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Artificially intelligent systems are slowly taking over tasks previously done by humans, and many processes involving repetitive, simple movements have already been fully automated. In the meantime, humans continue to be superior when it comes to abstract and creative tasks.
However, it seems like even when it comes to creativity, we’re now being challenged by our own creations.
In the last few years, we’ve seen the emergence of hundreds of “AI artists.” These complex algorithms are creating unique (and sometimes eerie) works of art. They’re generating stunning visuals, profound poetry, transcendent music, and even realistic movie scripts. The works of these AI artists are raising questions about the nature of art and the role of human creativity in future societies.
Here are a few works of art created by non-human entities.
Ai-Da Robot with Painting. Image Credit: Ai-Da portraits by Nicky Johnston. Published with permission from Midas Public Relations.
Earlier this month we saw the announcement of Ai.Da, considered the first ultra-realistic drawing robot artist. Her mechanical abilities, combined with AI-based algorithms, allow her to draw, paint, and even sculpt. She is able to draw people using her artificial eye and a pencil in her hand. Ai.Da’s artwork and first solo exhibition, Unsecured Futures, will be showcased at Oxford University in July.
Ai-Da Cartesian Painting. Image Credit: Ai-Da Artworks. Published with permission from Midas Public Relations.
Obviously Ai.Da has no true consciousness, thoughts, or feelings. Despite that, the (human) organizers of the exhibition believe that Ai.Da serves as a basis for crucial conversations about the ethics of emerging technologies. The exhibition will serve as a stimulant for engaging with critical questions about what kind of future we ought to create via such technologies.
The exhibition’s creators wrote, “Humans are confident in their position as the most powerful species on the planet, but how far do we actually want to take this power? To a Brave New World (Nightmare)? And if we use new technologies to enhance the power of the few, we had better start safeguarding the future of the many.”
Our transcendence adorns,
That society of the stars seem to be the secret.
The two lines of poetry above aren’t like any poetry you’ve come across before. They are generated by an algorithm that was trained via deep learning neural networks trained on 20 million words of 19th-century poetry.
Google’s latest art project, named PoemPortraits, takes a word of your suggestion and generates a unique poem (once again, a collaboration of man and machine). You can even add a selfie in the final “PoemPortrait.” Artist Es Devlin, the project’s creator, explains that the AI “doesn’t copy or rework existing phrases, but uses its training material to build a complex statistical model. As a result, the algorithm generates original phrases emulating the style of what it’s been trained on.”
The generated poetry can sometimes be profound, and sometimes completely meaningless.But what makes the PoemPortraits project even more interesting is that it’s a collaborative project. All of the generated lines of poetry are combined to form a consistently growing collective poem, which you can view after your lines are generated. In many ways, the final collective poem is a collaboration of people from around the world working with algorithms.
Faceless Portraits Transcending Time
AICAN + Ahmed Elgammal
Image Credit: AICAN + Ahmed Elgammal | Faceless Portrait #2 (2019) | Artsy.
In March of this year, an AI artist called AICAN and its creator Ahmed Elgammal took over a New York gallery. The exhibition at HG Commentary showed two series of canvas works portraying harrowing, dream-like faceless portraits.
The exhibition was not simply credited to a machine, but rather attributed to the collaboration between a human and machine. Ahmed Elgammal is the founder and director of the Art and Artificial Intelligence Laboratory at Rutgers University. He considers AICAN to not only be an autonomous AI artist, but also a collaborator for artistic endeavors.
How did AICAN create these eerie faceless portraits? The system was presented with 100,000 photos of Western art from over five centuries, allowing it to learn the aesthetics of art via machine learning. It then drew from this historical knowledge and the mandate to create something new to create an artwork without human intervention.
by AIVA Technologies
Listen to the score above. While you do, reflect on the fact that it was generated by an AI.
AIVA is an AI that composes soundtrack music for movies, commercials, games, and trailers. Its creative works span a wide range of emotions and moods. The scores it generates are indistinguishable from those created by the most talented human composers.
The AIVA music engine allows users to generate original scores in multiple ways. One is to upload an existing human-generated score and select the temp track to base the composition process on. Another method involves using preset algorithms to compose music in pre-defined styles, including everything from classical to Middle Eastern.
Currently, the platform is promoted as an opportunity for filmmakers and producers. But in the future, perhaps every individual will have personalized music generated for them based on their interests, tastes, and evolving moods. We already have algorithms on streaming websites recommending novel music to us based on our interests and history. Soon, algorithms may be used to generate music and other works of art that are tailored to impact our unique psyches.
The Future of Art: Pushing Our Creative Limitations
These works of art are just a glimpse into the breadth of the creative works being generated by algorithms and machines. Many of us will rightly fear these developments. We have to ask ourselves what our role will be in an era where machines are able to perform what we consider complex, abstract, creative tasks. The implications on the future of work, education, and human societies are profound.
At the same time, some of these works demonstrate that AI artists may not necessarily represent a threat to human artists, but rather an opportunity for us to push our creative boundaries. The most exciting artistic creations involve collaborations between humans and machines.
We have always used our technological scaffolding to push ourselves beyond our biological limitations. We use the telescope to extend our line of sight, planes to fly, and smartphones to connect with others. Our machines are not always working against us, but rather working as an extension of our minds. Similarly, we could use our machines to expand on our creativity and push the boundaries of art.
Image Credit: Ai-Da portraits by Nicky Johnston. Published with permission from Midas Public Relations. Continue reading
During the past 50 years, the frequency of recorded natural disasters has surged nearly five-fold.
In this blog, I’ll be exploring how converging exponential technologies (AI, robotics, drones, sensors, networks) are transforming the future of disaster relief—how we can prevent them in the first place and get help to victims during that first golden hour wherein immediate relief can save lives.
Here are the three areas of greatest impact:
AI, predictive mapping, and the power of the crowd
Next-gen robotics and swarm solutions
Aerial drones and immediate aid supply
Let’s dive in!
Artificial Intelligence and Predictive Mapping
When it comes to immediate and high-precision emergency response, data is gold.
Already, the meteoric rise of space-based networks, stratosphere-hovering balloons, and 5G telecommunications infrastructure is in the process of connecting every last individual on the planet.
Aside from democratizing the world’s information, however, this upsurge in connectivity will soon grant anyone the ability to broadcast detailed geo-tagged data, particularly those most vulnerable to natural disasters.
Armed with the power of data broadcasting and the force of the crowd, disaster victims now play a vital role in emergency response, turning a historically one-way blind rescue operation into a two-way dialogue between connected crowds and smart response systems.
With a skyrocketing abundance of data, however, comes a new paradigm: one in which we no longer face a scarcity of answers. Instead, it will be the quality of our questions that matters most.
This is where AI comes in: our mining mechanism.
In the case of emergency response, what if we could strategically map an almost endless amount of incoming data points? Or predict the dynamics of a flood and identify a tsunami’s most vulnerable targets before it even strikes? Or even amplify critical signals to trigger automatic aid by surveillance drones and immediately alert crowdsourced volunteers?
Already, a number of key players are leveraging AI, crowdsourced intelligence, and cutting-edge visualizations to optimize crisis response and multiply relief speeds.
Take One Concern, for instance. Born out of Stanford under the mentorship of leading AI expert Andrew Ng, One Concern leverages AI through analytical disaster assessment and calculated damage estimates.
Partnering with the cities of Los Angeles, San Francisco, and numerous cities in San Mateo County, the platform assigns verified, unique ‘digital fingerprints’ to every element in a city. Building robust models of each system, One Concern’s AI platform can then monitor site-specific impacts of not only climate change but each individual natural disaster, from sweeping thermal shifts to seismic movement.
This data, combined with that of city infrastructure and former disasters, are then used to predict future damage under a range of disaster scenarios, informing prevention methods and structures in need of reinforcement.
Within just four years, One Concern can now make precise predictions with an 85 percent accuracy rate in under 15 minutes.
And as IoT-connected devices and intelligent hardware continue to boom, a blooming trillion-sensor economy will only serve to amplify AI’s predictive capacity, offering us immediate, preventive strategies long before disaster strikes.
Beyond natural disasters, however, crowdsourced intelligence, predictive crisis mapping, and AI-powered responses are just as formidable a triage in humanitarian disasters.
One extraordinary story is that of Ushahidi. When violence broke out after the 2007 Kenyan elections, one local blogger proposed a simple yet powerful question to the web: “Any techies out there willing to do a mashup of where the violence and destruction is occurring and put it on a map?”
Within days, four ‘techies’ heeded the call, building a platform that crowdsourced first-hand reports via SMS, mined the web for answers, and—with over 40,000 verified reports—sent alerts back to locals on the ground and viewers across the world.
Today, Ushahidi has been used in over 150 countries, reaching a total of 20 million people across 100,000+ deployments. Now an open-source crisis-mapping software, its V3 (or “Ushahidi in the Cloud”) is accessible to anyone, mining millions of Tweets, hundreds of thousands of news articles, and geo-tagged, time-stamped data from countless sources.
Aggregating one of the longest-running crisis maps to date, Ushahidi’s Syria Tracker has proved invaluable in the crowdsourcing of witness reports. Providing real-time geographic visualizations of all verified data, Syria Tracker has enabled civilians to report everything from missing people and relief supply needs to civilian casualties and disease outbreaks— all while evading the government’s cell network, keeping identities private, and verifying reports prior to publication.
As mobile connectivity and abundant sensors converge with AI-mined crowd intelligence, real-time awareness will only multiply in speed and scale.
Imagining the Future….
Within the next 10 years, spatial web technology might even allow us to tap into mesh networks.
As I’ve explored in a previous blog on the implications of the spatial web, while traditional networks rely on a limited set of wired access points (or wireless hotspots), a wireless mesh network can connect entire cities via hundreds of dispersed nodes that communicate with each other and share a network connection non-hierarchically.
In short, this means that individual mobile users can together establish a local mesh network using nothing but the computing power in their own devices.
Take this a step further, and a local population of strangers could collectively broadcast countless 360-degree feeds across a local mesh network.
Imagine a scenario in which armed attacks break out across disjointed urban districts, each cluster of eye witnesses and at-risk civilians broadcasting an aggregate of 360-degree videos, all fed through photogrammetry AIs that build out a live hologram in real time, giving family members and first responders complete information.
Or take a coastal community in the throes of torrential rainfall and failing infrastructure. Now empowered by a collective live feed, verification of data reports takes a matter of seconds, and richly-layered data informs first responders and AI platforms with unbelievable accuracy and specificity of relief needs.
By linking all the right technological pieces, we might even see the rise of automated drone deliveries. Imagine: crowdsourced intelligence is first cross-referenced with sensor data and verified algorithmically. AI is then leveraged to determine the specific needs and degree of urgency at ultra-precise coordinates. Within minutes, once approved by personnel, swarm robots rush to collect the requisite supplies, equipping size-appropriate drones with the right aid for rapid-fire delivery.
This brings us to a second critical convergence: robots and drones.
While cutting-edge drone technology revolutionizes the way we deliver aid, new breakthroughs in AI-geared robotics are paving the way for superhuman emergency responses in some of today’s most dangerous environments.
Let’s explore a few of the most disruptive examples to reach the testing phase.
Autonomous Robots and Swarm Solutions
As hardware advancements converge with exploding AI capabilities, disaster relief robots are graduating from assistance roles to fully autonomous responders at a breakneck pace.
Born out of MIT’s Biomimetic Robotics Lab, the Cheetah III is but one of many robots that may form our first line of defense in everything from earthquake search-and-rescue missions to high-risk ops in dangerous radiation zones.
Now capable of running at 6.4 meters per second, Cheetah III can even leap up to a height of 60 centimeters, autonomously determining how to avoid obstacles and jump over hurdles as they arise.
Initially designed to perform spectral inspection tasks in hazardous settings (think: nuclear plants or chemical factories), the Cheetah’s various iterations have focused on increasing its payload capacity, range of motion, and even a gripping function with enhanced dexterity.
Cheetah III and future versions are aimed at saving lives in almost any environment.
And the Cheetah III is not alone. Just this February, Tokyo’s Electric Power Company (TEPCO) has put one of its own robots to the test. For the first time since Japan’s devastating 2011 tsunami, which led to three nuclear meltdowns in the nation’s Fukushima nuclear power plant, a robot has successfully examined the reactor’s fuel.
Broadcasting the process with its built-in camera, the robot was able to retrieve small chunks of radioactive fuel at five of the six test sites, offering tremendous promise for long-term plans to clean up the still-deadly interior.
Also out of Japan, Mitsubishi Heavy Industries (MHi) is even using robots to fight fires with full autonomy. In a remarkable new feat, MHi’s Water Cannon Bot can now put out blazes in difficult-to-access or highly dangerous fire sites.
Delivering foam or water at 4,000 liters per minute and 1 megapascal (MPa) of pressure, the Cannon Bot and its accompanying Hose Extension Bot even form part of a greater AI-geared system to conduct reconnaissance and surveillance on larger transport vehicles.
As wildfires grow ever more untameable, high-volume production of such bots could prove a true lifesaver. Paired with predictive AI forest fire mapping and autonomous hauling vehicles, not only will solutions like MHi’s Cannon Bot save numerous lives, but avoid population displacement and paralyzing damage to our natural environment before disaster has the chance to spread.
But even in cases where emergency shelter is needed, groundbreaking (literally) robotics solutions are fast to the rescue.
After multiple iterations by Fastbrick Robotics, the Hadrian X end-to-end bricklaying robot can now autonomously build a fully livable, 180-square-meter home in under three days. Using a laser-guided robotic attachment, the all-in-one brick-loaded truck simply drives to a construction site and directs blocks through its robotic arm in accordance with a 3D model.
Meeting verified building standards, Hadrian and similar solutions hold massive promise in the long-term, deployable across post-conflict refugee sites and regions recovering from natural catastrophes.
But what if we need to build emergency shelters from local soil at hand? Marking an extraordinary convergence between robotics and 3D printing, the Institute for Advanced Architecture of Catalonia (IAAC) is already working on a solution.
In a major feat for low-cost construction in remote zones, IAAC has found a way to convert almost any soil into a building material with three times the tensile strength of industrial clay. Offering myriad benefits, including natural insulation, low GHG emissions, fire protection, air circulation, and thermal mediation, IAAC’s new 3D printed native soil can build houses on-site for as little as $1,000.
But while cutting-edge robotics unlock extraordinary new frontiers for low-cost, large-scale emergency construction, novel hardware and computing breakthroughs are also enabling robotic scale at the other extreme of the spectrum.
Again, inspired by biological phenomena, robotics specialists across the US have begun to pilot tiny robotic prototypes for locating trapped individuals and assessing infrastructural damage.
Take RoboBees, tiny Harvard-developed bots that use electrostatic adhesion to ‘perch’ on walls and even ceilings, evaluating structural damage in the aftermath of an earthquake.
Or Carnegie Mellon’s prototyped Snakebot, capable of navigating through entry points that would otherwise be completely inaccessible to human responders. Driven by AI, the Snakebot can maneuver through even the most densely-packed rubble to locate survivors, using cameras and microphones for communication.
But when it comes to fast-paced reconnaissance in inaccessible regions, miniature robot swarms have good company.
Next-Generation Drones for Instantaneous Relief Supplies
Particularly in the case of wildfires and conflict zones, autonomous drone technology is fundamentally revolutionizing the way we identify survivors in need and automate relief supply.
Not only are drones enabling high-resolution imagery for real-time mapping and damage assessment, but preliminary research shows that UAVs far outpace ground-based rescue teams in locating isolated survivors.
As presented by a team of electrical engineers from the University of Science and Technology of China, drones could even build out a mobile wireless broadband network in record time using a “drone-assisted multi-hop device-to-device” program.
And as shown during Houston’s Hurricane Harvey, drones can provide scores of predictive intel on everything from future flooding to damage estimates.
Among multiple others, a team led by Texas A&M computer science professor and director of the university’s Center for Robot-Assisted Search and Rescue Dr. Robin Murphy flew a total of 119 drone missions over the city, from small-scale quadcopters to military-grade unmanned planes. Not only were these critical for monitoring levee infrastructure, but also for identifying those left behind by human rescue teams.
But beyond surveillance, UAVs have begun to provide lifesaving supplies across some of the most remote regions of the globe. One of the most inspiring examples to date is Zipline.
Created in 2014, Zipline has completed 12,352 life-saving drone deliveries to date. While drones are designed, tested, and assembled in California, Zipline primarily operates in Rwanda and Tanzania, hiring local operators and providing over 11 million people with instant access to medical supplies.
Providing everything from vaccines and HIV medications to blood and IV tubes, Zipline’s drones far outpace ground-based supply transport, in many instances providing life-critical blood cells, plasma, and platelets in under an hour.
But drone technology is even beginning to transcend the limited scale of medical supplies and food.
Now developing its drones under contracts with DARPA and the US Marine Corps, Logistic Gliders, Inc. has built autonomously-navigating drones capable of carrying 1,800 pounds of cargo over unprecedented long distances.
Built from plywood, Logistic’s gliders are projected to cost as little as a few hundred dollars each, making them perfect candidates for high-volume remote aid deliveries, whether navigated by a pilot or self-flown in accordance with real-time disaster zone mapping.
As hardware continues to advance, autonomous drone technology coupled with real-time mapping algorithms pose no end of abundant opportunities for aid supply, disaster monitoring, and richly layered intel previously unimaginable for humanitarian relief.
Perhaps one of the most consequential and impactful applications of converging technologies is their transformation of disaster relief methods.
While AI-driven intel platforms crowdsource firsthand experiential data from those on the ground, mobile connectivity and drone-supplied networks are granting newfound narrative power to those most in need.
And as a wave of new hardware advancements gives rise to robotic responders, swarm technology, and aerial drones, we are fast approaching an age of instantaneous and efficiently-distributed responses in the midst of conflict and natural catastrophes alike.
Empowered by these new tools, what might we create when everyone on the planet has the same access to relief supplies and immediate resources? In a new age of prevention and fast recovery, what futures can you envision?
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Spider-Man is one of the most popular superheroes of all time. It’s a bit surprising given that one of the more common phobias is arachnophobia—a debilitating fear of spiders.
Perhaps more fantastical is that young Peter Parker, a brainy high school science nerd, seemingly developed overnight the famous web-shooters and the synthetic spider silk that he uses to swing across the cityscape like Tarzan through the jungle.
That’s because scientists have been trying for decades to replicate spider silk, a material that is five times stronger than steel, among its many superpowers. In recent years, researchers have been untangling the protein-based fiber’s structure down to the molecular level, leading to new insights and new potential for eventual commercial uses.
The applications for such a material seem near endless. There’s the more futuristic visions, like enabling robotic “muscles” for human-like movement or ensnaring real-life villains with a Spider-Man-like web. Near-term applications could include the biomedical industry, such as bandages and adhesives, and as a replacement textile for everything from rope to seat belts to parachutes.
Spinning Synthetic Spider Silk
Randy Lewis has been studying the properties of spider silk and developing methods for producing it synthetically for more than three decades. In the 1990s, his research team was behind cloning the first spider silk gene, as well as the first to identify and sequence the proteins that make up the six different silks that web slingers make. Each has different mechanical properties.
“So our thought process was that you could take that information and begin to to understand what made them strong and what makes them stretchy, and why some are are very stretchy and some are not stretchy at all, and some are stronger and some are weaker,” explained Lewis, a biology professor at Utah State University and director of the Synthetic Spider Silk Lab, in an interview with Singularity Hub.
Spiders are naturally territorial and cannibalistic, so any intention to farm silk naturally would likely end in an orgy of arachnid violence. Instead, Lewis and company have genetically modified different organisms to produce spider silk synthetically, including inserting a couple of web-making genes into the genetic code of goats. The goats’ milk contains spider silk proteins.
The lab also produces synthetic spider silk through a fermentation process not entirely dissimilar to brewing beer, but using genetically modified bacteria to make the desired spider silk proteins. A similar technique has been used for years to make a key enzyme in cheese production. More recently, companies are using transgenic bacteria to make meat and milk proteins, entirely bypassing animals in the process.
The same fermentation technology is used by a chic startup called Bolt Threads outside of San Francisco that has raised more than $200 million for fashionable fibers made out of synthetic spider silk it calls Microsilk. (The company is also developing a second leather-like material, Mylo, using the underground root structure of mushrooms known as mycelium.)
Lewis’ lab also uses transgenic silkworms to produce a kind of composite material made up of the domesticated insect’s own silk proteins and those of spider silk. “Those have some fairly impressive properties,” Lewis said.
The researchers are even experimenting with genetically modified alfalfa. One of the big advantages there is that once the spider silk protein has been extracted, the remaining protein could be sold as livestock feed. “That would bring the cost of spider silk protein production down significantly,” Lewis said.
Building a Better Web
Producing synthetic spider silk isn’t the problem, according to Lewis, but the ability to do it at scale commercially remains a sticking point.
Another challenge is “weaving” the synthetic spider silk into usable products that can take advantage of the material’s marvelous properties.
“It is possible to make silk proteins synthetically, but it is very hard to assemble the individual proteins into a fiber or other material forms,” said Markus Buehler, head of the Department of Civil and Environmental Engineering at MIT, in an email to Singularity Hub. “The spider has a complex spinning duct in which silk proteins are exposed to physical forces, chemical gradients, the combination of which generates the assembly of molecules that leads to silk fibers.”
Buehler recently co-authored a paper in the journal Science Advances that found dragline spider silk exhibits different properties in response to changes in humidity that could eventually have applications in robotics.
Specifically, spider silk suddenly contracts and twists above a certain level of relative humidity, exerting enough force to “potentially be competitive with other materials being explored as actuators—devices that move to perform some activity such as controlling a valve,” according to a press release.
Studying Spider Silk Up Close
Recent studies at the molecular level are helping scientists learn more about the unique properties of spider silk, which may help researchers develop materials with extraordinary capabilities.
For example, scientists at Arizona State University used magnetic resonance tools and other instruments to image the abdomen of a black widow spider. They produced what they called the first molecular-level model of spider silk protein fiber formation, providing insights on the nanoparticle structure. The research was published last October in Proceedings of the National Academy of Sciences.
A cross section of the abdomen of a black widow (Latrodectus Hesperus) spider used in this study at Arizona State University. Image Credit: Samrat Amin.
Also in 2018, a study presented in Nature Communications described a sort of molecular clamp that binds the silk protein building blocks, which are called spidroins. The researchers observed for the first time that the clamp self-assembles in a two-step process, contributing to the extensibility, or stretchiness, of spider silk.
Another team put the spider silk of a brown recluse under an atomic force microscope, discovering that each strand, already 1,000 times thinner than a human hair, is made up of thousands of nanostrands. That helps explain its extraordinary tensile strength, though technique is also a factor, as the brown recluse uses a special looping method to reinforce its silk strands. The study also appeared last year in the journal ACS Macro Letters.
Making Spider Silk Stick
Buehler said his team is now trying to develop better and faster predictive methods to design silk proteins using artificial intelligence.
“These new methods allow us to generate new protein designs that do not naturally exist and which can be explored to optimize certain desirable properties like torsional actuation, strength, bioactivity—for example, tissue engineering—and others,” he said.
Meanwhile, Lewis’ lab has discovered a method that allows it to solubilize spider silk protein in what is essentially a water-based solution, eschewing acids or other toxic compounds that are normally used in the process.
That enables the researchers to develop materials beyond fiber, including adhesives that “are better than an awful lot of the current commercial adhesives,” Lewis said, as well as coatings that could be used to dampen vibrations, for example.
“We’re making gels for various kinds of of tissue regeneration, as well as drug delivery, and things like that,” he added. “So we’ve expanded the use profile from something beyond fibers to something that is a much more extensive portfolio of possible kinds of materials.”
And, yes, there’s even designs at the Synthetic Spider Silk Lab for developing a Spider-Man web-slinger material. The US Navy is interested in non-destructive ways of disabling an enemy vessel, such as fouling its propeller. The project also includes producing synthetic proteins from the hagfish, an eel-like critter that exudes a gelatinous slime when threatened.
Lewis said that while the potential for spider silk is certainly headline-grabbing, he cautioned that much of the hype is not focused on the unique mechanical properties that could lead to advances in healthcare and other industries.
“We want to see spider silk out there because it’s a unique material, not because it’s got marketing appeal,” he said.
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As over-hyped as artificial intelligence is—everyone’s talking about it, few fully understand it, it might leave us all unemployed but also solve all the world’s problems—its list of accomplishments is growing. AI can now write realistic-sounding text, give a debating champ a run for his money, diagnose illnesses, and generate fake human faces—among much more.
After training these systems on massive datasets, their creators essentially just let them do their thing to arrive at certain conclusions or outcomes. The problem is that more often than not, even the creators don’t know exactly why they’ve arrived at those conclusions or outcomes. There’s no easy way to trace a machine learning system’s rationale, so to speak. The further we let AI go down this opaque path, the more likely we are to end up somewhere we don’t want to be—and may not be able to come back from.
In a panel at the South by Southwest interactive festival last week titled “Ethics and AI: How to plan for the unpredictable,” experts in the field shared their thoughts on building more transparent, explainable, and accountable AI systems.
Not New, but Different
Ryan Welsh, founder and director of explainable AI startup Kyndi, pointed out that having knowledge-based systems perform advanced tasks isn’t new; he cited logistical, scheduling, and tax software as examples. What’s new is the learning component, our inability to trace how that learning occurs, and the ethical implications that could result.
“Now we have these systems that are learning from data, and we’re trying to understand why they’re arriving at certain outcomes,” Welsh said. “We’ve never actually had this broad society discussion about ethics in those scenarios.”
Rather than continuing to build AIs with opaque inner workings, engineers must start focusing on explainability, which Welsh broke down into three subcategories. Transparency and interpretability come first, and refer to being able to find the units of high influence in a machine learning network, as well as the weights of those units and how they map to specific data and outputs.
Then there’s provenance: knowing where something comes from. In an ideal scenario, for example, Open AI’s new text generator would be able to generate citations in its text that reference academic (and human-created) papers or studies.
Explainability itself is the highest and final bar and refers to a system’s ability to explain itself in natural language to the average user by being able to say, “I generated this output because x, y, z.”
“Humans are unique in our ability and our desire to ask why,” said Josh Marcuse, executive director of the Defense Innovation Board, which advises Department of Defense senior leaders on innovation. “The reason we want explanations from people is so we can understand their belief system and see if we agree with it and want to continue to work with them.”
Similarly, we need to have the ability to interrogate AIs.
Two Types of Thinking
Welsh explained that one big barrier standing in the way of explainability is the tension between the deep learning community and the symbolic AI community, which see themselves as two different paradigms and historically haven’t collaborated much.
Symbolic or classical AI focuses on concepts and rules, while deep learning is centered around perceptions. In human thought this is the difference between, for example, deciding to pass a soccer ball to a teammate who is open (you make the decision because conceptually you know that only open players can receive passes), and registering that the ball is at your feet when someone else passes it to you (you’re taking in information without making a decision about it).
“Symbolic AI has abstractions and representation based on logic that’s more humanly comprehensible,” Welsh said. To truly mimic human thinking, AI needs to be able to both perceive information and conceptualize it. An example of perception (deep learning) in an AI is recognizing numbers within an image, while conceptualization (symbolic learning) would give those numbers a hierarchical order and extract rules from the hierachy (4 is greater than 3, and 5 is greater than 4, therefore 5 is also greater than 3).
Explainability comes in when the system can say, “I saw a, b, and c, and based on that decided x, y, or z.” DeepMind and others have recently published papers emphasizing the need to fuse the two paradigms together.
Implications Across Industries
One of the most prominent fields where AI ethics will come into play, and where the transparency and accountability of AI systems will be crucial, is defense. Marcuse said, “We’re accountable beings, and we’re responsible for the choices we make. Bringing in tech or AI to a battlefield doesn’t strip away that meaning and accountability.”
In fact, he added, rather than worrying about how AI might degrade human values, people should be asking how the tech could be used to help us make better moral choices.
It’s also important not to conflate AI with autonomy—a worst-case scenario that springs to mind is an intelligent destructive machine on a rampage. But in fact, Marcuse said, in the defense space, “We have autonomous systems today that don’t rely on AI, and most of the AI systems we’re contemplating won’t be autonomous.”
The US Department of Defense released its 2018 artificial intelligence strategy last month. It includes developing a robust and transparent set of principles for defense AI, investing in research and development for AI that’s reliable and secure, continuing to fund research in explainability, advocating for a global set of military AI guidelines, and finding ways to use AI to reduce the risk of civilian casualties and other collateral damage.
Though these were designed with defense-specific aims in mind, Marcuse said, their implications extend across industries. “The defense community thinks of their problems as being unique, that no one deals with the stakes and complexity we deal with. That’s just wrong,” he said. Making high-stakes decisions with technology is widespread; safety-critical systems are key to aviation, medicine, and self-driving cars, to name a few.
Marcuse believes the Department of Defense can invest in AI safety in a way that has far-reaching benefits. “We all depend on technology to keep us alive and safe, and no one wants machines to harm us,” he said.
A Creation Superior to Its Creator
That said, we’ve come to expect technology to meet our needs in just the way we want, all the time—servers must never be down, GPS had better not take us on a longer route, Google must always produce the answer we’re looking for.
With AI, though, our expectations of perfection may be less reasonable.
“Right now we’re holding machines to superhuman standards,” Marcuse said. “We expect them to be perfect and infallible.” Take self-driving cars. They’re conceived of, built by, and programmed by people, and people as a whole generally aren’t great drivers—just look at traffic accident death rates to confirm that. But the few times self-driving cars have had fatal accidents, there’s been an ensuing uproar and backlash against the industry, as well as talk of implementing more restrictive regulations.
This can be extrapolated to ethics more generally. We as humans have the ability to explain our decisions, but many of us aren’t very good at doing so. As Marcuse put it, “People are emotional, they confabulate, they lie, they’re full of unconscious motivations. They don’t pass the explainability test.”
Why, then, should explainability be the standard for AI?
Even if humans aren’t good at explaining our choices, at least we can try, and we can answer questions that probe at our decision-making process. A deep learning system can’t do this yet, so working towards being able to identify which input data the systems are triggering on to make decisions—even if the decisions and the process aren’t perfect—is the direction we need to head.
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