IoT Innovation, Issue 5

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IoT INNOVATION JANUARY 2017 | #5

What’s Holding Back Predictive Maintenance In Rail?

Is the rail industry ready to handle one such innovation?

Autonomous Cars Gain A Powerful Ally

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The US government has released their first policy on autonomous vehicles.

Now it’s up to car companies and local authorities to formulate the future of driverless /16

5 AREAS AI WILL MAKE A DIFFERENCE IN 2017 Businesses who use AI, big data, and IoT technologies will be well ahead of their peers this year. James Ovenden looks at 5 areas where advancements in machine learning will become game changers /22

viva las data Big Data Innovation Summit January 25 & 26, 2017 | Las Vegas Speakers Include

Jordan Charalampous +1 415 614 4191 jc@theiegroup.com

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theinnovationenterprise.com

ISSUE 5

EDITOR’S LETTER Welcome to the 5th Edition of the IoT Innovation Magazine

The Internet of Things (IoT) continues to evolve and even more connected devices are set to appear on the market this year. With increasing amounts of data being shared and generated by these devices, though, the security question is more important than ever. Advancements in technology and improved connectivity mean that cyber criminals are maturing too, and last year’s DDoS attack on streaming applications like Spotify and Netflix demonstrated how vulnerable machines can be.

In order to protect users and connected devices, IoT security requires some serious measures.

With modern cyber attacks, it’s no longer enough for a user just to change their passwords once every couple of months. In the recent DDoS attack, users couldn’t change their passwords, which created the perfect conditions for Mirai opening - a malware that turns a computer system running Linux into remotely controlled bots. The botnet is capable

of targeting a wide range of connected devices, which are then unable to protect personal data.

a billion of connected devices are expected to be deployed by 2020, there is no room for mistakes.

Advanced security requires time, funding, and thorough testing, but in order to stay relevant in a hypercompetitive environment, some manufacturers try to reduce time to market by underdeveloping or ignoring vital security features. However, it is up to them to provide the right level of cyber security for users, otherwise, it won’t take long before someone launches another attack that could severely damage corporate reputation.

As always, if you have any comments on featured content or would like to submit an article, please email me at anastasia@theiegroup.com

Anastasia Anokhina managing editor

Protection of the IoT is a costly affair, however, it’s much cheaper to test and experiment at early product stages than deal with hacked smart devices post factum. One consequence of a failure to adequately invest early in testing is that more money has to be spent on the potential recall of products and it will take time for PR and marketing teams to fix the reputational damage. In such a competitive market, where over

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Premium online and on-site courses delivered by industry experts academy.theinnovationenterprise.com

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contents 6 | USING INTERNET OF THINGS TO IMPROVE CUSTOMER RELATIONSHIP

20 | EVERYTHING YOU NEED TO KNOW ABOUT SQL SERVER PERFORMANCE TUNING

We look at how the evolution of IoT will affect consumers and their preferences

Hayden Beck shares some tips on how to ensure your database is running as efficiently as possible and providing optimal management of various workloads

8 | WHAT’S HOLDING BACK PREDICTIVE MAINTENANCE IN RAIL?

22 | 5 AREAS AI WILL MAKE A DIFFERENCE IN 2017

With the continuing advancement of sensor technology and data analytics, predictive maintenance is the future of transport, but is industry ready to handle one such innovation? Duncan Rhodes investigates

Businesses who use AI, big data, and IoT technologies will be well ahead of their peers this year. James Ovenden looks at 5 areas where advancements in machine learning will become game changers

12 | WHY MORE PLAYERS ARE GETTING ON BOARD THE IOT CONNECTIVITY PLATFORM

26 | REVOLUTIONIZING DESIGN APPROACHES TO DELIVER INTELLIGENT MEDICAL EQUIPMENT

The recent DDoS attack on popular streaming apps demonstrated the perils of sharing personal data with machines and tech companies must react fast to ensure that the issues are dealt with

The amount of generated data and connected devices continues to grow exponentially, especially in health care.

Bhoopathi Rapolu explores how medical equipment manufacturers can improve product development

16 | AUTONOMOUS CARS GAIN A POWERFUL ALLY: THE US GOVERNMENT

WRITE FOR US

The US government has released their first policy on autonomous vehicles. Now it’s up to car companies and local authorities to formulate the future of driverless

managing editor anastasia anokhina

Do you want to contribute to our next issue?

Contact: ghill@theiegroup.com for details ADVERTISING

For advertising opportunities contact: achristofi@theiegroup.com for more information

| assistant editor james ovenden

creative director chelsea carpenter contributors phil oscarson, duncan rhodes, simon trend, johnny delgagio, hayden beck, bhoopathi rapolu

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Using Internet Of Things To Improve Customer Relationship Phil Oscarson, IoT Contributor

The Internet of Things is something most people can’t define There are many buzzwords in the information technology world that may leave some people confused.

This includes the cloud, big data and the Internet of Things. The Internet of Things is something most people can’t define. However, it’s a simple concept. It’s the use of ‘things’ including devices, objects, machines, buildings, animals, and people that have the ability to exchange and collect data with the use of embedded sensors, network connectors, software, and more. It goes beyond /6

the simple notion that only a person using a computer can interact with the internet.

According to estimates, there may be as many as 200 billion ‘things’ in the IoT by the year 2020. The internet of things comes under a wide tent. There are many ways this IT philosophy can help a business market and sell its products. Below are a few ways the IoT can help a company improve its customer relationships.

Products Will Be Smarter

Marketing Will Be Smarter

Customer Service Will Be Smarter

In the end, while service certainly matters a whole lot, the product itself is the biggest determinant of customer satisfaction. However, what if a product could tell what a customer wants and adapt accordingly? That is a very real possibility with the Internet of Things.

The Internet of Things also has the power to transform marketing forever.

The collection of massive amounts of data is known as ‘big data.’ Big data combined with the Internet of Things could be a powerful force. Right now, you are probably familiar with internet advertisements that target you based on your browsing history. Think of that but only apply it to how you use products in the real world other than your computer. In the future, you may be marketed to based on how you use products connected to the internet, including everything from kitchen appliances to your car.

Lastly, customer service will be transformed by the Internet of Things.

As we currently know it, customer service is reactionary. A customer has to make the effort to contact customer service or tech support about a problem. According to research, only 4% of dissatisfied customers contact the company that disappointed them.

In the future, everything from frying pans to mattresses and yoga mats will be connected directly to the internet.

These things will be able to receive and transmit data, and they will be able to operate differently based on the information received. They will also be able to analyze user input and receive instructions on how to adapt when that input is sent through a Wi-Fi connection to the cloud.

A television, for example, will be able to tell that the user speaks Spanish as a first language and will switch on spanish captioning automatically as a result. A treadmill will be able to tell when an athlete is at the breaking point and be able to slow down to safer pace as a result. There are nearly limitless possibilities for what can be accomplished, and all of them will surely help customize and improve a customer’s experience with products that are part of the internet of things.

This may seem like an invasion of privacy. That is certainly a real concern. However, consumers so far have shown great tolerance in regards to companies collecting their data for marketing purposes from their internet use. Unless the law changes to prevent it, the Internet of Things could become the greatest collector of marketing information in the history.

However, with the Internet of Things, customer service may become proactive. Tech support or customer service reps may be able to detect a customer is having a problem and contact that customer instead of the other way around.

Overall, the Internet of Things has the potential to completely change how people use the internet and products.

While some possibilities do seem a little questionable in regards to privacy concerns, the possibilities for businesses to make a huge profit by improving the customer experience are certainly there.

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Duncan Rhodes Global Account Manager of Transportation, Cyient

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Many trains already use predictive maintenance models, and we’re witnessing an increase in customer satisfaction levels as a result

What’s Holding Back Predictive Maintenance In Rail?

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The benefits of Predictive Maintenance (PM) in rail have been clear to see for a number of years. With the continuing advancement of sensor technology and data analytics, manufacturers and operators now have tools at their disposal to deploy an effective PM strategy, and ensure disruptions can be kept to a minimum, at a time when rail passengers remain frustrated with poor service.

Using asset monitoring and analysis, operators can predict the optimum time to carry out maintenance prior to a potential failure, minimizing interruption, promoting safety, and avoiding costly delays while replacement parts are ordered and fitted.

Why then, if such a strategy can improve punctuality, profitability, and customer service, has uptake so far been slower than expected in the rail industry? Here, I investigate why this is the case and explore what needs to change if PM is to revolutionise the industry.

The state of play: rail operators’ readiness for predictive maintenance Evidently, it would be incredibly difficult to shift from traditional, scheduled maintenance to a predictive model in one fell swoop. Currently, most operators are working to a conditionbased maintenance model (CBM), relying on automated monitors to signify when maintenance is required.

This demonstrates progress in a traditionally conservative industry, where many jobs are reliant on routine manual equipment checks and the installation of monitoring technology can be a slow process.

These factors could explain why PM hasn’t yet been more widely adopted. However, they also ignore the intricacies and idiosyncrasies of rail infrastructure, which represent another hurdle to the uptake of PM across the industry. Infrastructure, resources, and climate conditions vary from country to country, resulting in trains and networks that are tailored to their specific environments. For the moment, this is hindering the widespread implementation of a full PM strategy. /9

It’s also frequently overlooked that PM isn’t yet applicable to all aspects of rail maintenance. Large amounts of data are needed to build an effective solution, but currently the costs of collecting and formulating this information simply aren’t justifiable for all rail operators. Until the technology improves further, therefore, PM solutions will only be implemented on a case-by-case basis where they can be proven to reduce overall maintenance costs in the long-term.

If they are ready, are they willing?

That said, there are many trains which are suited to and are already more than viable for PM. So why then isn’t it more widely used? To a large extent, this can be attributed to the conservative nature of the rail industry; many train operating companies (TOCs) across Europe are still nationalized and lack the resources to invest in a comprehensive PM strategy. Therefore, many operators are disinclined to invest in retrofitting existing stock with PM solutions due to cost pressures, with most preferring to integrate PM into their new builds. From a technological perspective, practicality also plays a role. New builds are generally more suited to CBM and PM than existing legacy fleets, as the technology can be integrated into the design from the outset, rather than retrofitted 10 years down the line.

Of course, it makes sense that TOCs are choosing to find alternative routes and not make sweeping upgrades across their entire fleets all at once: to do so would be extremely costly, and would leave them without any stock to run their services. That said, failure to upgrade existing stock today means it could take them many years to implement it on a train by train basis in the future.

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Large amounts of data are needed to build an effective solution, but currently the costs of collecting and formulating this information simply aren’t justifiable for all rail operators

A matter of necessity Many TOCs are delaying investment in PM until the technology is sufficiently adaptable and affordable to be implemented on a large scale. Right now, many operators have Condition Based Maintenance systems in place to help safeguard the longevity of their networks. These systems typically involve the direct application of diagnostic monitoring in real-time, and are useful for assets and sub-systems where there is enough time to act after reading their condition to ensure they continue to be operated safely.

However, this does not address systems where there is insufficient time for technicians to act upon any issues, and leaves scope for delays, should these systems fail. These failures directly impact customer experience, and help illustrate why a more holistic solution based on predictive maintenance is the way forward to help operators boost their revenues.

Many trains already use predictive maintenance models, and we’re witnessing an increase in customer satisfaction levels as a result. In Hong Kong, for example, the MTR

Corporation is using AI to automate the planning and scheduling of engineering works, creating efficiency, time, and cost savings. These models incorporate the practice of replacing parts before they break down, which helps to reduce time spent on maintenance, and by extension, time lost to train delays through breakdowns or a lack of rolling stock.

This is critical in cultures where there are cost pressures associated with delays caused by failure – take the UK, where the breakdown of one packed commuter train, for instance, can incur a huge bill in compensation for passengers and hefty fines from the regulators.

So why should the industry care?

Ultimately, through a cohesive and universally implemented PM strategy, TOCs will be provided with live asset availability across the rail network, meaning they can pinpoint exactly where and when a train within their fleet needs maintenance. This means they can plan when to take trains out of service in advance, and allocate another train from within the fleet to

take its place, avoiding costly delays.

This then has a knock-on effect on customer service, by reducing breakdowns across the network and increasing the reliability and efficiency of both new and existing stock.

There’s only so long that TOCs can put off the advance of PM, with continued technological innovations now making it an absolute must for rail fleets. Implementing it makes sense for safety-critical systems and operations - such as brakes and signalling systems - regardless of the age of the fleet, because it allows TOCs to predict risks ahead and prevent them from happening, provided maintenance teams can act swiftly. Even on cost grounds, there’s no time for delay; once implemented, PM solutions can generate significant savings in maintenance costs, on top of the significant savings made from a reduction in delays. Given the fact that most TOCs will see return on investment in PM within two to four years, depending on the complexity of the solution, those who haven’t already done so should turn to it now, or risk being left behind in the safety and efficiency stakes.

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Why More Players Are Getting On Board The IoT Connectivity Platform Simon Trend, CIO, Wireless Logic

It may be the dying embers of 2016, but in the IoT world the issue of security is burning brightly as the industry gears up for an era of intense collaboration to address the challenges ahead.

The recent botnet attack which temporarily paralysed the running of Spotify and Netflix, among others, has been a factor in this renewed focus.

As wake up calls go, the message rang loud and clear, highlighting the ease with which the hackers can exploit the vulnerability of IoT devices.

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Interestingly, it has thrown a spotlight on where the responsibility lies when a device gets into the wrong hands and is turned into a bot programmed with malicious intent. To date, this largely unexplored area has seen only limited controls in place to protect against such activity but we’re on the cusp of change. I predict a greater onus will fall on the companies which are operating a large network of devices to take more responsibility. They could, indeed, find themselves liable for future botnet attacks in a similar way that

they are accountable for rogue online activity from their employees at the moment.

Just as importantly, the episode reminds us that despite an inherent caution when sharing our personal data with other people, less care is taken with machines which can (quite literally) provide a false sense of security. Take the anonymous data transmitted through vehicle tracking.

Information about a certain stretch of road may seem innocuous enough but the cumulative impact of this data over a period builds up patterns of behaviour that can be used to identify an individual, presenting a risk that is all too often overlooked.

This consumer apathy to machine security will soon be on the wane though as the industry is forced to rethink its approach to how anonymous data is processed and stored in response to a core challenge of our current landscape.

The heightened risks that come with an explosion in network data and

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Expectations that any kind of attack can be prevented from ever happening need to be replaced with an acceptance that ever-present threats must be managed on an ongoing basis

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ever rising entry points has meant that a trusted environment has never been more critical for users. However, heavy-handed Fort Knox style security solutions are now at odds with hyper connectivity and the wide distribution needed to serve more imaginative applications in complex and unstructured environments.

It’s a development reflected in the world of e-commerce where security solutions have transitioned from a hardened private network to a cloudbased approach, and I expect this to become common place across many other areas sectors In practical terms, this calls for a mindset that is vigilant but realistic and a solution that achieves this delicate balance. Expectations that any kind of attack can be prevented from ever happening need to be replaced with an acceptance that ever-present threats must be managed on an ongoing basis with an increased focus on impact mitigation and constant systems evolution.

This demands agile and intuitive security management aided by the new breed of smart, standalone connectivity platforms which foster interoperability and are equipped to distinguish between normal and rogue network activity. Making this distinction becomes even more critical as devices, fuelled by cheaper access points are more likely to operate in a way that can appear suspicious, but is in fact, normal business.

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Implement a strategy to capitalize on the growth of the iot

Internet of Things Summit April 19 & 20, 2017 | San Francisco

Speakers Include

Roy Asterley + 1 415 692 5426 rasterley@theiegroup.com theinnovationenterprise.com / 15

Johnny DelGagio, IoT Writer

Autonomous Cars Gain A Powerful Ally: The US Government / 16

Let me introduce you to my friends:

legislative, executive, judicial.

Safety and lack of federal regulations are some of the issues that have been hampering the growth of self-driving cars. The safety of self-driving vehicles has remained a controversial subject as some semi-autonomous vehicles have already been involved in road crashes.

Of notable concern are two cases of accidents involving Tesla’s driverless cars in Florida and China. Despite the strict regulations, companies like Tesla, Google, and Uber operate thousands of driverless cars on the roads. This has forced regulators to draft guidelines that will regulate the self-driving cars.

Federal Auto Safety Regulations on Self-Driving Cars Recently, the federal auto safety regulators opened up and attested that self-driving cars would be safer than human driven cars. The announcement helped relieve the growing suspense in the self-driving automotive industry regarding the government’s position on the new technology’s future.

Jeffrey Zients, a senior executive in National Economic Council, said that he was confident that self-driving cars would make the driving experience comfortable and productive. He added

that fully autonomous vehicles would save time spent on roads alongside reducing road accidents.

Besides the announcement, regulators also released a fifteen point guideline to govern the manufacturers and the operation of self-driving cars. The safety guidelines and regulations released targeted four areas.

Areas Targeted By the 15 Point Regulations The regulations require states to formulate uniform policies regarding the operation of autonomous vehicles.

States are also required to specify how existing transport regulations relate to driverless cars. The guideline also calls upon states to formulate new transport policies regarding the driverless cars.

Some of the state regulations that need to be formulated are how to insure the driverless car, its passengers, and/or the owner. The California insurance rates could even fall because self-driving cars are expected to cut down road accidents.

The 15 point safety guideline addresses the issue of how manufacturers should program driverless cars to act in the event of a technological difficulty. The guidance

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The federal auto safety regulators opened up and attested that self-driving cars would be safer than human driven cars clarifies the measures that auto manufacturers should put in place to enhance the privacy of passengers using driverless cars.

Furthermore, the 15 point guideline substantiates the issue of how driverless cars will be communicating with the passengers on board and pedestrians using the road.

The Federal guidelines called upon driverless vehicle manufacturers to clarify how their technology works alongside explaining how they will share data collected by the autonomous vehicles. Self-driving cars and semi-autonomous vehicles that would be found unsafe would be recalled.

However, the policies were not as clear as those governing the human driven cars. Bryan Thomas, a spokesman for the National Highway Traffic Safety Administration, said that their aim was only to address areas that needed the

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response of government regulators.

He further asserted that the areas they left out were to be dealt with by driverless car manufacturers.

Response on the Federal Regulations Most car manufacturing companies said that the federal regulations were good news to them. Among them is Ford, a leading car manufacturing company that aims to release its first driverless car in 2021. The company stated that the guidelines were a stepping stone to achieve a clear national framework regarding the operation of self-driving cars.

The company further expressed its dedication to participate in further development of the state regulations.

Big businesses that thrive on selfdriving cars, such as Uber, Lyft, and Google cars, applauded the guidelines. The auto manufacturers

urged states and local authorities to participate in coming up with more effective federal regulations.

Public safety advocates termed the move as a good step to extend the safety laws to companies that have been secretly engaged in the driverless car project. On the flipside, the advocates indicated that the regulations may appear to contradict some state laws.

Experts have also argued that the introduced guidelines will protect manufacturers in the event of an accident. As a result, the experts argue that the laws would achieve their core objective of fueling the development of self-driving cars, since manufacturers have been reluctant to release their projects for fear of government repercussions.

design, develop and discuss your big data journey Big Data Innovation Summit March 30 & 31 2017 | London

Speakers Include

Roy Asterley + 44 203 868 0033 rasterley@theiegroup.com theinnovationenterprise.com / 19

Everything You Need To Know About SQL Server Performance Tuning Hayden Beck, Community Outreach Specialist, DNSstuff

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Designed to be a full-featured database, the SQL Server is a relational database management system (RDBMS) with the primary function of storing and retrieving any data requested by other software applications. Microsoft has developed over a dozen different editions of the SQL Server in order to cater to different audiences and manage different workloads. From small, single-machine applications to large, cloud-based applications, the SQL Server supports different types of data while also allowing users to define and utilize various composite types.

By following these simple SQL Server performance tips, you can ensure your database is running as efficiently as possible, so it will continue optimal management of varying workloads.

Assessing and Analysing a Query In order to tune your SQL Server effectively and isolate the root cause of your slowed database, you’ll need to have a clear visibility into the layers of information ingrained in expensive queries. This will require you to know top SQL statements, wait types, SQL plans, blocked queries, resource contention, and the effects of missing indexes. Accessing a basic query is the best way to know exactly what you’re dealing with.

Use SSMS to hover over query elements to ensure that you’re indeed operating on a real table, not a view or table-valued function as these have different performance implications.

Check the row count in your table by querying the DMVs. Use this query to also examine the WHERE and JOIN clauses, and note the filtered row count. If the majority of the table is returned without any filters, this could be a red flag and significantly slow a query down.

Based on the table’s row count and the returned filters, you will know exactly how many rows you’ll be working with. This row count is also referred to as the actual, logical set.

Consider using an SQL diagramming tool to access queries and query selectivity.

Closely examine the SELECT* or scalar function to determine if the query has extra columns. The more columns returned, the less optimal it will become for your database to use the index operations needed to conduct an execution plan.

Discovering Flaws in a Written Query When tuning your SQL Server, knowing how to properly use constraints is a life saver. Review existing keys, constraints, and indexes to ensure there is no duplication of

effort or overlapping of indexes. Get information about these indexes by running the sp_helpindex stored procedure.

Estimated plans use estimated statistics in order to determine the estimated rows, whereas actual execution plans use actual statistics during runtime. Compare the actual and estimated plans, and be sure to note any differences. If you don’t record these results, you won’t be able to determine the true root cause of an underperforming database, nor will you be able to track the impact of your changes.

Start adjusting your query based on your findings from the previously run query. Make small, single changes at a time - starting with the most expensive operations first. By making too many changes at once, you risk nullifying your efforts. Run the query after each change and, again, be sure to record results. Continue this process until the logical I/Os provide satisfactory results.

Uncovering Hidden Opportunities for Improvement Though this manual tuning process is effective in improving the performance of your SQL Server, it can often take a significant amount of time and effort.

However, with the use of a database performance monitoring tool, you can optimize your query tuning process and even uncover hidden opportunities for improvement that you may have not otherwise seen.

Consider using a continuous database performance monitoring solution, like SolarWinds Database Performance Analyser, to consolidate performance information in a single, secure location.

With these tips and the help of reliable performance optimization tools, you can ensure your SQL Server is operating at the highest level possible.

If you’re not satisfied after several query adjustments and re-runs, consider altering the code, or adding or adjusting indexes if code alteration is not possible. Consider adjusting existing indexes, covering indexes, or filtering indexes for improvements.

After you’ve made adjustments, rerun the query and record results.

During this process, be sure to keep an eye out for frequently encountered inhibitors of performance, such as:

- Code first generators - Nested views - Abuse of wildcards - Scalar functions - Row-by-row processing - Cursors

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5 Areas AI Will Make A Difference In 2017 James Ovenden, Assistant Editor

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The success of television series Westworld, groundbreaking in its portrayal of AI, is testament to how the technology now pervades the public consciousness. It is no longer just futurists who concern themselves with how it will impact society, businesses and individuals are all using it every day and making real plans for adoption on a wide scale.

Almost every major tech giant is developing new applications for AI and machine learning. Facebook, Google and Microsoft invested more than $8.5 billion on AI research, acquisitions, and talent in 2015 alone. Over 2016, we saw a number of advances in the field, and the pace of evolution is only going to increase next year as investment goes up. According to Forrester, there will be an increase of more than 300% in investment in 2017 compared with 2016, while Gartner has put AI at the top of its new ‘Top 10 Strategic Technology Trends for 2017’ report.

Medical diagnostics and treatment However, AI implementations next year will be focused on incremental improvements. Earlier this year, Google’s Head of Machine Learning John Giannandrea told a Google I/O panel at the company’s developer’s conference that we are currently ‘kind of in an AI spring.’ It is heating up rapidly though, and summer is not far away. Businesses that use AI, big data and IoT technologies to uncover new business insights ‘will steal $1.2 trillion per annum from their less informed peers by 2020,’ according to Forrester, so organizations need to stay ahead of the game if they are to maintain competitive edge.

We’ve looked at some of the main areas that AI will have an impact next year.

Healthcare is one of the most pressing applications for AI, with hospitals collecting increasingly large amounts of data through wearables and other devices. CBI insights has identified 22 companies developing new programs for imaging and diagnostics, and the next year should see some significant advances.

In July, Google-owned DeepMind announced that it had partnered with Moorfield Eye Hospital in the UK. Machine learning algorithms will be applied to one million anonymous OCT (Optical Coherence Tomography) scans, the goal being a system that teaches itself how to recognize conditions that pose a threat to someone’s eyesight from just one digital eye scan. This could prevent a host of eye-related diseases, from age-related macular degeneration to sight loss that occurs as a result of diabetes.

Moorfields Professor Sir Peng Tee Khaw said of the partnership: ‘Our research with DeepMind has the potential to revolutionize the way professionals carry out eye tests and could lead to earlier detection and treatment of common eye

diseases such as age-related macular degeneration. With sight loss predicted to double by the year 2050 it is vital we explore the use of cutting-edge technology to prevent eye disease.’

AI can even be applied in emergency rooms. Beth Israel Deaconess Medical Center in Boston, for one, has applied machine learning algorithms to workflow processes to enable medical staff to better capture patients’ ‘chief complaints’ on arrival. Steven Horng, an emergency physician and computer programmer at Beth Israel Deaconess Medical Center in Boston, noted that: ‘Being able to capture chest pain as a discreet entity can be very valuable downstream for clinical care and in launching things like order sets and clinical pathways.’

Tech giants Microsoft and IBM are also deploying their vast resources to create AI that can analyze tumors and help design new medication regimes, and with such prestigious backing behind it, it is likely that next year we will see the first springs of a machineled healthcare system, one that’s more efficient, cost-effective, and accurate.

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The Home Mark Zuckerberg this summer announced that he has built an AI system. In his spare time. Which, aside from being another reason to be jealous of Mark Zuckerberg, is evidence that we may be closer to living like the Jetsons than we think.

There is even a robot that can fold your laundry set to go on sale next year. The ‘Laundroid’, developed over 10 years by Japanese firm Seven Dreamers with $49 million investment from Panasonic uses AI to fold a shirt in about four minutes, saving around 375 days of folding over your lifetime. While some critics have called the technology ‘ridiculous, frivolous, and a waste of engineering talent,’ it could help usher in a brave new world where clothes are just thrown into a laundry basket and then washed, dried, folded, and put back into the cupboard with no human involvement whatsoever.

Customer Support In a 2016 TechEmergence survey of AI executives and startup founders, 37% said virtual agents and chatbots were the AI applications most likely to take off in the next five years. Apple’s Siri, Microsoft’s Cortana, Google’s OK Google, and Amazon’s Echo services are already far advanced relative to where they were a couple of years ago, and developments made in speech analytics and natural-language processing means that they are getting better all the time. Even business app giant Oracle is creating chatbots for its apps. Market research firm, TMA Associates, estimate that the chatbot and digital assistant market will reach $600 billion market by 2020 as a result of such conversational user interfaces.

This has huge implications for the customer support industry. According to IBM, 65% of millennials prefer interacting with bots to talking to live agents, and as we get more accustomed to it, this number will only go up. Automated customer service would mean an end to the waiting

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in line and likely happier customers.

Equally, it should mean less call center workers spending their days getting screamed at for something that wasn’t their fault.

Google recently introduced two new AI tools that allow companies to analyze language and convert speech into text. They are already being used by UK-based grocery delivery service Ocado to rank and respond to customer queries based on how irate the complainant sounds, which seems to be the future, and next year should see more wide scale adoption.

Equally, once people cotton on to the fact that their AI customer service is bumping them up the queue based on how irate they sound, it is likely that they are going to be getting some truly horrific messages from people with minor complaints looking to get dealt with first.

Banking A 2015 report from McKinsey & Company revealed that a dozen European banks had moved from traditional statistical analysis modeling to machine learning. They cited increased new product sales of 10% and churn and capital expenditure falling by 20% as their reasons.

The financial services industry has long been at the forefront of technology, with anything that can enable greater speed in trading, financial analysis, and risk assessment likely to bring huge profits. AI and machine learning can provide this speed, and enables more in-depth risk assessment to help analysts and underwriters find information that may have been hidden, deliberately or otherwise, to ensure investments are the right ones.

The amount of information available to analysts already greatly outstrips their ability to comprehend it, and AI is really the only option.

Zest Finance, for example, helps lenders in different credit segments by assessing their clients by taking every bit of customer data they can

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Businesses that use AI, big data and IoT technologies to uncover new business insights will steal $1.2 trillion per annum from their less informed peers by 2020

legally get their hands on and applying machine learning algorithms to analzye it. Their model has been shown to beat the best-in-class industry score by 40%.

At the moment, AI in banking is still at a fairly nascent stage, a McKinsey survey found that just 3.5% have actively deployed AI. This number is going to rise rapidly over the next year, however, and banks look to find any way they can to hold on to their position.

Agriculture According to UN projections, the global population will reach 8.5 billion by 2030 and 9.7 billion by 2050. As a society, we cannot feed the 7.3 billion people we currently have, with roughly 795 million people in the world lacking sufficient food to lead a healthy active life.

There is vast scope for agricultural productivity to improve. Traditional farming practices are still shockingly outdated in many parts of the world. AI is one tool that can help best achieve this, and a number of startups are already making progress.

Switzerland-based agricultural tech firm Gamaya, for example, this year announced $3.2 million in funding for its AI project - drones equipped with

hyperspectral cameras that capture changes in water and fertilizer use, crop yields, and pests, data from which is analyzed using AI algorithms to highlight potential issues to farmers.

The technology can also be used for finding patterns that can predict outcomes of farmers’ decisions, giving them a better idea of where to invest and apply appropriate resources.

Another team of researchers at Penn State and the Swiss Federal Institute of Technology (EPFL) have fed a network of computers with over 53,000 photos of both healthy and unhealthy plants in an attempt to recognize specific plant diseases.

Such technology will provide the basis for field-based crop-disease identification using smartphones. The system has been able to identify both crops and diseases – from photos – with an accuracy rate of up to 99.35%.

Applications such as agriculture and healthcare are proof of AI’s ability to serve humanity and help us. There are justified concerns about what will happen to jobs, but we can be hopeful that AI will help 2017 be a better year than 2016.

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Revolutionizing Design Approaches To Deliver Intelligent Medical Equipment Bhoopathi Rapolu, Head of Analytics, EMEA, Cyient

In the connected world – according to Gartner, there will be over 20 billion connected devices globally by 2020 – the Internet of Things is increasingly impacting every facet of our lives, both professional and personal.

Take healthcare for instance. The proliferation of connectivity among both medical and personal health tracking devices is leading to an explosion in the amount of data generated. This, in turn, is opening up new possibilities for device manufacturers to embed Artificial Intelligence into their equipment.

Medical devices, personal health and fitness trackers collect terabytes of data every day – monitoring factors such as our heart rate, steps, calories burned and blood pressure – most of which goes unused. However, the application of advanced analytics and AI on healthcare data has far reaching implications on the industry overall.

As the number of connected devices and resultant data generated continues to grow exponentially, manufacturers are looking to new

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methods to process these huge volumes of information. Traditionally, this data has been processed in the cloud, but as the volume has increased, so too has the challenge of shifting it all to a remote server, analyzing it, and returning the actionable information back to the device. Cloud computing is suitable for infrequent data transfer – much like the way the average consumer may use DropBox or Flickr, for instance, to upload files occasionally. It is not as suitable for real-time insight and data processing. Which is why new methodologies, such as edge computing, are coming to the fore.

The rise of edge computing The relentless pace of technological development means that many devices now have the computational power to process more data themselves and adapt their performance accordingly. This is based on one of the multitude of sensors on a device generating data, which is subsequently run through a complex series of algorithms to be

processed. These algorithms can make predictions about the device and then recommendations to improve its performance - otherwise known as embedded intelligence.

Using edge computing, only the most insightful, actionable data is shipped to the cloud, freeing up a huge amount of capacity and improving efficiencies as a result. Without having to process endless reams of largely unusable data, equipment vendors can use edge computing to crunch the most meaningful information and apply the intelligence accrued to enhance device development, resulting in better quality of patient care in turn.

Shifting to the edge To take advantage of this shift, equipment manufacturers need to create the right conditions for their product development, by reengineering their design processes.

Here are my six guiding principles for designing connected medical equipment that supports embedded intelligence.

Software-defined

Monitor and report

Most of the manual functionality we had in medical devices previously – switches, buttons and dials, for instance – are now being replaced with software. Why? Because software-defined devices require less maintenance, enabling routine checks and updates to be undertaken quickly for minimal outlay. Software updates, for instance, can be completed remotely to alter the functionality of the device or add new features.

Every system and subsystem needs to have some form of monitoring while it is in the field, whether it be a heart monitor, MRI machine or fitness tracker. This enables each device or application to report back to the equipment vendor on its performance levels in the field, and in response helps them to redesign, or make any improvements or fixes where necessary. This also creates scope for interoperability between devices, which is critical to the further development of wider medical Internet of Things.

Autonomy We can make systems automonous by incorporating remote monitoring and self-learning capabilities. By introducing as much autonomy to each individual subsystem as possible, medical devices will be able to monitor themselves and self-heal if they develop any problems, removing the need for any manual intervention by the user.

Efficiency Improving the performance and efficiency of products in an aesthetic way has been one of the key drivers for product designers for some time now. We’re now able to take it a step further by using the capabilities afforded by Internet of Things and Artificial Intelligence. While the IoT allows us to generate the right data from the systems, AI can be used to make sense out of that data and generate actionable insights.

Incorporating these capabilities into the design process is the first step towards building intelligent medical equipment.

With efficiency gains derived from IoT and AI-driven design modifications, manufacturers can extend the uptime on production, creating equipment that can quickly take a new shape or direction if necessary, and connect to the wider system.

Enable remote control

as they use it more. As a result of providing improved customer service, manufacturers can begin to identify new business opportunities based on individual needs.

Ultimately, the connected medical equipment of tomorrow will have embedded intelligence at its heart, enabling clinicians and healthcare staff to benefit from improved devices in the field and better care for patients as a result. It’s critical, therefore, that equipment manufacturers look closely at their design processes to remain competitive in the market and create the digital-first devices that are needed for 21st century healthcare.

While monitoring is one-way, remote control creates a bilateral flow of information and recommended actions between the manufacturer and the device. This enables equipment manufacturers to act quickly if their product develops an issue in the field, by deploying software to clear any bugs. This will require a sea change in the way that products are designed, shifting the emphasis from user feedback to tangible device performance statistics.

Optimize for New Business Opportunities Ultimately, every manufacturer is seeking more business, and connected equipment can help unlock previously undiscovered opportunities for them. By paying more attention to product design, the data their devices generate may allow them to build new offerings such as remote service software, sell data to selected third parties, or combine data with other systems to create a more comprehensive, valuable package for other players in the industry to analyze and apply.

Design optimization to this extent enables manufacturers to alter medical equipment through remote action and to provide the user with an experience that continues to improve

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