Category Archives: Digital

Four Key Strategic Takeaways from the 2017 Open Data Science Conference in Boston: From Teams and Priorities to Communication and Crowdsourcing

Lux Research recently attended the 2017 Open Data Science Conference (ODSC) in Boston, a large, multi-day event with speakers ranging from Amazon’s Data and Analytics Practice Lead to the U.S. EPA’s Chief Data Scientist. The conference presented a solid mix of technical topics and strategic advice, covering trends in applied data science and specific techniques within analytics, big data, and machine learning. Although the agenda included a variety of technical deep-dives, four particular strategic themes stood out for us, each speaking to some of the bigger challenges and opportunities facing big data and analytics today: Continue reading

The Promise of Behavior Change: How Digital Solutions Deliver on Wellness

The ability to deliver on wellness, health, and safety by designing successful behavior change interventions is seeing an all-time high demand. Digital tools hold the promise of delivering scalable, personalized, and timely behavior change interventions.

While most digital behavior change tools have yet to showcase effectiveness, some interventions designed to align with behavior science are likely to deliver.

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Hacker-Farmers Break Connected Tractors’ DRM, and Deere’s Business Model

Smart, connected tractors from manufacturers like John Deere were intended to help bring about precision agriculture (see the report “Big Data in Precision Agriculture” [client registration required]), but their business model makes money by locking farmers into proprietary software and services. Such practices are not legal, but manufacturers skirt “right to repair” with End-User License Agreement (EULA) contracts that not only make farmers agree that only John Deere dealerships and “authorized” repair shops can work on the machines, they simultaneously indemnify the company from “crop loss, lost profits, loss of goodwill, loss of use of equipment … arising from the performance or non-performance of any aspect of the software.” Continue reading

Deutsche Telekom Joins the $578 Billion LPWAN Party With European City-Scale Rollouts

Late last month, Deutsche Telekom (DT) made a big announcement: the company has rolled out NarrowBand-IoT (NB-IoT) networks across Europe. Digging into the details, DT told us that it now has NB-IoT capability in eight countries, all across Europe: Germany, the Netherlands, Greece, Poland, Hungary, Austria, Slovakia, and Croatia. Other key details in the announcement are that ista is a partner of DT, offering the “first NB-IoT smart building solutions,” and that the latter has also developed a “Prototyping Hub” to develop solutions for different industry segments. Continue reading

Voice-Based Assistants Are Beginning to See More Uses Outside of the Smart Home

What They Said

With the increasing availability and competition between voice-controlled smart home assistants ([see the October 18, 2016 LRSJ] client registration required), Lux recently interviewed Dawn Brun, Senior Manager of Public Relations from Amazon, about its Alexa platform and its future direction. Dawn said that Alexa, like many other voice-based assistants, relies on four key components to drive its conversational interface – Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Dialogue Management, and Text to Speech (TTS):

  • The first step to answering correctly is speech recognition – hearing correctly. ASR is how we “hear” the user’s speech and convert it to text that we can then process. This is the challenge we had to overcome for Amazon Echo and Alexa – how do you get the machine to understand you from a distance, (i.e. in the far-field environment)?
  • Second, we need to make sure we understand the user correctly. NLU helps us parse the user’s request into their true intent. This enables us to find the meaning behind the speech. NLU is a particularly interesting problem, as we want to clearly understand what you are saying. A human-being is very good at disambiguating multiple responses, but with a voice interface you want to try to make the one, right choice from the very beginning for them.
  • Third, we need to decide how to respond to the user and take an action to address the request. We call this dialogue management. There’s also a personalization element here. We need to give the user the right response based on past behavior and preferences. So when a user asks to skip a song, we have to quickly deliver a new song that they will like.
  • Finally, TTS – we convert text back to speech to respond to the customer’s request. And of course, the TTS needs to be very natural.

When asked about the initial vision for Alexa’s implementation and its vision going forward, Dawn said, “We wanted to create a computer in the cloud that’s controlled entirely by your voice – you could ask it things, ask it to do things for you, find things for you, and it’s easy to converse with in a natural way. We’re always inventing and looking at ways to make customers’ lives easier. We believe voice is the most natural user interface and can really improve the way people interact with technology.”

Asking how Alexa compared to other voice-based assistants, such as Google Now, Microsoft’s Cortana, Apple’s Siri, or Facebook M, Dawn said, “Alexa is different than a voice assistant on a phone or tablet, which is designed to accompany a screen. Alexa was designed with the assumption that the user is not looking at a screen; therefore, the interactions become very different than with other voice assistants. Alexa isn’t a search engine giving you a list of choices on a screen; she’s making a decision on the best choice and delivering that back to the customer. We also leverage AWS, which is a huge advantage – things like huge processing power, Lambda, IoT.”

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WEBINAR – Blockchain Beyond Finance: Power, Supply Chain, IoT, & Much More

There is tremendous hype around blockchain, as venture firms throw billions at startups and developers begin porting the concept outside of the financial services industry. Beyond the hype, there is immense confusion around the appropriate use cases and the emerging participant ecosystem. Enterprises are uncertain about how blockchain will impact their businesses and they are even more uncertain about how to capitalize on the opportunity. In this webinar, we framed the evolving value chain, uncovered real world examples of industrial enterprise deployments, and explored the future of blockchain in industrial use cases beyond finance. Continue reading

Our Top Picks for Must-Attend Wearable Electronics Conferences in 2017

2017 is set to be the biggest year yet for wearable electronics conferences; currently, there are 70 scheduled wearables conferences globally. Many conferences look at the broader innovation happening in wearables, with 41% (28 total) focusing on overall wearable development; this can be in hardware, software, or niche use cases. Conferences focusing solely on software and app development follow closely behind with 33% of the total. Augmented reality (AR) and virtual reality (VR) have been receiving more attention recently in wearable electronics and similarly in digital health & wellness (client registration required).

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IBM Helps Walmart Leverage Blockchain to Improve Food Safety Outcomes

In October 2016, Walmart began testing blockchain’s capabilities to improve food safety across its complex global food supply chain. The initial proof-of-concept (POC) will track two products: one packaged produce item in the U.S. and pork in China. Using blockchain will enable the array of suppliers and distributors across the supply chain to record information on products in one central repository, rather than across a web of siloed, proprietary systems. In theory, this will increase transparency and create a permanent, traceable record of information as products move from supplier farms to Walmart shelves. Continue reading

Warehouse Robotics: Assistive Tools, or Replacements for Humans?

The e-commerce industry has seen a tremendous rate of growth, as is evident by the continuous increase in the volume of distribution centers around the globe. With this increase in the number of facilities, so too has the complexity and size of these installations grown. Despite this tremendous state of growth, the majority of logistics tasks are still primarily accomplished with manual labor. In order for distribution centers and other logistical operation sites to keep pace with this rate of growth, operation managers must, and indeed they have begun to, look at implementing automation products. Continue reading

Amazon Go Seeks to Break Big-Box Retailers With a Smaller, Smarter Store

Amazon recently announced Amazon Go, aiming to transform the ages-old brick-and-mortar retail experience. The official news broke via multiple channels, including a well-produced YouTube video showing shoppers entering a stylish grocery store. Central to the concept is the absence of any physical checkout system; shoppers check in upon arrival, and browse as they normally would. Amazon says it uses a combination of computer vision, machine learning, and artificial intelligence (AI) to track users and items throughout the store. When a shopper picks a product, this array of in-store sensors and back-end analytics automatically tabulate the final bill (deducting it from their Amazon account, of course), allowing shoppers to be on their way − “Just walk out technology.” According to initial reports, Amazon has actually built a 1,800 ft2 test site in one of its Seattle buildings. While today it is only open to Amazon employees, it says it may allow the public to shop in “early 2017.” To put the store’s scope in perspective, it is the size of a modest home, whereas most of the U.S.’s 38,000 supermarkets are 50,000 ft2 or more – more than 25 times the size of Amazon’s store. Continue reading