Qualtrics, the leader in customer experience and creator of the experience management category, announced Delighted AI, artificial intelligence, and machine learning engine built directly into Delighted’s customer experience platform. Delighted, a Qualtrics company, developed its AI technology to intelligently automate every aspect of the customer feedback process, from scheduling to analysis and reporting, so that companies can focus on closing feedback loops faster than ever. Delighted AI is complementary to Qualtrics’ existing Text iQ enterprise technology for CustomerXM, optimized for Delighted customers.

Today, the most successful customer experience programs are no longer measurement or metrics-based. Over the past few months, Net Promoter Scores have significantly declined in response to COVID-19, exposing customer experience gaps that companies have failed to address or identify. The companies who have emerged as customer experience leaders in the crisis have continuously listened to their customers, and more importantly, responded quickly to their preferences and expectations.

Delighted AI was created based on semantics and themes in the millions of customer feedback responses that Delighted and its customers have analyzed over several years to drive customer experience success.

“Delighted AI helped the right teams at our company understand customer feedback with more precision than ever before, which has been critical in the middle of a pandemic where we need to adapt and respond even more quickly to our customers’ needs and expectations,” said Roxana Turcanu, Growth Director for Adore Me, a New York-based e-commerce company. “We just recently launched a new try-at-home brand called Outlines, and we were able to do so with the help of Delighted AI by capturing and applying feedback early – this enabled us to pivot, at a rate we’ve never been able to do, towards what our customers actually wanted from our brand.”

Benefits of Delighted AI include:

• Ensuring the highest customer response rate. Delighted AI automatically knows when, and how, to ask your customers for feedback based on adaptive sampling and smart scheduling.
• Receiving quality customer feedback. Delighted AI evaluates customer feedback to detect and prevent any low-quality submissions.
• Surfacing feedback themes and insights with Smart Trends. Smart Trends is Delighted’s AI-based text analysis that immediately serves the top keywords and themes emerging in your customers’ open-ended feedback. It also creates and saves search filters and reports for ongoing analysis so your teams can prioritize and respond, down to the specific channel or point within the customer journey, such as onboarding or web support.

“Customer experience programs are rapidly evolving as companies have realized that relying on traditional metrics alone does not determine customer success. Instead, the customer experience leaders are winning based on gathering in-the-moment feedback that is immediately actionable and building a culture of continuous listening,” said Caleb Elston, co-founder of Delighted. “We created Delighted AI to empower companies to spend less time configuring, implementing, and analyzing so they can focus on acting on insights faster than any other technology or human could before.”

Acquired by Qualtrics in 2018, Delighted is one of the fastest and easiest ways to take action on customer feedback, which enables innovative brands and organizations of any size to quickly implement a customer experience program across every channel.

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Researchers at Binghamton University, who previously developed a method for detecting “butterfly” land mines using low-cost, commercial drones equipped with multispectral and infrared cameras, are now focusing on automated detection of land mines using convolutional neural networks.

Nicknamed “butterfly” land mines for their small size and butterfly-like shape, PFM-1 land mines are extremely difficult to locate and clear due to their small size, low trigger mass, and a design that mostly excludes metal components, making them virtually invisible to metal detectors.

The researchers analyzed multispectral and thermal data sets collected by an automated unmanned aerial vehicle (UAV) survey system featuring scattered PFM-1-type land mines as test objects. To automate land mine detection, they relied on supervised learning algorithms using a faster regional-convolutional neural network (Faster R-CNN).

The RGB visible light Faster R-CNN demo yielded a 99.3% testing accuracy for a partially withheld testing set and a 71.5% testing accuracy for a completely withheld testing set. The researchers found that across multiple test environments, the use of cm-scale, accurate, georeferenced data sets paired with Faster R-CNN allowed for accurate automated detection of the test PFM-1 land mines. The researchers said that their detection and mapping techniques could be calibrated to other types of scatterable antipersonnel mines in future trials to aid demining initiatives. For example, they could be adapted to detect and map disturbed soil for improvised explosive devices (IEDs).

The researchers said that the use of CNN-based approaches to automate the detection and mapping of land mines is much faster than manually counting land mines from an orthoimage (an aerial image that has been geometrically corrected). Also, a CNN-based method is quantitative and reproducible, unlike ocular detection, which is subjective and prone to human error. CNN-based methods can be generalized to detect and map any objects with distinct sizes and shapes from any remotely sensed raster images.

“Rapid drone-assisted mapping and automated detection of scatterable minefields would assist in addressing the deadly legacy of widespread use of small scatterable land mines in recent armed conflicts,” professor Alek Nikulin said,  

The post Using Deep Learning, Researchers Automate UAV-Based Land Mine Detection appeared first on Artificial Intelligence.

International Business Machines Corporation IBM recently updated Watson Studio with AutoAI capabilities.

As the name suggests, AutoAI enables enterprises to automate complex arduous processes, comprising optimizing, designing and other related tasks by leveraging robust AI capabilities.

Markedly, innovation in machine learning (ML) and improvement in data set quality drives development of AI tools. IBM rolled out AutoAI with an aim to free up time for data engineers to design sturdy ML models to expedite AI deployment.

AutoAI capabilities function in sync with Watson Machine Learning to accelerate AI processes considerably. AutoAI is available in “Watson Studio on the IBM Cloud.”

IBM is leaving no stone unturned to strategically infuse AI capabilities across its offerings, which hold promise. AutoAI is anticipated to boost adoption of IBM Watson Studio services, consequently bolstering the company’s top line in the days ahead.

Noteworthy Features

With AutoAI, data scientists can utilize “hyperparameter optimization capabilities” which facilitates development of AI models. Moreover, AutoAI is loaded with robust enterprise data science models, including gradient boosted trees which allow users to upscale ML deployment processes.

In a bid to empower users with deep learning (DL) capabilities, AutoAI allows access to IBM Neural Networks Synthesis (NeuNetS). Notably, NeuNetS utilizes AI to aid development of DL model and automates data synthesis across custom-designed neural networks. Further, NeuNetS provides users with the flexibility to prioritize accuracy or speed of the data model.

Increasing Spend on AI Favors Growth Prospects

Huge volume of data is being generated through a wide range of sources. In a bid to harness this data constructively, the companies intend to leverage AI to provide specific solutions and enhance solutions’ capabilities. However, deployment of AI tools is not trouble free.

For instance, per an IBM study, Shifting Toward Enterprise-Grade AI , conducted last year, 63% of surveyed individuals cited “lack of proper technical skills was a prime challenge to AI implementations.”

Moreover, according to a Forrester study, 60% of surveyed candidates claimed management of data quality to be one of the headwinds in delivering AI applications.

Markedly, IBM’s new Watson AutoAI capabilities provide companies with robust data models, consequently accelerating their AI processes.

We must note that the number of companies utilizing enterprise AI is increasing, which favors the prospects of AutoAI tool in Watson Studio. Per a report by IBM, customer satisfaction is the primary reason behind around 77% of organizations focusing on AI investments.

Further, IDC data projects worldwide spending on AI systems to hit $79.2 billion by 2022 at a CAGR of 38% from 2018 to 2022.

The move is expected to strengthen the company’s position in machine learning as a service (MLaaS) market, which is envisioned to hit $8.31 billion by 2023 per a MarketReportsWorld report, as revealed by MarketWatch.

The aforementioned secular trends reinforce IBM’s growth prospects in the longer haul.

Competition a Woe

Rising competition from Amazon Web Services (AWS) and Microsoft Azure, which dominate the cloud infrastructure services market, is a headwind. Notably, the cloud players are strengthening their IT infrastructure with advanced ML capabilities. For instance, Microsoft recently rolled out additional functionalities to Azure Machine Learning.

According to latest first quarter data from Synergy Research, IBM Cloud is classified as “strong niche player” with comparatively “lower growth rates.”

Zacks Rank & Key Picks

IBM carries a Zacks Rank #3 (Hold).

Some better-ranked stocks worth considering in the broader sector are eGain Corporation EGAN , Rosetta Stone Inc. RST and j2 Global, Inc. JCOM , each sporting Zacks Rank #1 (Strong Buy). You can see the complete list of today’s Zacks #1 Rank stocks here .

Long-term earnings growth rate for eGain, Rosetta Stone and j2 Global is pegged at 30%, 12.5% and 8%, respectively.

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For years, the sheer messiness of data slowed efforts to launch artificial intelligence (A.I.) and machine learning projects. Companies weren’t willing to wait a year or two while data analysts cleaned up a massive dataset, and executives sometimes had a hard time trusting the outputs of a platform or tool built on messy data.

Data pre-processing is a well-established art, and there are many tech pros out there who specialize in tweaking datasets for maximum validity, accuracy, and completeness. It’s a tough job, and someone has to do it (usually with the assistance of tools, as well as specialized libraries such as Pandas). But now IBM is trying to apply A.I. to this issue, via new data prep tools within AutoAI, itself a tool within the cloud-based Watson Studio.

“We have seen that complexity of data infrastructures can be daunting to the most sophisticated companies, but it can be overwhelming for those with little to no technical resources,” Rob Thomas, General Manager of IBM Data and AI, wrote in a statement.“The automation capabilities we’re putting Watson Studio are designed to smooth the process and help clients start building ML models and experiments faster.”

In addition to data cleanup, AutoAI includes a number of other tools for building A.I. and ML algorithms, including ones that set optimal hyperparameters (which are the parameters with values set before the machine’s learning begins). There’s also IBM Neural Networks Synthesis, or NeuNetS, which creates customized neural networks (users are asked to optimize for either speed or accuracy).

IBM is competing fiercely with Google (which is plunging into the ML-automation game with AutoML Video and AutoML Tables, with other tools surely on the way) and Microsoft (which has automation and recommendation tools built into its Azure Machine Learningplatform) to claim the attention of companies interested in the A.I./ML market. If that wasn’t enough of a crowded landscape, Amazon is plunging heavily into the enterprise-automation game with Amazon Personalize, which streamlines everything from mobile-app development to email marketing.

Of course, the rise of A.I./ML automation could lead to a new host of problems. Sure, having tech professionals build bespoke algorithms and tools in-house is a painstaking process with a fair amount of risk (if you fail, you’ve burned tons of time and resources), but there’s the reasonable expectation that you’ll have something tailored to your needs, based on reliable data and math. When you begin to automate these processes, you risk obfuscating at least a portion of the data and logic behind dashboards—which might lead some to question the output of the work.

Then again, many firms can’t afford to even begin an internal, customized A.I./ML program; in that context, these automated solutions are the best (and perhaps only) bet if they want to get into this particular game.

For tech professionals, these new tools are yet another sign that the A.I./ML market is maturing. Those professionals who understand how these tools work—as well as the underlying logic and theories—will have their pick of positions, as companies desperate for A.I./ML talent are willing to pay enormous salaries and benefits. Although these technologies might seem daunting, there are a number of resources designed to give you a solid education; check them out.

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