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#431081 How the Intelligent Home of the Future ...

As Dorothy famously said in The Wizard of Oz, there’s no place like home. Home is where we go to rest and recharge. It’s familiar, comfortable, and our own. We take care of our homes by cleaning and maintaining them, and fixing things that break or go wrong.
What if our homes, on top of giving us shelter, could also take care of us in return?
According to Chris Arkenberg, this could be the case in the not-so-distant future. As part of Singularity University’s Experts On Air series, Arkenberg gave a talk called “How the Intelligent Home of The Future Will Care For You.”
Arkenberg is a research and strategy lead at Orange Silicon Valley, and was previously a research fellow at the Deloitte Center for the Edge and a visiting researcher at the Institute for the Future.
Arkenberg told the audience that there’s an evolution going on: homes are going from being smart to being connected, and will ultimately become intelligent.
Market Trends
Intelligent home technologies are just now budding, but broader trends point to huge potential for their growth. We as consumers already expect continuous connectivity wherever we go—what do you mean my phone won’t get reception in the middle of Yosemite? What do you mean the smart TV is down and I can’t stream Game of Thrones?
As connectivity has evolved from a privilege to a basic expectation, Arkenberg said, we’re also starting to have a better sense of what it means to give up our data in exchange for services and conveniences. It’s so easy to click a few buttons on Amazon and have stuff show up at your front door a few days later—never mind that data about your purchases gets recorded and aggregated.
“Right now we have single devices that are connected,” Arkenberg said. “Companies are still trying to show what the true value is and how durable it is beyond the hype.”

Connectivity is the basis of an intelligent home. To take a dumb object and make it smart, you get it online. Belkin’s Wemo, for example, lets users control lights and appliances wirelessly and remotely, and can be paired with Amazon Echo or Google Home for voice-activated control.
Speaking of voice-activated control, Arkenberg pointed out that physical interfaces are evolving, too, to the point that we’re actually getting rid of interfaces entirely, or transitioning to ‘soft’ interfaces like voice or gesture.
Drivers of change
Consumers are open to smart home tech and companies are working to provide it. But what are the drivers making this tech practical and affordable? Arkenberg said there are three big ones:
Computation: Computers have gotten exponentially more powerful over the past few decades. If it wasn’t for processors that could handle massive quantities of information, nothing resembling an Echo or Alexa would even be possible. Artificial intelligence and machine learning are powering these devices, and they hinge on computing power too.
Sensors: “There are more things connected now than there are people on the planet,” Arkenberg said. Market research firm Gartner estimates there are 8.4 billion connected things currently in use. Wherever digital can replace hardware, it’s doing so. Cheaper sensors mean we can connect more things, which can then connect to each other.
Data: “Data is the new oil,” Arkenberg said. “The top companies on the planet are all data-driven giants. If data is your business, though, then you need to keep finding new ways to get more and more data.” Home assistants are essentially data collection systems that sit in your living room and collect data about your life. That data in turn sets up the potential of machine learning.
Colonizing the Living Room
Alexa and Echo can turn lights on and off, and Nest can help you be energy-efficient. But beyond these, what does an intelligent home really look like?
Arkenberg’s vision of an intelligent home uses sensing, data, connectivity, and modeling to manage resource efficiency, security, productivity, and wellness.
Autonomous vehicles provide an interesting comparison: they’re surrounded by sensors that are constantly mapping the world to build dynamic models to understand the change around itself, and thereby predict things. Might we want this to become a model for our homes, too? By making them smart and connecting them, Arkenberg said, they’d become “more biological.”
There are already several products on the market that fit this description. RainMachine uses weather forecasts to adjust home landscape watering schedules. Neurio monitors energy usage, identifies areas where waste is happening, and makes recommendations for improvement.
These are small steps in connecting our homes with knowledge systems and giving them the ability to understand and act on that knowledge.
He sees the homes of the future being equipped with digital ears (in the form of home assistants, sensors, and monitoring devices) and digital eyes (in the form of facial recognition technology and machine vision to recognize who’s in the home). “These systems are increasingly able to interrogate emotions and understand how people are feeling,” he said. “When you push more of this active intelligence into things, the need for us to directly interface with them becomes less relevant.”
Could our homes use these same tools to benefit our health and wellness? FREDsense uses bacteria to create electrochemical sensors that can be applied to home water systems to detect contaminants. If that’s not personal enough for you, get a load of this: ClinicAI can be installed in your toilet bowl to monitor and evaluate your biowaste. What’s the point, you ask? Early detection of colon cancer and other diseases.
What if one day, your toilet’s biowaste analysis system could link up with your fridge, so that when you opened it it would tell you what to eat, and how much, and at what time of day?
Roadblocks to intelligence
“The connected and intelligent home is still a young category trying to establish value, but the technological requirements are now in place,” Arkenberg said. We’re already used to living in a world of ubiquitous computation and connectivity, and we have entrained expectations about things being connected. For the intelligent home to become a widespread reality, its value needs to be established and its challenges overcome.
One of the biggest challenges will be getting used to the idea of continuous surveillance. We’ll get convenience and functionality if we give up our data, but how far are we willing to go? Establishing security and trust is going to be a big challenge moving forward,” Arkenberg said.
There’s also cost and reliability, interoperability and fragmentation of devices, or conversely, what Arkenberg called ‘platform lock-on,’ where you’d end up relying on only one provider’s system and be unable to integrate devices from other brands.
Ultimately, Arkenberg sees homes being able to learn about us, manage our scheduling and transit, watch our moods and our preferences, and optimize our resource footprint while predicting and anticipating change.
“This is the really fascinating provocation of the intelligent home,” Arkenberg said. “And I think we’re going to start to see this play out over the next few years.”
Sounds like a home Dorothy wouldn’t recognize, in Kansas or anywhere else.
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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
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
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
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
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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#430874 12 Companies That Are Making the World a ...

The Singularity University Global Summit in San Francisco this week brought brilliant minds together from all over the world to share a passion for using science and technology to solve the world’s most pressing challenges.
Solving these challenges means ensuring basic needs are met for all people. It means improving quality of life and mitigating future risks both to people and the planet.
To recognize organizations doing outstanding work in these fields, SU holds the Global Grand Challenge Awards. Three participating organizations are selected in each of 12 different tracks and featured at the summit’s EXPO. The ones found to have the most potential to positively impact one billion people are selected as the track winners.
Here’s a list of the companies recognized this year, along with some details about the great work they’re doing.
Global Grand Challenge Awards winners at Singularity University’s Global Summit in San Francisco.
Disaster Resilience
LuminAID makes portable lanterns that can provide 24 hours of light on 10 hours of solar charging. The lanterns came from a project to assist post-earthquake relief efforts in Haiti, when the product’s creators considered the dangerous conditions at night in the tent cities and realized light was a critical need. The lights have been used in more than 100 countries and after disasters, including Hurricane Sandy, Typhoon Haiyan, and the earthquakes in Nepal.

BreezoMeter uses big data and machine learning to deliver accurate air quality information in real time. Users can see pollution details as localized as a single city block, and data is impacted by real-time traffic. Forecasting is also available, with air pollution information available up to four days ahead of time, or several years in the past.
Aspire Food Group believes insects are the protein of the future, and that technology has the power to bring the tradition of eating insects that exists in many countries and cultures to the rest of the world. The company uses technologies like robotics and automated data collection to farm insects that have the protein quality of meat and the environmental footprint of plants.
Rafiki Power acts as a rural utility company, building decentralized energy solutions in regions that lack basic services like running water and electricity. The company’s renewable hybrid systems are packed and standardized in recycled 20-foot shipping containers, and they’re currently powering over 700 household and business clients in rural Tanzania.

MakeSense is an international community that brings together people in 128 cities across the world to help social entrepreneurs solve challenges in areas like education, health, food, and environment. Social entrepreneurs post their projects and submit challenges to the community, then participants organize workshops to mobilize and generate innovative solutions to help the projects grow.
Unima developed a fast and low-cost diagnostic and disease surveillance tool for infectious diseases. The tool allows health professionals to diagnose diseases at the point of care, in less than 15 minutes, without the use of any lab equipment. A drop of the patient’s blood is put on a diagnostic paper, where the antibody generates a visual reaction when in contact with the biomarkers in the sample. The result is evaluated by taking a photo with an app in a smartphone, which uses image processing, artificial intelligence and machine learning.
Egalite helps people with disabilities enter the labor market, and helps companies develop best practices for inclusion of the disabled. Egalite’s founders are passionate about the potential of people with disabilities and the return companies get when they invest in that potential.
Iris.AI is an artificial intelligence system that reads scientific paper abstracts and extracts key concepts for users, presenting concepts visually and allowing users to navigate a topic across disciplines. Since its launch, Iris.AI has read 30 million research paper abstracts and more than 2,000 TED talks. The AI uses a neural net and deep learning technology to continuously improve its output.
Hala Systems, Inc. is a social enterprise focused on developing technology-driven solutions to the world’s toughest humanitarian challenges. Hala is currently focused on civilian protection, accountability, and the prevention of violent extremism before, during, and after conflict. Ultimately, Hala aims to transform the nature of civilian defense during warfare, as well as to reduce casualties and trauma during post-conflict recovery, natural disasters, and other major crises.
Billion Bricks designs and provides shelter and infrastructure solutions for the homeless. The company’s housing solutions are scalable, sustainable, and able to create opportunities for communities to emerge from poverty. Their approach empowers communities to replicate the solutions on their own, reducing dependency on support and creating ownership and pride.

Tellus Labs uses satellite data to tackle challenges like food security, water scarcity, and sustainable urban and industrial systems, and drive meaningful change. The company built a planetary-scale model of all 170 million acres of US corn and soy crops to more accurately forecast yields and help stabilize the market fluctuations that accompany the USDA’s monthly forecasts.
Loowatt designed a toilet that uses a patented sealing technology to contain human waste within biodegradable film. The toilet is designed for linking to anaerobic digestion technology to provide a source of biogas for cooking, electricity, and other applications, creating the opportunity to offset capital costs with energy production.
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#430855 Why Education Is the Hardest Sector of ...

We’ve all heard the warning cries: automation will disrupt entire industries and put millions of people out of jobs. In fact, up to 45 percent of existing jobs can be automated using current technology.
However, this may not necessarily apply to the education sector. After a detailed analysis of more than 2,000-plus work activities for more than 800 occupations, a report by McKinsey & Co states that of all the sectors examined, “…the technical feasibility of automation is lowest in education.”
There is no doubt that technological trends will have a powerful impact on global education, both by improving the overall learning experience and by increasing global access to education. Massive open online courses (MOOCs), chatbot tutors, and AI-powered lesson plans are just a few examples of the digital transformation in global education. But will robots and artificial intelligence ever fully replace teachers?
The Most Difficult Sector to Automate
While various tasks revolving around education—like administrative tasks or facilities maintenance—are open to automation, teaching itself is not.
Effective education involves more than just transfer of information from a teacher to a student. Good teaching requires complex social interactions and adaptation to the individual student’s learning needs. An effective teacher is not just responsive to each student’s strengths and weaknesses, but is also empathetic towards the student’s state of mind. It’s about maximizing human potential.
Furthermore, students don’t just rely on effective teachers to teach them the course material, but also as a source of life guidance and career mentorship. Deep and meaningful human interaction is crucial and is something that is very difficult, if not impossible, to automate.
Automating teaching is an example of a task that would require artificial general intelligence (as opposed to narrow or specific intelligence). In other words, this is the kind of task that would require an AI that understands natural human language, can be empathetic towards emotions, plan, strategize and make impactful decisions under unpredictable circumstances.
This would be the kind of machine that can do anything a human can do, and it doesn’t exist—at least, not yet.
We’re Getting There
Let’s not forget how quickly AI is evolving. Just because it’s difficult to fully automate teaching, it doesn’t mean the world’s leading AI experts aren’t trying.
Meet Jill Watson, the teaching assistant from Georgia Institute of Technology. Watson isn’t your average TA. She’s an IBM-powered artificial intelligence that is being implemented in universities around the world. Watson is able to answer students’ questions with 97 percent certainty.
Technologies like this also have applications in grading and providing feedback. Some AI algorithms are being trained and refined to perform automatic essay scoring. One project has achieved a 0.945 correlation with human graders.
All of this will have a remarkable impact on online education as we know it and dramatically increase online student retention rates.

Any student with a smartphone can access a wealth of information and free courses from universities around the world. MOOCs have allowed valuable courses to become available to millions of students. But at the moment, not all participants can receive customized feedback for their work. Currently, this is limited by manpower, but in the future that may not be the case.
What chatbots like Jill Watson allow is the opportunity for hundreds of thousands, if not millions, of students to have their work reviewed and all their questions answered at a minimal cost.
AI algorithms also have a significant role to play in personalization of education. Every student is unique and has a different set of strengths and weaknesses. Data analysis can be used to improve individual student results, assess each student’s strengths and weaknesses, and create mass-customized programs. Algorithms can analyze student data and consequently make flexible programs that adapt to the learner based on real-time feedback. According to the McKinsey Global Institute, all of this data in education could unlock between $900 billion and $1.2 trillion in global economic value.
Beyond Automated Teaching
It’s important to recognize that technological automation alone won’t fix the many issues in our global education system today. Dominated by outdated curricula, standardized tests, and an emphasis on short-term knowledge, many experts are calling for a transformation of how we teach.
It is not enough to simply automate the process. We can have a completely digital learning experience that continues to focus on outdated skills and fails to prepare students for the future. In other words, we must not only be innovative with our automation capabilities, but also with educational content, strategy, and policies.
Are we equipping students with the most important survival skills? Are we inspiring young minds to create a better future? Are we meeting the unique learning needs of each and every student? There’s no point automating and digitizing a system that is already flawed. We need to ensure the system that is being digitized is itself being transformed for the better.
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#430761 How Robots Are Getting Better at Making ...

The multiverse of science fiction is populated by robots that are indistinguishable from humans. They are usually smarter, faster, and stronger than us. They seem capable of doing any job imaginable, from piloting a starship and battling alien invaders to taking out the trash and cooking a gourmet meal.
The reality, of course, is far from fantasy. Aside from industrial settings, robots have yet to meet The Jetsons. The robots the public are exposed to seem little more than over-sized plastic toys, pre-programmed to perform a set of tasks without the ability to interact meaningfully with their environment or their creators.
To paraphrase PayPal co-founder and tech entrepreneur Peter Thiel, we wanted cool robots, instead we got 140 characters and Flippy the burger bot. But scientists are making progress to empower robots with the ability to see and respond to their surroundings just like humans.
Some of the latest developments in that arena were presented this month at the annual Robotics: Science and Systems Conference in Cambridge, Massachusetts. The papers drilled down into topics that ranged from how to make robots more conversational and help them understand language ambiguities to helping them see and navigate through complex spaces.
Improved Vision
Ben Burchfiel, a graduate student at Duke University, and his thesis advisor George Konidaris, an assistant professor of computer science at Brown University, developed an algorithm to enable machines to see the world more like humans.
In the paper, Burchfiel and Konidaris demonstrate how they can teach robots to identify and possibly manipulate three-dimensional objects even when they might be obscured or sitting in unfamiliar positions, such as a teapot that has been tipped over.
The researchers trained their algorithm by feeding it 3D scans of about 4,000 common household items such as beds, chairs, tables, and even toilets. They then tested its ability to identify about 900 new 3D objects just from a bird’s eye view. The algorithm made the right guess 75 percent of the time versus a success rate of about 50 percent for other computer vision techniques.
In an email interview with Singularity Hub, Burchfiel notes his research is not the first to train machines on 3D object classification. How their approach differs is that they confine the space in which the robot learns to classify the objects.
“Imagine the space of all possible objects,” Burchfiel explains. “That is to say, imagine you had tiny Legos, and I told you [that] you could stick them together any way you wanted, just build me an object. You have a huge number of objects you could make!”
The infinite possibilities could result in an object no human or machine might recognize.
To address that problem, the researchers had their algorithm find a more restricted space that would host the objects it wants to classify. “By working in this restricted space—mathematically we call it a subspace—we greatly simplify our task of classification. It is the finding of this space that sets us apart from previous approaches.”
Following Directions
Meanwhile, a pair of undergraduate students at Brown University figured out a way to teach robots to understand directions better, even at varying degrees of abstraction.
The research, led by Dilip Arumugam and Siddharth Karamcheti, addressed how to train a robot to understand nuances of natural language and then follow instructions correctly and efficiently.
“The problem is that commands can have different levels of abstraction, and that can cause a robot to plan its actions inefficiently or fail to complete the task at all,” says Arumugam in a press release.
In this project, the young researchers crowdsourced instructions for moving a virtual robot through an online domain. The space consisted of several rooms and a chair, which the robot was told to manipulate from one place to another. The volunteers gave various commands to the robot, ranging from general (“take the chair to the blue room”) to step-by-step instructions.
The researchers then used the database of spoken instructions to teach their system to understand the kinds of words used in different levels of language. The machine learned to not only follow instructions but to recognize the level of abstraction. That was key to kickstart its problem-solving abilities to tackle the job in the most appropriate way.
The research eventually moved from virtual pixels to a real place, using a Roomba-like robot that was able to respond to instructions within one second 90 percent of the time. Conversely, when unable to identify the specificity of the task, it took the robot 20 or more seconds to plan a task about 50 percent of the time.
One application of this new machine-learning technique referenced in the paper is a robot worker in a warehouse setting, but there are many fields that could benefit from a more versatile machine capable of moving seamlessly between small-scale operations and generalized tasks.
“Other areas that could possibly benefit from such a system include things from autonomous vehicles… to assistive robotics, all the way to medical robotics,” says Karamcheti, responding to a question by email from Singularity Hub.
More to Come
These achievements are yet another step toward creating robots that see, listen, and act more like humans. But don’t expect Disney to build a real-life Westworld next to Toon Town anytime soon.
“I think we’re a long way off from human-level communication,” Karamcheti says. “There are so many problems preventing our learning models from getting to that point, from seemingly simple questions like how to deal with words never seen before, to harder, more complicated questions like how to resolve the ambiguities inherent in language, including idiomatic or metaphorical speech.”
Even relatively verbose chatbots can run out of things to say, Karamcheti notes, as the conversation becomes more complex.
The same goes for human vision, according to Burchfiel.
While deep learning techniques have dramatically improved pattern matching—Google can find just about any picture of a cat—there’s more to human eyesight than, well, meets the eye.
“There are two big areas where I think perception has a long way to go: inductive bias and formal reasoning,” Burchfiel says.
The former is essentially all of the contextual knowledge people use to help them reason, he explains. Burchfiel uses the example of a puddle in the street. People are conditioned or biased to assume it’s a puddle of water rather than a patch of glass, for instance.
“This sort of bias is why we see faces in clouds; we have strong inductive bias helping us identify faces,” he says. “While it sounds simple at first, it powers much of what we do. Humans have a very intuitive understanding of what they expect to see, [and] it makes perception much easier.”
Formal reasoning is equally important. A machine can use deep learning, in Burchfiel’s example, to figure out the direction any river flows once it understands that water runs downhill. But it’s not yet capable of applying the sort of human reasoning that would allow us to transfer that knowledge to an alien setting, such as figuring out how water moves through a plumbing system on Mars.
“Much work was done in decades past on this sort of formal reasoning… but we have yet to figure out how to merge it with standard machine-learning methods to create a seamless system that is useful in the actual physical world.”
Robots still have a lot to learn about being human, which should make us feel good that we’re still by far the most complex machines on the planet.
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