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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.

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Fully-automated roboadvisors to manage nearly $1tn assets by 2022, says Juniper Research

By Zenobia Hegde

New data from Juniper Research has found that roboadvisors (digital wealth management platforms) under full control of AI systems will reach $987 billion per annum in AUM (assets under management) by 2022.

These fully-automated roboadvisors will represent approximately 25% of total roboadvisor AUM in 2022, and their growth will considerably outpace semi-automated, supervised deployment types with lesser reliance on AI. These roboadvisors are forecast to grow their AUM at close to 155% per annum on average versus 69% growth for the overall market according to Juniper.

Building trust in AI

Juniper’s new research, AI in Fintech: Roboadvisors, Lending, Insurtech & Regtech 2018-2022 found that consumer trust would play a fundamental role in shaping the market during the projection period. For this reason, Juniper predicted that ‘hybrid’ roboadvisors would dominate the market, managing 66% of global roboadvisory AUM in 2022. It noted that human advisor input plays a key role here, serving to allay consumers’ fears of handing management of their cash over to an algorithm.

Nevertheless, the research predicted that while key market forces, such as economic uncertainty and increasing awareness of services would drive the overall market, changing demographics would kickstart demand for fully-automated roboadvisors.

“Digital-savvy millennials are rapidly reaching the age where the idea of financial planning is an important consideration,” noted research author Steffen Sorrell. “This demographic’s greater inherent trust in algorithms, alongside demand for ‘fire-and-forget’ convenience will drive take-up for AI fully-managed services.”

Market consolidation ahead

Meanwhile, the research predicted that market consolidation was highly likely in the near-term, particularly in more mature roboadvisory markets, such as the US.

It argued that strong competition and high customer acquisition costs meant that many services would be unable to reach the AUM ‘tipping point’ necessary to generate profits. Juniper noted this would impact semi-automated roboadvisory services the most, owing to their reliance on human advisors and relatively low AUMs. For these reasons, many service providers would make themselves a target for acquisition.

Juniper Research provides research and analytical services to the global hi-tech communications sector, providing consultancy, analyst reports and industry commentary.

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HPE, PTC, and Wind River join effort to speed IoT software purchasing

By Zenobia Hegde

Three of the biggest software vendors in IoT – HPE, PTC, and Wind River (Intel) – have agreed to join the IoT M2M Council’s (IMC) fledgling template RFP Program for IoT Software Platforms, which will be presented at the IMC’s conference at CES.

Using input from many vendors and more than 100 software buyers in an open-source process, the IMC developed a template reference document that will ease buying of IoT software, and later, hardware and connectivity solutions. HPE, PTC, and Wind River have agreed to have their platforms assessed by the IoT M2M Council which represents 25,000 enterprise users and OEMs that buy IoT solutions.

The RFP program will simplify sourcing of IoT platforms for buyers by providing reference documentation and demonstrating capabilities of established software platforms, and for participating vendors, it will ultimately shorten the sales cycle.

The RFP template will be discussed at this week’s Consumer Electronics Show in Las Vegas, where large numbers of OEMs that buy IoT solutions will see it for the first time. The IMC developed a template RFP document earlier this year in a wiki-based, open-source process with input from more than 100 IoT buyers, and has now retained a third-party consultancy to validate vendors against the RFP.

The validation process, conducted by UK-based Beecham Research, includes surveying vendors for responses to the RFP, contacting their customers anonymously for references, and a hands-on analysis of the platforms for ease-of-use.

“No other industry group or major consultancy is talking to buyers at scale and looking at the actual IoT sales process. My staff spends a lot of time responding to RFPs. The IMC’s RFP program gives us a report from a credible third-party that allows us to respond to RFPs more quickly, as well as a place to send potential buyers where they can access a template RFP document and learn more.

If this program reduces my sales cycle, even just incrementally, it will be well worth it,” says Volkhard Bregulla, VP of Global Industries, Manufacturing, & Distribution at HPE, with a seat on the IMC board.

IMC rank-and-file membership comes from 24 different vertical markets on every continent, and a plurality self-identify as “operations”, meaning that they are unlikely versed in communications technology. “The template RFP provides a non-technical reference, and can go a long way in establishing a common language for IoT technology among people actually doing the buying,” says Bregulla.

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Developers Will Adopt Sophisticated AI Model Training Tools in 2018

By James Kobielus

Training is the make-or-break task in every development project that involves artificial intelligence (AI). Determining an AI application’s fitness for its intended use involves training it with data from the solution domain into which it will be deployed.

In 2018, developers will come to regard training as a potential bottleneck in the AI application-development process and will turn to their AI solution providers for robust training tools. Developers will adopt robust tools for training AI models for disparate applications and deployment scenarios. By the end of this coming year, AI model training will emerge as the fastest growing platform segment in big data analytics. To keep pace with growing developer demand, most leading analytics solution providers will launch increasingly feature-rich training tools.

During the year, we’ll see AI solution providers continue to build robust support for a variety of AI-model training capabilities and patterned pipelines in their data science, application development, and big-data infrastructure tooling. Many of these enhancements will be to build out the automated ML capabilities in their DevOps tooling. By year-end 2018, most data science toolkits will include tools for automated feature engineering, hyperparameter tuning, model deployment, and other pipeline tasks. At the same time, vendors will continue to enhance their unsupervised learning algorithms to speed up cluster analysis and feature extraction on unlabeled data. And they will expand their support for semi-supervised learning in order to use small amounts of labeled data to accelerate pattern identification in large, unlabeled data sets.

In 2018, synthetic (aka artificial) training data, will become the lifeblood of most AI projects. Solution providers will roll out sophisticated tools for creation of synthetic training data and the labels and annotations needed to use it for supervised learning.

The surge in robotics projects and autonomous edge analytics will spur solution providers to add strong reinforcement learning to their AI training suites in 2018. This will involve building AI modules than can learn autonomously with little or no “ground truth” training data, though possible with human guidance. By the end of the year, more than 25 percent of enterprise AI app-dev projects will involve autonomous edge deployment, and more than 50 percent of those projects will involve reinforcement learning.

(ktsdesign/Shutterstock)

During the year, more AI solution providers will add collaborative learning to their neural-net training tools. This involves distributed AI modules collectively exploring, exchanging, and exploiting optimal hyperparameters so that all modules may converge dynamically on the optimal trade-off of learning speed vs. accuracy. Collaborative learning approaches, such as population-based training, will be a key technique for optimizing AI in that’s embedded in IoT&P (Internet of Things and People) edge devices.

It will also be useful in for optimizing distributed AI architectures such as generative adversarial networks (GANs) in the IoT, clouds, or even within server clusters in enterprise data centers. Many such training scenarios will leverage evolutionary algorithms, in which AI model fitness is assessed emergently by collective decisions of distributed, self-interested entities operating from local knowledge with limited sharing beyond their neighbor entities.

Another advanced AI-training feature we’ll see in AI suites in 2018 is transfer learning. This involves reuses of some or all of the training data, feature representations, neural-node layering, weights, training method, loss function, learning rate, and other properties of a prior model. Typically, a developer relies on transfer learning to tap into statistical knowledge that was gained on prior projects through supervised, semi-supervised, unsupervised, or reinforcement learning. Wikibon has seen industry progress in using transfer learning to reuse the hard-won knowledge gained in training one GAN on GANs in adjacent solution domains.

Also during the year, edge analytics will continue to spread throughout into enterprise AI architectures. During the year, edge-node on-device AI training will become a standard feature of mobile and IoT&P development tools. Already, we see it in many leading IoT and cloud providers’ AI tooling and middleware.

About the author: About the author: James Kobielus is a Data Science Evangelist for IBM. James spearheads IBM’s thought leadership activities in data science. He has spoken at such leading industry events as IBM Insight, Strata Hadoop World, and Hadoop Summit. He has published several business technology books and is a very popular provider of original commentary on blogs and many social media.

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Aeris CTO Hosain elected chair of IoT M2M Council

By Zenobia Hegde

Syed Zaeem “Z” Hosain, a founder and the chief technical officer at Aeris, was named the new chairman of the IoT M2M Council by the trade association’s Board of Governors at their annual meeting, held in November in Boston. With more than 25,000 members worldwide, covering 24 different vertical-market sectors, the IMC is the largest and fastest-growing trade organisation serving the IoT industry.

Hosain has been with Aeris – a technology provider in IoT – since 1996, when the company was founded, and has more than 38 years of experience in the semiconductor, telecommunications and computer industries. He has held leadership positions for several industry associations and technical standards bodies, and is the author of the book, “The Definitive Guide: The Internet of Things for Business.”

“By reaching out to buyers of IoT technology on such a large scale, the IMC can provide solutions providers with a natural platform to promote their products and services, and also learn about enterprise users and OEMs that are deploying the technology. For example, we have data that show a plurality of them self-identify as ‘operations’ – not ‘IT’ or ‘R&D’ – and now we’re digging deeper, and tracking if there are movements in these categories,” said Hosain.

To cultivate interaction between buyers and sellers in the IoT sector, the IMC has recently introduced a number of new programs, including a software widget survey-tool that tests a user’s readiness for IoT deployments and a template RFP program for its members that are interested in sourcing IoT software platforms.

The group has also been named as the exclusive organiser of the Consumer Electronics Show’s (CES) new area dedicated to IoT infrastructure – more than 6,500 square feet of exhibition space already has been booked for the January 2018 event to take place in Las Vegas.

“This is the first time CES will host a dedicated IoT area of this kind, and it’s a major achievement for the IMC. Our IMC mission is about bringing buyers and sellers together, and CES attracts tens of thousands of OEMs from markets that are crucial to the IoT,” said Hosain.

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