Several days ago, Rolls-Royce announced a brand-new “Airline Aircraft Availability Center.” According to a press release from the company, this facility is aimed at ensuring “every aircraft it powers departs and lands on time, every time.” The company monitors about 4,500 jet engines powering commercial airliners, which operate for a combined 14 million hours per year. The company said its new center will employ data analytics to optimize operations and plan and manage maintenance activities. Unsurprisingly, its SVP of Civil Aerospace Services said, “We are entering a new era of digital connectivity and new services technology which allows us to greatly expand the type of services we can offer….” Rolls-Royce also disclosed plans to make this availability center a hub for innovation, deploying new technologies such as “remote surgery” engineering tools; it is aiming to be using robots within the next five years. Continue reading
On June 16, Amazon announced it intends to buy U.S. grocery retailer Whole Foods for $13.7 billion. Whole Foods is a relatively young (founded in 1980), small chain with about 450 stores in mostly affluent neighborhoods (as well as nine in the U.K.), and focuses on organic, healthy, and relatively expensive products – earning it the nickname “Whole Paycheck.” In comparison, grocery giants like Albertsons and Kroger were founded in 1939 and 1883 respectively, and have more than 2,000 stores each. For its part, Amazon’s foray into grocery, Fresh, has not had much impact on the $700 billion industry. Still, this move is absolutely a big deal, and one that will reshape not only retail (news of the deal destroyed nearly $40 billion in market value of competing retailers) but also consumer packaged goods (CPG) categories like food, personal care, and home care, and impact industries beyond. We discussed five strategic implications with retail technology expert Jim Crawford of RED-LAB to ascertain what will happen now, what’s next for these industries, and where this positions Amazon to go in the future: Continue reading
For centuries, the way in which consumers interacted with food had been dictated by a set of fixed factors. With evolving consumer demand and growing innovation, however, thousands of tech developers are now trying to help a much more complex consumer choose, buy, and measure the impact of food. Continue reading
We’ve expressed in the past that accurate and reliable early-stage disease detection combined with non-invasive sample collection is the holy grail of molecular diagnostics. Previously, we discussed the growing popularity of non-invasive saliva-based diagnostics in the context of this theme (see the insight “Digital IVD sample of the future: Saliva” [client registration required]). While less mature, sweat based tests, too, present a compelling avenue for non-invasive sensing in medical, enterprise, and consumer applications. To gauge the state of innovation in sweat sensing, we surveyed the evolving landscape of sweat sensors patents. In total, we identified 1,009 patents for the search terms “sweat sensor” and “perspiration sensor” published in the past decade. As evident by Figure 1 below, sweat sensing technologies have seen consistent increase in patents applications. 2016 saw most activity, with a total of 194 patent grants and applications.
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 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.
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
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
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.”
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