum efficiency? 
  • Automated chefs replacing expensive cooks in a number of restaurants. Or think about a McDonald restaurant where the only human is a security guard - everything else being outsourced to AI-powered robots, including cleaning, preparing food, delivering to customers, processing payments, filing tax returns and accounting, ordering from vendors, and so forth. This would require significant system-to-system communications, but I believe it is feasible.
  • Automated policemen or soldiers is a source of concern, as you would have algorithms that decide who to kill or who to arrest. So this might not happen for a long time, though drones are replacing soldiers in a number of wars, and have the power to kill (based on some algorithm) with no one complaining about, as long as it is not happening in US. Terrorists might be attracted too by this type of technology.
  • AI will be present in many IoT applications such as smart cities, precision farming, transportation, monitoring (detecting when an offshore oil platform is going to collapse), and so on.

AI and automation has already replaced many data science tasks long ago

Many people talk about the threat of AI, but as of today, many jobs have already been automated, some more than 30 years ago. For instance, during my PhD years, a lot of data transited through tapes between big computer systems, and involved trips to the computer center, interacting with a number of people taking care of the data flow. This has entirely disappeared.

We used to have shared secretaries to write research papers (they could write LaTeX documents), I think this has all but disappeared.

One of the applications that I developed in the eighties was a remote sensing software that could perform image segmentation and clustering, for instance to compute the proportion of various crops in a specific area based on satellite images, without human interactions - thus eliminating all the expensive jobs that were previously performed by humans to accomplish this task.

Final note

Those who automate data science are still data scientists. Just like those developing robots to automate brain surgery work in a team, with many members being brain surgeons. it's just shifting the nature of the job rather than eliminating it.

 

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