Tag Archives: dialogue

#433907 How the Spatial Web Will Fix What’s ...

Converging exponential technologies will transform media, advertising and the retail world. The world we see, through our digitally-enhanced eyes, will multiply and explode with intelligence, personalization, and brilliance.

This is the age of Web 3.0.

Last week, I discussed the what and how of Web 3.0 (also known as the Spatial Web), walking through its architecture and the converging technologies that enable it.

To recap, while Web 1.0 consisted of static documents and read-only data, Web 2.0 introduced multimedia content, interactive web applications, and participatory social media, all of these mediated by two-dimensional screens—a flat web of sensorily confined information.

During the next two to five years, the convergence of 5G, AI, a trillion sensors, and VR/AR will enable us to both map our physical world into virtual space and superimpose a digital layer onto our physical environments.

Web 3.0 is about to transform everything—from the way we learn and educate, to the way we trade (smart) assets, to our interactions with real and virtual versions of each other.

And while users grow rightly concerned about data privacy and misuse, the Spatial Web’s use of blockchain in its data and governance layer will secure and validate our online identities, protecting everything from your virtual assets to personal files.

In this second installment of the Web 3.0 series, I’ll be discussing the Spatial Web’s vast implications for a handful of industries:

News & Media Coverage
Smart Advertising
Personalized Retail

Let’s dive in.

Transforming Network News with Web 3.0
News media is big business. In 2016, global news media (including print) generated 168 billion USD in circulation and advertising revenue.

The news we listen to impacts our mindset. Listen to dystopian news on violence, disaster, and evil, and you’ll more likely be searching for a cave to hide in, rather than technology for the launch of your next business.

Today, different news media present starkly different realities of everything from foreign conflict to domestic policy. And outcomes are consequential. What reporters and news corporations decide to show or omit of a given news story plays a tremendous role in shaping the beliefs and resulting values of entire populations and constituencies.

But what if we could have an objective benchmark for today’s news, whereby crowdsourced and sensor-collected evidence allows you to tour the site of journalistic coverage, determining for yourself the most salient aspects of a story?

Enter mesh networks, AI, public ledgers, and virtual reality.

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 protests break out across the country, each cluster of activists broadcasting an aggregate of 360-degree videos, all fed through photogrammetry AIs that build out a live hologram of the march in real time. Want to see and hear what the NYC-based crowds are advocating for? Throw on some VR goggles and explore the event with full access. Or cue into the southern Texan border to assess for yourself the handling of immigrant entry and border conflicts.

Take a front seat in the Capitol during tomorrow’s Senate hearing, assessing each Senator’s reactions, questions and arguments without a Fox News or CNN filter. Or if you’re short on time, switch on the holographic press conference and host 3D avatars of live-broadcasting politicians in your living room.

We often think of modern media as taking away consumer agency, feeding tailored and often partisan ideology to a complacent audience. But as wireless mesh networks and agnostic sensor data allow for immersive VR-accessible news sites, the average viewer will necessarily become an active participant in her own education of current events.

And with each of us interpreting the news according to our own values, I envision a much less polarized world. A world in which civic engagement, moderately reasoned dialogue, and shared assumptions will allow us to empathize and make compromises.

The future promises an era in which news is verified and balanced; wherein public ledgers, AI, and new web interfaces bring you into the action and respect your intelligence—not manipulate your ignorance.

Web 3.0 Reinventing Advertising
Bringing about the rise of ‘user-owned data’ and self-established permissions, Web 3.0 is poised to completely disrupt digital advertising—a global industry worth over 192 billion USD.

Currently, targeted advertising leverages tomes of personal data and online consumer behavior to subtly engage you with products you might not want, or sell you on falsely advertised services promising inaccurate results.

With a new Web 3.0 data and governance layer, however, distributed ledger technologies will require advertisers to engage in more direct interaction with consumers, validating claims and upping transparency.

And with a data layer that allows users to own and authorize third-party use of their data, blockchain also holds extraordinary promise to slash not only data breaches and identity theft, but covert advertiser bombardment without your authorization.

Accessing crowdsourced reviews and AI-driven fact-checking, users will be able to validate advertising claims more efficiently and accurately than ever before, potentially rating and filtering out advertisers in the process. And in such a streamlined system of verified claims, sellers will face increased pressure to compete more on product and rely less on marketing.

But perhaps most exciting is the convergence of artificial intelligence and augmented reality.

As Spatial Web networks begin to associate digital information with physical objects and locations, products will begin to “sell themselves.” Each with built-in smart properties, products will become hyper-personalized, communicating information directly to users through Web 3.0 interfaces.

Imagine stepping into a department store in pursuit of a new web-connected fridge. As soon as you enter, your AR goggles register your location and immediately grant you access to a populated register of store products.

As you move closer to a kitchen set that catches your eye, a virtual salesperson—whether by holographic video or avatar—pops into your field of view next to the fridge you’ve been examining and begins introducing you to its various functions and features. You quickly decide you’d rather disable the avatar and get textual input instead, and preferences are reset to list appliance properties visually.

After a virtual tour of several other fridges, you decide on the one you want and seamlessly execute a smart contract, carried out by your smart wallet and the fridge. The transaction takes place in seconds, and the fridge’s blockchain-recorded ownership record has been updated.

Better yet, you head over to a friend’s home for dinner after moving into the neighborhood. While catching up in the kitchen, your eyes fixate on the cabinets, which quickly populate your AR glasses with a price-point and selection of colors.

But what if you’d rather not get auto-populated product info in the first place? No problem!

Now empowered with self-sovereign identities, users might be able to turn off advertising preferences entirely, turning on smart recommendations only when they want to buy a given product or need new supplies.

And with user-centric data, consumers might even sell such information to advertisers directly. Now, instead of Facebook or Google profiting off your data, you might earn a passive income by giving advertisers permission to personalize and market their services. Buy more, and your personal data marketplace grows in value. Buy less, and a lower-valued advertising profile causes an ebb in advertiser input.

With user-controlled data, advertisers now work on your terms, putting increased pressure on product iteration and personalizing products for each user.

This brings us to the transformative future of retail.

Personalized Retail–Power of the Spatial Web
In a future of smart and hyper-personalized products, I might walk through a virtual game space or a digitally reconstructed Target, browsing specific categories of clothing I’ve predetermined prior to entry.

As I pick out my selection, my AI assistant hones its algorithm reflecting new fashion preferences, and personal shoppers—also visiting the store in VR—help me pair different pieces as I go.

Once my personal shopper has finished constructing various outfits, I then sit back and watch a fashion show of countless Peter avatars with style and color variations of my selection, each customizable.

After I’ve made my selection, I might choose to purchase physical versions of three outfits and virtual versions of two others for my digital avatar. Payments are made automatically as I leave the store, including a smart wallet transaction made with the personal shopper at a per-outfit rate (for only the pieces I buy).

Already, several big players have broken into the VR market. Just this year, Walmart has announced its foray into the VR space, shipping 17,000 Oculus Go VR headsets to Walmart locations across the US.

And just this past January, Walmart filed two VR shopping-related patents. In a new bid to disrupt a rapidly changing retail market, Walmart now describes a system in which users couple their VR headset with haptic gloves for an immersive in-store experience, whether at 3am in your living room or during a lunch break at the office.

But Walmart is not alone. Big e-commerce players from Amazon to Alibaba are leaping onto the scene with new software buildout to ride the impending headset revolution.

Beyond virtual reality, players like IKEA have even begun using mobile-based augmented reality to map digitally replicated furniture in your physical living room, true to dimension. And this is just the beginning….

As AR headset hardware undergoes breakneck advancements in the next two to five years, we might soon be able to project watches onto our wrists, swapping out colors, styles, brand, and price points.

Or let’s say I need a new coffee table in my office. Pulling up multiple models in AR, I can position each option using advanced hand-tracking technology and customize height and width according to my needs. Once the smart payment is triggered, the manufacturer prints my newly-customized piece, droning it to my doorstep. As soon as I need to assemble the pieces, overlaid digital prompts walk me through each step, and any user confusions are communicated to a company database.

Perhaps one of the ripest industries for Spatial Web disruption, retail presents one of the greatest opportunities for profit across virtual apparel, digital malls, AI fashion startups and beyond.

In our next series iteration, I’ll be looking at the tremendous opportunities created by Web 3.0 for the Future of Work and Entertainment.

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Posted in Human Robots

#433884 Designer Babies, and Their Babies: How ...

As if stand-alone technologies weren’t advancing fast enough, we’re in age where we must study the intersection points of these technologies. How is what’s happening in robotics influenced by what’s happening in 3D printing? What could be made possible by applying the latest advances in quantum computing to nanotechnology?

Along these lines, one crucial tech intersection is that of artificial intelligence and genomics. Each field is seeing constant progress, but Jamie Metzl believes it’s their convergence that will really push us into uncharted territory, beyond even what we’ve imagined in science fiction. “There’s going to be this push and pull, this competition between the reality of our biology with its built-in limitations and the scope of our aspirations,” he said.

Metzl is a senior fellow at the Atlantic Council and author of the upcoming book Hacking Darwin: Genetic Engineering and the Future of Humanity. At Singularity University’s Exponential Medicine conference last week, he shared his insights on genomics and AI, and where their convergence could take us.

Life As We Know It
Metzl explained how genomics as a field evolved slowly—and then quickly. In 1953, James Watson and Francis Crick identified the double helix structure of DNA, and realized that the order of the base pairs held a treasure trove of genetic information. There was such a thing as a book of life, and we’d found it.

In 2003, when the Human Genome Project was completed (after 13 years and $2.7 billion), we learned the order of the genome’s 3 billion base pairs, and the location of specific genes on our chromosomes. Not only did a book of life exist, we figured out how to read it.

Jamie Metzl at Exponential Medicine
Fifteen years after that, it’s 2018 and precision gene editing in plants, animals, and humans is changing everything, and quickly pushing us into an entirely new frontier. Forget reading the book of life—we’re now learning how to write it.

“Readable, writable, and hackable, what’s clear is that human beings are recognizing that we are another form of information technology, and just like our IT has entered this exponential curve of discovery, we will have that with ourselves,” Metzl said. “And it’s intersecting with the AI revolution.”

Learning About Life Meets Machine Learning
In 2016, DeepMind’s AlphaGo program outsmarted the world’s top Go player. In 2017 AlphaGo Zero was created: unlike AlphaGo, AlphaGo Zero wasn’t trained using previous human games of Go, but was simply given the rules of Go—and in four days it defeated the AlphaGo program.

Our own biology is, of course, vastly more complex than the game of Go, and that, Metzl said, is our starting point. “The system of our own biology that we are trying to understand is massively, but very importantly not infinitely, complex,” he added.

Getting a standardized set of rules for our biology—and, eventually, maybe even outsmarting our biology—will require genomic data. Lots of it.

Multiple countries already starting to produce this data. The UK’s National Health Service recently announced a plan to sequence the genomes of five million Britons over the next five years. In the US the All of Us Research Program will sequence a million Americans. China is the most aggressive in sequencing its population, with a goal of sequencing half of all newborns by 2020.

“We’re going to get these massive pools of sequenced genomic data,” Metzl said. “The real gold will come from comparing people’s sequenced genomes to their electronic health records, and ultimately their life records.” Getting people comfortable with allowing open access to their data will be another matter; Metzl mentioned that Luna DNA and others have strategies to help people get comfortable with giving consent to their private information. But this is where China’s lack of privacy protection could end up being a significant advantage.

To compare genotypes and phenotypes at scale—first millions, then hundreds of millions, then eventually billions, Metzl said—we’re going to need AI and big data analytic tools, and algorithms far beyond what we have now. These tools will let us move from precision medicine to predictive medicine, knowing precisely when and where different diseases are going to occur and shutting them down before they start.

But, Metzl said, “As we unlock the genetics of ourselves, it’s not going to be about just healthcare. It’s ultimately going to be about who and what we are as humans. It’s going to be about identity.”

Designer Babies, and Their Babies
In Metzl’s mind, the most serious application of our genomic knowledge will be in embryo selection.

Currently, in-vitro fertilization (IVF) procedures can extract around 15 eggs, fertilize them, then do pre-implantation genetic testing; right now what’s knowable is single-gene mutation diseases and simple traits like hair color and eye color. As we get to the millions and then billions of people with sequences, we’ll have information about how these genetics work, and we’re going to be able to make much more informed choices,” Metzl said.

Imagine going to a fertility clinic in 2023. You give a skin graft or a blood sample, and using in-vitro gametogenesis (IVG)—infertility be damned—your skin or blood cells are induced to become eggs or sperm, which are then combined to create embryos. The dozens or hundreds of embryos created from artificial gametes each have a few cells extracted from them, and these cells are sequenced. The sequences will tell you the likelihood of specific traits and disease states were that embryo to be implanted and taken to full term. “With really anything that has a genetic foundation, we’ll be able to predict with increasing levels of accuracy how that potential child will be realized as a human being,” Metzl said.

This, he added, could lead to some wild and frightening possibilities: if you have 1,000 eggs and you pick one based on its optimal genetic sequence, you could then mate your embryo with somebody else who has done the same thing in a different genetic line. “Your five-day-old embryo and their five-day-old embryo could have a child using the same IVG process,” Metzl said. “Then that child could have a child with another five-day-old embryo from another genetic line, and you could go on and on down the line.”

Sounds insane, right? But wait, there’s more: as Jason Pontin reported earlier this year in Wired, “Gene-editing technologies such as Crispr-Cas9 would make it relatively easy to repair, add, or remove genes during the IVG process, eliminating diseases or conferring advantages that would ripple through a child’s genome. This all may sound like science fiction, but to those following the research, the combination of IVG and gene editing appears highly likely, if not inevitable.”

From Crazy to Commonplace?
It’s a slippery slope from gene editing and embryo-mating to a dystopian race to build the most perfect humans possible. If somebody’s investing so much time and energy in selecting their embryo, Metzl asked, how will they think about the mating choices of their children? IVG could quickly leave the realm of healthcare and enter that of evolution.

“We all need to be part of an inclusive, integrated, global dialogue on the future of our species,” Metzl said. “Healthcare professionals are essential nodes in this.” Not least among this dialogue should be the question of access to tech like IVG; are there steps we can take to keep it from becoming a tool for a wealthy minority, and thereby perpetuating inequality and further polarizing societies?

As Pontin points out, at its inception 40 years ago IVF also sparked fear, confusion, and resistance—and now it’s as normal and common as could be, with millions of healthy babies conceived using the technology.

The disruption that genomics, AI, and IVG will bring to reproduction could follow a similar story cycle—if we’re smart about it. As Metzl put it, “This must be regulated, because it is life.”

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Posted in Human Robots

#433799 The First Novel Written by AI Is ...

Last year, a novelist went on a road trip across the USA. The trip was an attempt to emulate Jack Kerouac—to go out on the road and find something essential to write about in the experience. There is, however, a key difference between this writer and anyone else talking your ear off in the bar. This writer is just a microphone, a GPS, and a camera hooked up to a laptop and a whole bunch of linear algebra.

People who are optimistic that artificial intelligence and machine learning won’t put us all out of a job say that human ingenuity and creativity will be difficult to imitate. The classic argument is that, just as machines freed us from repetitive manual tasks, machine learning will free us from repetitive intellectual tasks.

This leaves us free to spend more time on the rewarding aspects of our work, pursuing creative hobbies, spending time with loved ones, and generally being human.

In this worldview, creative works like a great novel or symphony, and the emotions they evoke, cannot be reduced to lines of code. Humans retain a dimension of superiority over algorithms.

But is creativity a fundamentally human phenomenon? Or can it be learned by machines?

And if they learn to understand us better than we understand ourselves, could the great AI novel—tailored, of course, to your own predispositions in fiction—be the best you’ll ever read?

Maybe Not a Beach Read
This is the futurist’s view, of course. The reality, as the jury-rigged contraption in Ross Goodwin’s Cadillac for that road trip can attest, is some way off.

“This is very much an imperfect document, a rapid prototyping project. The output isn’t perfect. I don’t think it’s a human novel, or anywhere near it,” Goodwin said of the novel that his machine created. 1 The Road is currently marketed as the first novel written by AI.

Once the neural network has been trained, it can generate any length of text that the author desires, either at random or working from a specific seed word or phrase. Goodwin used the sights and sounds of the road trip to provide these seeds: the novel is written one sentence at a time, based on images, locations, dialogue from the microphone, and even the computer’s own internal clock.

The results are… mixed.

The novel begins suitably enough, quoting the time: “It was nine seventeen in the morning, and the house was heavy.” Descriptions of locations begin according to the Foursquare dataset fed into the algorithm, but rapidly veer off into the weeds, becoming surreal. While experimentation in literature is a wonderful thing, repeatedly quoting longitude and latitude coordinates verbatim is unlikely to win anyone the Booker Prize.

Data In, Art Out?
Neural networks as creative agents have some advantages. They excel at being trained on large datasets, identifying the patterns in those datasets, and producing output that follows those same rules. Music inspired by or written by AI has become a growing subgenre—there’s even a pop album by human-machine collaborators called the Songularity.

A neural network can “listen to” all of Bach and Mozart in hours, and train itself on the works of Shakespeare to produce passable pseudo-Bard. The idea of artificial creativity has become so widespread that there’s even a meme format about forcibly training neural network ‘bots’ on human writing samples, with hilarious consequences—although the best joke was undoubtedly human in origin.

The AI that roamed from New York to New Orleans was an LSTM (long short-term memory) neural net. By default, information contained in individual neurons is preserved, and only small parts can be “forgotten” or “learned” in an individual timestep, rather than neurons being entirely overwritten.

The LSTM architecture performs better than previous recurrent neural networks at tasks such as handwriting and speech recognition. The neural net—and its programmer—looked further in search of literary influences, ingesting 60 million words (360 MB) of raw literature according to Goodwin’s recipe: one third poetry, one third science fiction, and one third “bleak” literature.

In this way, Goodwin has some creative control over the project; the source material influences the machine’s vocabulary and sentence structuring, and hence the tone of the piece.

The Thoughts Beneath the Words
The problem with artificially intelligent novelists is the same problem with conversational artificial intelligence that computer scientists have been trying to solve from Turing’s day. The machines can understand and reproduce complex patterns increasingly better than humans can, but they have no understanding of what these patterns mean.

Goodwin’s neural network spits out sentences one letter at a time, on a tiny printer hooked up to the laptop. Statistical associations such as those tracked by neural nets can form words from letters, and sentences from words, but they know nothing of character or plot.

When talking to a chatbot, the code has no real understanding of what’s been said before, and there is no dataset large enough to train it through all of the billions of possible conversations.

Unless restricted to a predetermined set of options, it loses the thread of the conversation after a reply or two. In a similar way, the creative neural nets have no real grasp of what they’re writing, and no way to produce anything with any overarching coherence or narrative.

Goodwin’s experiment is an attempt to add some coherent backbone to the AI “novel” by repeatedly grounding it with stimuli from the cameras or microphones—the thematic links and narrative provided by the American landscape the neural network drives through.

Goodwin feels that this approach (the car itself moving through the landscape, as if a character) borrows some continuity and coherence from the journey itself. “Coherent prose is the holy grail of natural-language generation—feeling that I had somehow solved a small part of the problem was exhilarating. And I do think it makes a point about language in time that’s unexpected and interesting.”

AI Is Still No Kerouac
A coherent tone and semantic “style” might be enough to produce some vaguely-convincing teenage poetry, as Google did, and experimental fiction that uses neural networks can have intriguing results. But wading through the surreal AI prose of this era, searching for some meaning or motif beyond novelty value, can be a frustrating experience.

Maybe machines can learn the complexities of the human heart and brain, or how to write evocative or entertaining prose. But they’re a long way off, and somehow “more layers!” or a bigger corpus of data doesn’t feel like enough to bridge that gulf.

Real attempts by machines to write fiction have so far been broadly incoherent, but with flashes of poetry—dreamlike, hallucinatory ramblings.

Neural networks might not be capable of writing intricately-plotted works with charm and wit, like Dickens or Dostoevsky, but there’s still an eeriness to trying to decipher the surreal, Finnegans’ Wake mish-mash.

You might see, in the odd line, the flickering ghost of something like consciousness, a deeper understanding. Or you might just see fragments of meaning thrown into a neural network blender, full of hype and fury, obeying rules in an occasionally striking way, but ultimately signifying nothing. In that sense, at least, the RNN’s grappling with metaphor feels like a metaphor for the hype surrounding the latest AI summer as a whole.

Or, as the human author of On The Road put it: “You guys are going somewhere or just going?”

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Posted in Human Robots

#433594 Technology and Compassion: A ...

From how we get around to how we spend our time to how we manage our health, technology is changing our lives—not to mention economies, governments, and cities around the world. Tech has brought good to individuals and societies by, for example, democratizing access to information and lowering the cost of many products and services. But it’s also brought less-desirable effects we can’t ignore, like a rise in mental health problems and greater wealth inequality.

To keep pushing tech in a direction that will benefit humanity as a whole—rather than benefiting a select few—we must encourage open dialogues about these topics among leading figures in business, government, and spirituality.

To that end, SingularityU The Netherlands recently hosted a dialogue about compassion and technology with His Holiness the Dalai Lama. The event was attended by students and tech innovators, ambassadors, members of the Dutch royal family, and other political and business leaders.

The first half of the conversation focused on robotics, telepresence, and artificial intelligence. His Holiness spoke with Tilly Lockey, a British student helping tech companies create bionic limbs, Karen Dolva, CEO of telepresence company No Isolation, and Maarten Steinbuch, faculty chair of robotics at SingularityU the Netherlands and a professor of systems and control at TU Eindhoven.

When asked what big tech companies could be doing to help spread good around the world, His Holiness pointed out that while technology has changed many aspects of life in developed countries, there is still immense suffering in less-developed nations, and tech companies should pay more attention to the poorer communities around the world.

In the second half of the event, focus switched to sickness, aging, and death. Speakers included Liz Parrish, CEO of BioViva Sciences, Kris Verburgh, faculty chair of health and medicine at SingularityU the Netherlands, Jeantine Lunshof, a bio-ethicist at MIT Media Lab, and Selma Boulmalf, a religious studies student at University of Amsterdam. Among other topics, they talked with His Holiness about longevity research and the drawbacks of trying to extend our lifespans or achieve immortality.

Both sessions were moderated by Christa Meindersma, founder and chair of the Himalaya Initiative for Culture and Society. The event served as the ceremonial opening of an exhibition called The Life of the Buddha, Path to the Present, on display in Amsterdam’s 15-century De Nieuwe Kerk church through February 2019.

In the 21st century, His Holiness said, “There is real possibility to create a happier world, peaceful world. So now we need vision. A peaceful world on the basis of a sense of oneness of humanity.”

Technology’s role in that world is being developed and refined every day, and we must maintain an ongoing awareness of its positive and negative repercussions—on everyone.

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Posted in Human Robots

#431159 How Close Is Turing’s Dream of ...

The quest for conversational artificial intelligence has been a long one.
When Alan Turing, the father of modern computing, racked his considerable brains for a test that would truly indicate that a computer program was intelligent, he landed on this area. If a computer could convince a panel of human judges that they were talking to a human—if it could hold a convincing conversation—then it would indicate that artificial intelligence had advanced to the point where it was indistinguishable from human intelligence.
This gauntlet was thrown down in 1950 and, so far, no computer program has managed to pass the Turing test.
There have been some very notable failures, however: Joseph Weizenbaum, as early as 1966—when computers were still programmed with large punch-cards—developed a piece of natural language processing software called ELIZA. ELIZA was a machine intended to respond to human conversation by pretending to be a psychotherapist; you can still talk to her today.
Talking to ELIZA is a little strange. She’ll often rephrase things you’ve said back at you: so, for example, if you say “I’m feeling depressed,” she might say “Did you come to me because you are feeling depressed?” When she’s unsure about what you’ve said, ELIZA will usually respond with “I see,” or perhaps “Tell me more.”
For the first few lines of dialogue, especially if you treat her as your therapist, ELIZA can be convincingly human. This was something Weizenbaum noticed and was slightly alarmed by: people were willing to treat the algorithm as more human than it really was. Before long, even though some of the test subjects knew ELIZA was just a machine, they were opening up with some of their deepest feelings and secrets. They were pouring out their hearts to a machine. When Weizenbaum’s secretary spoke to ELIZA, even though she knew it was a fairly simple computer program, she still insisted Weizenbaum leave the room.
Part of the unexpected reaction ELIZA generated may be because people are more willing to open up to a machine, feeling they won’t be judged, even if the machine is ultimately powerless to do or say anything to really help. The ELIZA effect was named for this computer program: the tendency of humans to anthropomorphize machines, or think of them as human.

Weizenbaum himself, who later became deeply suspicious of the influence of computers and artificial intelligence in human life, was astonished that people were so willing to believe his script was human. He wrote, “I had not realized…that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people.”

“Consciously, you know you’re talking to a big block of code stored somewhere out there in the ether. But subconsciously, you might feel like you’re interacting with a human.”

The ELIZA effect may have disturbed Weizenbaum, but it has intrigued and fascinated others for decades. Perhaps you’ve noticed it in yourself, when talking to an AI like Siri, Alexa, or Google Assistant—the occasional response can seem almost too real. Consciously, you know you’re talking to a big block of code stored somewhere out there in the ether. But subconsciously, you might feel like you’re interacting with a human.
Yet the ELIZA effect, as enticing as it is, has proved a source of frustration for people who are trying to create conversational machines. Natural language processing has proceeded in leaps and bounds since the 1960s. Now you can find friendly chatbots like Mitsuku—which has frequently won the Loebner Prize, awarded to the machines that come closest to passing the Turing test—that aim to have a response to everything you might say.
In the commercial sphere, Facebook has opened up its Messenger program and provided software for people and companies to design their own chatbots. The idea is simple: why have an app for, say, ordering pizza when you can just chatter to a robot through your favorite messenger app and make the order in natural language, as if you were telling your friend to get it for you?
Startups like Semantic Machines hope their AI assistant will be able to interact with you just like a secretary or PA would, but with an unparalleled ability to retrieve information from the internet. They may soon be there.
But people who engineer chatbots—both in the social and commercial realm—encounter a common problem: the users, perhaps subconsciously, assume the chatbots are human and become disappointed when they’re not able to have a normal conversation. Frustration with miscommunication can often stem from raised initial expectations.
So far, no machine has really been able to crack the problem of context retention—understanding what’s been said before, referring back to it, and crafting responses based on the point the conversation has reached. Even Mitsuku will often struggle to remember the topic of conversation beyond a few lines of dialogue.

“For everything you say, there could be hundreds of responses that would make sense. When you travel a layer deeper into the conversation, those factors multiply until you end up with vast numbers of potential conversations.”

This is, of course, understandable. Conversation can be almost unimaginably complex. For everything you say, there could be hundreds of responses that would make sense. When you travel a layer deeper into the conversation, those factors multiply until—like possible games of Go or chess—you end up with vast numbers of potential conversations.
But that hasn’t deterred people from trying, most recently, tech giant Amazon, in an effort to make their AI voice assistant, Alexa, friendlier. They have been running the Alexa Prize competition, which offers a cool $500,000 to the winning AI—and a bonus of a million dollars to any team that can create a ‘socialbot’ capable of sustaining a conversation with human users for 20 minutes on a variety of themes.
Topics Alexa likes to chat about include science and technology, politics, sports, and celebrity gossip. The finalists were recently announced: chatbots from universities in Prague, Edinburgh, and Seattle. Finalists were chosen according to the ratings from Alexa users, who could trigger the socialbots into conversation by saying “Hey Alexa, let’s chat,” although the reviews for the socialbots weren’t always complimentary.
By narrowing down the fields of conversation to a specific range of topics, the Alexa Prize has cleverly started to get around the problem of context—just as commercially available chatbots hope to do. It’s much easier to model an interaction that goes a few layers into the conversational topic if you’re limiting those topics to a specific field.
Developing a machine that can hold almost any conversation with a human interlocutor convincingly might be difficult. It might even be a problem that requires artificial general intelligence to truly solve, rather than the previously-employed approaches of scripted answers or neural networks that associate inputs with responses.
But a machine that can have meaningful interactions that people might value and enjoy could be just around the corner. The Alexa Prize winner is announced in November. The ELIZA effect might mean we will relate to machines sooner than we’d thought.
So, go well, little socialbots. If you ever want to discuss the weather or what the world will be like once you guys take over, I’ll be around. Just don’t start a therapy session.
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