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AI Investment Up, ROI Remains Iffy

By George Leopold

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.

Recent items:

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AI Adopters Confront Barriers to ROI

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Akita raises $700,000 crowd-based funding on Kickstarter

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.

Instant Privacy for Smart Homes

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 uses a custom form of WiFi sniffing to detect questionable packets and send them to our servers to be analyzed (this is why the hardware can run on low memory/CPU, because it’s mainly a cloud-based system). Once the packets are determined to be malicious, a command is sent from our servers back to the Akita device; this includes custom protocol commands to disconnect the device from the network router.“

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 at CES 2018: Test and Hear Personalized Voices for Autonomous Vehicles, Smart Toys and IoT from Acapela Group

By IoT – Internet of Things

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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Scaling a small data team with the power of machine learning

By Zenobia Hegde

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.

The situation

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 […]

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Case Study: Controlling quality of irrigation water with IoT to improve crops production

By Zenobia Hegde

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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