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#436488 Tech’s Biggest Leaps From the Last 10 ...

Posted on January 3, 2020 by Android

As we enter our third decade in the 21st century, it seems appropriate to reflect on the ways technology developed and note the breakthroughs that were achieved in the last 10 years.

The 2010s saw IBM’s Watson win a game of Jeopardy, ushering in mainstream awareness of machine learning, along with DeepMind’s AlphaGO becoming the world’s Go champion. It was the decade that industrial tools like drones, 3D printers, genetic sequencing, and virtual reality (VR) all became consumer products. And it was a decade in which some alarming trends related to surveillance, targeted misinformation, and deepfakes came online.

For better or worse, the past decade was a breathtaking era in human history in which the idea of exponential growth in information technologies powered by computation became a mainstream concept.

As I did last year for 2018 only, I’ve asked a collection of experts across the Singularity University faculty to help frame the biggest breakthroughs and moments that gave shape to the past 10 years. I asked them what, in their opinion, was the most important breakthrough in their respective fields over the past decade.

My own answer to this question, focused in the space of augmented and virtual reality, would be the stunning announcement in March of 2014 that Facebook acquired Oculus VR for $2 billion. Although VR technology had been around for a while, it was at this precise moment that VR arrived as a consumer technology platform. Facebook, largely fueled by the singular interest of CEO Mark Zuckerberg, has funded the development of this industry, keeping alive the hope that consumer VR can become a sustainable business. In the meantime, VR has continued to grow in sophistication and usefulness, though it has yet to truly take off as a mainstream concept. That will hopefully be a development for the 2020s.

Below is a decade in review across the technology areas that are giving shape to our modern world, as described by the SU community of experts.

Digital Biology
Dr. Tiffany Vora | Faculty Director and Vice Chair, Digital Biology and Medicine, Singularity University

In my mind, this decade of astounding breakthroughs in the life sciences and medicine rests on the achievement of the $1,000 human genome in 2016. More-than-exponentially falling costs of DNA sequencing have driven advances in medicine, agriculture, ecology, genome editing, synthetic biology, the battle against climate change, and our fundamental understanding of life and its breathtaking connections. The “digital” revolution in DNA constituted an important model for harnessing other types of biological information, from personalized bio data to massive datasets spanning populations and species.

Crucially, by aggressively driving down the cost of such analyses, researchers and entrepreneurs democratized access to the source code of life—with attendant financial, cultural, and ethical consequences. Exciting, but take heed: Veritas Genetics spearheaded a $600 genome in 2019, only to have to shutter USA operations due to a money trail tangled with the trade war with China. Stay tuned through the early 2020s to see the pricing of DNA sequencing fall even further … and to experience the many ways that cheaper, faster harvesting of biological data will enrich your daily life.

Cryptocurrency
Alex Gladstein | Chief Strategy Officer, Human Rights Foundation

The past decade has seen Bitcoin go from just an idea on an obscure online message board to a global financial network carrying more than 100 billion dollars in value. And we’re just getting started. One recent defining moment in the cryptocurrency space has been a stunning trend underway in Venezuela, where today, the daily dollar-denominated value of Bitcoin traded now far exceeds the daily dollar-denominated value traded on the Caracas Stock Exchange. It’s just one country, but it’s a significant country, and a paradigm shift.

Governments and corporations are following Bitcoin’s success too, and are looking to launch their own digital currencies. China will launch its “DC/EP” project in the coming months, and Facebook is trying to kickstart its Libra project. There are technical and regulatory uncertainties for both, but one thing is for certain: the era of digital currency has arrived.

Business Strategy and Entrepreneurship
Pascal Finnette | Chair, Entrepreneurship and Open Innovation, Singularity University

For me, without a doubt, the most interesting and quite possibly ground-shifting development in the fields of entrepreneurship and corporate innovation in the last ten years is the rapid maturing of customer-driven product development frameworks such as Lean Startup, and its subsequent adoption by corporates for their own innovation purposes.

Tools and frameworks like the Business Model Canvas, agile (software) development and the aforementioned Lean Startup methodology fundamentally shifted the way we think and go about building products, services, and companies, with many of these tools bursting onto the startup scene in the late 2000s and early 2010s.

As these tools matured they found mass adoption not only in startups around the world, but incumbent companies who eagerly adopted them to increase their own innovation velocity and success.

Energy
Ramez Naam | Co-Chair, Energy and Environment, Singularity University

The 2010s were the decade that saw clean electricity, energy storage, and electric vehicles break through price and performance barriers around the world. Solar, wind, batteries, and EVs started this decade as technologies that had to be subsidized. That was the first phase of their existence. Now they’re entering their third, most disruptive phase, where shifting to clean energy and mobility is cheaper than continuing to use existing coal, gas, or oil infrastructure.

Consider that at the start of 2010, there was no place on earth where building new solar or wind was cheaper than building new coal or gas power generation. By 2015, in some of the sunniest and windiest places on earth, solar and wind had entered their second phase, where they were cost-competitive for new power. And then, in 2018 and 2019, we started to see the edge of the third phase, as building new solar and wind, in some parts of the world, was cheaper than operating existing coal or gas power plants.

Food Technology
Liz Specht, Ph. D | Associate Director of Science & Technology, The Good Food Institute

The arrival of mainstream plant-based meat is easily the food tech advance of the decade. Meat analogs have, of course, been around forever. But only in the last decade have companies like Beyond Meat and Impossible Foods decided to cut animals out of the process and build no-compromise meat directly from plants.

Plant-based meat is already transforming the fast-food industry. For example, the introduction of the Impossible Whopper led Burger King to their most profitable quarter in many years. But the global food industry as a whole is shifting as well. Tyson, JBS, Nestle, Cargill, and many others are all embracing plant-based meat.

Augmented and Virtual Reality
Jody Medich | CEO, Superhuman-x

The breakthrough moment for augmented and virtual reality came in 2013 when Palmer Lucky took apart an Android smartphone and added optic lenses to make the first version of the Oculus Rift. Prior to that moment, we struggled with miniaturizing the components needed to develop low-latency head-worn devices. But thanks to the smartphone race started in 2006 with the iPhone, we finally had a suite of sensors, chips, displays, and computing power small enough to put on the head.

What will the next 10 years bring? Look for AR/VR to explode in a big way. We are right on the cusp of that tipping point when the tech is finally “good enough” for our linear expectations. Given all it can do today, we can’t even picture what’s possible. Just as today we can’t function without our phones, by 2029 we’ll feel lost without some AR/VR product. It will be the way we interact with computing, smart objects, and AI. Tim Cook, Apple CEO, predicts it will replace all of today’s computing devices. I can’t wait.

Philosophy of Technology
Alix Rübsaam | Faculty Fellow, Singularity University, Philosophy of Technology/Ethics of AI

The last decade has seen a significant shift in our general attitude towards the algorithms that we now know dictate much of our surroundings. Looking back at the beginning of the decade, it seems we were blissfully unaware of how the data we freely and willingly surrendered would feed the algorithms that would come to shape every aspect of our daily lives: the news we consume, the products we purchase, the opinions we hold, etc.

If I were to isolate a single publication that contributed greatly to the shift in public discourse on algorithms, it would have to be Cathy O’Neil’s Weapons of Math Destruction from 2016. It remains a comprehensive, readable, and highly informative insight into how algorithms dictate our finances, our jobs, where we go to school, or if we can get health insurance. Its publication represents a pivotal moment when the general public started to question whether we should be OK with outsourcing decision making to these opaque systems.

The ubiquity of ethical guidelines for AI and algorithms published just in the last year (perhaps most comprehensively by the AI Now Institute) fully demonstrates the shift in public opinion of this decade.

Data Science
Ola Kowalewski | Faculty Fellow, Singularity University, Data Innovation

In the last decade we entered the era of internet and smartphone ubiquity. The number of internet users doubled, with nearly 60 percent of the global population connected online and now over 35 percent of the globe owns a smartphone. With billions of people in a state of constant connectedness and therefore in a state of constant surveillance, the companies that have built the tech infrastructure and information pipelines have dominated the global economy. This shift from tech companies being the underdogs to arguably the world’s major powers sets the landscape we enter for the next decade.

Global Grand Challenges
Darlene Damm | Vice Chair, Faculty, Global Grand Challenges, Singularity University

The biggest breakthrough over the last decade in social impact and technology is that the social impact sector switched from seeing technology as something problematic to avoid, to one of the most effective ways to create social change. We now see people using exponential technologies to solve all sorts of social challenges in areas ranging from disaster response to hunger to shelter.

The world’s leading social organizations, such as UNICEF and the World Food Programme, have launched their own venture funds and accelerators, and the United Nations recently declared that digitization is revolutionizing global development.

Digital Biology
Raymond McCauley | Chair, Digital Biology, Singularity University, Co-Founder & Chief Architect, BioCurious; Principal, Exponential Biosciences

CRISPR is bringing about a revolution in genetic engineering. It’s obvious, and it’s huge. What may not be so obvious is the widespread adoption of genetic testing. And this may have an even longer-lasting effect. It’s used to test new babies, to solve medical mysteries, and to catch serial killers. Thanks to holiday ads from 23andMe and Ancestry.com, it’s everywhere. Testing your DNA is now a common over-the-counter product. People are using it to set their diet, to pick drugs, and even for dating (or at least picking healthy mates).

And we’re just in the early stages. Further down the line, doing large-scale studies on more people, with more data, will lead to the use of polygenic risk scores to help us rank our genetic potential for everything from getting cancer to being a genius. Can you imagine what it would be like for parents to pick new babies, GATTACA-style, to get the smartest kids? You don’t have to; it’s already happening.

Artificial Intelligence
Neil Jacobstein | Chair, Artificial Intelligence and Robotics, Singularity University

The convergence of exponentially improved computing power, the deep learning algorithm, and access to massive data resulted in a series of AI breakthroughs over the past decade. These included: vastly improved accuracy in identifying images, making self driving cars practical, beating several world champions in Go, and identifying gender, smoking status, and age from retinal fundus photographs.

Combined, these breakthroughs convinced researchers and investors that after 50+ years of research and development, AI was ready for prime-time applications. Now, virtually every field of human endeavor is being revolutionized by machine learning. We still have a long way to go to achieve human-level intelligence and beyond, but the pace of worldwide improvement is blistering.

Hod Lipson | Professor of Engineering and Data Science, Columbia University

The biggest moment in AI in the past decade (and in its entire history, in my humble opinion) was midnight, Pacific time, September 30, 2012: the moment when machines finally opened their eyes. It was the moment when deep learning took off, breaking stagnant decades of machine blindness, when AI couldn’t reliably tell apart even a cat from a dog. That seemingly trivial accomplishment—a task any one-year-old child can do—has had a ripple effect on AI applications from driverless cars to health diagnostics. And this is just the beginning of what is sure to be a Cambrian explosion of AI.

Neuroscience
Divya Chander | Chair, Neuroscience, Singularity University

If the 2000s were the decade of brain mapping, then the 2010s were the decade of brain writing. Optogenetics, a technique for precisely mapping and controlling neurons and neural circuits using genetically-directed light, saw incredible growth in the 2010s.

Also in the last 10 years, neuromodulation, or the ability to rewire the brain using both invasive and non-invasive interfaces and energy, has exploded in use and form. For instance, the Braingate consortium showed us how electrode arrays implanted into the motor cortex could be used by paralyzed people to use their thoughts to direct a robotic arm. These technologies, alone or in combination with robotics, exoskeletons, and flexible, implantable, electronics also make possible a future of human augmentation.

Image Credit: Image by Jorge Guillen from Pixabay Continue reading →

Posted in Human Robots

#436256 Alphabet Is Developing a Robot to Take ...

Posted on December 2, 2019 by Android

Robots excel at carrying out specialized tasks in controlled environments, but put them in your average office and they’d be lost. Alphabet wants to change that by developing what they call the Everyday Robot, which could learn to help us out with our daily chores.

For a long time most robots were painstakingly hand-coded to carry out their functions, but since the deep learning revolution earlier this decade there’s been a growing effort to imbue them with AI that lets them learn new tasks through experience.

That’s led to some impressive breakthroughs, like a robotic hand nimble enough to solve a Rubik’s cube and a robotic arm that can accurately toss bananas across a room.

And it turns out Alphabet’s early-stage research and development division, Alphabet X, has also secretly been using similar machine learning techniques to develop robots adaptable enough to carry out a range of tasks in cluttered and unpredictable human environments like homes and offices.

The robots they’ve built combine a wheeled base with a single arm and a head full of sensors (including LIDAR) for 3D scanning, borrowed from Alphabet’s self-driving car division, Waymo.

At the minute, though, they’re largely restricted to sorting trash for recycling, project leader Hans Peter Brondmo writes in a blog post. While that might sound mundane, identifying different kinds of trash, grasping it, and moving it to the correct bin is still a difficult thing for a robot to do consistently. Some of the robots also have to navigate around the office to sort trash at various recycling stations.

Alphabet says even its human staff were getting it wrong 20 percent of the time, but after several months of training the robots have managed to get that down to 3.5 percent.

Every day, 30 robots toil away in what’s been dubbed the “playpen” sorting trash, and then every night thousands of virtual robots continue to practice in a simulation. This experience is then used to update the robots’ control algorithms each night. All the robots also share their experiences with the others through a process called collaborative learning.

The process isn’t flawless, though. Simonite notes that while the robots exhibit some uncannily smart behaviors, like stirring piles of rubbish to make it easier to grab specific items, they also frequently miss or fumble the objects they’re trying to grasp.

Nonetheless, the project’s leaders are happy with their progress so far. And the hope is that creating robots that are able to learn from little more than experience in complex environments like an office should be a first step towards general-purpose robots that can pick up a variety of useful skills to assist humans.

Taking that next step will be the major test of the project. So far there’s been limited evidence that experience gained by robots in one task can be transferred to learning another. That’s something the group hopes to demonstrate next year.

And it seems there may be more robot news coming out of Alphabet X soon. The group has several other robotics “moonshots” in the pipeline, built on technology and talent transferred over in 2016 from the remains of a broadly unsuccessful splurge on robotics startups by former Google executive Andy Rubin.

Whether this robotics renaissance at Alphabet will finally help robots break into our homes and offices remains to be seen, but with the resources they have at hand, they just may be able to make it happen.

Image Credit: Everyday Robot, Alphabet X Continue reading →

Posted in Human Robots

#436218 An AI Debated Its Own Potential for Good ...

Posted on November 27, 2019 by Android

Artificial intelligence is going to overhaul the way we live and work. But will the changes it brings be for the better? As the technology slowly develops (let’s remember that right now, we’re still very much in the narrow AI space and nowhere near an artificial general intelligence), whether it will end up doing us more harm than good is a question at the top of everyone’s mind.

What kind of response might we get if we posed this question to an AI itself?

Last week at the Cambridge Union in England, IBM did just that. Its Project Debater (an AI that narrowly lost a debate to human debating champion Harish Natarajan in February) gave the opening arguments in a debate about the promise and peril of artificial intelligence.

Critical thinking, linking different lines of thought, and anticipating counter-arguments are all valuable debating skills that humans can practice and refine. While these skills are tougher for an AI to get good at since they often require deeper contextual understanding, AI does have a major edge over humans in absorbing and analyzing information. In the February debate, Project Debater used IBM’s cloud computing infrastructure to read hundreds of millions of documents and extract relevant details to construct an argument.

This time around, Debater looked through 1,100 arguments for or against AI. The arguments were submitted to IBM by the public during the week prior to the debate, through a website set up for that purpose. Of the 1,100 submissions, the AI classified 570 as anti-AI, or of the opinion that the technology will bring more harm to humanity than good. 511 arguments were found to be pro-AI, and the rest were irrelevant to the topic at hand.

Debater grouped the arguments into five themes; the technology’s ability to take over dangerous or monotonous jobs was a pro-AI theme, and on the flip side was its potential to perpetuate the biases of its creators. “AI companies still have too little expertise on how to properly assess datasets and filter out bias,” the tall black box that houses Project Debater said. “AI will take human bias and will fixate it for generations.”
After Project Debater kicked off the debate by giving opening arguments for both sides, two teams of people took over, elaborating on its points and coming up with their own counter-arguments.

In the end, an audience poll voted in favor of the pro-AI side, but just barely; 51.2 percent of voters felt convinced that AI can help us more than it can hurt us.

The software’s natural language processing was able to identify racist, obscene, or otherwise inappropriate comments and weed them out as being irrelevant to the debate. But it also repeated the same arguments multiple times, and mixed up a statement about bias as being pro-AI rather than anti-AI.

IBM has been working on Project Debater for over six years, and though it aims to iron out small glitches like these, the system’s goal isn’t to ultimately outwit and defeat humans. On the contrary, the AI is meant to support our decision-making by taking in and processing huge amounts of information in a nuanced way, more quickly than we ever could.

IBM engineer Noam Slonim envisions Project Debater’s tech being used, for example, by a government seeking citizens’ feedback about a new policy. “This technology can help to establish an interesting and effective communication channel between the decision maker and the people that are going to be impacted by the decision,” he said.

As for the question of whether AI will do more good or harm, perhaps Sylvie Delacroix put it best. A professor of law and ethics at the University of Birmingham who argued on the pro-AI side of the debate, she pointed out that the impact AI will have depends on the way we design it, saying “AI is only as good as the data it has been fed.”

She’s right; rather than asking what sort of impact AI will have on humanity, we should start by asking what sort of impact we want it to have. The people working on AI—not AIs themselves—are ultimately responsible for how much good or harm will be done.

Image Credit: IBM Project Debater at Cambridge Union Society, photo courtesy of IBM Research Continue reading →

Posted in Human Robots

#436207 This Week’s Awesome Tech Stories From ...

Posted on November 23, 2019 by Android

COMPUTING
A Giant Superfast AI Chip Is Being Used to Find Better Cancer Drugs
Karen Hao | MIT Technology Review
“Thus far, Cerebras’s computer has checked all the boxes. Thanks to its chip size—it is larger than an iPad and has 1.2 trillion transistors for making calculations—it isn’t necessary to hook multiple smaller processors together, which can slow down model training. In testing, it has already shrunk the training time of models from weeks to hours.”

MEDICINE
Humans Put Into Suspended Animation for First Time
Ian Sample | The Guardian
“The process involves rapidly cooling the brain to less than 10C by replacing the patient’s blood with ice-cold saline solution. Typically the solution is pumped directly into the aorta, the main artery that carries blood away from the heart to the rest of the body.”

DRONES
This Transforming Drone Can Be Fired Straight Out of a Cannon
James Vincent | The Verge
“Drones are incredibly useful machines in the air, but getting them up and flying can be tricky, especially in crowded, windy, or emergency scenarios when speed is a factor. But a group of researchers from Caltech university and NASA’s Jet Propulsion Laboratory have come up with an elegant and oh-so-fun solution: fire the damn thing out of a cannon.”

ROBOTICS
Alphabet’s Dream of an ‘Everyday Robot’ Is Just Out of Reach
Tom Simonite | Wired
“Sorting trash was chosen as a convenient challenge to test the project’s approach to creating more capable robots. It’s using artificial intelligence software developed in collaboration with Google to make robots that learn complex tasks through on-the-job experience. The hope is to make robots less reliant on human coding for their skills, and capable of adapting quickly to complex new tasks and environments.”

ENVIRONMENT
The Electric Car Revolution May Take a Lot Longer Than Expected
James Temple | MIT Technology Review
“A new report from the MIT Energy Initiative warns that EVs may never reach the same sticker price so long as they rely on lithium-ion batteries, the energy storage technology that powers most of today’s consumer electronics. In fact, it’s likely to take another decade just to eliminate the difference in the lifetime costs between the vehicle categories, which factors in the higher fuel and maintenance expenses of standard cars and trucks.”

SPACE
How Two Intruders From Interstellar Space Are Upending Astronomy
Alexandra Witze | Nature
“From the tallest peak in Hawaii to a high plateau in the Andes, some of the biggest telescopes on Earth will point towards a faint smudge of light over the next few weeks. …What they’re looking for is a rare visitor that is about to make its closest approach to the Sun. After that, they have just months to grab as much information as they can from the object before it disappears forever into the blackness of space.”

Image Credit: Simone Hutsch / Unsplash Continue reading →

Posted in Human Robots

#436188 The Blogger Behind “AI ...

Posted on November 21, 2019 by Android

Sure, artificial intelligence is transforming the world’s societies and economies—but can an AI come up with plausible ideas for a Halloween costume?

Janelle Shane has been asking such probing questions since she started her AI Weirdness blog in 2016. She specializes in training neural networks (which underpin most of today’s machine learning techniques) on quirky data sets such as compilations of knitting instructions, ice cream flavors, and names of paint colors. Then she asks the neural net to generate its own contributions to these categories—and hilarity ensues. AI is not likely to disrupt the paint industry with names like “Ronching Blue,” “Dorkwood,” and “Turdly.”

Shane’s antics have a serious purpose. She aims to illustrate the serious limitations of today’s AI, and to counteract the prevailing narrative that describes AI as well on its way to superintelligence and complete human domination. “The danger of AI is not that it’s too smart,” Shane writes in her new book, “but that it’s not smart enough.”

The book, which came out on Tuesday, is called You Look Like a Thing and I Love You. It takes its odd title from a list of AI-generated pick-up lines, all of which would at least get a person’s attention if shouted, preferably by a robot, in a crowded bar. Shane’s book is shot through with her trademark absurdist humor, but it also contains real explanations of machine learning concepts and techniques. It’s a painless way to take AI 101.

She spoke with IEEE Spectrum about the perils of placing too much trust in AI systems, the strange AI phenomenon of “giraffing,” and her next potential Halloween costume.

Janelle Shane on . . .

The un-delicious origin of her blog
“The narrower the problem, the smarter the AI will seem”
Why overestimating AI is dangerous
Giraffing!
Machine and human creativity

The un-delicious origin of her blog IEEE Spectrum: You studied electrical engineering as an undergrad, then got a master’s degree in physics. How did that lead to you becoming the comedian of AI?
Janelle Shane: I’ve been interested in machine learning since freshman year of college. During orientation at Michigan State, a professor who worked on evolutionary algorithms gave a talk about his work. It was full of the most interesting anecdotes–some of which I’ve used in my book. He told an anecdote about people setting up a machine learning algorithm to do lens design, and the algorithm did end up designing an optical system that works… except one of the lenses was 50 feet thick, because they didn’t specify that it couldn’t do that.
I started working in his lab on optics, doing ultra-short laser pulse work. I ended up doing a lot more optics than machine learning, but I always found it interesting. One day I came across a list of recipes that someone had generated using a neural net, and I thought it was hilarious and remembered why I thought machine learning was so cool. That was in 2016, ages ago in machine learning land.
Spectrum: So you decided to “establish weirdness as your goal” for your blog. What was the first weird experiment that you blogged about?
Shane: It was generating cookbook recipes. The neural net came up with ingredients like: “Take ¼ pounds of bones or fresh bread.” That recipe started out: “Brown the salmon in oil, add creamed meat to the mixture.” It was making mistakes that showed the thing had no memory at all.
Spectrum: You say in the book that you can learn a lot about AI by giving it a task and watching it flail. What do you learn?
Shane: One thing you learn is how much it relies on surface appearances rather than deep understanding. With the recipes, for example: It got the structure of title, category, ingredients, instructions, yield at the end. But when you look more closely, it has instructions like “Fold the water and roll it into cubes.” So clearly this thing does not understand water, let alone the other things. It’s recognizing certain phrases that tend to occur, but it doesn’t have a concept that these recipes are describing something real. You start to realize how very narrow the algorithms in this world are. They only know exactly what we tell them in our data set.
BACK TO TOP↑ “The narrower the problem, the smarter the AI will seem” Spectrum: That makes me think of DeepMind’s AlphaGo, which was universally hailed as a triumph for AI. It can play the game of Go better than any human, but it doesn’t know what Go is. It doesn’t know that it’s playing a game.
Shane: It doesn’t know what a human is, or if it’s playing against a human or another program. That’s also a nice illustration of how well these algorithms do when they have a really narrow and well-defined problem.
The narrower the problem, the smarter the AI will seem. If it’s not just doing something repeatedly but instead has to understand something, coherence goes down. For example, take an algorithm that can generate images of objects. If the algorithm is restricted to birds, it could do a recognizable bird. If this same algorithm is asked to generate images of any animal, if its task is that broad, the bird it generates becomes an unrecognizable brown feathered smear against a green background.
Spectrum: That sounds… disturbing.
Shane: It’s disturbing in a weird amusing way. What’s really disturbing is the humans it generates. It hasn’t seen them enough times to have a good representation, so you end up with an amorphous, usually pale-faced thing with way too many orifices. If you asked it to generate an image of a person eating pizza, you’ll have blocks of pizza texture floating around. But if you give that image to an image-recognition algorithm that was trained on that same data set, it will say, “Oh yes, that’s a person eating pizza.”
BACK TO TOP↑ Why overestimating AI is dangerous Spectrum: Do you see it as your role to puncture the AI hype?
Shane: I do see it that way. Not a lot of people are bringing out this side of AI. When I first started posting my results, I’d get people saying, “I don’t understand, this is AI, shouldn’t it be better than this? Why doesn't it understand?” Many of the impressive examples of AI have a really narrow task, or they’ve been set up to hide how little understanding it has. There’s a motivation, especially among people selling products based on AI, to represent the AI as more competent and understanding than it actually is.
Spectrum: If people overestimate the abilities of AI, what risk does that pose?
Shane: I worry when I see people trusting AI with decisions it can’t handle, like hiring decisions or decisions about moderating content. These are really tough tasks for AI to do well on. There are going to be a lot of glitches. I see people saying, “The computer decided this so it must be unbiased, it must be objective.”

“If the algorithm’s task is to replicate human hiring decisions, it’s going to glom onto gender bias and race bias.”
—Janelle Shane, AI Weirdness blogger
That’s another thing I find myself highlighting in the work I’m doing. If the data includes bias, the algorithm will copy that bias. You can’t tell it not to be biased, because it doesn’t understand what bias is. I think that message is an important one for people to understand.
If there’s bias to be found, the algorithm is going to go after it. It’s like, “Thank goodness, finally a signal that’s reliable.” But for a tough problem like: Look at these resumes and decide who’s best for the job. If its task is to replicate human hiring decisions, it’s going to glom onto gender bias and race bias. There’s an example in the book of a hiring algorithm that Amazon was developing that discriminated against women, because the historical data it was trained on had that gender bias.
Spectrum: What are the other downsides of using AI systems that don’t really understand their tasks?
Shane: There is a risk in putting too much trust in AI and not examining its decisions. Another issue is that it can solve the wrong problems, without anyone realizing it. There have been a couple of cases in medicine. For example, there was an algorithm that was trained to recognize things like skin cancer. But instead of recognizing the actual skin condition, it latched onto signals like the markings a surgeon makes on the skin, or a ruler placed there for scale. It was treating those things as a sign of skin cancer. It’s another indication that these algorithms don’t understand what they’re looking at and what the goal really is.
BACK TO TOP↑ Giraffing Spectrum: In your blog, you often have neural nets generate names for things—such as ice cream flavors, paint colors, cats, mushrooms, and types of apples. How do you decide on topics?
Shane: Quite often it’s because someone has written in with an idea or a data set. They’ll say something like, “I’m the MIT librarian and I have a whole list of MIT thesis titles.” That one was delightful. Or they’ll say, “We are a high school robotics team, and we know where there’s a list of robotics team names.” It’s fun to peek into a different world. I have to be careful that I’m not making fun of the naming conventions in the field. But there’s a lot of humor simply in the neural net’s complete failure to understand. Puns in particular—it really struggles with puns.
Spectrum: Your blog is quite absurd, but it strikes me that machine learning is often absurd in itself. Can you explain the concept of giraffing?
Shane: This concept was originally introduced by [internet security expert] Melissa Elliott. She proposed this phrase as a way to describe the algorithms’ tendency to see giraffes way more often than would be likely in the real world. She posted a whole bunch of examples, like a photo of an empty field in which an image-recognition algorithm has confidently reported that there are giraffes. Why does it think giraffes are present so often when they’re actually really rare? Because they’re trained on data sets from online. People tend to say, “Hey look, a giraffe!” And then take a photo and share it. They don’t do that so often when they see an empty field with rocks.
There’s also a chatbot that has a delightful quirk. If you show it some photo and ask it how many giraffes are in the picture, it will always answer with some non zero number. This quirk comes from the way the training data was generated: These were questions asked and answered by humans online. People tended not to ask the question “How many giraffes are there?” when the answer was zero. So you can show it a picture of someone holding a Wii remote. If you ask it how many giraffes are in the picture, it will say two.
BACK TO TOP↑ Machine and human creativity Spectrum: AI can be absurd, and maybe also creative. But you make the point that AI art projects are really human-AI collaborations: Collecting the data set, training the algorithm, and curating the output are all artistic acts on the part of the human. Do you see your work as a human-AI art project?
Shane: Yes, I think there is artistic intent in my work; you could call it literary or visual. It’s not so interesting to just take a pre-trained algorithm that’s been trained on utilitarian data, and tell it to generate a bunch of stuff. Even if the algorithm isn’t one that I’ve trained myself, I think about, what is it doing that’s interesting, what kind of story can I tell around it, and what do I want to show people.

The Halloween costume algorithm “was able to draw on its knowledge of which words are related to suggest things like sexy barnacle.”
—Janelle Shane, AI Weirdness blogger
Spectrum: For the past three years you’ve been getting neural nets to generate ideas for Halloween costumes. As language models have gotten dramatically better over the past three years, are the costume suggestions getting less absurd?
Shane: Yes. Before I would get a lot more nonsense words. This time I got phrases that were related to real things in the data set. I don’t believe the training data had the words Flying Dutchman or barnacle. But it was able to draw on its knowledge of which words are related to suggest things like sexy barnacle and sexy Flying Dutchman.
Spectrum: This year, I saw on Twitter that someone made the gothy giraffe costume happen. Would you ever dress up for Halloween in a costume that the neural net suggested?
Shane: I think that would be fun. But there would be some challenges. I would love to go as the sexy Flying Dutchman. But my ambition may constrict me to do something more like a list of leg parts.
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