Tag Archives: popular

#431368 This Week’s Awesome Stories From ...

INTERNET OF THINGSAmazon Key Is a New Service That Lets Couriers Unlock Your Front DoorBen Popper | The Verge“When a courier arrives with a package for in-home delivery, they scan the barcode, sending a request to Amazon’s cloud. If everything checks out, the cloud grants permission by sending a message back to the camera, which starts recording. The courier then gets a prompt on their app, swipes the screen, and voilà, your door unlocks.”
ROBOTICSWatch Yamaha’s Humanoid Robot Ride a Motorcycle Around a RacetrackPhilip E. Ross | IEEE Spectrum“What’s striking is that the bike is unmodified: the robot is a hunched-over form on top. It senses the environment, calculates what to do, keeps the bike stable, manages acceleration and deceleration—all while factoring in road conditions, air resistance, and engine braking.”
ARTIFICIAL INTELLIGENCETech Giants Are Paying Huge Salaries for Scarce A.I. TalentCade Metz | The New York Times“Typical A.I. specialists, including both Ph.D.s fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them. All of them requested anonymity because they did not want to damage their professional prospects.”
HEALTH This Doctor Diagnosed His Own Cancer With an iPhone UltrasoundAntonio Regalado | MIT Technology Review“The device he used, called the Butterfly IQ, is the first solid-state ultrasound machine to reach the market in the U.S. Ultrasound works by shooting sound into the body and capturing the echoes. Usually, the sound waves are generated by a vibrating crystal. But Butterfly’s machine instead uses 9,000 tiny drums etched onto a semiconductor chip.”
ENTREPRENEURSHIPWeWork: A $20 Billion Startup Fueled by Silicon Valley Pixie DustEliot Brown | Wall Street Journal“WeWork’s strategy carries the costs and risks associated with traditional real estate. Its client list is heavily weighted toward startups that may or may not be around for long. WeWork is on the hook for long-term leases, and it doesn’t own its own buildings. Vacancy rates have risen recently, and the company is increasing incentives to draw tenants… The model has proved popular, with 150,000 individuals renting space in more than 170 locations globally.”
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#431238 AI Is Easy to Fool—Why That Needs to ...

Con artistry is one of the world’s oldest and most innovative professions, and it may soon have a new target. Research suggests artificial intelligence may be uniquely susceptible to tricksters, and as its influence in the modern world grows, attacks against it are likely to become more common.
The root of the problem lies in the fact that artificial intelligence algorithms learn about the world in very different ways than people do, and so slight tweaks to the data fed into these algorithms can throw them off completely while remaining imperceptible to humans.
Much of the research into this area has been conducted on image recognition systems, in particular those relying on deep learning neural networks. These systems are trained by showing them thousands of examples of images of a particular object until they can extract common features that allow them to accurately spot the object in new images.
But the features they extract are not necessarily the same high-level features a human would be looking for, like the word STOP on a sign or a tail on a dog. These systems analyze images at the individual pixel level to detect patterns shared between examples. These patterns can be obscure combinations of pixel values, in small pockets or spread across the image, that would be impossible to discern for a human, but highly accurate at predicting a particular object.

“An attacker can trick the object recognition algorithm into seeing something that isn’t there, without these alterations being obvious to a human.”

What this means is that by identifying these patterns and overlaying them over a different image, an attacker can trick the object recognition algorithm into seeing something that isn’t there, without these alterations being obvious to a human. This kind of manipulation is known as an “adversarial attack.”
Early attempts to trick image recognition systems this way required access to the algorithm’s inner workings to decipher these patterns. But in 2016 researchers demonstrated a “black box” attack that enabled them to trick such a system without knowing its inner workings.
By feeding the system doctored images and seeing how it classified them, they were able to work out what it was focusing on and therefore generate images they knew would fool it. Importantly, the doctored images were not obviously different to human eyes.
These approaches were tested by feeding doctored image data directly into the algorithm, but more recently, similar approaches have been applied in the real world. Last year it was shown that printouts of doctored images that were then photographed on a smartphone successfully tricked an image classification system.
Another group showed that wearing specially designed, psychedelically-colored spectacles could trick a facial recognition system into thinking people were celebrities. In August scientists showed that adding stickers to stop signs in particular configurations could cause a neural net designed to spot them to misclassify the signs.
These last two examples highlight some of the potential nefarious applications for this technology. Getting a self-driving car to miss a stop sign could cause an accident, either for insurance fraud or to do someone harm. If facial recognition becomes increasingly popular for biometric security applications, being able to pose as someone else could be very useful to a con artist.
Unsurprisingly, there are already efforts to counteract the threat of adversarial attacks. In particular, it has been shown that deep neural networks can be trained to detect adversarial images. One study from the Bosch Center for AI demonstrated such a detector, an adversarial attack that fools the detector, and a training regime for the detector that nullifies the attack, hinting at the kind of arms race we are likely to see in the future.
While image recognition systems provide an easy-to-visualize demonstration, they’re not the only machine learning systems at risk. The techniques used to perturb pixel data can be applied to other kinds of data too.

“Bypassing cybersecurity defenses is one of the more worrying and probable near-term applications for this approach.”

Chinese researchers showed that adding specific words to a sentence or misspelling a word can completely throw off machine learning systems designed to analyze what a passage of text is about. Another group demonstrated that garbled sounds played over speakers could make a smartphone running the Google Now voice command system visit a particular web address, which could be used to download malware.
This last example points toward one of the more worrying and probable near-term applications for this approach: bypassing cybersecurity defenses. The industry is increasingly using machine learning and data analytics to identify malware and detect intrusions, but these systems are also highly susceptible to trickery.
At this summer’s DEF CON hacking convention, a security firm demonstrated they could bypass anti-malware AI using a similar approach to the earlier black box attack on the image classifier, but super-powered with an AI of their own.
Their system fed malicious code to the antivirus software and then noted the score it was given. It then used genetic algorithms to iteratively tweak the code until it was able to bypass the defenses while maintaining its function.
All the approaches noted so far are focused on tricking pre-trained machine learning systems, but another approach of major concern to the cybersecurity industry is that of “data poisoning.” This is the idea that introducing false data into a machine learning system’s training set will cause it to start misclassifying things.
This could be particularly challenging for things like anti-malware systems that are constantly being updated to take into account new viruses. A related approach bombards systems with data designed to generate false positives so the defenders recalibrate their systems in a way that then allows the attackers to sneak in.
How likely it is that these approaches will be used in the wild will depend on the potential reward and the sophistication of the attackers. Most of the techniques described above require high levels of domain expertise, but it’s becoming ever easier to access training materials and tools for machine learning.
Simpler versions of machine learning have been at the heart of email spam filters for years, and spammers have developed a host of innovative workarounds to circumvent them. As machine learning and AI increasingly embed themselves in our lives, the rewards for learning how to trick them will likely outweigh the costs.
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#431170 This Week’s Awesome Stories From ...

AUGMENTED REALITY
ZED Mini Turns Rift and Vive Into an AR Headset From the FutureBen Lang | Road to VR“When attached, the camera provides stereo pass-through video and real-time depth and environment mapping, turning the headsets into dev kits emulating the capabilities of high-end AR headsets of the future. The ZED Mini will launch in November.”
ROBOTICS
Life-Size Humanoid Robot Is Designed to Fall Over (and Over and Over)Evan Ackerman | IEEE Spectrum “The researchers came up with a new strategy for not worrying about falls: not worrying about falls. Instead, they’ve built their robot from the ground up with an armored structure that makes it totally okay with falling over and getting right back up again.”
SPACE
Russia Will Team up With NASA to Build a Lunar Space StationAnatoly Zak | Popular Mechanics “NASA and its partner agencies plan to begin the construction of the modular habitat known as the Deep-Space Gateway in orbit around the Moon in the early 2020s. It will become the main destination for astronauts for at least a decade, extending human presence beyond the Earth’s orbit for the first time since the end of the Apollo program in 1972. Launched on NASA’s giant SLS rocket and serviced by the crews of the Orion spacecraft, the outpost would pave the way to a mission to Mars in the 2030s.”
TRANSPORTATION
Dubai Starts Testing Crewless Two-Person ‘Flying Taxis’Thuy Ong | The Verge“The drone was uncrewed and hovered 200 meters high during the test flight, according to Reuters. The AAT, which is about two meters high, was supplied by specialist German manufacturer Volocopter, known for its eponymous helicopter drone hybrid with 18 rotors…Dubai has a target for autonomous transport to account for a quarter of total trips by 2030.”
AUTONOMOUS CARS
Toyota Is Trusting a Startup for a Crucial Part of Its Newest Self-Driving CarsJohana Bhuiyan | Recode “Toyota unveiled the next generation of its self-driving platform today, which features more accurate object detection technology and mapping, among other advancements. These test cars—which Toyota is testing on both a closed driving course and on some public roads—will also be using Luminar’s lidar sensors, or radars that use lasers to detect the distance to an object.”
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#431058 How to Make Your First Chatbot With the ...

You’re probably wondering what Game of Thrones has to do with chatbots and artificial intelligence. Before I explain this weird connection, I need to warn you that this article may contain some serious spoilers. Continue with your reading only if you are a passionate GoT follower, who watches new episodes immediately after they come out.
Why are chatbots so important anyway?
According to the study “When Will AI Exceed Human Performance?,” researchers believe there is a 50% chance artificial intelligence could take over all human jobs by around the year 2060. This technology has already replaced dozens of customer service and sales positions and helped businesses make substantial savings.
Apart from the obvious business advantages, chatbot creation can be fun. You can create an artificial personality with a strong attitude and a unique set of traits and flaws. It’s like creating a new character for your favorite TV show. That’s why I decided to explain the most important elements of the chatbot creation process by using the TV characters we all know and love (or hate).
Why Game of Thrones?
Game of Thrones is the most popular TV show in the world. More than 10 million viewers watched the seventh season premiere, and you have probably seen internet users fanatically discussing the series’ characters, storyline, and possible endings.
Apart from writing about chatbots, I’m also a GoT fanatic, and I will base this chatbot on one of the characters from my favorite series. But before you find out the name of my bot, you should read a few lines about incredible free tools that allow us to build chatbots without coding.
Are chatbots expensive?
Today, you can create a chatbot even if you don’t know how to code. Most chatbot building platforms offer at least one free plan that allows you to use basic functionalities, create your bot, deploy it to Facebook Messenger, and analyze its performance. Free plans usually allow your bot to talk to a limited number of users.
Why should you personalize your bot?
Every platform will ask you to write a bot’s name before you start designing conversations. You will also be able to add the bot’s photograph and bio. Personalizing your bot is the only way to ensure that you will stick to the same personality and storyline throughout the building process. Users often see chatbots as people, and by giving your bot an identity, you will make sure that it doesn’t sound like it has multiple personality disorder.
I think connecting my chatbot with a GoT character will help readers understand the process of chatbot creation.
And the name of our GoT chatbot is…
…Cersei. She is mean, pragmatic, and fearless and she would do anything to stay on the Iron Throne. Many people would rather hang out with Daenerys or Jon Snow. These characters are honest, noble and good-hearted, which means their actions are often predictable.
Cersei, on the other hand, is the queen of intrigues. As the meanest and the most vengeful character in the series, she has an evil plan for everybody who steps on her toes. While viewers can easily guess where Jon and Daenerys stand, there are dozens of questions they would like to ask Cersei. But before we start talking to our bot, we need to build her personality by using the most basic elements of chatbot interaction.
Choosing the bot’s name on Botsify.
Welcome / Greeting Message
The welcome message is the greeting Cersei says to every commoner who clicks on the ‘start conversation’ button. She is not a welcoming person (ask Sansa), except if you are a banker from Braavos. Her introductory message may sound something like this:
“Dear {{user_full_name}}, My name is Cersei of the House Lannister, the First of Her Name, Queen of the Andals and the First Men, Protector of the Seven Kingdoms. You can ask me questions, and I will answer them. If the question is not worth answering, I will redirect you to Ser Gregor Clegane, who will give you a step-by-step course on how to talk to the Queen of Westeros.”
Creating the welcome message on Chatfuel
Default Message / Answer
In the bot game, users, bots, and their creators often need to learn from failed attempts and mistakes. The default message is the text Cersei will send whenever you ask her a question she doesn’t understand. Knowing Cersei, it would sound something like this:
“Ser Gregor, please escort {{user_full_name}} to the dungeon.”
Creating default message on Botsify
Menu
To avoid calling out the Mountain every time someone asks her a question, Cersei might give you a few (safe) options to choose. The best way to do this is by using a menu function. We can classify the questions people want to ask Cersei in several different categories:

Iron Throne
Relationship with Jaime — OK, this isn’t a “safe option,” get ready to get close and personal with Sir Gregor Clegane.
War plans
Euron Greyjoy

After users choose a menu item, Cersei can give them a default response on the topic or set up a plot that will make their lives miserable. Knowing Cersei, she will probably go for the second option.
Adding chatbot menu on Botsify
Stories / Blocks
This feature allows us to build a longer Cersei-to-user interaction. The structure of stories and blocks is different on every chatbot platform, but most of them use keywords and phrases for finding out the user’s intention.

Keywords — where the bot recognizes a certain keyword within the user’s reply. Users who have chosen the ‘war plans’ option might ask Cersei how is she planning to defeat Daenerys’s dragons. We can add ‘dragon’ and ‘dragons’ as keywords, and connect them with an answer that will sound something like this:

“Dragons are not invulnerable as you may think. Maester Qyburn is developing a weapon that will bring them down for good!”
Adding keywords on Chatfuel
People may also ask her about White Walkers. Do you plan to join Daenerys and Jon Snow in a fight against White Walkers? After we add ‘White Walker’ and ‘White Walkers’ on the keyword list, Cersei will answer:
“White Walkers? Do you think the Queen of Westeros has enough free time to think about creatures from fairy tales and legends?”
Adding Keywords on Botsify

Phrases — are more complex syntaxes that the bot can be trained to recognize. Many people would like to ask Cersei if she’s going to marry Euron Greyjoy after the war ends. We can add ‘Euron’ as a keyword, but then we won’t be sure what answer the user is expecting. Instead, we can use the phrase ‘(Will you) marry Euron Greyjoy (after the war?)’. Just to be sure, we should also add a few alternative phrases like ‘(Do you plan on) marrying Euron Greyjoy (after the war),’ ‘(Will you) end up with Euron Greyjoy (after the war?)’, ‘(Will) Euron Greyjoy be the new King?’ etc. Cersei would probably answer this inquiry in her style:

“Of course not, Euron is a useful idiot. I will use his fleet and send him back to the Iron Islands, where he belongs.”
Adding phrases on Botsify
Forms
We have already asked Cersei several questions, and now she would like to ask us something. She can do so by using the form/user input feature. Most tools allow us to add a question and the criteria for checking the users’ answer. If the user provides us the answer that is compliant to the predefined form (like email address, phone number, or a ZIP code), the bot will identify and extract the answer. If the answer doesn’t fit into the predefined criteria, the bot will notify the user and ask him/her to try again.
If Cersei would ask you a question, she would probably want to know your address so she could send her guards to fill your basement with barrels of wildfire.
Creating forms on Botsify
Templates
If you have problems building your first chatbot, templates can help you create the basic conversation structure. Unfortunately, not all platforms offer this feature for free. Snatchbot currently has the most comprehensive list of free templates. There you can choose a pre-built layout. The template selection ranges from simple FAQ bots to ones created for a specific industry, like banking, airline, healthcare, or e-commerce.
Choosing templates on Snatchbot
Plugins
Most tools also provide plugins that can be used for making the conversations more meaningful. These plugins allow Cersei to send images, audio and video files. She can unleash her creativity and make you suffer by sending you her favorite GoT execution videos.

With the help of integrations, Cersei can talk to you on Facebook Messenger, Telegram, WeChat, Slack, and many other communication apps. She can also sell her fan gear and ask you for donations by integrating in-bot payments from PayPal accounts. Her sales pitch will probably sound something like this:
“Gold wins wars! Would you rather invest your funds in a member of a respected family, who always pays her debts, or in the chaotic war endeavor of a crazy revolutionary, whose strength lies in three flying lizards? If your pockets are full of gold, you are already on my side. Now you can complete your checkout on PayPal.”
Chatbot building is now easier than ever, and even small businesses are starting to use the incredible benefits of artificial intelligence. If you still don’t believe that chatbots can replace customer service representatives, I suggest you try to develop a bot based on your favorite TV show, movie or book character and talk with him/her for a while. This way, you will be able to understand the concept that stands behind this amazing technology and use it to improve your business.
Now I’m off to talk to Cersei. Maybe she will feed me some Season 8 spoilers.
This article was originally published by Chatbots Magazine. Read the original post here.
Image credits for screenshots in post: Branislav Srdanovic
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#430668 Why Every Leader Needs to Be Obsessed ...

This article is part of a series exploring the skills leaders must learn to make the most of rapid change in an increasingly disruptive world. The first article in the series, “How the Most Successful Leaders Will Thrive in an Exponential World,” broadly outlines four critical leadership skills—futurist, technologist, innovator, and humanitarian—and how they work together.
Today’s post, part five in the series, takes a more detailed look at leaders as technologists. Be sure to check out part two of the series, “How Leaders Dream Boldly to Bring New Futures to Life,” part three of the series, “How All Leaders Can Make the World a Better Place,” and part four of the series, “How Leaders Can Make Innovation Everyone’s Day Job”.
In the 1990s, Tower Records was the place to get new music. Successful and popular, the California chain spread far and wide, and in 1998, they took on $110 million in debt to fund aggressive further expansion. This wasn’t, as it turns out, the best of timing.
The first portable digital music player went on sale the same year. The following year brought Napster, a file sharing service allowing users to freely share music online. By 2000, Napster hosted 20 million users swapping songs. Then in 2001, Apple’s iPod and iTunes arrived, and when the iTunes Music Store opened in 2003, Apple sold over a million songs the first week.
As music was digitized, hard copies began to go out of style, and sales and revenue declined.
Tower first filed for bankruptcy in 2004 and again (for the last time) in 2006. The internet wasn’t the only reason for Tower’s demise. Mismanagement and price competition from electronics retailers like Best Buy also played a part. Still, today, the vast majority of music is purchased or streamed entirely online, and record stores are for the most part a niche market.
The writing was on the wall, but those impacted most had trouble reading it.
Why is it difficult for leaders to see technological change coming and right the ship before it’s too late? Why did Tower go all out on expansion just as the next big thing took the stage?
This is one story of many. Digitization has moved beyond music and entertainment, and now many big retailers operating physical stores are struggling to stay relevant. Meanwhile, the pace of change is accelerating, and new potentially disruptive technologies are on the horizon.
More than ever, leaders need to develop a strong understanding of and perspective on technology. They need to survey new innovations, forecast their pace, gauge the implications, and adopt new tools and strategy to change course as an industry shifts, not after it’s shifted.
Simply, leaders need to adopt the mindset of a technologist. Here’s what that means.
Survey the Landscape
Nurturing curiosity is the first step to understanding technological change. To know how technology might disrupt your industry, you have to know what’s in the pipeline and identify which new inventions are directly or indirectly related to your industry.
Becoming more technologically minded takes discipline and focus as well as unstructured time to explore the non-obvious connections between what is right in front of us and what might be. It requires a commitment to ongoing learning and discovery.
Read outside your industry and comfort zone, not just Fast Company and Wired, but Science and Nature to expand your horizons. Identify experts with the ability to demystify specific technology areas—many have a solid following on Twitter or a frequently cited blog.
But it isn’t all about reading. Consider going where the change is happening too.
Visit one of the technology hubs around the world or a local university research lab in your own back yard. Or bring the innovation to you by building an internal exploration lab stocked with the latest technologies, creating a technology advisory board, hosting an internal innovation challenge, or a local pitch night where aspiring entrepreneurs can share their newest ideas.
You might even ask the crowd by inviting anyone to suggest what innovation is most likely to disrupt your product, service, or sector. And don’t hesitate to engage younger folks—the digital natives all around you—by asking questions about what technology they are using or excited about. Consider going on a field trip with them to see how they use technology in different aspects of their lives. Invite the seasoned executives on your team to explore long-term “reverse mentoring” with someone who can expose them to the latest technology and teach them to use it.
Whatever your strategy, the goal should be to develop a healthy obsession with technology.
By exploring fresh perspectives outside traditional work environments and then giving ourselves permission to see how these new ideas might influence existing products and strategies, we have a chance to be ready for what we’re not ready for—but is likely right around the corner.
Estimate the Pace of Progress
The next step is forecasting when a technology will mature.
One of the most challenging aspects of the changes underway is that in many technology arenas, we are quickly moving from a linear to an exponential pace. It is hard enough to envision what is needed in an industry buffeted by progress that is changing 10% per year, but what happens when technological progress doubles annually? That is another world altogether.
This kind of change can be deceiving. For example, machine learning and big data are finally reaching critical momentum after more than twenty years of being right around the corner. The advances in applications like speech and image recognition that we’ve seen in recent years dwarf what came before and many believe we’ve just begun to understand the implications.
Even as we begin to embrace disruptive change in one technology arena, far more exciting possibilities unfold when we explore how multiple arenas are converging.
Artificial intelligence and big data are great examples. As Hod Lipson, professor of Mechanical Engineering and Data Science at Columbia University and co-author of Driverless: Intelligent Cars and the Road Ahead, says, “AI is the engine, but big data is the fuel. They need each other.”
This convergence paired with an accelerating pace makes for surprising applications.
To keep his research lab agile and open to new uses of advancing technologies, Lipson routinely asks his PhD students, “How might AI disrupt this industry?” to prompt development of applications across a wide spectrum of sectors from healthcare to agriculture to food delivery.
Explore the Consequences
New technology inevitably gives rise to new ethical, social, and moral questions that we have never faced before. Rather than bury our heads in the sand, as leaders we must explore the full range of potential consequences of whatever is underway or still to come.
We can add AI to kids’ toys, like Mattel’s Hello Barbie or use cutting-edge gene editing technology like CRISPR-Cas9 to select for preferred gene sequences beyond basic health. But just because we can do something doesn’t mean we should.
Take time to listen to skeptics and understand the risks posed by technology.
Elon Musk, Stephen Hawking, Steve Wozniak, Bill Gates, and other well-known names in science and technology have expressed concern in the media and via open letters about the risks posed by AI. Microsoft’s CEO, Satya Nadella, has even argued tech companies shouldn’t build artificial intelligence systems that will replace people rather than making them more productive.
Exploring unintended consequences goes beyond having a Plan B for when something goes wrong. It requires broadening our view of what we’re responsible for. Beyond customers, shareholders, and the bottom line, we should understand how our decisions may impact employees, communities, the environment, our broader industry, and even our competitors.
The minor inconvenience of mitigating these risks now is far better than the alternative. Create forums to listen to and value voices outside of the board room and C-Suite. Seek out naysayers, ethicists, community leaders, wise elders, and even neophytes—those who may not share our preconceived notions of right and wrong or our narrow view of our role in the larger world.
The question isn’t: If we build it, will they come? It’s now: If we can build it, should we?
Adopt New Technologies and Shift Course
The last step is hardest. Once you’ve identified a technology (or technologies) as a potential disruptor and understand the implications, you need to figure out how to evolve your organization to make the most of the opportunity. Simply recognizing disruption isn’t enough.
Take today’s struggling brick-and-mortar retail business. Online shopping isn’t new. Amazon isn’t a plucky startup. Both have been changing how we buy stuff for years. And yet many who still own and operate physical stores—perhaps most prominently, Sears—are now on the brink of bankruptcy.
There’s hope though. Netflix began as a DVD delivery service in the 90s, but quickly realized its core business didn’t have staying power. It would have been laughable to stream movies when Netflix was founded. Still, computers and bandwidth were advancing fast. In 2007, the company added streaming to its subscription. Even then it wasn’t a totally compelling product.
But Netflix clearly saw a streaming future would likely end their DVD business.
In recent years, faster connection speeds, a growing content library, and the company’s entrance into original programming have given Netflix streaming the upper hand over DVDs. Since 2011, DVD subscriptions have steadily declined. Yet the company itself is doing fine. Why? It anticipated the shift to streaming and acted on it.
Never Stop Looking for the Next Big Thing
Technology is and will increasingly be a driver of disruption, destabilizing entrenched businesses and entire industries while also creating new markets and value not yet imagined.
When faced with the rapidly accelerating pace of change, many companies still default to old models and established practices. Leading like a technologist requires vigilant understanding of potential sources of disruption—what might make your company’s offering obsolete? The answers may not always be perfectly clear. What’s most important is relentlessly seeking them.
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