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#436252 After AI, Fashion and Shopping Will ...

AI and broadband are eating retail for breakfast. In the first half of 2019, we’ve seen 19 retailer bankruptcies. And the retail apocalypse is only accelerating.

What’s coming next is astounding. Why drive when you can speak? Revenue from products purchased via voice commands is expected to quadruple from today’s US$2 billion to US$8 billion by 2023.

Virtual reality, augmented reality, and 3D printing are converging with artificial intelligence, drones, and 5G to transform shopping on every dimension. And as a result, shopping is becoming dematerialized, demonetized, democratized, and delocalized… a top-to-bottom transformation of the retail world.

Welcome to Part 1 of our series on the future of retail, a deep-dive into AI and its far-reaching implications.

Let’s dive in.

A Day in the Life of 2029
Welcome to April 21, 2029, a sunny day in Dallas. You’ve got a fundraising luncheon tomorrow, but nothing to wear. The last thing you want to do is spend the day at the mall.

No sweat. Your body image data is still current, as you were scanned only a week ago. Put on your VR headset and have a conversation with your AI. “It’s time to buy a dress for tomorrow’s event” is all you have to say. In a moment, you’re teleported to a virtual clothing store. Zero travel time. No freeway traffic, parking hassles, or angry hordes wielding baby strollers.

Instead, you’ve entered your own personal clothing store. Everything is in your exact size…. And I mean everything. The store has access to nearly every designer and style on the planet. Ask your AI to show you what’s hot in Shanghai, and presto—instant fashion show. Every model strutting down the runway looks exactly like you, only dressed in Shanghai’s latest.

When you’re done selecting an outfit, your AI pays the bill. And as your new clothes are being 3D printed at a warehouse—before speeding your way via drone delivery—a digital version has been added to your personal inventory for use at future virtual events.

The cost? Thanks to an era of no middlemen, less than half of what you pay in stores today. Yet this future is not all that far off…

Digital Assistants
Let’s begin with the basics: the act of turning desire into purchase.

Most of us navigate shopping malls or online marketplaces alone, hoping to stumble across the right item and fit. But if you’re lucky enough to employ a personal assistant, you have the luxury of describing what you want to someone who knows you well enough to buy that exact right thing most of the time.

For most of us who don’t, enter the digital assistant.

Right now, the four horsemen of the retail apocalypse are waging war for our wallets. Amazon’s Alexa, Google’s Now, Apple’s Siri, and Alibaba’s Tmall Genie are going head-to-head in a battle to become the platform du jour for voice-activated, AI-assisted commerce.

For baby boomers who grew up watching Captain Kirk talk to the Enterprise’s computer on Star Trek, digital assistants seem a little like science fiction. But for millennials, it’s just the next logical step in a world that is auto-magical.

And as those millennials enter their consumer prime, revenue from products purchased via voice-driven commands is projected to leap from today’s US$2 billion to US$8 billion by 2023.

We are already seeing a major change in purchasing habits. On average, consumers using Amazon Echo spent more than standard Amazon Prime customers: US$1,700 versus US$1,300.

And as far as an AI fashion advisor goes, those too are here, courtesy of both Alibaba and Amazon. During its annual Singles’ Day (November 11) shopping festival, Alibaba’s FashionAI concept store uses deep learning to make suggestions based on advice from human fashion experts and store inventory, driving a significant portion of the day’s US$25 billion in sales.

Similarly, Amazon’s shopping algorithm makes personalized clothing recommendations based on user preferences and social media behavior.

Customer Service
But AI is disrupting more than just personalized fashion and e-commerce. Its next big break will take place in the customer service arena.

According to a recent Zendesk study, good customer service increases the possibility of a purchase by 42 percent, while bad customer service translates into a 52 percent chance of losing that sale forever. This means more than half of us will stop shopping at a store due to a single disappointing customer service interaction. These are significant financial stakes. They’re also problems perfectly suited for an AI solution.

During the 2018 Google I/O conference, CEO Sundar Pichai demoed the Google Duplex, their next generation digital assistant. Pichai played the audience a series of pre-recorded phone calls made by Google Duplex. The first call made a reservation at a restaurant, the second one booked a haircut appointment, amusing the audience with a long “hmmm” mid-call.

In neither case did the person on the other end of the phone have any idea they were talking to an AI. The system’s success speaks to how seamlessly AI can blend into our retail lives and how convenient it will continue to make them. The same technology Pichai demonstrated that can make phone calls for consumers can also answer phones for retailers—a development that’s unfolding in two different ways:

(1) Customer service coaches: First, for organizations interested in keeping humans involved, there’s Beyond Verbal, a Tel Aviv-based startup that has built an AI customer service coach. Simply by analyzing customer voice intonation, the system can tell whether the person on the phone is about to blow a gasket, is genuinely excited, or anything in between.

Based on research of over 70,000 subjects in more than 30 languages, Beyond Verbal’s app can detect 400 different markers of human moods, attitudes, and personality traits. Already it’s been integrated in call centers to help human sales agents understand and react to customer emotions, making those calls more pleasant, and also more profitable.

For example, by analyzing word choice and vocal style, Beyond Verbal’s system can tell what kind of shopper the person on the line actually is. If they’re an early adopter, the AI alerts the sales agent to offer them the latest and greatest. If they’re more conservative, it suggests items more tried-and-true.

(2) Replacing customer service agents: Second, companies like New Zealand’s Soul Machines are working to replace human customer service agents altogether. Powered by IBM’s Watson, Soul Machines builds lifelike customer service avatars designed for empathy, making them one of many helping to pioneer the field of emotionally intelligent computing.

With their technology, 40 percent of all customer service interactions are now resolved with a high degree of satisfaction, no human intervention needed. And because the system is built using neural nets, it’s continuously learning from every interaction—meaning that percentage will continue to improve.

The number of these interactions continues to grow as well. Software manufacturer Autodesk now includes a Soul Machine avatar named AVA (Autodesk Virtual Assistant) in all of its new offerings. She lives in a small window on the screen, ready to soothe tempers, troubleshoot problems, and forever banish those long tech support hold times.

For Daimler Financial Services, Soul Machines built an avatar named Sarah, who helps customers with arguably three of modernity’s most annoying tasks: financing, leasing, and insuring a car.

This isn’t just about AI—it’s about AI converging with additional exponentials. Add networks and sensors to the story and it raises the scale of disruption, upping the FQ—the frictionless quotient—in our frictionless shopping adventure.

Final Thoughts
AI makes retail cheaper, faster, and more efficient, touching everything from customer service to product delivery. It also redefines the shopping experience, making it frictionless and—once we allow AI to make purchases for us—ultimately invisible.

Prepare for a future in which shopping is dematerialized, demonetized, democratized, and delocalized—otherwise known as “the end of malls.”

Of course, if you wait a few more years, you’ll be able to take an autonomous flying taxi to Westfield’s Destination 2028—so perhaps today’s converging exponentials are not so much spelling the end of malls but rather the beginning of an experience economy far smarter, more immersive, and whimsically imaginative than today’s shopping centers.

Either way, it’s a top-to-bottom transformation of the retail world.

Over the coming blog series, we will continue our discussion of the future of retail. Stay tuned to learn new implications for your business and how to future-proof your company in an age of smart, ultra-efficient, experiential retail.

Want a copy of my next book? If you’ve enjoyed this blogified snippet of The Future is Faster Than You Think, sign up here to be eligible for an early copy and access up to $800 worth of pre-launch giveaways!

Join Me
(1) A360 Executive Mastermind: If you’re an exponentially and abundance-minded entrepreneur who would like coaching directly from me, consider joining my Abundance 360 Mastermind, a highly selective community of 360 CEOs and entrepreneurs who I coach for 3 days every January in Beverly Hills, Ca. Through A360, I provide my members with context and clarity about how converging exponential technologies will transform every industry. I’m committed to running A360 for the course of an ongoing 25-year journey as a “countdown to the Singularity.”

If you’d like to learn more and consider joining our 2020 membership, apply here.

(2) Abundance-Digital Online Community: I’ve also created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is Singularity University’s ‘onramp’ for exponential entrepreneurs — those who want to get involved and play at a higher level. Click here to learn more.

(Both A360 and Abundance-Digital are part of Singularity University — your participation opens you to a global community.)

This article originally appeared on diamandis.com. Read the original article here.

Image Credit: Image by Pexels from Pixabay Continue reading

Posted in Human Robots

#436234 Robot Gift Guide 2019

Welcome to the eighth edition of IEEE Spectrum’s Robot Gift Guide!

This year we’re featuring 15 robotic products that we think will make fantastic holiday gifts. As always, we tried to include a broad range of robot types and prices, focusing mostly on items released this year. (A reminder: While we provide links to places where you can buy these items, we’re not endorsing any in particular, and a little bit of research may result in better deals.)

If you need even more robot gift ideas, take a look at our past guides: 2018, 2017, 2016, 2015, 2014, 2013, and 2012. Some of those robots are still great choices and might be way cheaper now than when we first posted about them. And if you have suggestions that you’d like to share, post a comment below to help the rest of us find the perfect robot gift.

Skydio 2

Image: Skydio

What makes robots so compelling is their autonomy, and the Skydio 2 is one of the most autonomous robots we’ve ever seen. It uses an array of cameras to map its environment and avoid obstacles in real-time, making flight safe and effortless and enabling the kinds of shots that would be impossible otherwise. Seriously, this thing is magical, and it’s amazing that you can actually buy one.
$1,000
Skydio
UBTECH Jimu MeeBot 2

Image: UBTECH

The Jimu MeeBot 2.0 from UBTECH is a STEM education robot designed to be easy to build and program. It includes six servo motors, a color sensor, and LED lights. An app for iPhone or iPad provides step-by-step 3D instructions, and helps you code different behaviors for the robot. It’s available exclusively from Apple.
$130
Apple
iRobot Roomba s9+

Image: iRobot

We know that $1,400 is a crazy amount of money to spend on a robot vacuum, but the Roomba s9+ is a crazy robot vacuum. As if all of its sensors and mapping intelligence wasn’t enough, it empties itself, which means that you can have your floors vacuumed every single day for a month and you don’t have to even think about it. This is what home robots are supposed to be.
$1,400
iRobot
PFF Gita

Photo: Piaggio Fast Forward

Nobody likes carrying things, which is why Gita is perfect for everyone with an extra $3,000 lying around. Developed by Piaggio Fast Forward, this autonomous robot will follow you around with a cargo hold full of your most important stuff, and do it in a way guaranteed to attract as much attention as possible.
$3,250
Gita
DJI Mavic Mini

Photo: DJI

It’s tiny, it’s cheap, and it takes good pictures—what more could you ask for from a drone? And for $400, this is an excellent drone to get if you’re on a budget and comfortable with manual flight. Keep in mind that while the Mavic Mini is small enough that you don’t need to register it with the FAA, you do still need to follow all the same rules and regulations.
$400
DJI
LEGO Star Wars Droid Commander

Image: LEGO

Designed for kids ages 8+, this LEGO set includes more than 1,000 pieces, enough to build three different droids: R2-D2, Gonk Droid, and Mouse Droid. Using a Bluetooth-controlled robotic brick called Move Hub, which connects to the LEGO BOOST Star Wars app, kids can change how the robots behave and solve challenges, learning basic robotics and coding skills.
$200
LEGO
Sony Aibo

Photo: Sony

Robot pets don’t get much more sophisticated (or expensive) than Sony’s Aibo. Strictly speaking, it’s one of the most complex consumer robots you can buy, and Sony continues to add to Aibo’s software. Recent new features include user programmability, and the ability to “feed” it.
$2,900 (free aibone and paw pads until 12/29/2019)
Sony
Neato Botvac D4 Connected

Photo: Neato

The Neato Botvac D4 may not have all of the features of its fancier and more expensive siblings, but it does have the features that you probably care the most about: The ability to make maps of its environment for intelligent cleaning (using lasers!), along with user-defined no-go lines that keep it where you want it. And it cleans quite well, too.
$530 $350 (sale)
Neato Robotics
Cubelets Curiosity Set

Photo: Modular Robotics

Cubelets are magnetic blocks that you can snap together to make an endless variety of robots with no programming and no wires. The newest set, called Curiosity, is designed for kids ages 4+ and comes with 10 robotic cubes. These include light and distance sensors, motors, and a Bluetooth module, which connects the robot constructions to the Cubelets app.
$250
Modular Robotics
Tertill

Photo: Franklin Robotics

Tertill does one simple job: It weeds your garden. It’s waterproof, dirt proof, solar powered, and fully autonomous, meaning that you can leave it out in your garden all summer and just enjoy eating your plants rather than taking care of them.
$350
Tertill
iRobot Root

Photo: iRobot

Root was originally developed by Harvard University as a tool to help kids progressively learn to code. iRobot has taken over Root and is now supporting the curriculum, which starts for kids before they even know how to read and should keep them busy for years afterwards.
$200
iRobot
LOVOT

Image: Lovot

Let’s be honest: Nobody is really quite sure what LOVOT is. We can all agree that it’s kinda cute, though. And kinda weird. But cute. Created by Japanese robotics startup Groove X, LOVOT does have a whole bunch of tech packed into its bizarre little body and it will do its best to get you to love it.
$2,750 (¥300,000)
LOVOT
Sphero RVR

Photo: Sphero

RVR is a rugged, versatile, easy to program mobile robot. It’s a development platform designed to be a bridge between educational robots like Sphero and more sophisticated and expensive systems like Misty. It’s mostly affordable, very expandable, and comes from a company with a lot of experience making robots.
$250
Sphero
“How to Train Your Robot”

Image: Lawrence Hall of Science

Aimed at 4th and 5th graders, “How to Train Your Robot,” written by Blooma Goldberg, Ken Goldberg, and Ashley Chase, and illustrated by Dave Clegg, is a perfect introduction to robotics for kids who want to get started with designing and building robots. But the book isn’t just for beginners: It’s also a fun, inspiring read for kids who are already into robotics and want to go further—it even introduces concepts like computer simulations and deep learning. You can download a free digital copy or request hardcopies here.
Free
UC Berkeley
MIT Mini Cheetah

Photo: MIT

Yes, Boston Dynamics’ Spot, now available for lease, is probably the world’s most famous quadruped, but MIT is starting to pump out Mini Cheetahs en masse for researchers, and while we’re not exactly sure how you’d manage to get one of these things short of stealing one directly for MIT, a Mini Cheetah is our fantasy robotics gift this year. Mini Cheetah looks like a ton of fun—it’s portable, highly dynamic, super rugged, and easy to control. We want one!
Price N/A
MIT Biomimetic Robotics Lab

For more tech gift ideas, see also IEEE Spectrum’s annual Gift Guide. Continue reading

Posted in Human Robots

#436209 Video Friday: Robotic Endoscope Travels ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

DARPA SubT Urban Circuit – February 18-27, 2020 – Olympia, WA, USA
Let us know if you have suggestions for next week, and enjoy today's videos.

Kuka has just announced the results of its annual Innovation Award. From an initial batch of 30 applicants, five teams reached the finals (we were part of the judging committee). The five finalists worked for nearly a year on their applications, which they demonstrated this week at the Medica trade show in Düsseldorf, Germany. And the winner of the €20,000 prize is…Team RoboFORCE, led by the STORM Lab in the U.K., which developed a “robotic magnetic flexible endoscope for painless colorectal cancer screening, surveillance, and intervention.”

The system could improve colonoscopy procedures by reducing pain and discomfort as well as other risks such as bleeding and perforation, according to the STORM Lab researchers. It uses a magnetic field to control the endoscope, pulling rather than pushing it through the colon.

The other four finalists also presented some really interesting applications—you can see their videos below.

“Because we were so please with the high quality of the submissions, we will have next year’s finals again at the Medica fair, and the challenge will be named ‘Medical Robotics’,” says Rainer Bischoff, vice president for corporate research at Kuka. He adds that the selected teams will again use Kuka’s LBR Med robot arm, which is “already certified for integration into medical products and makes it particularly easy for startups to use a robot as the main component for a particular solution.”

Applications are now open for Kuka’s Innovation Award 2020. You can find more information on how to enter here. The deadline is 5 January 2020.

[ Kuka ]

Oh good, Aibo needs to be fed now.

You know what comes next, right?

[ Aibo ]

Your cat needs this robot.

It's about $200 on Kickstarter.

[ Kickstarter ]

Enjoy this tour of the Skydio offices courtesy Skydio 2, which runs into not even one single thing.

If any Skydio employees had important piles of papers on their desks, well, they don’t anymore.

[ Skydio ]

Artificial intelligence is everywhere nowadays, but what exactly does it mean? We asked a group MIT computer science grad students and post-docs how they personally define AI.

“When most people say AI, they actually mean machine learning, which is just pattern recognition.” Yup.

[ MIT ]

Using event-based cameras, this drone control system can track attitude at 1600 degrees per second (!).

[ UZH ]

Introduced at CES 2018, Walker is an intelligent humanoid service robot from UBTECH Robotics. Below are the latest features and technologies used during our latest round of development to make Walker even better.

[ Ubtech ]

Introducing the Alpha Prime by #VelodyneLidar, the most advanced lidar sensor on the market! Alpha Prime delivers an unrivaled combination of field-of-view, range, high-resolution, clarity and operational performance.

Performance looks good, but don’t expect it to be cheap.

[ Velodyne ]

Ghost Robotics’ Spirit 40 will start shipping to researchers in January of next year.

[ Ghost Robotics ]

Unitree is about to ship the first batch of their AlienGo quadrupeds as well:

[ Unitree ]

Mechanical engineering’s Sarah Bergbreiter discusses her work on micro robotics, how they draw inspiration from insects and animals, and how tiny robots can help humans in a variety of fields.

[ CMU ]

Learning contact-rich, robotic manipulation skills is a challenging problem due to the high-dimensionality of the state and action space as well as uncertainty from noisy sensors and inaccurate motor control. To combat these factors and achieve more robust manipulation, humans actively exploit contact constraints in the environment. By adopting a similar strategy, robots can also achieve more robust manipulation. In this paper, we enable a robot to autonomously modify its environment and thereby discover how to ease manipulation skill learning. Specifically, we provide the robot with fixtures that it can freely place within the environment. These fixtures provide hard constraints that limit the outcome of robot actions. Thereby, they funnel uncertainty from perception and motor control and scaffold manipulation skill learning.

[ Stanford ]

Since 2016, Verity's drones have completed more than 200,000 flights around the world. Completely autonomous, client-operated and designed for live events, Verity is making the magic real by turning drones into flying lights, characters, and props.

[ Verity ]

To monitor and stop the spread of wildfires, University of Michigan engineers developed UAVs that could find, map and report fires. One day UAVs like this could work with disaster response units, firefighters and other emergency teams to provide real-time accurate information to reduce damage and save lives. For their research, the University of Michigan graduate students won first place at a competition for using a swarm of UAVs to successfully map and report simulated wildfires.

[ University of Michigan ]

Here’s an important issue that I haven’t heard talked about all that much: How first responders should interact with self-driving cars.

“To put the car in manual mode, you must call Waymo.” Huh.

[ Waymo ]

Here’s what Gitai has been up to recently, from a Humanoids 2019 workshop talk.

[ Gitai ]

The latest CMU RI seminar comes from Girish Chowdhary at the University of Illinois at Urbana-Champaign on “Autonomous and Intelligent Robots in Unstructured Field Environments.”

What if a team of collaborative autonomous robots grew your food for you? In this talk, I will discuss some key advances in robotics, machine learning, and autonomy that will one day enable teams of small robots to grow food for you in your backyard in a fundamentally more sustainable way than modern mega-farms! Teams of small aerial and ground robots could be a potential solution to many of the serious problems that modern agriculture is facing. However, fully autonomous robots that operate without supervision for weeks, months, or entire growing season are not yet practical. I will discuss my group’s theoretical and practical work towards the underlying challenging problems in robotic systems, autonomy, sensing, and learning. I will begin with our lightweight, compact, and autonomous field robot TerraSentia and the recent successes of this type of undercanopy robots for high-throughput phenotyping with deep learning-based machine vision. I will also discuss how to make a team of autonomous robots learn to coordinate to weed large agricultural farms under partial observability. These direct applications will help me make the case for the type of reinforcement learning and adaptive control that are necessary to usher in the next generation of autonomous field robots that learn to solve complex problems in harsh, changing, and dynamic environments. I will then end with an overview of our new MURI, in which we are working towards developing AI and control that leverages neurodynamics inspired by the Octopus brain.

[ CMU RI ] Continue reading

Posted in Human Robots

#436188 The Blogger Behind “AI ...

Sure, artificial intelligence is transforming the world’s societies and economies—but can an AI come up with plausible ideas for a Halloween costume?

Janelle Shane has been asking such probing questions since she started her AI Weirdness blog in 2016. She specializes in training neural networks (which underpin most of today’s machine learning techniques) on quirky data sets such as compilations of knitting instructions, ice cream flavors, and names of paint colors. Then she asks the neural net to generate its own contributions to these categories—and hilarity ensues. AI is not likely to disrupt the paint industry with names like “Ronching Blue,” “Dorkwood,” and “Turdly.”

Shane’s antics have a serious purpose. She aims to illustrate the serious limitations of today’s AI, and to counteract the prevailing narrative that describes AI as well on its way to superintelligence and complete human domination. “The danger of AI is not that it’s too smart,” Shane writes in her new book, “but that it’s not smart enough.”

The book, which came out on Tuesday, is called You Look Like a Thing and I Love You. It takes its odd title from a list of AI-generated pick-up lines, all of which would at least get a person’s attention if shouted, preferably by a robot, in a crowded bar. Shane’s book is shot through with her trademark absurdist humor, but it also contains real explanations of machine learning concepts and techniques. It’s a painless way to take AI 101.

She spoke with IEEE Spectrum about the perils of placing too much trust in AI systems, the strange AI phenomenon of “giraffing,” and her next potential Halloween costume.

Janelle Shane on . . .

The un-delicious origin of her blog
“The narrower the problem, the smarter the AI will seem”
Why overestimating AI is dangerous
Giraffing!
Machine and human creativity

The un-delicious origin of her blog IEEE Spectrum: You studied electrical engineering as an undergrad, then got a master’s degree in physics. How did that lead to you becoming the comedian of AI?
Janelle Shane: I’ve been interested in machine learning since freshman year of college. During orientation at Michigan State, a professor who worked on evolutionary algorithms gave a talk about his work. It was full of the most interesting anecdotes–some of which I’ve used in my book. He told an anecdote about people setting up a machine learning algorithm to do lens design, and the algorithm did end up designing an optical system that works… except one of the lenses was 50 feet thick, because they didn’t specify that it couldn’t do that.
I started working in his lab on optics, doing ultra-short laser pulse work. I ended up doing a lot more optics than machine learning, but I always found it interesting. One day I came across a list of recipes that someone had generated using a neural net, and I thought it was hilarious and remembered why I thought machine learning was so cool. That was in 2016, ages ago in machine learning land.
Spectrum: So you decided to “establish weirdness as your goal” for your blog. What was the first weird experiment that you blogged about?
Shane: It was generating cookbook recipes. The neural net came up with ingredients like: “Take ¼ pounds of bones or fresh bread.” That recipe started out: “Brown the salmon in oil, add creamed meat to the mixture.” It was making mistakes that showed the thing had no memory at all.
Spectrum: You say in the book that you can learn a lot about AI by giving it a task and watching it flail. What do you learn?
Shane: One thing you learn is how much it relies on surface appearances rather than deep understanding. With the recipes, for example: It got the structure of title, category, ingredients, instructions, yield at the end. But when you look more closely, it has instructions like “Fold the water and roll it into cubes.” So clearly this thing does not understand water, let alone the other things. It’s recognizing certain phrases that tend to occur, but it doesn’t have a concept that these recipes are describing something real. You start to realize how very narrow the algorithms in this world are. They only know exactly what we tell them in our data set.
BACK TO TOP↑ “The narrower the problem, the smarter the AI will seem” Spectrum: That makes me think of DeepMind’s AlphaGo, which was universally hailed as a triumph for AI. It can play the game of Go better than any human, but it doesn’t know what Go is. It doesn’t know that it’s playing a game.
Shane: It doesn’t know what a human is, or if it’s playing against a human or another program. That’s also a nice illustration of how well these algorithms do when they have a really narrow and well-defined problem.
The narrower the problem, the smarter the AI will seem. If it’s not just doing something repeatedly but instead has to understand something, coherence goes down. For example, take an algorithm that can generate images of objects. If the algorithm is restricted to birds, it could do a recognizable bird. If this same algorithm is asked to generate images of any animal, if its task is that broad, the bird it generates becomes an unrecognizable brown feathered smear against a green background.
Spectrum: That sounds… disturbing.
Shane: It’s disturbing in a weird amusing way. What’s really disturbing is the humans it generates. It hasn’t seen them enough times to have a good representation, so you end up with an amorphous, usually pale-faced thing with way too many orifices. If you asked it to generate an image of a person eating pizza, you’ll have blocks of pizza texture floating around. But if you give that image to an image-recognition algorithm that was trained on that same data set, it will say, “Oh yes, that’s a person eating pizza.”
BACK TO TOP↑ Why overestimating AI is dangerous Spectrum: Do you see it as your role to puncture the AI hype?
Shane: I do see it that way. Not a lot of people are bringing out this side of AI. When I first started posting my results, I’d get people saying, “I don’t understand, this is AI, shouldn’t it be better than this? Why doesn't it understand?” Many of the impressive examples of AI have a really narrow task, or they’ve been set up to hide how little understanding it has. There’s a motivation, especially among people selling products based on AI, to represent the AI as more competent and understanding than it actually is.
Spectrum: If people overestimate the abilities of AI, what risk does that pose?
Shane: I worry when I see people trusting AI with decisions it can’t handle, like hiring decisions or decisions about moderating content. These are really tough tasks for AI to do well on. There are going to be a lot of glitches. I see people saying, “The computer decided this so it must be unbiased, it must be objective.”

“If the algorithm’s task is to replicate human hiring decisions, it’s going to glom onto gender bias and race bias.”
—Janelle Shane, AI Weirdness blogger
That’s another thing I find myself highlighting in the work I’m doing. If the data includes bias, the algorithm will copy that bias. You can’t tell it not to be biased, because it doesn’t understand what bias is. I think that message is an important one for people to understand.
If there’s bias to be found, the algorithm is going to go after it. It’s like, “Thank goodness, finally a signal that’s reliable.” But for a tough problem like: Look at these resumes and decide who’s best for the job. If its task is to replicate human hiring decisions, it’s going to glom onto gender bias and race bias. There’s an example in the book of a hiring algorithm that Amazon was developing that discriminated against women, because the historical data it was trained on had that gender bias.
Spectrum: What are the other downsides of using AI systems that don’t really understand their tasks?
Shane: There is a risk in putting too much trust in AI and not examining its decisions. Another issue is that it can solve the wrong problems, without anyone realizing it. There have been a couple of cases in medicine. For example, there was an algorithm that was trained to recognize things like skin cancer. But instead of recognizing the actual skin condition, it latched onto signals like the markings a surgeon makes on the skin, or a ruler placed there for scale. It was treating those things as a sign of skin cancer. It’s another indication that these algorithms don’t understand what they’re looking at and what the goal really is.
BACK TO TOP↑ Giraffing Spectrum: In your blog, you often have neural nets generate names for things—such as ice cream flavors, paint colors, cats, mushrooms, and types of apples. How do you decide on topics?
Shane: Quite often it’s because someone has written in with an idea or a data set. They’ll say something like, “I’m the MIT librarian and I have a whole list of MIT thesis titles.” That one was delightful. Or they’ll say, “We are a high school robotics team, and we know where there’s a list of robotics team names.” It’s fun to peek into a different world. I have to be careful that I’m not making fun of the naming conventions in the field. But there’s a lot of humor simply in the neural net’s complete failure to understand. Puns in particular—it really struggles with puns.
Spectrum: Your blog is quite absurd, but it strikes me that machine learning is often absurd in itself. Can you explain the concept of giraffing?
Shane: This concept was originally introduced by [internet security expert] Melissa Elliott. She proposed this phrase as a way to describe the algorithms’ tendency to see giraffes way more often than would be likely in the real world. She posted a whole bunch of examples, like a photo of an empty field in which an image-recognition algorithm has confidently reported that there are giraffes. Why does it think giraffes are present so often when they’re actually really rare? Because they’re trained on data sets from online. People tend to say, “Hey look, a giraffe!” And then take a photo and share it. They don’t do that so often when they see an empty field with rocks.
There’s also a chatbot that has a delightful quirk. If you show it some photo and ask it how many giraffes are in the picture, it will always answer with some non zero number. This quirk comes from the way the training data was generated: These were questions asked and answered by humans online. People tended not to ask the question “How many giraffes are there?” when the answer was zero. So you can show it a picture of someone holding a Wii remote. If you ask it how many giraffes are in the picture, it will say two.
BACK TO TOP↑ Machine and human creativity Spectrum: AI can be absurd, and maybe also creative. But you make the point that AI art projects are really human-AI collaborations: Collecting the data set, training the algorithm, and curating the output are all artistic acts on the part of the human. Do you see your work as a human-AI art project?
Shane: Yes, I think there is artistic intent in my work; you could call it literary or visual. It’s not so interesting to just take a pre-trained algorithm that’s been trained on utilitarian data, and tell it to generate a bunch of stuff. Even if the algorithm isn’t one that I’ve trained myself, I think about, what is it doing that’s interesting, what kind of story can I tell around it, and what do I want to show people.

The Halloween costume algorithm “was able to draw on its knowledge of which words are related to suggest things like sexy barnacle.”
—Janelle Shane, AI Weirdness blogger
Spectrum: For the past three years you’ve been getting neural nets to generate ideas for Halloween costumes. As language models have gotten dramatically better over the past three years, are the costume suggestions getting less absurd?
Shane: Yes. Before I would get a lot more nonsense words. This time I got phrases that were related to real things in the data set. I don’t believe the training data had the words Flying Dutchman or barnacle. But it was able to draw on its knowledge of which words are related to suggest things like sexy barnacle and sexy Flying Dutchman.
Spectrum: This year, I saw on Twitter that someone made the gothy giraffe costume happen. Would you ever dress up for Halloween in a costume that the neural net suggested?
Shane: I think that would be fun. But there would be some challenges. I would love to go as the sexy Flying Dutchman. But my ambition may constrict me to do something more like a list of leg parts.
BACK TO TOP↑ Continue reading

Posted in Human Robots

#436100 Labrador Systems Developing Affordable ...

Developing robots for the home is still a challenge, especially if you want those robots to interact with people and help them do practical, useful things. However, the potential markets for home robots are huge, and one of the most compelling markets is for home robots that can assist humans who need them. Today, Labrador Systems, a startup based in California, is announcing a pre-seed funding round of $2 million (led by SOSV’s hardware accelerator HAX with participation from Amazon’s Alexa Fund and iRobot Ventures, among others) with the goal of expanding development and conducting pilot studies of “a new [assistive robot] platform for supporting home health.”

Labrador was founded two years ago by Mike Dooley and Nikolai Romanov. Both Mike and Nikolai have backgrounds in consumer robotics at Evolution Robotics and iRobot, but as an ’80s gamer, Mike’s bio (or at least the parts of his bio on LinkedIn) caught my attention: From 1995 to 1997, Mike worked at Brøderbund Software, helping to manage play testing for games like Myst and Riven and the Where in the World is Carmen San Diego series. He then spent three years at Lego as the product manager for MindStorms. After doing some marginally less interesting things, Mike was the VP of product development at Evolution Robotics from 2006 to 2012, where he led the team that developed the Mint floor sweeping robot. Evolution was acquired by iRobot in 2012, and Mike ended up as the VP of product development over there until 2017, when he co-founded Labrador.

I was pretty much sold at Where in the World is Carmen San Diego (the original version of which I played from a 5.25” floppy on my dad’s Apple IIe)*, but as you can see from all that other stuff, Mike knows what he’s doing in robotics as well.

And according to Labrador’s press release, what they’re doing is this:

Labrador Systems is an early stage technology company developing a new generation of assistive robots to help people live more independently. The company’s core focus is creating affordable solutions that address practical and physical needs at a fraction of the cost of commercial robots. … Labrador’s technology platform offers an affordable solution to improve the quality of care while promoting independence and successful aging.

Labrador’s personal robot, the company’s first offering, will enter pilot studies in 2020.

That’s about as light on detail as a press release gets, but there’s a bit more on Labrador’s website, including:

Our core focus is creating affordable solutions that address practical and physical needs. (we are not a social robot company)
By affordable, we mean products and technologies that will be available at less than 1/10th the cost of commercial robots.
We achieve those low costs by fusing the latest technologies coming out of augmented reality with robotics to move things in the real world.

The only hardware we’ve actually seen from Labrador at this point is a demo that they put together for Amazon’s re:MARS conference, which took place a few months ago, showing a “demonstration project” called Smart Walker:

This isn’t the home assistance robot that Labrador got its funding for, but rather a demonstration of some of their technology. So of course, the question is, what’s Labrador working on, then? It’s still a secret, but Mike Dooley was able to give us a few more details.

IEEE Spectrum: Your website shows a smart walker concept—how is that related to the assistive robot that you’re working on?

Mike Dooley: The smart walker was a request from a major senior living organization to have our robot (which is really good at navigation) guide residents from place to place within their communities. To test the idea with residents, it turned out to be much quicker to take the navigation system from the robot and put it on an existing rollator walker. So when you see the clips of the technology in the smart walker video on our website, that’s actually the robot’s navigation system localizing in real time and path planning in an environment.

“Assistive robot” can cover a huge range of designs and capabilities—can you give us any more detail about your robot, and what it’ll be able to do?

One of the core features of our robot is to help people move things where they have difficulty moving themselves, particularly in the home setting. That may sound trivial, but to someone who has impaired mobility, it can be a major daily challenge and negatively impact their life and health in a number of ways. Some examples we repeatedly hear are people not staying hydrated or taking their medication on time simply because there is a distance between where they are and the items they need. Once we have those base capabilities, i.e. the ability to navigate around a home and move things within it, then the robot becomes a platform for a wider variety of applications.

What made you decide to develop assistive robots, and why are robots a good solution for seniors who want to live independently?

Supporting independent living has been seen as a massive opportunity in robotics for some time, but also as something off in the future. The turning point for me was watching my mother enter that stage in her life and seeing her transition to using a cane, then a walker, and eventually to a wheelchair. That made the problems very real for me. It also made things much clearer about how we could start addressing specific needs with the tools that are becoming available now.

In terms of why robots can be a good solution, the basic answer is the level of need is so overwhelming that even helping with “basic” tasks can make an appreciable difference in the quality of someone’s daily life. It’s also very much about giving individuals a degree of control back over their environment. That applies to seniors as well as others whose world starts getting more complex to manage as their abilities become more impaired.

What are the particular challenges of developing assistive robots, and how are you addressing them? Why do you think there aren’t more robotics startups in this space?

The setting (operating in homes and personal spaces) and the core purpose of the product (aiding a wide variety of individuals) bring a lot of complexity to any capability you want to build into an assistive robot. Our approach is to put as much structure as we can into the system to make it functional, affordable, understandable and reliable.

I think one of the reasons you don’t see more startups in the space is that a lot of roboticists want to skip ahead and do the fancy stuff, such as taking on human-level capabilities around things like manipulation. Those are very interesting research topics, but we think those are also very far away from being practical solutions you can productize for people to use in their homes.

How do you think assistive robots and human caregivers should work together?

The ideal scenario is allowing caregivers to focus more of their time on the high-touch, personal side of care. The robot can offload the more basic support tasks as well as extend the impact of the caregiver for the long hours of the day they can’t be with someone at their home. We see that applying to both paid care providers as well as the 40 million unpaid family members and friends that provide assistance.

The robot is really there as a tool, both for individuals in need and the people that help them. What’s promising in the research discussions we’ve had so far, is that even when a caregiver is present, giving control back to the individual for simple things can mean a lot in the relationship between them and the caregiver.

What should we look forward to from Labrador in 2020?

Our big goal in 2020 is to start placing the next version of the robot with individuals with different types of needs to let them experience it naturally in their own homes and provide feedback on what they like, what don’t like and how we can make it better. We are currently reaching out to companies in the healthcare and home health fields to participate in those studies and test specific applications related to their services. We plan to share more detail about those studies and the robot itself as we get further into 2020.

If you’re an organization (or individual) who wants to possibly try out Labrador’s prototype, the company encourages you to connect with them through their website. And as we learn more about what Labrador is up to, we’ll have updates for you, presumably in 2020.

[ Labrador Systems ]

* I just lost an hour of my life after finding out that you can play Where in the World is Carmen San Diego in your browser for free. Continue reading

Posted in Human Robots