Tag Archives: Age
#436488 Tech’s Biggest Leaps From the Last 10 ...
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
#436437 Why AI Will Be the Best Tool for ...
Dmitry Kaminskiy speaks as though he were trying to unload everything he knows about the science and economics of longevity—from senolytics research that seeks to stop aging cells from spewing inflammatory proteins and other molecules to the trillion-dollar life extension industry that he and his colleagues are trying to foster—in one sitting.
At the heart of the discussion with Singularity Hub is the idea that artificial intelligence will be the engine that drives breakthroughs in how we approach healthcare and healthy aging—a concept with little traction even just five years ago.
“At that time, it was considered too futuristic that artificial intelligence and data science … might be more accurate compared to any hypothesis of human doctors,” said Kaminskiy, co-founder and managing partner at Deep Knowledge Ventures, an investment firm that is betting big on AI and longevity.
How times have changed. Artificial intelligence in healthcare is attracting more investments and deals than just about any sector of the economy, according to data research firm CB Insights. In the most recent third quarter, AI healthcare startups raised nearly $1.6 billion, buoyed by a $550 million mega-round from London-based Babylon Health, which uses AI to collect data from patients, analyze the information, find comparable matches, then make recommendations.
Even without the big bump from Babylon Health, AI healthcare startups raised more than $1 billion last quarter, including two companies focused on longevity therapeutics: Juvenescence and Insilico Medicine.
The latter has risen to prominence for its novel use of reinforcement learning and general adversarial networks (GANs) to accelerate the drug discovery process. Insilico Medicine recently published a seminal paper that demonstrated how such an AI system could generate a drug candidate in just 46 days. Co-founder and CEO Alex Zhavoronkov said he believes there is no greater goal in healthcare today—or, really, any venture—than extending the healthy years of the human lifespan.
“I don’t think that there is anything more important than that,” he told Singularity Hub, explaining that an unhealthy society is detrimental to a healthy economy. “I think that it’s very, very important to extend healthy, productive lifespan just to fix the economy.”
An Aging Crisis
The surge of interest in longevity is coming at a time when life expectancy in the US is actually dropping, despite the fact that we spend more money on healthcare than any other nation.
A new paper in the Journal of the American Medical Association found that after six decades of gains, life expectancy for Americans has decreased since 2014, particularly among young and middle-aged adults. While some of the causes are societal, such as drug overdoses and suicide, others are health-related.
While average life expectancy in the US is 78, Kaminskiy noted that healthy life expectancy is about ten years less.
To Zhavoronkov’s point about the economy (a topic of great interest to Kaminskiy as well), the US spent $1.1 trillion on chronic diseases in 2016, according to a report from the Milken Institute, with diabetes, cardiovascular conditions, and Alzheimer’s among the most costly expenses to the healthcare system. When the indirect costs of lost economic productivity are included, the total price tag of chronic diseases in the US is $3.7 trillion, nearly 20 percent of GDP.
“So this is the major negative feedback on the national economy and creating a lot of negative social [and] financial issues,” Kaminskiy said.
Investing in Longevity
That has convinced Kaminskiy that an economy focused on extending healthy human lifespans—including the financial instruments and institutions required to support a long-lived population—is the best way forward.
He has co-authored a book on the topic with Margaretta Colangelo, another managing partner at Deep Knowledge Ventures, which has launched a specialized investment fund, Longevity.Capital, focused on the longevity industry. Kaminskiy estimates that there are now about 20 such investment funds dedicated to funding life extension companies.
In November at the inaugural AI for Longevity Summit in London, he and his collaborators also introduced the Longevity AI Consortium, an academic-industry initiative at King’s College London. Eventually, the research center will include an AI Longevity Accelerator program to serve as a bridge between startups and UK investors.
Deep Knowledge Ventures has committed about £7 million ($9 million) over the next three years to the accelerator program, as well as establishing similar consortiums in other regions of the world, according to Franco Cortese, a partner at Longevity.Capital and director of the Aging Analytics Agency, which has produced a series of reports on longevity.
A Cure for What Ages You
One of the most recent is an overview of Biomarkers for Longevity. A biomarker, in the case of longevity, is a measurable component of health that can indicate a disease state or a more general decline in health associated with aging. Examples range from something as simple as BMI as an indicator of obesity, which is associated with a number of chronic diseases, to sophisticated measurements of telomeres, the protective ends of chromosomes that shorten as we age.
While some researchers are working on moonshot therapies to reverse or slow aging—with a few even arguing we could expand human life on the order of centuries—Kaminskiy said he believes understanding biomarkers of aging could make more radical interventions unnecessary.
In this vision of healthcare, people would be able to monitor their health 24-7, with sensors attuned to various biomarkers that could indicate the onset of everything from the flu to diabetes. AI would be instrumental in not just ingesting the billions of data points required to develop such a system, but also what therapies, treatments, or micro-doses of a drug or supplement would be required to maintain homeostasis.
“Consider it like Tesla with many, many detectors, analyzing the behavior of the car in real time, and a cloud computing system monitoring those signals in real time with high frequency,” Kaminskiy explained. “So the same shall be applied for humans.”
And only sophisticated algorithms, Kaminskiy argued, can make longevity healthcare work on a mass scale but at the individual level. Precision medicine becomes preventive medicine. Healthcare truly becomes a system to support health rather than a way to fight disease.
Image Credit: Photo by h heyerlein on Unsplash Continue reading →
Posted in Human Robots
#436252 After AI, Fashion and Shopping Will ...
AI and broadband are eating retail for breakfast. In the first half of 2019, we’ve seen 19 retailer bankruptcies. And the retail apocalypse is only accelerating.
What’s coming next is astounding. Why drive when you can speak? Revenue from products purchased via voice commands is expected to quadruple from today’s US$2 billion to US$8 billion by 2023.
Virtual reality, augmented reality, and 3D printing are converging with artificial intelligence, drones, and 5G to transform shopping on every dimension. And as a result, shopping is becoming dematerialized, demonetized, democratized, and delocalized… a top-to-bottom transformation of the retail world.
Welcome to Part 1 of our series on the future of retail, a deep-dive into AI and its far-reaching implications.
Let’s dive in.
A Day in the Life of 2029
Welcome to April 21, 2029, a sunny day in Dallas. You’ve got a fundraising luncheon tomorrow, but nothing to wear. The last thing you want to do is spend the day at the mall.
No sweat. Your body image data is still current, as you were scanned only a week ago. Put on your VR headset and have a conversation with your AI. “It’s time to buy a dress for tomorrow’s event” is all you have to say. In a moment, you’re teleported to a virtual clothing store. Zero travel time. No freeway traffic, parking hassles, or angry hordes wielding baby strollers.
Instead, you’ve entered your own personal clothing store. Everything is in your exact size…. And I mean everything. The store has access to nearly every designer and style on the planet. Ask your AI to show you what’s hot in Shanghai, and presto—instant fashion show. Every model strutting down the runway looks exactly like you, only dressed in Shanghai’s latest.
When you’re done selecting an outfit, your AI pays the bill. And as your new clothes are being 3D printed at a warehouse—before speeding your way via drone delivery—a digital version has been added to your personal inventory for use at future virtual events.
The cost? Thanks to an era of no middlemen, less than half of what you pay in stores today. Yet this future is not all that far off…
Digital Assistants
Let’s begin with the basics: the act of turning desire into purchase.
Most of us navigate shopping malls or online marketplaces alone, hoping to stumble across the right item and fit. But if you’re lucky enough to employ a personal assistant, you have the luxury of describing what you want to someone who knows you well enough to buy that exact right thing most of the time.
For most of us who don’t, enter the digital assistant.
Right now, the four horsemen of the retail apocalypse are waging war for our wallets. Amazon’s Alexa, Google’s Now, Apple’s Siri, and Alibaba’s Tmall Genie are going head-to-head in a battle to become the platform du jour for voice-activated, AI-assisted commerce.
For baby boomers who grew up watching Captain Kirk talk to the Enterprise’s computer on Star Trek, digital assistants seem a little like science fiction. But for millennials, it’s just the next logical step in a world that is auto-magical.
And as those millennials enter their consumer prime, revenue from products purchased via voice-driven commands is projected to leap from today’s US$2 billion to US$8 billion by 2023.
We are already seeing a major change in purchasing habits. On average, consumers using Amazon Echo spent more than standard Amazon Prime customers: US$1,700 versus US$1,300.
And as far as an AI fashion advisor goes, those too are here, courtesy of both Alibaba and Amazon. During its annual Singles’ Day (November 11) shopping festival, Alibaba’s FashionAI concept store uses deep learning to make suggestions based on advice from human fashion experts and store inventory, driving a significant portion of the day’s US$25 billion in sales.
Similarly, Amazon’s shopping algorithm makes personalized clothing recommendations based on user preferences and social media behavior.
Customer Service
But AI is disrupting more than just personalized fashion and e-commerce. Its next big break will take place in the customer service arena.
According to a recent Zendesk study, good customer service increases the possibility of a purchase by 42 percent, while bad customer service translates into a 52 percent chance of losing that sale forever. This means more than half of us will stop shopping at a store due to a single disappointing customer service interaction. These are significant financial stakes. They’re also problems perfectly suited for an AI solution.
During the 2018 Google I/O conference, CEO Sundar Pichai demoed the Google Duplex, their next generation digital assistant. Pichai played the audience a series of pre-recorded phone calls made by Google Duplex. The first call made a reservation at a restaurant, the second one booked a haircut appointment, amusing the audience with a long “hmmm” mid-call.
In neither case did the person on the other end of the phone have any idea they were talking to an AI. The system’s success speaks to how seamlessly AI can blend into our retail lives and how convenient it will continue to make them. The same technology Pichai demonstrated that can make phone calls for consumers can also answer phones for retailers—a development that’s unfolding in two different ways:
(1) Customer service coaches: First, for organizations interested in keeping humans involved, there’s Beyond Verbal, a Tel Aviv-based startup that has built an AI customer service coach. Simply by analyzing customer voice intonation, the system can tell whether the person on the phone is about to blow a gasket, is genuinely excited, or anything in between.
Based on research of over 70,000 subjects in more than 30 languages, Beyond Verbal’s app can detect 400 different markers of human moods, attitudes, and personality traits. Already it’s been integrated in call centers to help human sales agents understand and react to customer emotions, making those calls more pleasant, and also more profitable.
For example, by analyzing word choice and vocal style, Beyond Verbal’s system can tell what kind of shopper the person on the line actually is. If they’re an early adopter, the AI alerts the sales agent to offer them the latest and greatest. If they’re more conservative, it suggests items more tried-and-true.
(2) Replacing customer service agents: Second, companies like New Zealand’s Soul Machines are working to replace human customer service agents altogether. Powered by IBM’s Watson, Soul Machines builds lifelike customer service avatars designed for empathy, making them one of many helping to pioneer the field of emotionally intelligent computing.
With their technology, 40 percent of all customer service interactions are now resolved with a high degree of satisfaction, no human intervention needed. And because the system is built using neural nets, it’s continuously learning from every interaction—meaning that percentage will continue to improve.
The number of these interactions continues to grow as well. Software manufacturer Autodesk now includes a Soul Machine avatar named AVA (Autodesk Virtual Assistant) in all of its new offerings. She lives in a small window on the screen, ready to soothe tempers, troubleshoot problems, and forever banish those long tech support hold times.
For Daimler Financial Services, Soul Machines built an avatar named Sarah, who helps customers with arguably three of modernity’s most annoying tasks: financing, leasing, and insuring a car.
This isn’t just about AI—it’s about AI converging with additional exponentials. Add networks and sensors to the story and it raises the scale of disruption, upping the FQ—the frictionless quotient—in our frictionless shopping adventure.
Final Thoughts
AI makes retail cheaper, faster, and more efficient, touching everything from customer service to product delivery. It also redefines the shopping experience, making it frictionless and—once we allow AI to make purchases for us—ultimately invisible.
Prepare for a future in which shopping is dematerialized, demonetized, democratized, and delocalized—otherwise known as “the end of malls.”
Of course, if you wait a few more years, you’ll be able to take an autonomous flying taxi to Westfield’s Destination 2028—so perhaps today’s converging exponentials are not so much spelling the end of malls but rather the beginning of an experience economy far smarter, more immersive, and whimsically imaginative than today’s shopping centers.
Either way, it’s a top-to-bottom transformation of the retail world.
Over the coming blog series, we will continue our discussion of the future of retail. Stay tuned to learn new implications for your business and how to future-proof your company in an age of smart, ultra-efficient, experiential retail.
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This article originally appeared on diamandis.com. Read the original article here.
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Posted in Human Robots
#436184 Why People Demanded Privacy to Confide ...
This is part four of a six-part series on the history of natural language processing.
Between 1964 and 1966, Joseph Weizenbaum, a German American computer scientist at MIT’s artificial intelligence lab, developed the first-ever chatbot [PDF].
While there were already some rudimentary digital language generators in existence—programs that could spit out somewhat coherent lines of text—Weizenbaum’s program was the first designed explicitly for interactions with humans. The user could type in some statement or set of statements in their normal language, press enter, and receive a response from the machine. As Weizenbaum explained, his program made “certain kinds of natural-language conversation between man and computer possible.”
He named the program Eliza after Eliza Doolittle, the working-class hero of George Bernard Shaw’s Pygmalion who learns how to talk with an upper-class accent. The new Eliza was written for the 36-bit IBM 7094, an early transistorized mainframe computer, in a programming language that Weizenbaum developed called MAD-SLIP.
Because computer time was a valuable resource, Eliza could only be run via a time-sharing system; the user interacted with the program remotely via an electric typewriter and printer. When the user typed in a sentence and pressed enter, a message was sent to the mainframe computer. Eliza scanned the message for the presence of a keyword and used it in a new sentence to form a response that was sent back, printed out, and read by the user.
To encourage ongoing dialogue, Weizenbaum designed Eliza to simulate the type of conversational style used by a Rogerian psychoanalyst. The program would take something the user said and reflect it back with a question, as can be seen in the example below. (Notice how the program takes keywords like “boyfriend” and “depressed” and re-uses them.)
Human: Men are all alike.
Computer: IN WHAT WAY?
Human: They're always bugging us about something or other.
Computer: CAN YOU THINK OF A SPECIFIC EXAMPLE?
Human: Well, my boyfriend made me come here.
Computer: YOUR BOYFRIEND MADE YOU COME HERE
Human: He says I'm depressed much of the time.
Computer: I AM SORRY TO HEAR YOU ARE DEPRESSED
Human: It's true. I am unhappy.
Weizenbaum chose this mode of dialogue for Eliza because it gave the impression that the computer understood what was being said without having to offer anything new to the conversation. It created the illusion of comprehension and engagement in a mere 200 lines of code.
To test Eliza’s capacity to engage an interlocutor, Weizenbaum invited students and colleagues into his office and let them chat with the machine while he looked on. He noticed, with some concern, that during their brief interactions with Eliza, many users began forming emotional attachments to the algorithm. They would open up to the machine and confess problems they were facing in their lives and relationships.
During their brief interactions with Eliza, many users began forming emotional attachments to the algorithm.
Even more surprising was that this sense of intimacy persisted even after Weizenbaum described how the machine worked and explained that it didn’t really understand anything that was being said. Weizenbaum was most troubled when his secretary, who had watched him build the program from scratch over many months, insisted that he leave the room so she could talk to Eliza in private.
For Weizenbaum, this experiment with Eliza made him question an idea that Alan Turing had proposed in 1950 about machine intelligence. In his paper, entitled “Computing Machinery and Intelligence,” Turing suggested that if a computer could conduct a convincingly human conversation in text, one could assume it was intelligent—an idea that became the basis of the famous Turing Test.
But Eliza demonstrated that convincing communication between a human and a machine could take place even if comprehension only flowed from one side: The simulation of intelligence, rather than intelligence itself, was enough to fool people. Weizenbaum called this the Eliza effect, and believed it was a type of “delusional thinking” that humanity would collectively suffer from in the digital age. This insight was a profound shock for Weizenbaum, and one that came to define his intellectual trajectory over the next decade.
The simulation of intelligence, rather than intelligence itself, was enough to fool people.
In 1976, he published Computing Power and Human Reason: From Judgment to Calculation [PDF], which offered a long meditation on why people are willing to believe that a simple machine might be able to understand their complex human emotions.
In this book, he argues that the Eliza effect signifies a broader pathology afflicting “modern man.” In a world conquered by science, technology, and capitalism, people had grown accustomed to viewing themselves as isolated cogs in a large and uncaring machine. In such a diminished social world, Weizenbaum reasoned, people had grown so desperate for connection that they put aside their reason and judgment in order to believe that a program could care about their problems.
Weizenbaum spent the rest of his life developing this humanistic critique of artificial intelligence and digital technology. His mission was to remind people that their machines were not as smart as they were often said to be. And that even though it sometimes appeared as though they could talk, they were never really listening.
This is the fourth installment of a six-part series on the history of natural language processing. Last week’s post described Andrey Markov and Claude Shannon’s painstaking efforts to create statistical models of language for text generation. Come back next Monday for part five, “In 2016, Microsoft’s Racist Chatbot Revealed the Dangers of Conversation.”
You can also check out our prior series on the untold history of AI. Continue reading →
Posted in Human Robots
#436151 Natural Language Processing Dates Back ...
This is part one of a six-part series on the history of natural language processing.
We’re in the middle of a boom time for natural language processing (NLP), the field of computer science that focuses on linguistic interactions between humans and machines. Thanks to advances in machine learning over the past decade, we’ve seen vast improvements in speech recognition and machine translation software. Language generators are now good enough to write coherent news articles, and virtual agents like Siri and Alexa are becoming part of our daily lives.
Most trace the origins of this field back to the beginning of the computer age, when Alan Turing, writing in 1950, imagined a smart machine that could interact fluently with a human via typed text on a screen. For this reason, machine-generated language is mostly understood as a digital phenomenon—and a central goal of artificial intelligence (AI) research.
This six-part series will challenge that common understanding of NLP. In fact, attempts to design formal rules and machines that can analyze, process, and generate language go back hundreds of years.
Attempts to design formal rules and machines that can analyze, process, and generate language go back hundreds of years.
While specific technologies have changed over time, the basic idea of treating language as a material that can be artificially manipulated by rule-based systems has been pursued by many people in many cultures and for many different reasons. These historical experiments reveal the promise and perils of attempting to simulate human language in non-human ways—and they hold lessons for today’s practitioners of cutting-edge NLP techniques.
The story begins in medieval Spain. In the late 1200s, a Jewish mystic by the name of Abraham Abulafia sat down at a table in his small house in Barcelona, picked up a quill, dipped it in ink, and began combining the letters of the Hebrew alphabet in strange and seemingly random ways. Aleph with Bet, Bet with Gimmel, Gimmel with Aleph and Bet, and so on.
Abulafia called this practice “the science of the combination of letters.” He wasn’t actually combining letters at random; instead he was carefully following a secret set of rules that he had devised while studying an ancient Kabbalistic text called the Sefer Yetsirah. This book describes how God created “all that is formed and all that is spoken” by combining Hebrew letters according to sacred formulas. In one section, God exhausts all possible two-letter combinations of the 22 Hebrew letters.
By studying the Sefer Yetsirah, Abulafia gained the insight that linguistic symbols can be manipulated with formal rules in order to create new, interesting, insightful sentences. To this end, he spent months generating thousands of combinations of the 22 letters of the Hebrew alphabet and eventually emerged with a series of books that he claimed were endowed with prophetic wisdom.
For Abulafia, generating language according to divine rules offered insight into the sacred and the unknown, or as he put it, allowed him to “grasp things which by human tradition or by thyself thou would not be able to know.”
Combining letters to generate language allows thou to “grasp things which by human tradition or by thyself thou would not be able to know.”
—Abraham Abulafia, mystic
But other Jewish scholars considered this rudimentary language generation a dangerous act that bordered on the profane. The Talmud tells stories of rabbis who, by the magical act of permuting language according to the formulas set out in the Sefer Yetsirah, created artificial creatures called golems. In these tales, rabbis manipulated the letters of the Hebrew alphabet to replicate God’s act of creation, using the sacred formulas to imbue inanimate objects with life.
In some of these myths, the rabbis used this skill for practical reasons, to make animals to eat when hungry or servants to help them with domestic duties. But many of these golem stories end badly. In one particularly well-known fable, Judah Loew ben Bezalel, the 16th century rabbi of Prague, used the sacred practice of letter combinatorics to conjure a golem to protect the Jewish community from antisemitic attacks, only to see the golem turn violently on him instead.
This “science of the combination of letters” was a rudimentary form of natural language processing, as it involved combining letters of the Hebrew alphabet according to specific rules. For Kabbalists, it was a double-edged sword: a way to access new forms of knowledge and wisdom, but also an inherently dangerous practice that could bring about unintended consequences.
This tension reappears throughout the long history of language processing, and still echoes in discussions about the most cutting-edge NLP technology of our digital era.
This is the first installment of a six-part series on the history of natural language processing. Come back next Monday for part two, “In the 17th Century, Leibniz Dreamed of a Machine That Could Calculate Ideas.”
You can also check out our prior series on the untold history of AI. Continue reading →
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