Real-world applications for artificial intelligence are emerging in areas such as boosting the productivity of dispersed workforces. However, early adopters are still struggling to determine the return on initial AI investments, according to a pair of new vendor reports.
Red Hat released research this week indicating that AI deployments have yielded some tangible results in areas such as transportation and utilities that rely heavily on field workers. A separate forecast released Wednesday (Jan.17) by Narrative Science found growing enterprise adoption of AI technologies but little in the way of investment returns.
Chicago-based Narrative Science, which sells natural language generation technology, found that 61 percent of those companies it surveyed deployed AI technologies in 2017. Early deployments focused on business intelligence, finance and product management. “In 2018, the focus will be on ensuring enterprises get value from their AI investments,” company CEO Stuart Frankel noted in releasing the survey.
Early adopters are also encountering many of the hurdles associated with a “first mover” advantage. “More and more organizations are deploying AI-powered technologies, with goals such as improving worker productivity and enhancing the customer experience that are not only laudable, but achievable,” Narrative Science concluded. “A focus on realistic deployment timeframes and accurately measuring the effectiveness and [return on investment] of AI is critical to keeping the current momentum around the technology moving forward.”
Meanwhile, the Red Hat (NYSE: RHT) survey also found an uptick in AI deployments, with 30 percent of respondents planning to implement AI for “field service workers” this year. Other applications include predictive analytics, machine learning and robotics.
While issues such as securing data access and a lack of standards persist, Red Hat found that field workers are “now at the forefront of digital transformation where artificial intelligence, smart mobile devices, the Internet of Things (IoT) and business process management technologies have created new opportunities to better streamline and transform traditional workflows and workforce management practices.”
A predicted 25 percent increase in AI investment through November 2018 is seen transforming field service operations, Red Hat noted in a blog posted on Thursday (Jan. 18). Early movers cited increase field worker productivity (46 percent), streamlining field operations (40 percent) and improving customer service (37 percent) as the top business factors for investing in AI.
Along with a lack of standards, respondents said deployment challenges include keeping pace with technological change and integrating AI deployments with legacy systems. The survey notes that industry groups are focusing on standards and interoperability among IoT devices along with data security while improving integration technologies.
Earlier vendor surveys also have identified barriers to implementation ranging from a lack of IT infrastructure suited to AI applications to a lack AI expertise. For instance, a survey released last fall by data analytics vendor Teradata Corp. (NYSE: TDC) found that 30 percent of those it polled said greater investments would be required to expand AI deployments.
Despite the promise and pitfalls of AI—ranging from freeing workers from drudgery to displacing those same workers—early AI deployments appear to underscore the reality that the technology remains a solution in search of a problem.
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Akita, an IoT device watchdog station raised approximately $700,000 crowdfunding on Kickstarter. With 7000 plus backers, the startup promises to provide instant privacy for connected products.
The device performs three core activities i.e. scans connected gadgets/devices, blocks compromised devices and notifies the users of known issues. Akita comes with full support and help desk monitoring powered by Axius.
This device connects to a LAN port on users’ home router (not inline). The startup describes the device working as follows:
Akita’s Kickstarter received significant backing (both in terms o the number of backers and funds raised from the campaign), though, it only aimed to raise $30,000 initially.
The rise in popularity of privacy and network security devices is understandable. A home network, with several connected devices, need robust protections. That’s where other startups like Dojo and F-Secure also promise to secure network traffic and identify rouge devices.
Readers might visit the Postscapes Connected Device Security guide to understand how other devices in the same niche work and how Akita stacks up against its competitors.
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Voice first interfaces are reinventing the way we engage with devices. Acapela Group, leading player in voice solutions for more than 30 years, is constantly creating new voices to better interact with users, whatever their age or skills, thanks to voices that adapt to the context. Voices that convey meaning, intent and emotions. Voices for […]
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In an effort to continue to grow their business in existing and new markets, DAZN – a live and on-demand sports streaming service – wanted a fast, low-maintenance way to enable their small data team to run predictive analytics and machine learning projects at scale.
The company wanted to find a way to allow data analysts who were not necessarily technical or experienced in machine learning to be able to contribute in meaningful ways to impactful data projects. Ultimately, they wanted to support an underlying data culture with advanced analytics and machine learning at the heart of the business.
Until recently, the sports entertainment industry was dominated by cable or satellite TV systems and companies; if a customer wanted to watch a particular sporting event, he had little or no choice in how to do so. Now that consumers are breaking free from traditional TV, they are increasingly turning to specialised services streaming exactly the content they’re looking for, whether live or on-demand. And while they are willing to pay for these services, it means that entertainment companies – in the absence of the a fore mentioned virtual monopoly of TV broadcasts – are held to increasingly higher standards when it comes to quality and offerings.
In other words, because customers can turn elsewhere, entertainment companies have had to up their game, so to speak. Today, that means bringing innovation by way of predictive analytics and machine learning to optimise every aspect of the business, from marketing to customer service to product offerings. To do this efficiently, they must also bring this innovation at scale, hiring fewer people to do more such that insights grow exponentially along with the amount of data being collected.
The need for Big Data with a small staff
DAZN knew that in order to accomplish their goals quickly, they would need technologies that were simple and in the cloud. They turned to Amazon Web Services (AWS) and Dataiku in combination for their simplicity in setup, connection, integration, and usability, and they got up and running in under one hour.
With AWS and Dataiku, the small data team built and now manages more than 30 models in parallel, all without needing to do any coding so that the processes are completely accessible to non-technical team members.
They use these models as the basis for a variety of critical processes throughout all areas of the business, specifically:
Content attribution to determine what fixtures are driving sales, enabling contextual information on key fixtures in each market.
Advanced customer segmentation to identify user behaviours, particularly regarding content and devices on which customers use the product.
Propensity modeling to identify customers that are likely to churn, enabling improved customer targeting for retention activities.
Survival analysis to understand customer stickiness, enabling calculation of expected revenues to understand customer return on investment.
Natural language processing on social networks for market research
Results of more effective team members = More data science
AWS and Dataiku have noticeably shifted the data culture at DAZN and have brought innovations in advanced analytics and machine learning into the spotlight throughout […]
The post Scaling a small data team with the power of machine learning appeared first on IoT Now – How to run an IoT enabled business.
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The quality of irrigation water, as well as the correct management of water resources, is essential for the productivity and efficiency of the crops. Controlling and analysing water before irrigating is crucial and its quality may vary significantly depending on the time of the year. So frequent measurements are recommended.
The Spanish company GMV has developed a water quality monitoring system based on Libelium technology. The nodes were installed at the “El Portal” irrigation dam, located on the Guadalete river where it passes through Jerez de la Frontera (Spain).
Location of Jerez de la Frontera
GMV, which was founded in 1984, has wide experience in hi-tech sectors with a growing order book in all five continents. It has experienced an important technology transfer along its trajectory and nowadays the company focuses its efforts on two business lines: transport and telecommunication sectors and applications of information technologies.
The regional government detected a high cost of maintenance of the old measurement equipments along with high costs of transport and possible inconsistencies due to manual handling of the tools.
“El Portal” irrigation dam at Jerez de la Frontera, Spain
The main goals of the project were to reduce the costs of measurement and data network management as well as to avoid manual processing that may lead to inaccuracy. In the same way, the electrical consumption of the previous equipment had a handicap to solve, together with the fact that this location usually suffers from frequent acts of vandalism against power lines, automatically ceasing the normal functioning of the monitoring system.
GMV and the regional government of Andalusia trusted Libelium technology to deploy this project to monitor different water quality parameters in an irrigation dam on the Guadalete river, close to Jerez de la Frontera.
Installation of the Waspmote Plug & Sense Smart Water sensors
Two measuring nodes Waspmote Plug & Sense! Smart Water were installed in the location to measure levels of temperature, pH, dissolved oxygen and conductivity every 30 minutes. Sigfox was the protocol chosen by GMV, with a view to enlarge the deployment in the future.
Waspmote Plug & Sense! Smart Water at “El Portal” dam
The data collected by the sensors is sent to the proprietary software SEMS (Smart Environment Monitor System), which allows monitoring of any kind of parameter, managing sensors, executing custom queries, managing users, reporting alarms and many other operations.
Diagram of GMV project
This platform gives the irrigators access to real-time information on water quality to help decision-making in aspects such as the opening and closing of gates or the hours when water quality is higher. Additionally, manual collection is not necessary anymore so access to the information is now easier and quicker.
GMV highlights the adaptability of the Waspmote wireless sensor platform to any need and any environment along to the interoperability and compatibility with Sigfox and the low electrical consumption, which were ideal for the challenge they had to face.
GMV SEMS dashboard for the Andalusian Government
This new water quality monitoring system meant savings of around 50% in development time. The company is currently carrying out a technical report to present the results obtained after controlling the deployment in terms of sensorisation cost savings.
The Andalusian government (Junta de Andalucía in […]
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Powercast Corporation, the provider of radio-frequency (RF)-based long-range power-over-distance wireless charging technology, announced that it will unveil at CES its FCC-approved (Part 15, FCC ID: YESTX91503) and ISED-approved (Canada IC: 8985A-TX91503) three-watt PowerSpot transmitter which works in the far field(up to 80 feet) for over-the-air charging of multiple devices – no charging mats or direct line of sight needed.
Powercast used the experience it gained powering industrial and commercial devices with its initial Powercaster® transmitter (FCC and ISED approved in 2010) to develop the new smaller, smarter and less expensive PowerSpot transmitter specifically for the consumer market. The PowerSpot is the industry’s first long-range, far-field, power-over-distance wireless recharging transmitter for consumer devices to gain FCC and ISED approval.
How Powercast’s patented remote wireless charging technology works
Creating a coverage area like Wi-Fi, a Powercast transmitter automatically charges enabled devices when within range. The transmitter uses the 915-MHz ISM band to send RF energy to a tiny Powercast receiver chip embedded in a device, which converts it to direct current (DC) to directly power or recharge that device’s batteries.
Powercast will begin production of its standalone PowerSpot charger now that it is FCC approved and is also offering a PowerSpot subassembly that consumer goods manufacturers can integrate into their own products.
Consider lamps, appliances, set-top boxes, gaming systems, computer monitors, furniture or vehicle dashboards that become “PowerSpots” able to charge multiple enabled devices around them.
Powercast is in discussions with several manufacturers, and has inked deals with two household names, since releasing a wireless power development kit in early 2017 containing the PowerSpot subassembly.
“Consumer electronics manufacturers can now confidently build our FCC-approved technology into their wireless charging ecosystems, and offer their customers convenient far-field charging where devices charge over the air from a power source without needing direct contact, like inductive charging requires, or near direct contact, like magnetic resonance requires,” said Powercast’s COO/CTO Charles Greene, Ph.D.
The company’s vision is to enable long-range, true wireless charging where consumers simply place all Powercast-enabled devices for charging within range of a PowerSpot in their home or a public place.
“Others might be talking RF power possibilities, but we have consistently delivered far-field wireless power solutions that work, safely and responsibly, under FCC and other global standards providing power up to 80 feet,” said Greene. “Our robust technology has capabilities beyond today’s permitted standards, so our product releases will evolve as regulations do.”
The PowerSpot creates an overnight charging zone of up to 80 feet free of wires or charging mats
Enabled devices charge when in range, but don’t need direct line of sight to the PowerSpot. Powercast expects up to 30 devices left in the zone on a countertop or desktop overnight can charge by morning, sharing the transmitter’s three-watt (EIRP) power output. Charging rates will vary with distance, type and power consumption of a device.
TX91503 – PowerSpot Transmitter
Power-hungry, heavily used devices like game controllers, smart watches, fitness bands, hearing aids, ear buds, or headphones charge best up to two feet away; keyboards and mice up to six feet away; TV […]
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It’s been more than a decade since we first heard the phrase “data is the new oil.” But while this idea may well define the next generation of business, there’s important context surrounding it that often gets overlooked.
Since making this statement, many others have echoed these same words. What we now have come to realise is that data is a commodity, a raw material, and it’s only valuable when it can be turned into intelligence. Without the right tools to refine it, it’s just a bunch of ones and zeros. Study after study shows that while most enterprises understand the importance of data, they continue to struggle to draw real value from it, says Matt Mills, CEO and Board Member at MapR Technologies.
Amongst the most powerful and largest tech titans in the world, the idea of data turned into intelligence is gospel. It’s why companies like Apple, Amazon, Google, Facebook, and Microsoft dominate the charts these days. They not only know the value of data, but also how to transform it from raw insight into a competitive advantage.
Unlike these companies, many organisations today are using 30-year old technologies to “refine” their data and are frustrated with the little progress they are making. The simple fact is that older technologies are often too fragile and simply aren’t built for the diversity or the sheer volume of data today. The fact of the matter is this is just the beginning. Many believe that from now on, data and the digital universe will double in size every two years.
Successful data-driven companies in the early stages of their digital transformation journeys are choosing a modern data platform that is both optimised for performance today and provides the speed, scale and reliability that are required for next-generation intelligent solutions.
The modern data platform has 10 key characteristics:
A single platform that performs analytics and applications at once
Manages all data from big to small, structured and unstructured, tables, streams, or files – all data types from any source
A database that runs rich data-intensive applications and in-place analytics
Global cloud data fabric that brings together all data from across every cloud to ingest, store, manage, process, apply, and analyse data as it happens
Diverse compute engines to take advantage of analytics, machine learning and artificial intelligence
Delivers cloud economics by operating on any and every cloud of your choice, public or private
No lock-in, supporting open APIs
DataOps ready to champion the new process to create and deploy new, intelligent modern applications, products and services
Trusted with security built from the ground up
Streaming and edge first for all data-in-motion from any data source as data happens and enabling microservices natively
Many software products today can handle some aspects of modern day data platforms, but few if any can actually deliver on all of these requirements. This is where most companies get into trouble. They try to extend the software to do things it was never intended to do. These limitations are a key reason why many companies never reach their goals and objectives with data.
Here at MapR we have built the premier data platform for today — and tomorrow’s — leading enterprises. […]
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The justice system is known for many things, but efficiency is not one of them. Neither is being up-to-speed with technology. One joke goes that the unofficial IT slogan of the courts is, “Yesterday’s technology, tomorrow!”
Into this space comes LegalThings, an Amsterdam-based digital contracts company that’s aiming to update how those accused of a crime move through the justice system by making the law accessible while making judicial record-keeping more open and secure.
After winning a “blockathon” competition in September hosted by the Dutch Ministry of Justice and Security, LegalThings began a pilot project with the Public Prosecution Service of the Netherlands, known as the “Openbaar Ministerie,” or OM, in Dutch. The project aims to build a system to process low-level criminal offenders quickly and with more transparency. If successful, it could be a huge time- and money-saving enterprise for the government.
“What you see now [in the justice system] is there is a lot of procedures, and those procedures are important to create a fair legal system, but they’re also really labor-intensive,” said Arnold Daniels, a co-founder of LegalThings and its chief software engineer. “What we’re trying to do is create an alternative to that.”
How might that work in practice? Imagine someone nabbed for possession of a small amount of illicit drugs, a crime that, in the Netherlands, can carry a fine of a few hundred euros. There are a number of parties involved in processing such a law enforcement action: the police who catch the alleged offender, the forensics expert that examines the drugs, and the OM.
Depending on whether the forensic expert is on-site to test the drugs, processing such an enforcement action can take anywhere from several hours to a couple days, said Sanne Giphart, innovation manager at OM. While some record-keeping systems have been made digital, that’s an ongoing process, Giphart explained. Things can move slowly.
By contrast, with the LegalThings application, the accused can get an explanation of the relevant law, choose whether to be represented by counsel, and agree to pay the relevant fine—all on their smartphone. All told, the actual processing of the offender takes about 30 minutes, and every step of the exchange is recorded, time-stamped, and made unchangeable using cryptography to ensure records can’t be fudged.
So far, OM, which is comparable to a mashup of the Department of Justice and local district attorneys in the U.S., has only experimented with the technology on “dummy data” involving a drug offense and a domestic violence offense, Giphart said. “The next step is to let people get familiar with this type of technology within the [OM] and then hopefully we can implement on one stream of cases.”
The challenges to implementing such a system are not purely technological. It also will likely require some changes in both public and institutional attitudes toward judicial record-keeping, said Daniels. “With this system, there’s really no backsies,” he explained. “You can correct it, but you can always see your initial action.”
Unlike other blockchain systems that use a publicly distributed ledger, the LegalThings project with OM allows […]
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Alchemy IoT, a provider of AI-powered applications for industrial IoT, introduced “IoT Asset Intelligence,” a new industry approach that addresses the complexity and market fragmentation associated with Big Data, IoT and artificial intelligence. It outlines today’s disruptive mega-trends, and Alchemy’s industry insights and vision, before outlining practical steps organisations can use to align company goals, resources and teams toward value streams centered on industrial equipment, fleets and operational performance.
“IoT Asset Intelligence combines our best thinking into an actionable framework to plan, implement and gain value from AI-based IoT initiatives – especially for smaller organisations that may lack the resources for expensive consultants and data scientists,” said Victor Perez, CEO of Alchemy. “We are committed to the customer journey as much as the technology that is transforming the way industrial companies operate and win in the market.”
Recent studies by the International Data Corporation (IDC) estimate that by the year 2020 there will be 30 billion connected devices globally. With connected devices outputting sensor data 24 hours a day, seven days a week, the sheer scale of data production is staggering. The IoT Asset Intelligence framework provides practical steps for improving operational efficiencies, empowering better decision making, promoting collaboration and innovation, and delivering more value to the organisation.
The central theme of the IoT Asset Intelligence framework is visibility and efficiency through a blend of data-driven processes and a renewed culture around innovation and critical thinking. Like Lean and other operational improvement methods, IoT Asset Intelligence requires executive support and leadership to achieve sustainable success – as change and breaking with entrenched norms is as much a cultural “transformation” exercise as it is a process and technology play.
The following are the IoT Asset Intelligence core tenets:
Appoint an IoT champion: to be the single-point-of-contact, for the initiative and to help shift company culture.
Establish top-line goals and ROI opportunities: establish goals early in the process to have a clear path to success.
Conduct asset and process assessments: identify new data points to gain a strong understanding of what existing assets and processes exist in the organisation to better optimise them.
Identify processes for improvement and align new value streams: these processes are the best candidates for improvement.
Apply AI and machine learning for data analytics and automated workflows: not all data is good data – leverage AI to filter out irrelevant data to make data streams more powerful.
Present performance data in graphs for contextual 360-degree views: teams need to access performance data in a clear, visual representation that will allow them not only to see progress, but identify areas for improvement.
Integrate with existing ERP, EAM, MRO and MRP platforms: work with existing tools to optimise and enhance current processes, not just create new ones.
Provide graphical intelligence for executive decision-making and strategic planning: leverage data intelligence to make meaningful business decisions at the highest levels.
Practice continuous improvement: always improve – never be satisfied.
Alchemy will soon publish a comprehensive paper on the IoT Asset Intelligence framework that will be available from its website. For more information on the company, please […]
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Longsys Technology has announced a revolutionary turnkey IoT End-to-End solution to enable existing products to go smart within 30 days, directly changing the period of development for an IoT device. In the past, companies have spent a lot time and money on initial their IoT solutions, to work with different parties like cloud service companies, […]
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