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Amazon sends Alexa developers on quest for ‘holy grail of voice science’

June 15, 2019 Big Data

At Amazon’s re:Mars conference last week, the company rolled out Alexa Conversations in preview. Conversations is a module within the Alexa Skills Kit that stitches together Alexa voice apps into experiences that help you accomplish complex tasks.

Alexa Conversations may be Amazon’s most intriguing and substantial pitch to voice developers in years. Conversations will make creating skills possible with fewer lines of code. It will also do away with the need to understand the many different ways a person can ask to complete an action, as a recurrent neural network will automatically generate dialogue flow.

For users, Alexa Conversations will make it easier to complete tasks that require the incorporation of multiple skills and will cut down on the number of interactions needed to do things like reserve a movie ticket or order food.

Amazon VP David Limp refers to Conversations as a great next step forward. “It has been sort of the holy grail of voice science, which is how can you make a conversation string together when you didn’t actually programmatically think about it end-to-end. […] I think a year or two ago I would have said we didn’t see a way out of that tunnel, but now I think the science is showing us that [although] it will take us years to get more and more conversational, […] this breakthrough is very big for us, tip of the iceberg,” Limp said.

It begins with a night out and casual conversation

The Alexa Conversations journey is first emerging with a night-out scenario. In an onstage demo last week at re:Mars, a woman buys a movie ticket, makes dinner reservations, and hails a ride in about one minute. (Atom tickets, Uber, and OpenTable are early Alexa Conversations partners.)

The night-out scenario is the first of what Amazon says will become a collection of bundled experiences to get things done.

Conversations may someday power more difficult tasks such as a weekend trip scenario that Limp demonstrated last fall at an event to introduce nearly a dozen new Alexa-powered devices. Limp’s talk of a holy grail is a transformation that every major tech company in the world with an AI assistant is chasing: to evolve assistants from a voice interface that completes basic tasks one at a time to an assistant that can handle complex and complicated tasks.

Two years ago, during a rare onstage gathering of current or former leaders from Alexa, Google Assistant, Siri, and Cortana teams, Viv cofounder and Siri co-creator Adam Cheyer — a person who’s pondered the future of voice assistants since the 1990s – wondered aloud about an assistant that can guide you through the scenario of planning for your sister’s wedding. (Samsung acquired Viv in October 2016 to enhance their Bixby AI assistant.)

At the event, Cheyer talked about how voice will define the next decade of computing and the importance of bridging first-party AI assistant services with a third-party voice app ecosystem. “I don’t want to have to remember what a car assistant can do, the TV system do, the Alexa versus Cortana versus … too much. I want one assistant on every device to access every service without any differentiation between what’s core and what’s third-party,” Cheyer said.

Amazon is working towards that end, starting by reducing the number of interactions you need to get things done with Alexa. Last fall, Amazon introduced Follow-Up Mode, so you can engage in multiple interactions but only have to say the “Alexa” wake word once. With Conversations, the number of interactions necessary to execute the night-out scenario is cut down from 40 to about a dozen back-and-forth interactions.

To further increase the perception that Alexa is capable of natural conversation, the AI assistant learned to whisper when a person is whispering, and can now respond to name-free skill invocation. That means you can say “Get me a ride” instead of first having to launch the skill by saying, “Alexa, launch the Uber skill.”

Creating the perception of intelligence

Amazon isn’t alone in its ambition to make an assistant capable of fluid conversation like the kind you’d expect from another person. Google introduced Continued Conversations so you don’t have the say the wake word to continue to talk about something. Alexa Conversations also gives Amazon’s AI assistant the power to quickly take care of things or engage in commerce akin to Google Assistant’s new food ordering powers and Google’s Duplex. Duplex for the Web and deep connections between Android apps and Google Assistant made their debut last month. Microsoft is also bringing similar intelligence to workplace assistants with Semantic Machines, a startup it acquired in 2018.

It all points to the issue that more complex tasks require more than a single exchange, which Alexa AI senior product manager Sanju Pancholi emphasized. “When you’re starting to solve more complex problems, there is more give and take of information, there are more decisions at each point in time, and hence there are multiple actions that can come in context of the same conversation with different individuals,” he said.

He led a session at re:Mars to make a pitch for Alexa Conversations for businesses and developers, and talked about an assistant that can “solve their product and service needs in the moment of recognition when they realize they need it.”

To be seen as intelligent, Amazon thinks an assistant should understand natural language, remember context, and make proactive predictive suggestions, traits that can prove an assistant is smart enough to accomplish more complex tasks. Doing away with a need to repeat yourself is also critical.

“If you make [customers] repeat information again and again and again, you are forcing them to believe that they are talking to a dumb entity, and if that’s the rapport you’re building with them from the get-go, the chances are they’re never going to delegate higher order tasks to you, because they will never think you’re capable of solving higher-order problems for them,” he said.

The Alexa Skills Store now has more than 90,000 skills, and 325,000 developers have used the Alexa Skills Kit, Pancholi said. Alexa is now available in 100 million devices.

Pancholi shared with developers that potential next steps for Alexa Conversations scenarios may include collections of skills to help people watch content at home, get food delivered, or buy a gift.

Skills on skills

In an interview with VentureBeat, Alexa chief scientist Rohit Prasad declined to share details about use cases that may be taken up next, but believes this could include ways to help plan a weekend. Prasad, who has led Alexa AI initiatives for language understanding and emotional intelligence, said Conversations is designed to stitch together the voice ecosystem for engagement increases for skills and Alexa alike.

“The developer proposition is that you start getting more traffic and more discovery as the more cross skilled we become, like the fact that night out experience is now getting you to order a cab. So Uber and Lyft will see more traffic as well and more customer engagement. So that, and plus skill discovery will happen naturally as part of that. So that’s a huge piece of our value proposition in this case.

Even Blueprints — voice app templates for private, custom Echo skills — may soon incorporate Conversations, Prasad said. Batches of custom skills for the home could, for example, walk kids through multi-step routines, do chores, and help countdown to important dates.

The first proactive Alexa features — Hunches, which suggests event reminders and smart home actions, and Alexa Guard for detecting the sound of broken glass or smoke alarm — were rolled out last fall.

Conversations could someday also become part of Amazon’s voice assistant for the workplace offering if the module is incorporated into Alexa for Business, which added support for Blueprints in March.

Brands, indie developers, and

In January 2018, CNBC reported that Amazon was in talks with brands like Procter & Gamble and Clorox to ink deals to promote their products to Alexa users.

Amazon Alexa VP Steve Rabuchin insists there’s no way for businesses or developers to get prioritized by Alexa’s voice app recommendation system, but the Alexa voice app ecosystem may face another problem. Because of the nature of how voice apps work often without a screen, packaging skills means some skills may inevitably be left out or won’t be ranked.

This is especially important for voice apps. Unlike searching for apps on a smartphone, Alexa delivers voice app recommendation engine only serves up three skills at a time.

“Our vision isn’t to end up where it’s just the biggest brands or most popular,” Rabuchin said in an interview with VentureBeat. “A lot of our most popular skills are indie developers, individuals developers.”

Amazon’s skills recommendation engine that responds when you say things like “Alexa, get me a ride,” recommends voice apps based on measurements like engagement levels, which Amazon started paying developers for in 2017. Whether a skill works

Conversations will incorporate skill quality measurements like user ratings, engagement levels Factors like regional significance, whether a skill works on a smart display, and personal information may also decide which skills appear during Alexa Conversations interactions.

“I think we have a good playbook to start from like, I don’t think it’s a perfect playbook, but it’s a great one to start with,” Prasad said.

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Big Data – VentureBeat

Help improve Power BI – Quarterly survey

June 15, 2019 Self-Service BI

We love hearing feedback from the Power BI community and today we have another quarterly survey to keep improving Power BI (both the Desktop and Service) with your feedback. This survey helps us to understand what we’re doing well, and what we can change to make your experience even better. This is a great opportunity to provide feedback directly to the product team and influence our development directly.

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AI in Power BI Survey

June 15, 2019 Self-Service BI

We’re exploring a new AI visual and would love to get some information about how you’re currently working. Please take this quick survey so that we can get a baseline understanding of how (if at all) you are using process mining in your organization. Be a part of shaping the future of AI in Power BI! It should take about 5 minutes.

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Nick Cannon To Host Morning Show On LA Radio Station Power 106

June 15, 2019 Humor

LOS ANGELES (AP) — Nick Cannon is coming to radio.

Meruelo Media on Friday announced the rapper, comedian and actor is joining Los Angeles hip-hop radio station Power 106 as host of its morning show. “Nick Cannon Mornings” launches Monday from 5 to 10 a.m.

In a statement, Cannon says he’s honored for the opportunity to “make our community and culture proud.”

Cannon hosts Fox’s “The Masked Singer” and MTV’s “Wild ’N Out.”

Meruelo Media president and COO Otto Padron says Cannon’s “tremendous success across television, film and music is a testimony of his unique talent, work ethic and impeccable reputation.”

BET Networks And Irv ‘Gotti’ Lorenzo Announce The Season Two Premiere Of ‘Tales’

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AI Weekly: ICML 2019 top papers and highlights

June 15, 2019 Big Data

This week in sunny Southern California, the world’s community of AI researchers had one of its biggest annual gatherings, as the International Conference on Machine Learning (ICML) took place in Long Beach.

VentureBeat didn’t have a reporter at ICML, but you don’t have to be in Long Beach to read the latest state-of-the-art research and breakthrough advances that move the AI needle.

Top papers

Conference organizers gave best paper honors to “Rates of Convergence for Sparse Variational Gaussian Process Regression” from University of Cambridge and “Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.”

With authors from ETH Zurich, Max-Planck Institute for Intelligent Systems, and Google Brain, the latter work evaluates more than 12,000 disentanglement models to dispel some beliefs. It asserts, for example, that unsupervised learning of disentangled representations is impossible without inductive biases on models and data.

“Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets,” the paper’s abstract reads.

The group also released a library of 10,000 pretrained disentanglement models for training, evaluation, and future research.

Another paper aimed at challenging AI industry assumptions was among top honorable mentions. “Analogies Explained: Towards Understanding Word Embeddings” by University of Edinburgh researchers examines neural word embeddings like word2vec that power natural language processing.

Researchers at MIT’s Media Lab, Princeton University’s Institute for Advanced Study, and Google’s DeepMind devised methods for coordination and communication of multi-agent reinforcement learning to earn an honorable mention, as as did two University of Oxford Department of Statistics papers.

Popular author presentation videos together with slides can be found on the SlidesLive website.

AI reproducibility

For the first time, this year event chairs asked researchers to include code to prove their findings at the same time that they shared their paper manuscripts. Researchers around the world submitted more than 3,000 papers, and organizers accepted nearly 800 manuscripts.

Sharing code helps verify scientific results shared in research papers. Sharing code at manuscript submission time instead of upon acceptance also appears to be good for judges evaluating papers, as more than half said they found it helpful when deciding what’s worthy of being accepted for publication.

The code-at-submit-time experiment found that 67% of accepted papers submitted code along with research to back the validity of their claims. In a Medium post that shares experiment results, co-chairs suggested a common code repository and common archive to further back reproducibility over time.

A breakdown of ICML participants by affiliation found that top contributors include Google; Microsoft; Facebook; MIT; Stanford University; and University of California, Berkeley.

In another initiative this week to encourage replication of results, Facebook introduced PyTorch Hub, an API and workflow for research reproducibility and support. The hub in beta comes with about 20 pretrained models for replicating at launch.

Other notable research we came across this week includes work by Intel AI to combine reinforcement learning methods to train a 3D humanoid to walk and another that demonstrates how to compress models without accuracy loss.

Beyond ICML, we wrote about AI created by University of York researchers that can predict when Dota 2 players will die, Facebook AI’s MelNet generative model that can sound like Bill Gates delivering a TED Talk, and evidence that Alexa’s speech error rate continues to decline.

ICML continues Saturday with a number of workshops like reinforcement learning for real life, the 6th AutoML workshop, and using AI for fighting deepfakes and climate change.

More research is coming. The Computer Vision and Pattern Recognition (CVPR) event — also in Long Beach and also considered one of the largest annual AI conferences, according to the 2018 AI Index report — begins Monday.

The above research isn’t meant to be a comprehensive list of noteworthy research from ICML — just a glance at work that caught our eye this week. So if you know about great research from ICML or other conferences that you think merits coverage, send news tips to Khari Johnson and Kyle Wiggers — and be sure to bookmark our AI Channel and subscribe to the AI Weekly newsletter.

Thanks for reading,

Khari Johnson

Senior AI staff writer

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Big Data – VentureBeat

An Outcome-Driven Enterprise Data Strategy: Ongoing Data Maintenance

June 14, 2019 BI News and Info

Part 4 in the 5-part Enterprise Data Strategy series, which explores the importance of leadership and accountability in directing an overall data strategy tied to business outcomes.

People relocate, transfer jobs, and switch companies. Businesses fail and merge. Email addresses change. If you have data, it’s going to decay – that’s a given. In fact, 94% of businesses suspect that their customer and prospect data is inaccurate (Zoomdata). Yet ongoing data maintenance is the most overlooked aspect of an outcome-driven enterprise data strategy.

That’s why in this fourth installment of our data strategy series, I’m going to dive into ongoing, proactive maintenance: why it matters, what it encompasses, and how to get started.

data blog 4 An Outcome Driven Enterprise Data Strategy: Ongoing Data Maintenance

Why is ongoing data maintenance important?

What good is data if the quality is poor? It’s not worth much and could quite possibly be costly. According to Gartner, “organizations believe poor data quality to be responsible for an average of $ 15 million per year in losses… and this is likely to worsen as information environments become increasingly complex – a challenge faced by organizations of all sizes.”

When building an analytic platform or moving data from legacy systems to a new solution, companies tend to put a lot of effort into profiling, cleansing, and enriching data. However, building an always-on data-maintenance capability is often neglected – and given that change is a fact of life, this is a risky proposition.

It’s essential that your outcome-driven enterprise data strategy defines how you will manage your company’s most critical data on a continuing basis, specifically:

Data-quality business rules and data operations

Data-maintenance shared services

Service-level agreements (SLAs)

Required data-maintenance processes and KPIs

Accountable owners

What are the keys to success?

The number-one key to success is ensuring that your maintenance program is proactive, coordinated, and always on. Automation is recommended, but you must still have accountable owners in business and IT who are responsible for:

Creating and updating the business rules

Reviewing the ongoing data operations and quality reports for issues that require resolution

Establishing remediation efforts for any discovered issues

What are the “gotchas”?

The biggest gotcha is assuming that people will keep their data clean if you give them the tools. This rarely happens unless they’re motivated to do so, such as their paycheck or invoice payment being dependent upon accurate information. However, even if they maintain these needed data fields, there may be other critical fields that are ignored simply because they aren’t as important to the person.

Employees, for example, will typically keep their banking information up-to-date but maybe not their business unit. Similarly, a sales executive is incentivized to maintain bill-to-contact information but not ship-to-contact. That’s why you need accountable business and IT owners to oversee the program.

How do you get started?

You’ve done a lot of the work already when establishing the business rules in the organization and governance section of your data strategy. Reuse them – again and again. Many of the rules you created to get your data ready for a big project will be the same ones you should use to maintain the fields.

The second thing you should do is transform your workflow-based systems into proactive, always-on processes. What is the difference? Workflow-only systems require a workflow before anything happens. With an always-on process, on the other hand, there’s always a program running an email validation every month or twice a year, for example.

Setting up this type of approach requires a shift in mindset. We tend to think people keep all their accounts clean everywhere. But do you update your phone number, address, title, etc. everywhere? Probably not. That’s why tools and workflows alone aren’t enough to keep your company’s data from decaying. You need an ongoing, proactive data maintenance program as part of your overall data strategy.

For more information

Reach out to us (Maria for North America and Tina for everywhere else) to inquire about a 1:1 enterprise data strategy discussion

And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.

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Digitalist Magazine

Sizing The Prize: The Digital Finance Advantage

June 14, 2019 SAP

Everywhere we look, we see finance experimenting with and deploying smart automation solutions, like intelligent data capture, cognitive computing, and robotic process automation (RPA). Finance executives tell us they expect that digital transformation will have a dramatic impact on their performance, their operating model, and their skills and role definitions in the next two to three years. The results of The Hackett Group 2019 Key Issues Study predict a steep upward trajectory in the adoption of digital tools over the same period. For example, we foresee a more than threefold increase in the implementation of robotics.

But just how much impact can digital have on finance’s performance?

As finance people, we like numbers. So at The Hackett Group, we leveraged our extensive finance benchmark database to quantify the effects of smart automation on functional efficiency – at least for now – by looking at the effects of automation on full-time equivalent (FTE) requirements. We then used that information to calculate finance operating cost as a percentage of revenue.

We didn’t select efficiency metrics randomly. Nor did we do it just because it’s easier to collect and analyze the data. The reason we chose to focus on cost optimization first is that it is a huge driver of finance going digital. Seventy-five percent of finance organizations in our 2018 Digital Transformation Performance Study listed cost reduction as the number-one business objective for going digital.

The reality is that cost is a front-and-center issue for finance executives today (see image below). They are under pressure to improve margins by shrinking the bottom line, as senior executives worry about a weaker global economy, escalating trade wars, and the relentless introduction of new technologies and business models. The Hackett Group’s 2019 Key Issues Study revealed that cost reduction was finance’s top improvement priority for this year.

Nilly figure 1 Sizing The Prize: The Digital Finance Advantage

Quantifying the digital advantage

Is digital transformation helping finance meet these imperatives?

When we ran the numbers, we found exciting news. Our analysis of the efficiency impact of digitalization shows that the typical (i.e., peer) finance organization can slash its operating cost by 42% through digital transformation. Thus, by taking full advantage of digital tools, peers can match the efficiency levels of today’s world-class finance functions. Plus, even the best of the best (we label them world-class) can significantly widen the efficiency gap with peers (see image below).

Nilly figure 2 Sizing The Prize: The Digital Finance Advantage

Moving all the way to the right on this chart (from peer to digital world-class) takes significant time and resources. The shift must involve not just technological advancement, but also re-tuning processes and a massive effort to reskill talent.

But finance cannot wait that long. Our data also shows that by just deploying smart automation solutions, peers can accelerate their journey to world-class by 13%. Emergent digital technologies take a lot less time to implement and yield results faster. Thus, while it’s important that finance takes a holistic approach to digital transformation, it must also take immediate steps so as not to fall behind. From both our research and our work with clients, we know that this is already happening, and at an ever-faster pace.

Of course, efficiency is only one of the measures of digitalization success; the other two are effectiveness and experience. (Taken together, we call them the 3 E’s.) It’s harder to quantify how smart automation impacts the other two E’s. But we’ve made headway there, too. On the effectiveness front, we see that digitally enabled finance functions make fewer mistakes and complete their planning cycle much faster. On the stakeholder experience side, we found that internal customers of highly technology-enabled finance organizations are significantly more likely to perceive the function as a valued business partner.

At the most fundamental level (beyond the 3 E’s), digital technologies will alter the very nature of finance work. According to our analysis, the digitally enabled finance function will be able to perform today’s work with half as many people.

That does not mean half of finance professionals will lose their jobs, although some jobs will become obsolete. What it means is that the kind of work finance does will expand and evolve into more value-creating activities, such as analytics. We already see signs of this shift. According to our 2019 benchmarks, world-class finance functions allocate 18% of their staff to analytics and value-creating work, compared to only 10% for peers. They also dedicate 30% more staff to strategy and transformation work.

Here’s a fact that may surprise you: Our data shows that for the past decade, the efficiency gap between peer and world-class finance organizations has remained fairly fixed at 40%-45%. Digital transformation can disrupt the pattern and ultimately create a whole new category of world-class finance functions. What’s world-class today is not what’s going to be world-class tomorrow. Digital will change everything. The numbers foretell it. It’s therefore incumbent upon finance leaders to rev up their digital transformation engines.

Read more in The Hackett Group report, “World-Class Finance: Redefining Performance in a Digital Era.”

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Digitalist Magazine

Steve Harvey’s ‘Boys Mentoring Weekend’ Underway In Georgia

June 14, 2019 Humor

Steve Harvey is back in Georgia for his 11th annual “Boys Mentoring Weekend.” The five-day event began on Wednesday, June 12 and runs through the weekend. The event also includes “The Single Mothers’ Enrichment Summit” and the inaugural “Steve Harvey Invitational Golf Tournament.”

This Father’s Day weekend, over 200 teenage boys from fatherless households will join the radio and TV personality at The Rock Ranch in The Rock, GA, for a life changing experience. For the first time, the camp has partnered with Bethany Christian Services and will host youth in foster care and adoption programs. The U.S. National Guard and U.S. Fish and Wildlife have also partnered with the 2019 mentoring camp.

Guests include recording artists Ne-Yo and Cee Lo Green, author, life coach and TV personality Iyanla Vanzant and former NBA star Dikembe Mutombo.

“A young man without a father in his life has a hole in his heart the size of this father,” Harvey said in a release. “Through mentorship, these young men have received the tools and guidance to propel them both professionally and emotionally.”

Activities at the camp include STEM programs, educational forums, career development, financial literacy, motivational sessions and health and wellness clinics.

“The Single Mothers’ Enrichment Summit,” led by former Essence Magazine’s Editor-at-Large, Mikki Taylor, will host single mothers for conversations and educational sessions while providing them with the tools to help support and raise productive young men.

Tracee Ellis Ross To Lead ‘Daria’ Spinoff For MTV Studios

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Need Decision Maker Buy-In on a Business App? We can help!

June 14, 2019 Microsoft Dynamics CRM

You’ve seen a demo of a Rockton product and you are SOLD! But your decision maker may not be up to speed yet. Sometimes it may require a little more information on the benefits, support, features, and functionality to get things moving forward. We can help make your work simpler and easier® and that includes getting the decision maker buy-in.

Let’s talk about price

Is spending money on the software causing them to pause? Our products are competitively and affordably priced. Not only does the monthly subscription pay for the product, it includes unlimited product support as well. It also includes updates for supported versions so when you upgrade your system you can upgrade our software too.

Benefits are key

Does your company’s decision maker understand the benefits of the Rockton product? We can help communicate the time you will save, errors that will be eliminated, and repetitive tasks that will be avoided. This, in turn, will allow time to be spent on things needed to make your business grow and run smoothly. Our products are easy to install and user-friendly, making adoption a breeze. Here are some ways we can help them see the benefits:

Personalized demo
Trial the product in your system
Setup & Configure page in the product to help you configure and test the product
Access to blogs, KBs, video’s, and much more

Support – we are in it for the long haul

Is your decision maker concerned about adequate technical support? Rest assured, we have an excellent support team with years of knowledge specializing in Microsoft Products such as Microsoft Dynamics 365 & CRM. You have unlimited product support with the monthly subscription, and we are here to make sure your questions are answered.

Confidence – get the reassurance you need

Maybe your decision maker simply doesn’t know Rockton yet. We are happy to provide references or schedule a call to answer any questions or concerns. Customer and partner feedback along with continued subscription renewals lets us know that they trust and rely on our products. We hope we can make you a fan, too!

For more information about our products, you can view our Dynamics 365 Solutions page or feel free to contact us at [email protected] or 877-4-ROCKTON. If you already have a product and have some questions, please contact [email protected].

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How Microsoft hopes to preserve privacy while gathering AR map data

June 14, 2019 Big Data

As augmented reality (AR) technologies barrel toward ubiquity, a key concern among ethicists remains preserving privacy in data on which the underpinning AI models train. In order to localize themselves in space, AR apps require access to device cameras, which they use to construct digital maps of their surroundings. These maps are often stored persistently either on-device or remotely, and although images aren’t retained along with them, malicious actors with access to them could theoretically infer geometry, appearance, the layout of private spaces, knowledge about objects contained in those spaces, and other sensitive information.

That’s why a team at Microsoft and academic collaborators are investigating techniques that obfuscate data without sacrificing performance or accuracy. In a pair of papers scheduled to be presented at the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2019) conference next week in Long Beach, California, they demonstrate that 3D point clouds and camera localization features are prone to privacy attacks, and they offer a solution that involves transforming the 3D points such that scene geometry is concealed.

In the first of the two papers (“Revealing Scenes by Inverting Structure from Motion Reconstructions”), scientists from Microsoft Research and the University of Florida show that the aforementioned AR point clouds, reconstructed using structure from motion or simultaneous localization and mapping (SLAM), retain enough information to rebuild detailed scene images even after the source pictures have been discarded. Their novel AI system reconstructs color images in the scene given 2D projections of sparse 3D points and their features, even in the absence of attributes like visibility and in cases where the points are irregularly distributed.

How Microsoft hopes to preserve privacy while gathering AR map data

Above: The 3D line cloud representation (shown on the right) protects user privacy by concealing the scene geometry.

Image Credit: Microsoft Research

The second paper (“Privacy Preserving Images-Based Localization”) — the work of ETH Zurich and Microsoft’s Mixed Reality & AI lab — posits a privacy-preserving approach to AR localization that lifts 3D points in maps to randomly oriented 3D lines that pass through the original points. The new representations bear little resemblance to the original scenes on their face, but still allow for precise positioning because of the close correlation among the 2D image points and the 3D lines.

The teams say they’re investigating algorithms for camera localization services that will run on the cloud, and they believe that the mitigation technique in the second paper will enable users to share maps with AR apps in a privacy-preserving fashion.

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Big Data – VentureBeat

Amazon sends Alexa developers on quest for ‘holy grail of voice science’

It begins with a night out and casual conversation

Creating the perception of intelligence

Skills on skills

Brands, indie developers, and

Help improve Power BI – Quarterly survey

AI in Power BI Survey

Nick Cannon To Host Morning Show On LA Radio Station Power 106

Like this:

BET Networks And Irv ‘Gotti’ Lorenzo Announce The Season Two Premiere Of ‘Tales’

AI Weekly: ICML 2019 top papers and highlights

Top papers

AI reproducibility

An Outcome-Driven Enterprise Data Strategy: Ongoing Data Maintenance