{"id":4138,"date":"2020-12-11T02:54:42","date_gmt":"2020-12-11T02:54:42","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2020\/12\/11\/cities-are-only-as-smart-as-their-businesses\/"},"modified":"2020-12-11T02:54:42","modified_gmt":"2020-12-11T02:54:42","slug":"cities-are-only-as-smart-as-their-businesses","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2020\/12\/11\/cities-are-only-as-smart-as-their-businesses\/","title":{"rendered":"Cities are only as smart as their businesses"},"content":{"rendered":"

Cities are only as smart as their businesses<\/a><\/p>\n

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Las Vegas is using IoT technology to help city personnel make better decisions<\/strong><\/figcaption><\/figure>\n

Science fiction provides fantastic visions of a connected city, but as this futuristic reality dawns, its success will be driven by companies using the internet of things (IoT).<\/p>\n

From smart energy grids to traffic logistics, public transportation<\/a> to waste management and street lighting to connected living or working, vast networks of sensors across smart cities will harness masses of data collected in ways we\u2019ve never seen before.<\/p>\n

But unless the C-suite prepares right now to lead, and is willing to fund innovation, it may fail to satisfy the demands of employees, customers, suppliers and citizens.<\/p>\n

Jos\u00e9 Manuel Benedetti, director of strategy and digital transformation at Insight, says: \u201cA smart city means more than allocating free parking spaces or optimising street lighting with smart lamps. C-suite executives need the technology to take advantage of the huge amount of data connected cities will create.<\/p>\n

\u201cMany organisations still rely on human employees to review data from IoT applications and make decisions. The volume of data from even a small smart city would make this impossible. They need layers of automated decision-making algorithms to complement the process and give human decision-makers only the information they need.\u201d<\/p>\n

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Cities that invest, cities that learn, cities that understand the technology, will be the cities of the future<\/p>\n<\/blockquote>\n<\/figure>\n

Establishing itself at the heart of a smart city ecosystem will also be a key challenge for business. As Benedetti explains, each must consume and use data, while generating and feeding back its own so the streams react to each other.<\/p>\n

\u201cOne of our clients uses drones with smart image processing software to monitor railway tracks for faults,\u201d he says. \u201cIn a smart city, a similar application for roads would, when combined with businesses\u2019 own data from their vehicles, identify when infrastructure, such as bridges, is overloaded and needs repair. Data from these vehicles then helps the city plan any extra traffic control measures.\u201d<\/p>\n

Smart city solutions gather data in urban areas<\/h2>\n

With so much being promised, experts believe the C-suite needs a long-term, structured approach to harness these opportunities and help them cope with new policies and rules.<\/p>\n

Nick Sacke, head of IoT and products at Comms365, says digital-twin programmes can be the answer. Cities create a digital copy of the infrastructure and operations and this updates dynamically when data from sensors and other sources is received and processed.<\/p>\n

He explains: \u201cThis is a fantastic resource and facility for companies that want to play a role in the planning and delivery of infrastructure, utilities and services, as potential complexities and problems can be modelled upfront.<\/p>\n

\u201cAccess to the digital-twin data in many cities is planned to be made available to all businesses, with some data sources freely available, while others are chargeable. The return on investment for using their enhanced data should be well worth the investment.\u201d<\/p>\n

One current example of smart city ideas using IoT is in Las Vegas. With 40 million visitors a year, it worked with NTT, in partnership with VMware and Dell Technologies, to create a real-time network of information that uses artificial intelligence and machine-learning to remove a significant burden from key city personnel when it comes to critical decision-making.<\/p>\n

Michael Sherwood, director of innovation and technology for the City of Las Vegas, says: \u201cCities that invest, cities that learn, cities that understand the technology, will be the cities of the future<\/a>.\u201d<\/p>\n

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Smart city projects need IoT technology <\/h2>\n

Smart cities will also offer companies the chance to develop better logistics over time, creating agility for stock supplies and storage plus efficiencies in delivery, while informing demand-driven manufacturing in smart factories. Much will be driven by 5G, resulting in data transfer speeds and the responsiveness of multiple devices being used at once increasing greatly.<\/p>\n

Kevin Hasley, chief executive at RootMetrics, says: \u201cSmart cities can be crucial for businesses by enabling them to better understand the urban realm and powering game-changing tech applications like autonomous vehicles and drones.<\/p>\n

\u201cPitfalls though could lie in timing the investment needed, varying 5G adoption rates and speeds of implementation, plus regulatory barriers, which could cause issues and delays in future. Understanding the local performance standards of 5G is going to be crucial in helping businesses to navigate this and make the most of the smart cities opportunity.\u201d<\/p>\n

Applied futurist Tom Cheesewright thinks data literacy as another challenge. \u201cMost organisations are struggling to make good use of the data they have to drive evidence-based decisions. Companies won\u2019t realise the benefits of IoT technologies unless they address this,\u201d he says.<\/p>\n

\u201cThe technology investment is probably quite small. It\u2019s more about skills<\/a> and culture. Who has access to the data? Who is responsible for extracting answers? How do different functions collaborate around that data?<\/p>\n

\u201cA technology-first approach is the biggest trap; a whole city monitored and controlled from a tablet is attractive to some in leadership, but the most successful cities are necessarily messy and organic. You need to start by laying out a coherent framework, but then pick single problems you can solve and build those as point solutions.\u201d<\/p>\n

Alicia As\u00edn, chief executive of Libelium, says companies must not forget the end-user. \u201cFor any of these smart city solutions to work, experience tells us that citizen buy-in is critical,\u201d she says. \u201cNone of us would have thought we would need to encourage people to use contact-tracing and social-distancing apps. It has required building trust and ensuring transparency as not everyone will be skilled at using some of the new technologies. More than ever, we need to address this end-user audience and make sure they become part of the smart city solution.\u201d<\/p>\n

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Published at Fri, 11 Dec 2020 00:45:00 +0000<\/p>\n

Amazon SageMaker grows ever more powerful as machine learning models head for the edge<\/a><\/p>\n

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Machine learning has experienced an incredible increase in usage in the past couple of years. In 2017, deploying ML models was considered extremely difficult, and something only major organizations had the resources to consider.<\/p>\n

Then, Amazon Web Services Inc. released the Amazon SageMaker<\/a> machine learning service.<\/p>\n

\u201cCustomers saw it was much easier to do machine learning once they were using tools like SageMaker,\u201d said Bratin Saha<\/a> (pictured), vice president and general manager of machine learning services and Amazon AI at Amazon. \u201cMachine learning was no longer niche; machine learning was no longer a fictional thing. It was something that was giving real business value.\u201d<\/p>\n

Saha spoke with Dave Vellante<\/a>, host of theCUBE, SiliconANGLE Media\u2019s livestreaming studio, during AWS re:Invent<\/a>. They discussed trends in machine intelligence learning and artificial intelligence and how they are supported by Amazon SageMaker.  (* Disclosure below.)<\/em><\/p>\n

SageMaker makes machine learning accessible to every business<\/h3>\n

As customers saw the value in machine learning, they went from deploying tens of models to deploying hundreds of thousands of models, making SageMaker one of the fastest-growing services in AWS history, according to Saha.<\/p>\n

\u201cToday, we have one customer who is deploying more than a million models,\u201d he said.<\/p>\n

But as usage grew, so did the problems customers were reporting. So, AWS grew SageMaker in response. More than 50 new capabilities have been added to SageMaker in just the past year, and re:Invent 2020 saw the addition of nine more<\/a>.<\/p>\n

Running through these in his discussion with theCUBE, Saha described each in detail and gave key use cases. For example, SageMaker Data Wrangler<\/a> is an addition to SageMaker Studio, a fully integrated environment for machine learning. By giving customers the ability to do data preparation in the same service as machine learning, it reduces the time spent cleaning data.<\/p>\n

\u201cWith a few clicks you can connect to a variety of data stores \u2026 and do all of your data preparation,\u201d Saha stated. \u201cYou get your data in; you do some interactive processing. Once you\u2019re happy with the results of your data, you can just send it off as an automated data pipeline job. It\u2019s the easiest and fastest way to do ML and take out that 80% [of time wrangling data].\u201d<\/p>\n

Another new service is Amazon SageMaker Clarify<\/a>, which provides visibility and transparency into the modeling process in order to eliminate unintentional biases.<\/p>\n

\u201c[Clarify] helps to convert insights that you get from model predictions into actionable insights because you now know why the model is predicting what it is predicting,\u201c Saha said.<\/p>\n

Distributed Training on Amazon SageMaker<\/a> increases the efficiency for customers training really large models, some of which now have billions of parameters, according to Saha. The new capability uses two techniques<\/a>, model parallelism and data parallelism. The first enables these massive models that could take weeks to train on a single graphics processing unit to be trained in parallel across multiple GPUs. The second takes models that are too large to fit in the memory of a single GPU and automatically distributes them across multiple GPUs.<\/p>\n

These capabilities are \u201cmaking it much faster and much easier for customers to work with large models,\u201d Saha said.<\/p>\n

Bringing machine learning to edge devices and helping fulfill Amazon\u2019s mission of \u201cAWS Everywhere\u201d is Amazon SageMaker Edge Manager<\/a>. Optimizing models so they can run on an edge device increases the benefit by 25x, according to Saha. Not only does Edge Manager enable local inference, but it helps maintain model quality.<\/p>\n

\u201cOnce deployed it monitors quality of the models by letting you upload data samples to SageMaker so you can see if there is drift in your models or any other degradation,\u201d Saha stated.<\/p>\n

For complete descriptions and discussion on all the new Amazon SageMaker capabilities, watch the complete video interview below. Then be sure to check out more of SiliconANGLE\u2019s and theCUBE\u2019s coverage of AWS re:Invent<\/a>. (* Disclosure: Amazon Web Services Inc. sponsored this segment of theCUBE. Neither AWS nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)<\/em><\/p>\n