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#431392 What AI Can Now Do Is Remarkable—But ...

Major websites all over the world use a system called CAPTCHA to verify that someone is indeed a human and not a bot when entering data or signing into an account. CAPTCHA stands for the “Completely Automated Public Turing test to tell Computers and Humans Apart.” The squiggly letters and numbers, often posted against photographs or textured backgrounds, have been a good way to foil hackers. They are annoying but effective.
The days of CAPTCHA as a viable line of defense may, however, be numbered.
Researchers at Vicarious, a Californian artificial intelligence firm funded by Amazon founder Jeff Bezos and Facebook’s Mark Zuckerberg, have just published a paper documenting how they were able to defeat CAPTCHA using new artificial intelligence techniques. Whereas today’s most advanced artificial intelligence (AI) technologies use neural networks that require massive amounts of data to learn from, sometimes millions of examples, the researchers said their system needed just five training steps to crack Google’s reCAPTCHA technology. With this, they achieved a 67 percent success rate per character—reasonably close to the human accuracy rate of 87 percent. In answering PayPal and Yahoo CAPTCHAs, the system achieved an accuracy rate of greater than 50 percent.
The CAPTCHA breakthrough came hard on the heels of another major milestone from Google’s DeepMind team, the people who built the world’s best Go-playing system. DeepMind built a new artificial-intelligence system called AlphaGo Zero that taught itself to play the game at a world-beating level with minimal training data, mainly using trial and error—in a fashion similar to how humans learn.
Both playing Go and deciphering CAPTCHAs are clear examples of what we call narrow AI, which is different from artificial general intelligence (AGI)—the stuff of science fiction. Remember R2-D2 of Star Wars, Ava from Ex Machina, and Samantha from Her? They could do many things and learned everything they needed on their own.
Narrow AI technologies are systems that can only perform one specific type of task. For example, if you asked AlphaGo Zero to learn to play Monopoly, it could not, even though that is a far less sophisticated game than Go. If you asked the CAPTCHA cracker to learn to understand a spoken phrase, it would not even know where to start.
To date, though, even narrow AI has been difficult to build and perfect. To perform very elementary tasks such as determining whether an image is of a cat or a dog, the system requires the development of a model that details exactly what is being analyzed and massive amounts of data with labeled examples of both. The examples are used to train the AI systems, which are modeled on the neural networks in the brain, in which the connections between layers of neurons are adjusted based on what is observed. To put it simply, you tell an AI system exactly what to learn, and the more data you give it, the more accurate it becomes.
The methods that Vicarious and Google used were different; they allowed the systems to learn on their own, albeit in a narrow field. By making their own assumptions about what the training model should be and trying different permutations until they got the right results, they were able to teach themselves how to read the letters in a CAPTCHA or to play a game.
This blurs the line between narrow AI and AGI and has broader implications in robotics and virtually any other field in which machine learning in complex environments may be relevant.
Beyond visual recognition, the Vicarious breakthrough and AlphaGo Zero success are encouraging scientists to think about how AIs can learn to do things from scratch. And this brings us one step closer to coexisting with classes of AIs and robots that can learn to perform new tasks that are slight variants on their previous tasks—and ultimately the AGI of science fiction.
So R2-D2 may be here sooner than we expected.
This article was originally published by The Washington Post. Read the original article here.
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#431203 Could We Build a Blade Runner-Style ...

The new Blade Runner sequel will return us to a world where sophisticated androids made with organic body parts can match the strength and emotions of their human creators. As someone who builds biologically inspired robots, I’m interested in whether our own technology will ever come close to matching the “replicants” of Blade Runner 2049.
The reality is that we’re a very long way from building robots with human-like abilities. But advances in so-called soft robotics show a promising way forward for technology that could be a new basis for the androids of the future.
From a scientific point of view, the real challenge is replicating the complexity of the human body. Each one of us is made up of millions and millions of cells, and we have no clue how we can build such a complex machine that is indistinguishable from us humans. The most complex machines today, for example the world’s largest airliner, the Airbus A380, are composed of millions of parts. But in order to match the complexity level of humans, we would need to scale this complexity up about a million times.
There are currently three different ways that engineering is making the border between humans and robots more ambiguous. Unfortunately, these approaches are only starting points and are not yet even close to the world of Blade Runner.
There are human-like robots built from scratch by assembling artificial sensors, motors, and computers to resemble the human body and motion. However, extending the current human-like robot would not bring Blade Runner-style androids closer to humans, because every artificial component, such as sensors and motors, are still hopelessly primitive compared to their biological counterparts.
There is also cyborg technology, where the human body is enhanced with machines such as robotic limbs and wearable and implantable devices. This technology is similarly very far away from matching our own body parts.
Finally, there is the technology of genetic manipulation, where an organism’s genetic code is altered to modify that organism’s body. Although we have been able to identify and manipulate individual genes, we still have a limited understanding of how an entire human emerges from genetic code. As such, we don’t know the degree to which we can actually program code to design everything we wish.
Soft robotics: a way forward?
But we might be able to move robotics closer to the world of Blade Runner by pursuing other technologies and, in particular, by turning to nature for inspiration. The field of soft robotics is a good example. In the last decade or so, robotics researchers have been making considerable efforts to make robots soft, deformable, squishable, and flexible.
This technology is inspired by the fact that 90% of the human body is made from soft substances such as skin, hair, and tissues. This is because most of the fundamental functions in our body rely on soft parts that can change shape, from the heart and lungs pumping fluid around our body to the eye lenses generating signals from their movement. Cells even change shape to trigger division, self-healing and, ultimately, the evolution of the body.
The softness of our bodies is the origin of all their functionality needed to stay alive. So being able to build soft machines would at least bring us a step closer to the robotic world of Blade Runner. Some of the recent technological advances include artificial hearts made out of soft functional materials that are pumping fluid through deformation. Similarly, soft, wearable gloves can help make hand grasping stronger. And “epidermal electronics” has enabled us to tattoo electronic circuits onto our biological skins.
Softness is the keyword that brings humans and technologies closer together. Sensors, motors, and computers are all of a sudden integrated into human bodies once they became soft, and the border between us and external devices becomes ambiguous, just like soft contact lenses became part of our eyes.
Nevertheless, the hardest challenge is how to make individual parts of a soft robot body physically adaptable by self-healing, growing, and differentiating. After all, every part of a living organism is also alive in biological systems in order to make our bodies totally adaptable and evolvable, the function of which could make machines totally indistinguishable from ourselves.
It is impossible to predict when the robotic world of Blade Runner might arrive, and if it does, it will probably be very far in the future. But as long as the desire to build machines indistinguishable from humans is there, the current trends of robotic revolution could make it possible to achieve that dream.
This article was originally published on The Conversation. Read the original article.
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#431159 How Close Is Turing’s Dream of ...

The quest for conversational artificial intelligence has been a long one.
When Alan Turing, the father of modern computing, racked his considerable brains for a test that would truly indicate that a computer program was intelligent, he landed on this area. If a computer could convince a panel of human judges that they were talking to a human—if it could hold a convincing conversation—then it would indicate that artificial intelligence had advanced to the point where it was indistinguishable from human intelligence.
This gauntlet was thrown down in 1950 and, so far, no computer program has managed to pass the Turing test.
There have been some very notable failures, however: Joseph Weizenbaum, as early as 1966—when computers were still programmed with large punch-cards—developed a piece of natural language processing software called ELIZA. ELIZA was a machine intended to respond to human conversation by pretending to be a psychotherapist; you can still talk to her today.
Talking to ELIZA is a little strange. She’ll often rephrase things you’ve said back at you: so, for example, if you say “I’m feeling depressed,” she might say “Did you come to me because you are feeling depressed?” When she’s unsure about what you’ve said, ELIZA will usually respond with “I see,” or perhaps “Tell me more.”
For the first few lines of dialogue, especially if you treat her as your therapist, ELIZA can be convincingly human. This was something Weizenbaum noticed and was slightly alarmed by: people were willing to treat the algorithm as more human than it really was. Before long, even though some of the test subjects knew ELIZA was just a machine, they were opening up with some of their deepest feelings and secrets. They were pouring out their hearts to a machine. When Weizenbaum’s secretary spoke to ELIZA, even though she knew it was a fairly simple computer program, she still insisted Weizenbaum leave the room.
Part of the unexpected reaction ELIZA generated may be because people are more willing to open up to a machine, feeling they won’t be judged, even if the machine is ultimately powerless to do or say anything to really help. The ELIZA effect was named for this computer program: the tendency of humans to anthropomorphize machines, or think of them as human.

Weizenbaum himself, who later became deeply suspicious of the influence of computers and artificial intelligence in human life, was astonished that people were so willing to believe his script was human. He wrote, “I had not realized…that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people.”

“Consciously, you know you’re talking to a big block of code stored somewhere out there in the ether. But subconsciously, you might feel like you’re interacting with a human.”

The ELIZA effect may have disturbed Weizenbaum, but it has intrigued and fascinated others for decades. Perhaps you’ve noticed it in yourself, when talking to an AI like Siri, Alexa, or Google Assistant—the occasional response can seem almost too real. Consciously, you know you’re talking to a big block of code stored somewhere out there in the ether. But subconsciously, you might feel like you’re interacting with a human.
Yet the ELIZA effect, as enticing as it is, has proved a source of frustration for people who are trying to create conversational machines. Natural language processing has proceeded in leaps and bounds since the 1960s. Now you can find friendly chatbots like Mitsuku—which has frequently won the Loebner Prize, awarded to the machines that come closest to passing the Turing test—that aim to have a response to everything you might say.
In the commercial sphere, Facebook has opened up its Messenger program and provided software for people and companies to design their own chatbots. The idea is simple: why have an app for, say, ordering pizza when you can just chatter to a robot through your favorite messenger app and make the order in natural language, as if you were telling your friend to get it for you?
Startups like Semantic Machines hope their AI assistant will be able to interact with you just like a secretary or PA would, but with an unparalleled ability to retrieve information from the internet. They may soon be there.
But people who engineer chatbots—both in the social and commercial realm—encounter a common problem: the users, perhaps subconsciously, assume the chatbots are human and become disappointed when they’re not able to have a normal conversation. Frustration with miscommunication can often stem from raised initial expectations.
So far, no machine has really been able to crack the problem of context retention—understanding what’s been said before, referring back to it, and crafting responses based on the point the conversation has reached. Even Mitsuku will often struggle to remember the topic of conversation beyond a few lines of dialogue.

“For everything you say, there could be hundreds of responses that would make sense. When you travel a layer deeper into the conversation, those factors multiply until you end up with vast numbers of potential conversations.”

This is, of course, understandable. Conversation can be almost unimaginably complex. For everything you say, there could be hundreds of responses that would make sense. When you travel a layer deeper into the conversation, those factors multiply until—like possible games of Go or chess—you end up with vast numbers of potential conversations.
But that hasn’t deterred people from trying, most recently, tech giant Amazon, in an effort to make their AI voice assistant, Alexa, friendlier. They have been running the Alexa Prize competition, which offers a cool $500,000 to the winning AI—and a bonus of a million dollars to any team that can create a ‘socialbot’ capable of sustaining a conversation with human users for 20 minutes on a variety of themes.
Topics Alexa likes to chat about include science and technology, politics, sports, and celebrity gossip. The finalists were recently announced: chatbots from universities in Prague, Edinburgh, and Seattle. Finalists were chosen according to the ratings from Alexa users, who could trigger the socialbots into conversation by saying “Hey Alexa, let’s chat,” although the reviews for the socialbots weren’t always complimentary.
By narrowing down the fields of conversation to a specific range of topics, the Alexa Prize has cleverly started to get around the problem of context—just as commercially available chatbots hope to do. It’s much easier to model an interaction that goes a few layers into the conversational topic if you’re limiting those topics to a specific field.
Developing a machine that can hold almost any conversation with a human interlocutor convincingly might be difficult. It might even be a problem that requires artificial general intelligence to truly solve, rather than the previously-employed approaches of scripted answers or neural networks that associate inputs with responses.
But a machine that can have meaningful interactions that people might value and enjoy could be just around the corner. The Alexa Prize winner is announced in November. The ELIZA effect might mean we will relate to machines sooner than we’d thought.
So, go well, little socialbots. If you ever want to discuss the weather or what the world will be like once you guys take over, I’ll be around. Just don’t start a therapy session.
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#431134 Anthouse Pet Companion Robot Kickstarter

Press Release by: Anthouse.pet
New Ultimate Pet Companion Robot will Turn Heads and Make Your Dog Absolutely Love You.
Man’s Best Friend will soon have a new companion to play with this Fall. Introducing The Anthouse Pet Companion Robot, from the creators at Anthouse Technology Co., Ltd. The Anthouse Robot is the best pet robot for dogs that the market has ever seen. The product includes a range of smart functions all controlled via a smart phone app that pet owners can control to interact with and attend to their loving pets. Features include a camera that’s capable of recording video and taking photos of your pet, with a one-touch social media share button enabled; a walki-talki megaphone to speak to your pet directly; a dog food treat dispenser that can dispense treat servings depending a measure you select; self-directed automated charging (the robot will find it’s charging station whenever its batteries is nearly depleted); automated obstacle avoidance, and our very favorite, a mini-tennis ball launcher for non-stop fun and exercise for your pet. Never again will you have to wonder what your pet is doing. It’s the perfect user-friendly tech product for pet owners and their faithful friends to keep close despite the physical distance between. The Anthouse Pet Companion Robot is set to launch on Kickstarter on August 15th, 9AM PST with an early-bird pice offering of $349. For media review details, and to get an invitation to the official press kit and pre-launch Kickstarter video viewing, please contact Sarah Miller of the Anthouse team for details.
Photo By: Anthouse.pet

Contact Information:
Name: Sarah Miller
Email: hello@anthouse.pet
Phone: 1 (512) 333-2950
Facebook: @anthousepetrobot
Website:
www.anthouse.pet
On Kickstarter: August 15th, 9AM PST
General Press Kit: http://bit.ly/AnthousePressKit

Photo By: Anthouse.pet

Robotic Magazine’s Note: The press release above was provided by anthouse.pet to us. Robotic Magazine do not necessarily endorse any kickstarter campaigns. We publish relevant kickstarter campaigns at the request of the project owners, for free, to support development of robotics.

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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
Banner stock media provided by new_vision_studio / Pond5 Continue reading

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