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Archivo del Autor: Belen De Leon

FCC takes another swipe at illegal robocalls and text spam – CNET

The agency stirs up controversy with its latest proposal to give wireless carriers wide latitude to block and filter text messages.
Source: CNET

Tech’s New Harassment Policies Are Too Late for Some Women

Companies say they will let employees sue over claims of harassment, rather than go through arbitration; but many policies don’t cover older cases, or other claims.
Source: Wired

Watch 2 Helicopter Pilots Pull Off A Daring Wildfire Rescue

Two chopper pilots pulled off a daring rescue from the Woolsey Fire, plucking three people and two dogs off a ridge in hideous flying conditions.
Source: Wired

Kindred’s robots help retailers handle fulfillment centers — and take on Amazon

Since taking the reins as chief executive of Kindred at the beginning of the year, Jim Liefer has been focused on commercializing his company’s autonomous robots. But unlike forward-projecting use-cases for robots that may (or may not) one day take over for human beings in a wide swath of functions, Kindred’s current robots are purpose-built for the floor of retail fulfillment centers. That puts Kindred in the middle of an interesting business question: Given rising consumer expectations associated with online ordering, can anyone match or beat Amazon when it comes to speed, accuracy and efficiency?

With a background in operations at Walmart and One Kings Lane, Liefer asserts that his company’s core IP represents a significant advancement in retail operations. That’s because while industrial robots have worked well on manufacturing floors, robots have historically underperformed in e-commerce fulfillment centers, which require systems to handle objects of various shapes and sizes. Kindred’s approach is also notable because of its low-risk model that doesn’t require customers to make major capital investments. Instead of paying for the robot hardware, clients such as Gap pay based on the robots being able to successfully pick and sort items in a warehouse.

In the interview below, Liefer was eager to elaborate on his company’s core product, SORT. He was also happy to address the labor and throughput challenges facing Kindred’s clients as they look to thrive this holiday season. Finally, he offered his candid perspective on the ongoing debate over AI and jobs.

Gregg Schoenberg: Jim, it’s good to see you. I was interested in talking with you because Kindred is focused on the unsexy, but very important part of robot and AI technology that deals with e-commerce and gives insight into how our economy is changing. And by unsexy, I mean that your robots don’t do parkour.

Jim Liefer: Thanks, Gregg.  I’ll start out by saying that sexy is in the eye of the beholder. If you came from retail operations companies like Walmart, sexy would be not having to re-engineer or re-architect my building every year to handle the next peak.

GS: Fair enough. So where has that “sexy” journey taken the company today?

JL: We’ve evolved from a research and engineering company into a customer-focused organization. Today, there are four primary components that Kindred is working on: vision capability, grasping/manipulation capability, ability to identify what’s being held onto and then placing an item somewhere.

GS: And today, your solution is being applied to retail fulfillment centers?

JL: Yes, in retail fulfillment distribution centers, but not the consumer-facing side of retail. Still, there is a tremendous amount of automation in these centers. There are sorters and power conveyance, and there are forklifts running around. But we saw gaps in those in-between moments, the need to take individual pieces from automation A to automation B. That’s where Kindred now can fill those gaps, and it’s a big market.

GS: Do you make robots or do you make cobots?

JL: We’re absolutely collaborating with the humans, but we’re not letting them get that close to the robot. We’re letting the humans do what they do best, like higher-level thinking and dealing with more ambiguity than the robot can handle.

GS: But your solution is designed with the intent that there are going to be people that interact with it?

JL: For some period of time to come, I believe that is going to be true. That’s the design of what we have now. The reason I say it that way is that even today, the aspects of how product arrives at our solution varies, and some day, there might be another mobile robot that serves our robot, that brings the product to us.

Product

GS: At the core of the solution is your autograsp technology, right?

JL: The autonomous grasp algorithm is the core of our AI technology, which is combined with vision and grasping capabilities.

GS: I’m guessing that even though that grasp technology looks simple, it’s actually a big feat of both software and hardware innovation.

JL: Yes, absolutely. The grasping technology is a combination of AI that can understand the ambiguity that it’s dealing with. But there’s also the the physical side of it. Not only do you have to be able to get to a grasp-point, but you also have to grip it correctly.

Some day, there might be another mobile robot that serves our robot, that brings the product to us.

GS: What’s the inherent challenge with getting the gripping correct?

JL: It needs to be precise enough to pick up the item you want. It also has to have enough torque to be able to hold onto the item when you’re moving it.  

GS: Why is that so critical?

JL: Because you have to move at a speed that’s equivalent to a human or better in order to not lose it.

GS: What’s the installation process associated with putting a system into a facility?

JL: We literally roll them off the truck, roll them into place, plug them into 110 power and a data port, and maybe do some final provisioning of software. All in, it takes us anywhere from five to eight hours to set up a robot. So it’s definitely plug and play.

Business Model

GS: I know you don’t actually sell the solution to a customer. Can you walk me through your model?

JL: In the days when I was in a Walmart facility and I wanted to implement a new solution, I would go out to a service provider and they would tell me how many millions of dollars to plunk down. I would pay for it and then someone would come in and build it, and then they would go away and I would try to operate it.

GS: How antiquated.

JL: In our world today, they tell us their throughput need and how many products they are trying to serve with robots. We then deploy the number of robots to the customer. We have an agreement that says you need 10 robots or whatever the number is, and we deploy robots that will serve X amount of products.

GS: How does the money flow work?

JL: It’s a robots-as-a-service model, where every time we successfully grasp and stow a product or item, they pay us something.

It takes us anywhere from five to eight hours to set up a robot. So it’s definitely plug and play.

GS: A commission of sorts.

JL A commission, right. So it’s not a purchase and walk away. And there are several reasons why we think that’s compelling for the customer. One, because it’s not a capital expenditure play for them; it doesn’t have to be multiple weeks, months or even years to get onto the capital budget. It’s an operating expense play.

GS: That sounds like a key consideration.

JL: Think of it this way. When an operating expense comes into play, in many cases, a director-level person of a fulfillment center can make the choice: Am I going to hire a human to do the job, or I can hire a robot to do that job?  The other is that because we’re providing a service to the customer, we’re right there alongside them. It’s not as though we gave them something and said figure it out.

GS: Aren’t you making it very easy for clients to keep the robots around? Because it’s not costing them to have the robots sit on the fulfillment center floor.

JL: Well, okay, good question. In our model, we still have a minimum for the customer,  because we’re paying down the robot. We don’t want to have a robot sitting there idle.

GS: In that case, what’s the break-even on how long the robot needs to be on site with the client?

JL: It’s somewhere between a year and a year and half to get the payback to cover the cost of building the robot.

The Kindred.AI sorting robot in the lab.

GS: Does the counterparty risk become a factor? Because these machines are obviously expensive.

JL: Yes, that comes into play. At the same time, the robots themselves are quite… I want to say the word mobile. It’s relatively low-pain for us to roll them out and roll them to another customer facility that’s probably nearby. Of course, we don’t want to do that, but it’s possible to do it.

Amazon

GS: Of course not. But you’ve spent many years at Walmart, and you’re obviously very aware of the existential threat that Amazon poses to just about everybody that isn’t Amazon. Does Kindred aspire to help others thrive in a retail economy that is increasingly dominated by Amazon?

JL: Yes. It levels the playing field, because if our customer, the retailer, is able to have better throughput, get the products into the hands of the customer faster, then they have the ability to hold onto their customers. If they don’t do it, then those customers are going to go somewhere else.

Am I going to hire a human to do the job, or I can hire a robot to do that job?

GS: Looking to the future, do you want to go deeper within the apparel channel, or do you see other retail applications for your grasping technology?

JL: To recap, we figured out a very difficult problem, which is how to handle clothing in a polybag with a label on it. What seems like the most logical and reasonable place to go is to smaller items and maybe toy items or jewelry.

GS: But it has to be in a bag?

JL: It doesn’t have to be in a bag. In testing, we can pick up a pen or a pencil. We can pick up an iPhone and even general merchandise-related items like baby wipes or rubber balls.

Technology

GS: Let’s dive into your technology a little deeper. Is your tech based on reinforcement learning or deep reinforcement learning?

JL: Actually, both. The way that we’re operating the current SORT robot is that there are multiple AI algorithms that are running in concert together. So there’s the autonomous grasp algorithm, there’s a grasp verification algorithm, there’s a stow algorithm; there are multiple algorithms that are running to maintain that speed and accuracy. Then, there’s our team in the Toronto office—

GS: —That’s the team working on deploying more reinforcement learning?

JL: Yes, the reinforcement learning which would replace some of the deep learning algorithms that we have in place today.

GS: I read up on Rich Sutton, who, based on my research, is a big deal in reinforcement learning—

JL: —Yes. He’s a big deal and is a mentor to several of our people.

It’s relatively low-pain for us to roll them out and roll them to another customer facility that’s probably nearby.

GS: Sutton describes reinforcement learning as a learning system that wants something. Can you describe in lay terms how this is central to Kindred’s technology and how it is different than deep Learning?

JL: Here’s how I think of reinforcement learning versus deep Learning. Reinforcement learning is allowing the algorithm to determine all of the possible outcomes and all of the possible permutations. Think about something in a space where you want you to go from point A to point B. In reinforcement learning, that robot will achieve the goal by doing something called body babbling, which looks like it’s jittering around, looking at all the different possible solutions.

GS: So it takes longer to train a reinforcement algo?

JL: Yes, because in deep Learning, you are going to give it some sort of structure within parameters, because you sort of know what you want it to do. Then you look at body babbling, which is a much cleaner solution because the algorithm knows how to deal with all these variables because it’s explored every permutation.

GS: I saw that Kindred released a research paper last month. My top-line takeaway is that while reinforcement learning has made progress, it’s tough to train robots.

JL: I view it this way: In the last two years that I’ve been here at Kindred, I’ve seen things on a daily basis that I didn’t think were possible the week before or the month before. That’s a blanket statement, though, which is one of the reasons why I think people are anxious about AI and automation.

AI Anxiety

GS: So let’s talk about AI anxiety. Yesterday, I was on Bush Street and I watched this Cafe X robot serve coffee. Meanwhile, across the street, you’ve got this Blue Bottle that’s teeming with people, keeping its staff quite busy. Is that the future you see? Where workers are in demand, even in an era of well functioning robots that can grasp stuff?

JL: I  think back to Tower Records in San Francisco in the 90s. It used to be packed. I mean, that’s where I spent every weekend. You never thought that would end, perhaps like some people at the Blue Bottle today. But there’s that flip point.

GS: I appreciate that honest comment.

JL: To me, I just think it’s inevitable, and I don’t think it’s bad. But I believe that we will embrace it, just as we embrace technology in our phones, because it will improve our lives in many ways and it will also make our lives more complicated.

GS: We’ve discussed previously, too, this idea that in the fulfillment centers where the Kindred robots are operating, there’s a labor shortage.

JL: Yes, there’s no employee there to do the job.

We can pick up an iPhone and even general merchandise-related items like baby wipes or rubber balls.

GS: And that’s because fulfillment centers are in locations that are often—

JL: —They’re clustered. They’re fighting for the same resources. Big Amazon has come in, they’re paying those workers more, so they’re siphoning all those workers away.

GS: What about temporary workers around the holidays?

JL: We said earlier that the robots are collaborative, working alongside and collaborating with the humans. Absolutely, there are places for the temporary workers to come in, and I want those humans to be fulfilled. In terms of helping our customer, it’s so painful to get even a temporary worker, give them a job that’s very mundane, have them leave and then have to hire another temporary worker.

GS: But Kindred is giving jobs to people with gamer skills, too, right?

JL: Yes, on the tele-operation side. About 85 percent of the time, our algorithm can do everything on its own. But 15 percent of the time, we have a human in the loop who steps in and assists the robot for about a second and half, and then steps back out.

GS: When you’re recruiting for these people, are you recruiting in the typical places that tech companies look?

JL: These people have a wide variety of backgrounds and skill sets. They might be gamer types, but some of them have marketing degrees and some of them have engineering backgrounds. There’s also a pool of generalists, those jack-of-all-trades kind of people.

GS: But they need to have pretty good dexterity, right?

JL: I don’t think it’s highly required. A lot of it is just point and click.

GS: Well, on that non-techy note, Jim, thanks so much for your time.

JL: Thanks very much, Gregg.

This interview has been edited for content, length and clarity.


Source: TechCrunch

Instagram’s Crackdown on Fake Followers Just Might Work

Instagram’s promise to root out “inauthentic activity” sends some social media marketers into crisis.
Source: Wired

Five years and one pivot later, Trueface emerges with a promise for better facial recognition tech

Shaun Moore and Nezare Chafni didn’t initially intend to develop a new standalone facial recognition technology, when they first got started developing the technology that would become their new company, Trueface.ai.

When the two serial entrepreneurs were planning their next act five years ago, they wanted to ride the wave of smart home technologies with the development of a new smart doorbell — called Chui.

That doorbell would be equipped with facial recognition software as a service. The company raised $500,000 in angel funding and opened a manufacturing facility in Medellin, Colombia.

What the two entrepreneurs discovered was that most existing facial recognition tools lacked the ability to identify spoof or presentation attacks, which rendered the tech unfeasible for the access control functions they were trying to develop.

So Moore and Chafni set out to develop better software for facial recognition.

 

“In 2014 we focused our engineering efforts on deploying face recognition on the edge in highly constrained environments that could identify hack or spoof attempts,” Moore, the chief executive of Trueface.ai said in an email. “This technology is the core of what has become Trueface.”

With the upgrades to the product, Chui began tackling the commercial access control market, and while customers loved the software, they wanted to use their own hardware for the product, according to Moore.

So the two entrepreneurs shuttered the factory in 2017 and began focusing on selling the facial recognition product on its own. Thus Trueface was born.

It’s actually the third company that the two founders have worked on together. Friends since their days studying business at Southern Methodist University, Moore and Chafni previously worked on a content management startup, before moving on to Chui’s smart doorbell.

The company spun Trueface out of Chui in June 2017 and raised seed capital from investors including Scout Ventures with Harvard Business Angels and GSV Labs. That $1.5 million round has powered the company’s development since (including the integration with IFTT earlier this year to prove that its system worked).

But over the past few years, as damning stories around the risks associated with potentially bad training data being applied to facial recognition technologies continued to appear, the company set itself another task — aligning its training data with the real world.

To that end the company has partnered with a global non-profit which is collecting facial images from Africa, Asia and Southeast Asia to create a more robust portfolio of images to train its recognition software.

“Like many facial recognition companies, we acknowledge the implicit bias in publicly available training data that can result in misidentification of certain ethnicities,” the company’s chief executive has written. “We think that is unacceptable, and have pioneered methods to collect a multiplicity of anonymized face data from around the world in order to balance our training models. For example, we partnered with non-profits in Africa and Southeast Asia to ensure our training data is diverse and inclusive, resulting in reduced bias and more accurate face recognition – for all.

The company has also established three principles by which its technology will be applied. The first is an explicit commitment to reduce bias in training data; the second, an agreement with its customers that in any case that goes to court, human decision making is privileged over any data from its software; and finally, an explicit focus on data security to prevent breaches and data transparency so that customers discloes what information they’re collecting.

“When implemented responsibly, people will demand this technology for its daily benefits and utility, not fear it,” writes Moore.


Source: TechCrunch

Our 3 favorite startups from Morgan Stanley’s 2nd Multicultural Innovation Lab Demo Day 

The Morgan Stanley Multicultural Innovation LabMorgan Stanley’s in-house accelerator focused on companies founded by multicultural and female entrepreneurs, hosted its second Annual Showcase and Demo Day.  The event also featured companies from accelerators HearstLab, Newark Venture Partner Labs and PS27 Ventures.  (Note: I was formerly employed by Morgan Stanley and have no financial ties.)

The showcase represented the culmination of the program’s second year, which followed an initial five company class that has already seen two acquisitions.  Through the six-month program, Morgan Stanley provides early-stage companies with a wide range of benefits including an equity investment from Morgan Stanley, office space at Morgan Stanley headquarters, access to Morgan Stanley’s extensive network, and others.  Applications are now open for its third cohort of companies with the application window closing on January 4th, 2019.

The 16 presenting startups, all led by a female or multicultural founder, offered solutions to structural inefficiencies across a wide array of categories including fintech, developer tools, and health.  Though all of the companies offered impressive presentations and strong value propositions, here are three of the companies that stood out to us.

Hatch Apps

In hopes of democratizing software and app development, Hatch Apps provides a platform that allows users and companies to build iOS, Android and web applications without any code through pre-built templates and custom plug-and-play functions.  In essence, Hatch Apps provides a solution for application building similar to what Squarespace or Wix provide for websites.

In the modern economy, every company is in one way or another a tech or tech-enabled company.  Now the demand for strong engineers has made the fight for talent increasingly competitive and has made engineering quite costly, even when only needed for simple tasks. 

For an implementation and subscription fee, Hatch Apps allows companies with less sophisticated engineering DNA to reduce entering costs by launch native apps on their own, across platforms, and often on faster timelines than those seen through third-party developers.  Once an app is launched, Hatch Apps provides customers with detailed analytics and allows them to send targeted push notifications, export data and make in-app changes that can automatically go live in app stores.

The company initially took a bootstrapping approach to financing and raised funds by selling a 2016 election-themed “Cards Against Humanity”-style game created on the platform.  Since then, Hatch Apps has already received funding from the Y Combinator Fellowship, Morgan Stanley, and a number of other investors.

FreeWill

While estate planning is a topic many don’t like to think about, it’s a critical issue for managing cross-generational wealth. But will drafting can often be very complex, time-consuming, and costly, requiring hours of legal consultation and coordination between various parties.

Founded by two former classmates at Stanford Business School, FreeWill looks to simplify the estate planning process by providing a free online platform that automates will drafting, in a similar function to what TurboTax does for taxes.  Using FreeWill, users can quickly set allocations for their estate and select personal recipients, charitable donations, executor specifications, and other ancillary requests.  The platform then creates a finalized legal document that is legally valid in all 50 states, which users can also quickly make changes to and replace without incurring expensive legal costs.

FreeWill is able to provide the platform to consumers for free due to the proceeds it receives from its non-profit customers, who pay to be featured on the platform as a partner organization.  FreeWill offers a compelling value proposition for partnering companies.  By acting as a channel to funnel user donations to listed organizations, FreeWill has been able to drive a 600% increase in charitable giving to partner organizations on average.  FreeWill also provides partner organizations with backing analytics that allow non-profits to track bequests and donors through monthly reports. 

FreeWill currently boasts an impressive roster of 75 paying non-profit partners that include American Red Cross, Amnesty International and many others.  In the long-run hopes to be the go-to solution financial and legal end of life planning for investment advisors, life insurance and employee benefits providers.

Shoobs

Shoobs is looking to be the go-to platform for local “urban” events, which the company defined as events centered on local nightlife, comedy and concerts in the hip-hop, R&B, and reggae genres to name a few.  But unlike the genre-agnostic, transaction-focused event management platforms that can make the space seem pretty crowded, Shoobs focused on providing genre-specific even discovery.  Shoobs matches urban event goers with artists of their choice and related smaller scale events that can be harder to discover, acting as a form of curation, quality control and discovery.

For event organizers, Shoobs helps provide digital ticketing and promotion services, with event recommendation capabilities that target the most promising potential customers.  Through its offering to event organizers, Shoobs is able to monetize its services through ticket sale commission, advertising and brand partnerships.

Since its initial launch in London, Shoobs notes it has become one of the top urban events platforms in the city, with an extensive base of recurring registered users and event organizers.  After previously working with AEG for its London launch, Shoobs is looking to expand stateside with the help of organizers like Live Nation.  Shoobs joins a long list of promising Y Combinator alumni companies with YC also acting as one of Shows initial investors

Other presenting companies included:

Morgan Stanley Multicultural Innovation Lab

  • BeautyLynk “is an on-demand hair and makeup service provider, specializing in customizable services for women.”
  • Broadway Roulette “is an events marketplace that pairs consumers with surprise cultural events, beginning with Broadway theater.”
  • CariClub “is an enterprise software platform to connect young professionals with nonprofit opportunities.”
  • COI Energy Services “is an integrated platform for electric utilities and business users to optimize and manage energy usage.”
  • CoSign “is an API and application that allows anyone to create, distribute and monetize visual content.”
  • Goalsetter “is a goals-based gifting, savings, and investing platform designed for children.”
  • myLAB Box “offers customizable at home health-test kits and relevant telemedicine consultations / prescription services.”

Hearst Labs

  • Priori “is a global legal marketplace changing the way in-house teams find, hire, and manage outside counsel.”
  • TRENCH “is an online fashion marketplace that makes use of the unworn items in every woman’s closet.”

Newark Venture Partners Labs

  • Floss Bar “is a new type of preventive brand for oral health care. The company offers high-quality, routine dental care across flexible locations at thoughtful prices.”
  • Upsider “is a software solution allowing recruiters to leverage AI technology to identify a comprehensive set of candidates who align with their business and role requirements, resulting in a more strategic understanding of the best possible talent for the job.”

PS27 Ventures

  • BlueWave Technologies “is a cleantech company and the creators of the BlueWave™ Cleaning System — a water free, detergent free, and chemical free plasma device that cleans items that are extremely hard or impossible to clean with a washer and dryer.”
  • OnPay Solutions “focuses exclusively on business-to-business payments. They create payment software and offer payment web services to enhance efficiency and productivity for Accounts Payable and Accounts Receivable.”


Source: TechCrunch

Black Friday 2018 Best Buy phone deals available now: iPhone XS, Pixel 3, Note 9, Moto G6 and more – CNET

If you’re in the market for a shiny new phone, Best Buy has some great deals for Black Friday.
Source: CNET

Facebook fallout: Civil rights groups call for firing of executive – CNET

They want to see the company’s VP of global public policy handed a pink slip. They also want Facebook to release a civil rights audit and any opposition research done on them by Definers Public Affairs.
Source: CNET

Facebook sued by Russian firm linked to election meddling – CNET

Company says it’s legitimate news agency.
Source: CNET

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