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#431186 The Coming Creativity Explosion Belongs ...
Does creativity make human intelligence special?
It may appear so at first glance. Though machines can calculate, analyze, and even perceive, creativity may seem far out of reach. Perhaps this is because we find it mysterious, even in ourselves. How can the output of a machine be anything more than that which is determined by its programmers?
Increasingly, however, artificial intelligence is moving into creativity’s hallowed domain, from art to industry. And though much is already possible, the future is sure to bring ever more creative machines.
What Is Machine Creativity?
Robotic art is just one example of machine creativity, a rapidly growing sub-field that sits somewhere between the study of artificial intelligence and human psychology.
The winning paintings from the 2017 Robot Art Competition are strikingly reminiscent of those showcased each spring at university exhibitions for graduating art students. Like the works produced by skilled artists, the compositions dreamed up by the competition’s robotic painters are aesthetically ambitious. One robot-made painting features a man’s bearded face gazing intently out from the canvas, his eyes locking with the viewer’s. Another abstract painting, “inspired” by data from EEG signals, visually depicts the human emotion of misery with jagged, gloomy stripes of black and purple.
More broadly, a creative machine is software (sometimes encased in a robotic body) that synthesizes inputs to generate new and valuable ideas, solutions to complex scientific problems, or original works of art. In a process similar to that followed by a human artist or scientist, a creative machine begins its work by framing a problem. Next, its software specifies the requirements the solution should have before generating “answers” in the form of original designs, patterns, or some other form of output.
Although the notion of machine creativity sounds a bit like science fiction, the basic concept is one that has been slowly developing for decades.
Nearly 50 years ago while a high school student, inventor and futurist Ray Kurzweil created software that could analyze the patterns in musical compositions and then compose new melodies in a similar style. Aaron, one of the world’s most famous painting robots, has been hard at work since the 1970s.
Industrial designers have used an automated, algorithm-driven process for decades to design computer chips (or machine parts) whose layout (or form) is optimized for a particular function or environment. Emily Howell, a computer program created by David Cope, writes original works in the style of classical composers, some of which have been performed by human orchestras to live audiences.
What’s different about today’s new and emerging generation of robotic artists, scientists, composers, authors, and product designers is their ubiquity and power.
“The recent explosion of artificial creativity has been enabled by the rapid maturation of the same exponential technologies that have already re-drawn our daily lives.”
I’ve already mentioned the rapidly advancing fields of robotic art and music. In the realm of scientific research, so-called “robotic scientists” such as Eureqa and Adam and Eve develop new scientific hypotheses; their “insights” have contributed to breakthroughs that are cited by hundreds of academic research papers. In the medical industry, creative machines are hard at work creating chemical compounds for new pharmaceuticals. After it read over seven million words of 20th century English poetry, a neural network developed by researcher Jack Hopkins learned to write passable poetry in a number of different styles and meters.
The recent explosion of artificial creativity has been enabled by the rapid maturation of the same exponential technologies that have already re-drawn our daily lives, including faster processors, ubiquitous sensors and wireless networks, and better algorithms.
As they continue to improve, creative machines—like humans—will perform a broad range of creative activities, ranging from everyday problem solving (sometimes known as “Little C” creativity) to producing once-in-a-century masterpieces (“Big C” creativity). A creative machine’s outputs could range from a design for a cast for a marble sculpture to a schematic blueprint for a clever new gadget for opening bottles of wine.
In the coming decades, by automating the process of solving complex problems, creative machines will again transform our world. Creative machines will serve as a versatile source of on-demand talent.
In the battle to recruit a workforce that can solve complex problems, creative machines will put small businesses on equal footing with large corporations. Art and music lovers will enjoy fresh creative works that re-interpret the style of ancient disciplines. People with a health condition will benefit from individualized medical treatments, and low-income people will receive top-notch legal advice, to name but a few potentially beneficial applications.
How Can We Make Creative Machines, Unless We Understand Our Own Creativity?
One of the most intriguing—yet unsettling—aspects of watching robotic arms skillfully oil paint is that we humans still do not understand our own creative process. Over the centuries, several different civilizations have devised a variety of models to explain creativity.
The ancient Greeks believed that poets drew inspiration from a transcendent realm parallel to the material world where ideas could take root and flourish. In the Middle Ages, philosophers and poets attributed our peculiarly human ability to “make something of nothing” to an external source, namely divine inspiration. Modern academic study of human creativity has generated vast reams of scholarship, but despite the value of these insights, the human imagination remains a great mystery, second only to that of consciousness.
Today, the rise of machine creativity demonstrates (once again), that we do not have to fully understand a biological process in order to emulate it with advanced technology.
Past experience has shown that jet planes can fly higher and faster than birds by using the forward thrust of an engine rather than wings. Submarines propel themselves forward underwater without fins or a tail. Deep learning neural networks identify objects in randomly-selected photographs with super-human accuracy. Similarly, using a fairly straightforward software architecture, creative software (sometimes paired with a robotic body) can paint, write, hypothesize, or design with impressive originality, skill, and boldness.
At the heart of machine creativity is simple iteration. No matter what sort of output they produce, creative machines fall into one of three categories depending on their internal architecture.
Briefly, the first group consists of software programs that use traditional rule-based, or symbolic AI, the second group uses evolutionary algorithms, and the third group uses a variation of a form of machine learning called deep learning that has already revolutionized voice and facial recognition software.
1) Symbolic creative machines are the oldest artificial artists and musicians. In this approach—also known as “good old-fashioned AI (GOFAI) or symbolic AI—the human programmer plays a key role by writing a set of step-by-step instructions to guide the computer through a task. Despite the fact that symbolic AI is limited in its ability to adapt to environmental changes, it’s still possible for a robotic artist programmed this way to create an impressively wide variety of different outputs.
2) Evolutionary algorithms (EA) have been in use for several decades and remain powerful tools for design. In this approach, potential solutions “compete” in a software simulator in a Darwinian process reminiscent of biological evolution. The human programmer specifies a “fitness criterion” that will be used to score and rank the solutions generated by the software. The software then generates a “first generation” population of random solutions (which typically are pretty poor in quality), scores this first generation of solutions, and selects the top 50% (those random solutions deemed to be the best “fit”). The software then takes another pass and recombines the “winning” solutions to create the next generation and repeats this process for thousands (and sometimes millions) of generations.
3) Generative deep learning (DL) neural networks represent the newest software architecture of the three, since DL is data-dependent and resource-intensive. First, a human programmer “trains” a DL neural network to recognize a particular feature in a dataset, for example, an image of a dog in a stream of digital images. Next, the standard “feed forward” process is reversed and the DL neural network begins to generate the feature, for example, eventually producing new and sometimes original images of (or poetry about) dogs. Generative DL networks have tremendous and unexplored creative potential and are able to produce a broad range of original outputs, from paintings to music to poetry.
The Coming Explosion of Machine Creativity
In the near future as Moore’s Law continues its work, we will see sophisticated combinations of these three basic architectures. Since the 1950s, artificial intelligence has steadily mastered one human ability after another, and in the process of doing so, has reduced the cost of calculation, analysis, and most recently, perception. When creative software becomes as inexpensive and ubiquitous as analytical software is today, humans will no longer be the only intelligent beings capable of creative work.
This is why I have to bite my tongue when I hear the well-intended (but shortsighted) advice frequently dispensed to young people that they should pursue work that demands creativity to help them “AI-proof” their futures.
Instead, students should gain skills to harness the power of creative machines.
There are two skills in which humans excel that will enable us to remain useful in a world of ever-advancing artificial intelligence. One, the ability to frame and define a complex problem so that it can be handed off to a creative machine to solve. And two, the ability to communicate the value of both the framework and the proposed solution to the other humans involved.
What will happen to people when creative machines begin to capably tread on intellectual ground that was once considered the sole domain of the human mind, and before that, the product of divine inspiration? While machines engaging in Big C creativity—e.g., oil painting and composing new symphonies—tend to garner controversy and make the headlines, I suspect the real world-changing application of machine creativity will be in the realm of everyday problem solving, or Little C. The mainstream emergence of powerful problem-solving tools will help people create abundance where there was once scarcity.
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#431175 Servosila introduces Mobile Robots ...
Servosila introduces a new member of the family of Servosila “Engineer” robots, a UGV called “Radio Engineer”. This new variant of the well-known backpack-transportable robot features a Software Defined Radio (SDR) payload module integrated into the robotic vehicle.
“Several of our key customers had asked us to enable an Electronic Warfare (EW) or Cognitive Radio applications in our robots”, – says a spokesman for the company, “By integrating a Software Defined Radio (SDR) module into our robotic platforms we cater to both requirements. Radio spectrum analysis, radio signal detection, jamming, and radio relay are important features for EOD robots such as ours. Servosila continues to serve the customers by pushing the boundaries of what their Servosila robots can do. Our partners in the research world and academia shall also greatly benefit from the new functionality that gives them more means of achieving their research goals.”
Photo Credit: Servosila – www.servosila.com
Coupling a programmable mobile robot with a software-defined radio creates a powerful platform for developing innovative applications that mix mobility and artificial intelligence with modern radio technologies. The new robotic radio applications include localized frequency hopping pattern analysis, OFDM waveform recognition, outdoor signal triangulation, cognitive mesh networking, automatic area search for radio emitters, passive or active mobile robotic radars, mobile base stations, mobile radio scanners, and many others.
A rotating head of the robot with mounts for external antennae acts as a pan-and-tilt device thus enabling various scanning and tracking applications. The neck of the robotic head is equipped with a pair of highly accurate Servosila-made servos with a pointing precision of 3.0 angular minutes. This means that the robot can point its antennae with an unprecedented accuracy.
Researchers and academia can benefit from the platform’s support for GnuRadio, an open source software framework for developing SDR applications. An on-board Intel i7 computer capable of executing OpenCL code, is internally connected to the SDR payload module. This makes it possible to execute most existing GnuRadio applications directly on the robot’s on-board computer. Other sensors of the robot such as a GPS sensor, an IMU or a thermal vision camera contribute into sensor fusion algorithms.
Since Servosila “Engineer” mobile robots are primarily designed for outdoor use, the SDR module is fully enclosed into a hardened body of the robot which provides protection in case of dust, rain, snow or impacts with obstacles while the robot is on the move. The robot and its SDR payload module are both powered by an on-board battery thus making the entire robotic radio platform independent of external power supplies.
Servosila plans to start shipping the SDR-equipped robots to international customers in October, 2017.
Web: https://www.servosila.com
YouTube: https://www.youtube.com/user/servosila/videos
About the Company
Servosila is a robotics technology company that designs, produces and markets a range of mobile robots, robotic arms, servo drives, harmonic reduction gears, robotic control systems as well as software packages that make the robots intelligent. Servosila provides consulting, training and operations support services to various customers around the world. The company markets its products and services directly or through a network of partners who provide tailored and localized services that meet specific procurement, support or operational needs.
Press Release above is by: Servosila
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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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Posted in Human Robots
#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.
Environment
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.
Food
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.
Energy
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.
Governance
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.
Health
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.
Prosperity
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.
Learning
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.
Security
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.
Shelter
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.
Space
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.
Water
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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Posted in Human Robots
#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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