Blog posts tagged as 'basaap'

Preface

This is a blog post about a large design research project we completed in 2011 in close partnership with Google Creative Lab.

There wasn’t an opportunity for publication at the time, but it represented a large proportion of the studio’s efforts for that period – nearly everyone in the studio was involved at some point – so we’ve decided to document the work and its context here a year on.

I’m still really proud of it, and some of the end results the team produced are both thought-provoking and gorgeous.

We’ve been wanting to share it for a while.

It’s a long post covering a lot of different ideas, influences, side-projects and outputs, so I’ve broken it up into chapters… but I recommend you begin at the beginning…

• Introduction
• Material Exploration
• Influences and Starting Points
• The $700 Lightbulb
• “Lamps that see”
• Sketching in Video, Code and Hardware
• Little Brain, Big Brain
• Rules for Smart Light
• Smart Light, Dumb Products
• What happened next: Universe-B
• Postscript: the scenius of Lamps

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Introduction

 

At the beginning of 2011 we started a wide-ranging conversation with Google Creative Lab, around near-future experiences of Google and its products.

During our discussions with them, a strong theme emerged. We were both curious about how it would feel to have Google in the world with us, rather than on a screen.

If Google wasn’t trapped behind glass, what would it do?

What would it behave like?

How would we react to it?

This traces back to our studio’s long preoccupation with embodied interaction. Also, our explorations of the technologies of computer vision and projection that we’ve talked about previously under the banner of the “Robot-Readable World”.

Our project through the spring and summer of 2011 concentrated on making evidence around this – investigating computer vision and projection as ‘material’ for designing with, in partnership with Google Creative Lab.

Material Exploration

 

We find that treating ‘immaterial’ new technologies as if they were physical materials is useful in finding rules-of-thumb and exploring opportunities in their “grain”. We try as a studio to pursue this approach as much as someone trying to craft something from wood, stone, or metal.

We looked at computer-vision and projection in a close relationship – almost as one ‘material’.

That material being a bound-together expression of the computer’s understanding of the world around it and its agency or influence in that environment.



Influences and starting points

 

One of the very early departure points for our thinking was a quote by (then-)Google’s Marissa Meyer at the Le Web conference in late 2010: “We’re trying to build a virtual mirror of the world at all times”

This quote struck a particular chord for me, reminding me greatly of the central premise of David Gelernter‘s 1991 book “Mirror Worlds“.

I read “Mirror Worlds” while I was in architecture school in the 90s. Gelernter’s vision of shared social simulations based on real-world sensors, information feeds and software bots still seems incredibly prescient 20 years later.

Gelernter saw the power to simply build sophisticated, shared models of reality that all could see, use and improve as a potentially revolutionary technology.

What if Google’s mirror world were something out in the real world with us, that we could see, touch and play with together?

Seymour Papert – another incredibly influential computer science and education academic – also came to our minds. Not only did he maintain similar views about the importance of sharing and constructing our own models of reality, but was also a pioneer of computer vision. in 1966 he sent the ‘Summer Vision Memo‘ “Spend the summer linking a camera to a computer, and getting the computer to describe what it saw…”


Nearly fifty years on, we have Kinects in our houses, internet-connected face-tracking cameras in our pockets and ‘getting the computer to describe (or at least react to) what it saw seems to be one of the most successful component tracks of the long quest for ‘artificial intelligence’.

Our thinking and discussion continued this line toward the cheapness and ubiquity of computer vision.

The $700 Lightbulb

 

Early on, Jack invoked the metaphor of a “$700 lightbulb”:

Lightbulbs and electric light went from a scientific curiosity to a cheap, accessible ubiquity in the late 19th and early 20th century.

What if lightbulbs were still $700?

We’d carry one around carefully in a case and screw it in when/where we needed light. They are not, so we leave them screwed in wherever we want, and just flip the switch when we need light. Connected computers with eyes cost $500, and so we carry them around in our pockets.

But – what if we had lots of cheap computer vision, processing, connectivity and display all around our environments – like light bulbs?

Ubiquitous Computing has of course been a long held vision in academia, which in some ways has been derailed by the popularity of the smartphone

But smartphones are getting cheaper, Android is embedding itself in new contexts, with other I/Os than a touchscreen, and increasingly we keep our data in the cloud rather than in dedicated devices at the edge.

Ubiquitous computing has been seen by many as in the past as a future of cheap, plentiful ‘throw-away’ I/O clients to the cloud.

It seems like we’re nearly there.

In 2003, I remember being captivated by Neil Gershenfeld’s vision of computing that you could ‘paint’ onto any surface:

“a paintable computer, a viscous medium with tiny silicon fragments that makes a pour-out computer, and if it’s not fast enough or doesn’t store enough, you put another few pounds or paint out another few square inches of computing.”

Updating this to the present-day, post web2.0 world – where if ‘it’s not fast enough or doesn’t store enough’ we request more resources from centralised, elastic compute-clouds.

“Clouds” that can see our context, our environment through sensors and computer vision, and have a picture of us built up through our continued interactions with it to deliver appropriate information on-demand.

To this we added speculation that not only computer-vision would be cheap and ubiquitous, but excellent quality projection would become as cheap and widespread as touch screens in the near-future.

This would mean that the cloud could act in the world with us, come out from behind the glass and relate what it sees to what we see.

In summary: computer vision, depth-sensing and projection can be combined as materials – so how can we use them to make Google services bubble through from the Mirror World into your lap?

How would that feel? How should it feel?

This is the question we took as our platform for design exploration.

“Lamps that see”

 

One of our first departure points was to fuse computer-vision and projection into one device – a lamp that saw.

Here’s a really early sketch of mine where we see a number of domestic lamps, that saw and understood their context, projecting and illuminating the surfaces around them with information and media in response.

We imagined that the type of lamp would inform the lamp’s behaviour – more static table lamps might be less curious or more introverted than a angle-poise for instance.

Jack took the idea of the angle-poise lamp on, thinking about how servo-motors might allow the lamp to move around within the degrees of freedom its arm gives it on a desk to inquire about its context with computer vision, track objects and people, and surfaces that it can ‘speak’ onto with projected light.





Of course, in the back of our minds was the awesome potential for injecting character and playfulness into the angle-poise as an object – familiar to all from the iconic Pixar animation Luxo Jr.


And very recently, students from the University of Wellington in New Zealand created something very similar at first glance, although the projection aspect is missing here.

Alongside these sketching activities around proposed form and behaviour we started to pursue material exploration.

Sketching in Video, Code & Hardware

 

We’d been keenly following work by friends such as James George and Greg Borenstein in the space of projection and computer vision, and a number of projects in the domain emerged during the course of the project, but we wanted to understand it as ‘a material to design with’ from first principles.

Timo, Jack, Joe and Nick – with Chris Lauritzen (then of Google Creative Lab), and Elliot Woods of Kimchi & Chips, started a series of tests to investigate both the interactive and aesthetic qualities of the combination of projection and computervision – which we started to call “Smart Light” internally.

First of all, the team looked at the different qualities of projected light on materials, and in the world.

This took the form or a series of very quick experiments, looking for different ways in which light could act in inhabited spaces, on surfaces, interact with people and things.

In a lot of these ‘video sketches’ there was little technology beyond the projector and photoshop being used – but it enabled us to imagine what a computer-vision directed ‘smart light’ might behave like, look like and feel like at human scale very quickly.

Here are a few example video sketches from that phase of the work:

Sketch 04 Sticky search from BERG on Vimeo.



Sketch 06: Interfaces on things from BERG on Vimeo.

One particularly compelling video sketch projected an image of a piece of media (in this case a copy of Wired magazine) back onto the media – the interaction and interference of one with the other is spellbinding at close-quarters, and we thought it could be used to great effect to direct the eye as part of an interaction.



Sketch 09: Media on media from BERG on Vimeo.

Alongside these aesthetic investigations, there were technical explorations for instance, into using “structured light” techniques with a projector to establish a depth map of a scene…



Sketch 13: Structured light from BERG on Vimeo.

Quickly, the team reached a point where more technical exploration was necessary and built a test-rig that could be used to prototype a “Smart Light Lamp” comprising a projector, a HD webcam, a PrimeSense / Asus depth camera and bespoke software.

At the time of the project the Kinect SDK now ubiquitous in computer vision projects was not officially available. The team plumped for the component approach over the integration of the Kinect for a number of reasons, including wanting the possibility of using HD video in capture and projection.

Testing the Lamps rig from BERG on Vimeo.

Nick recalls:

Actually by that stage the OpenNI libraries were out (http://openni.org/), but the “official” Microsoft SDK wasn’t out (http://www.microsoft.com/en-us/kinectforwindows/develop/developer-downloads.aspx). The OpenNI libraries were more focussed on skeletal tracking, and were difficult to get up and running.

Since we didn’t have much need for skeletal tracking in this project, we used very low-level access to the IR camera and depth sensor facilitated by various openFrameworks plugins. This approach gave us the correct correlation of 3D position, high definition colour image, and light projection to allow us to experiment with end-user applications in a unified, calibrated 3D space.

The proto rig became a great test bed for us to start to explore high-level behaviours of Smart Light – rules for interaction, animation and – for want of a better term – ‘personality’.

Little Brain, Big Brain

 

One of our favourite things of the last few years is Sticky Light.


It’s a great illustration of how little a system needs to do, for us to ascribe personality to its behaviour.

We imagined that the Smart Light Lamp might manifest itself as a companion species in the physical world, a creature that could act as a go-between for you and the mirror-worlds of the digital.

We’ve written about digital companion species before: when our digital tools become more than just tools – acquiring their own behaviour, personality and agency.


You might recall Bit from the original Tron movie, or the Daemons from the Philip Pullman “His Dark Materials” trilogy. Companions that are “on your side” but have abilities and senses that extend you.

We wanted the Lamp to act as companion species for the mirror-worlds of data that we all live within, and Google has set out to organise.

We wanted the lamp to act as a companion species that illustrated – through its behaviour – the powers of perception that Google has through computer vision, context-sensing and machine-learning.

Having a companion species that is a native of the cloud, but on your side, could make evident the vast power of such technologies in an intuitive and understandable way.

Long-running themes of the studio’s work are at play here – beautiful seams, shelf-evidence, digital companion species and BASAAP – which we tried to sum up in our Gardens and Zoos talk/blog post , which in turn was informed by the work we’d done in the studios on Lamps.

One phrase that came up again and again around this areas of the lamps behaviour was “Big Brain, Little Brain” i.e. the Smart Light companion would be the Little Brain, on your side, that understood you and the world immediately around you, and talked on your behalf to the Big Brain in ‘the cloud’.

This intentional division, this hopefully ‘beautiful seam’ would serve to emphasise your control over what you let the the Big Brain know in return for its knowledge and perspective, and also make evident the sense (or nonsense) that the Little Brain makes of your world before it communicates that to anyone else.

One illustration we made of this is the following sketch of a ‘Text Camera’

Text Camera from BERG on Vimeo.

Text Camera is about making the inputs and inferences the phone sees around it to ask a series of friendly questions that help to make clearer what it can sense and interpret.

It reports back on what it sees in text, rather than through a video. Your smartphone camera has a bunch of software to interpret the light it’s seeing around you – in order to adjust the exposure automatically. So, we look to that and see if it’s reporting ‘tungsten light’ for instance, and can infer from that whether to ask the question “Am I indoors?”.

Through the dialog we feel the seams – the capabilities and affordances of the smartphone, and start to make a model of what it can do.

The Smart Light Companion in the Lamp could similarly create a dialog with its ‘owner’, so that the owner could start to build up a model of what its Little Brain could do, and where it had to refer to the Big Brain in the cloud to get the answers.

All of this serving to playfully, humanely build a literacy in how computer vision, context-sensing and machine learning interpret the world.

Rules for Smart Light

 

The team distilled all of the sketches, code experiments, workshop conversations and model-making into a few rules of thumb for designing with this new material – a platform for further experiments and invention we could use as we tried to imagine products and services that used Smart Light.

Reflecting our explorations, some of the rules-of-thumb are aesthetic, some are about context and behaviour, and some are about the detail of interaction.

24 Rules for smart light from BERG on Vimeo.

We wrote the ‘rules’ initially as a list of patterns that we saw as fruitful in the experiments. Our ambition was to evolve this in the form of a speculative “HIG” or Human Interface Guideline – for an imagined near-future where Smart Light is as ubiquitous as the touchscreen is now…

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Smart Light HIG

1. Projection is three-dimensional. We are used to projection turning a flat ‘screen’ into an image, but there is really a cone of light that intersects with the physical world all the way back to the projector lens. Projection is not the flatland display surfaces that we have become used to through cinema, tv and computers.
2. Projection is additive. Using a projector we can’t help but add light to the world. Projecting black means that a physical surface is unaffected, projecting white means that an object is fully illuminated up to the brightness of the projector.
3. Enchanted objects. Unless an object has been designed with blank spaces for projection, it should not have information projected onto it. Because augmenting objects with information is so problematic (clutter, space, text on text) objects can only be ‘spotlighted’, ‘highlighted’ or have their own image re-projected onto themselves.
4. Light feels touchable (but it’s not). Through phones and pads we are conditioned into interacting with bright surfaces. It feels intuitive to want to touch, grab, slide and scroll projected things around. However, it is difficult to make it interactive.
5. The new rules of depth. A lamp sees the world as a stream of images, but also as a three-dimensional space. There is no consistent interaction surface to interact with like in mouse or touch-based systems, light hits any and all surfaces and making them respond to ‘touch’ is difficult. This is due to finger-based interaction being very difficult to achieve with projection and computer vision. Tracking fingers is technically difficult, fingers are small, there is limited/no existing skeletal recognition software for detecting hands.
6. Smart light should be respectful. Projected light inhabits the world alongside us, it augments and affects the things we use every day. Unlike interfaces that are contained in screens, the boundaries of the lamps vision and projection are much more obscure. Lamps ‘look’ at the world through cameras, which mean that they should be trustworthy companions.

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Next, we started to create some speculative products using these rules, particularly focussed around the idea of “Enchanted Objects”…

Smart Light, Dumb Products

 

These are a set of physical products based on digital media and services such as YouTube watching, Google calendar, music streaming that have no computation or electronics in them at all.

All of the interaction and media is served from a Smart Light Lamp that acts on the product surface to turn it from a block into an ‘enchanted object’.

Joe started with a further investigation of the aesthetic qualities of light on materials.



Projection materials from BERG on Vimeo.

This lead to sketches exploring techniques of projection mapping on desktop scales. It’s something often seen at large scales, manipulating our perceptions of architectural facades with animated projected light, but we wanted to understand how it felt at more intimate human scale of projecting onto everyday objects.

In the final film you might notice some of the lovely effects this can create to attract attention to the surface of the object – guiding perhaps to notifications from a service in the cloud, or alerts in a UI.

Then some sketching in code – using computer vision to create optical switches – that make or break a recognizable optical marker depending on movement. In a final product these markers could be invisible to the human eye but observable by computer vision. Similarly – tracking markers to provide controls for video navigation, calendar alerts etc.



Fiducial switch from BERG on Vimeo.

Joe worked with physical prototypes – first simple nets in card and then more finished models to uncover some of the challenges of form in relation to angles of projection and computer vision.



For instance in the Video object, a pulley system has to connect the dial the user operates to the marker that the Lamp sees, so that it’s not obscured from the computer vision software.

Here’s the final output from these experiments:

Dumb things, smart light from BERG on Vimeo.

This sub-project was a fascinating test of our Smart Light HIG – which lead to more questions and opportunities.

For instance, one might imagine that the physical product – as well as housing dedicated and useful controls for the service it is matched to – could act as a ‘key’ to be recognised by computer vision to allow access to the service.

What if subscriptions to digital services were sold as beautiful robot-readable objects, each carved at point-of-purchase with a wonderful individually-generated pattern to unlock access?

What happened next: Universe-B

 

From the distance of a year since we finished this work, it’s interesting to compare its outlook to that of the much-more ambitious and fully-realised Google Glass project that was unveiled this summer.

Google Glass inherits a vision of ubiquitous computing that has been strived after for decades.

As a technical challenge it’s been one that academics and engineers in industry have failed to make compelling to the general populace. The Google team’s achievement in realising this vision is undoubtedly impressive. I can’t wait to try them! (hint, hint!)

It’s also a vision that is personal and, one might argue, introverted – where the Big Brain is looking at the same things as you and trying to understand them, but the results are personal, never shared with the people you are with. The result could be an incredibly powerful, but subjective overlay on the world.

In other words, the mirrorworld has a population of 1. You.

Lamps uses similar techniques of computer vision, context-sensing and machine learning but its display is in the world, the cloud is painted on the world. In the words of William Gibson, the mirrorworld is becoming part of our world – everted into the spaces we live in.

The mirrorworld is shared with you, and those you are with.

This brings with it advantages (collaboration, evidence) and disadvantages (privacy, physical constraints) – but perhaps consider it as a complementary alternative future… A Universe-B where Google broke out of the glass.

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Postscript: the scenius of Lamps

 

No design happens in a vacuum, and culture has a way of bubbling up a lot of similar things all at the same time. While not an exhaustive list, we want to acknowledge that! Some of these projects are precedent to our work, and some emerged in the nine months of the project or since.

• Steve Mann’s Telepointer project (1998) http://en.wikipedia.org/wiki/Telepointer
• Moveable Projected Displays using Projector Based Tracking (2005-2007) by Jonny Chung Lee, Scott E. Hudson, Jay W. Summet and Paul H. Dietz
• Microsoft research: 6th sense (2009)
• Jamie Tunnard’s anglepoise projector (2010) http://www.hhc.rca.ac.uk/3147/all/1/Desklampprojector.aspx
• Omnitouch by Chris Harrison, Hrvoje Benko and Andrew D. Wilson (2011)
• Camara-Lucida by Christian Parsons and Mariano Tepper (2011)
• University of Tokyo: “Invoked Computing” (2011)
• Pico projection of interfaces onto surfaces from Darmstadt University (2012)
• SketchSynth by Billy Keyes of CMU (2012)
  

Here are a selection of less-academic projects using projection and computer-vision that Joe picked out from the last year or so:

• Display Cabinet by Ben Bashford : http://journal.benbashford.com/post/4530562245
  
• TSUMIKI by PPP : http://vimeo.com/31836098
• Evolution of Mobile by VodaphoneUK : http://www.youtube.com/watch?v=883UlMNOuX4
• Receipt racer by undef and Joshua Noble : http://vimeo.com/24987120
• Real-life Minecraft Block by Ben Purdy : http://vimeo.com/28124800
• SHOWROOM RICHTI-AREAL by PROJEKTIL : http://vimeo.com/22946428
• Playstation 3 Promos : http://www.youtube.com/watch?v=VrgWH1KUDt4, http://www.youtube.com/watch?v=N_cKsOe7hLI

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Huge thanks to Tom Uglow, Sara Rowghani, Chris Lauritzen, Ben Malbon, Chris Wiggins, Robert Wong, Andy Berndt and all those we worked with at Google Creative Lab for their collaboration and support throughout the project.

Will Wiles, the Deputy Editor of the design magazine Icon, asked us recently to contribute to a special issue on Mobiles Phones alongside James Bridle, Kazys Varnelis, Marko Ahtisaari and Will Self, among others.

I wrote a short piece on smartphones as ‘companion species’, that reflects a lot of ongoing themes and discussions in the studio around designing the behaviour of sensate devices with ‘fractional intelligence‘.

They see the world differently to us, picking up on things we miss.

They adapt to us, our routines. They look to us for attention, guidance and sustenance. We imagine what they are thinking, and vice-versa.

Dogs? Or smartphones?

Mobile devices (can we still call them phones?) are being packed full of sensors, processing power. They are animated by ever-more-sophisticated software, dedicated to understanding the world around them (in terms of advances in computer vision and context-awareness) and understanding us (speech recognition and adaptive ‘agent’ software such as Apple’s ‘Siri’)

They are moving – somewhat awkwardly – from being our tools to becoming our newest companion species.

Donna Haraway, theorist on our transformation into cyborgs, published ‘The Companion Species Manifesto’ in 2003. It addresses the relationship between domestic dogs and humans, but there is much in there to inspire designers of smartphones, apps and agents.

“Cyborgs and companion species each bring together the human and non-human, the organic and technological, carbon and silicon, freedom and structure, history and myth, the rich and the poor, the state and the subject, diversity and depletion, modernity and postmodernity, and nature and culture in unexpected ways.”

Using inspirations from theory such as Haraway, and fiction – such as Philip Pullman’s ‘Daemons’ from his ‘Dark Materials’ books – we can perhaps imagine a near-future that is richer and weirder than the current share-everything-all-the-time/total-gamified-personal-productivity obsessions of silicon valley.

A future of digital daemons would be one of close relationships with software that learned and acted intuitively – perhaps inscrutably at first, but with a maxim of ‘do no harm, with maximum charm’.

Intel’s Genevieve Bell recently spoke of the importance of designing relationships with – and crucially, between our technologies – so that we not in the centre of an arms-race of ever-more-complex 1-to-1 interactions with our phones, tablets and apps. She memorably quoted a research subject that likened her collection of digital devices to a ‘needy backpack of baby birds’

Much better to have one faithful, puppy-smart daemon device, working at our side to round everything (and every thing) up and relate what it senses to us?

At BERG we are fond of quoting MIT roboticist Rodney Brooks – who said that fifty years of sustained work by the brightest and the best in artificial intelligence would get us things that were ‘smart as puppies’ if we’re lucky.

This seems like a fine goal to us, rather than creating uncanny, flawed and frustrating analogues of human intelligence and interactions – such as Siri, or if we cast our minds back a decade – Microsoft’s ‘Clippy’.

This future might also free the form of our devices – from glowing rectangles that suck our attention from the world, to subtler physical avatars representing our companions – things that listen, watch, speak – to us and for us.

Our companion species as are likely to inhabit the biomimetic descendants of the Nike fuelband or the now-mundane bluetooth headsets as Ive’s perfectionist slabs of glass and alloy.

Also, companion species might be shared, as a family pet is now – bound to home and hearth rather than the predominant 1-to-1 ‘personal computing’ paradigm of the last 40 years or so.

What forms might these ‘household spirits’ take? Nest’s smart thermostat has pursued the Ives/Rams route of tasteful (if ironically, cold) elegance, whereas our own Little Printer takes a rather different approach…

There will be more diverse responses to these new categories of digital/physical extensions to ourselves, our homes, cars and cities. Which is as it should be.

I hope it triggers explosion of form and interaction beyond the glowing touchscreen hegemony. The advent of ‘digital companion species’ should be a cambrian moment for design.

This is a version of a talk that I gave at the “In Progress” event, staged by ‘It’s Nice That‘ magazine.

It builds on some thoughts that I’ve spoken about at some other events in 2011, but I think this version put it forward in the way I’m happiest with.

Having said that, I took the name of the event literally – it’s a bit of a work-in-progress, still.

It might more properly be entitled ‘Pets & Pot-plants’ rather than ‘Gardens & Zoos’ – but the audience seemed to enjoy it, and hopefully it framed some of the things we’re thinking about and discussing in the studio over the last year or so, as we’ve been working on http://bergcloud.com and other projects looking at the near-future of connected products.

And – with that proviso… Here it is.

Let me introduce a few characters…

This is my frying pan. I bought it in Helsinki. It’s very good at making omelettes.

This is Sukie. She’s a pot-plant that we adopted from our friend Heather’s ‘Wayward Plants‘ project, at the Radical Nature exhibit at the Barbican (where “In Progress” is!)

This is a puppy – we’ll call him ‘Bruno’.

I have no idea if that’s his name, but it’s from our friend Matt Cottam’s “Dogs I Meet” flickr set, and Matt’s dog is called Bruno – so it seemed fitting.

And finally, this is Siri – a bot.

And, I’m Matt Jones – a designer and one of the principals at BERG, a design and invention studio.

There are currently 13 of us – half-technologists, half-designers, sharing a room in East London where we invent products for ourselves and for other people – generally large technology and media companies.

This is Availabot, one of the first products that we designed – it’s a small connected product that represents your online status physically…

But I’m going to talk today about the near-future of connected products.

And it is a near-future, not far from the present.

In fact, one of our favourite quotes about the future is from William Burroughs: “When you cut into the present, the future leaks out…“

A place we like to ‘cut into the present’ is the Argos catalogue! Matt Webb’s talked about this before.

It’s really where you see Moore’s Law hit the high-street.

Whether it’s toys, kitchen gear or sports equipment – it’s getting hard to find consumer goods that don’t have software inside them.

This is near-future where the things around us start to display behaviour – acquiring motive and agency as they act and react to the context around them according to the software they have inside them, and increasingly the information they get from (and publish back to) the network.

In this near-future, it’s very hard to identify the ‘U’ in UI’ – that is, the User in User-Interface. It’s not so clear anymore what these things are. Tools… or something more.

Of course, I choose to illustrate this slightly-nuanced point with a video of kittens riding a Roomba that Matt Webb found, so you might not be convinced.

However, this brings us back to our new friends, the Bots.

By bot – I guess I mean a piece of software that displays a behaviour, that has motive and agency.

Let me show a clip about Siri, and how having bots in our lives might affect us [Contains Strong Language!]

Perhaps, like me – you have more sympathy for the non-human in that clip…

But how about some other visions of what it might be like to have non-human companions in our lives? For instance, the ‘daemons’ of Phillip Pullman’s ‘Dark Materials‘ trilogy. They are you, but not you – able to reveal things about you and reveal things to you. Able to interact naturally with you and each other.

Creatures we’ve made that play and explore the world don’t seem that far-fetched anymore. This is a clip of work on juggling robot quadcopters by ETH Zurich.

Which brings me back to my earlier thought – that it’s hard to see where the User in User-Interfaces might be. User-Centred Design has been the accepted wisdom for decades in interaction design.

I like this quote that my friend Karsten introduced me to, by Prof Bertrand Meyer (coincidentally at professor at ETH) that might offer an alternative view…

A more fruitful stance for interaction design in this new landscape might be that offered by Actor-Network Theory?

I like this snippet from a formulation of ANT based on work by Geoff Walsham et al.

“Creating a body of allies, human and non-human…”

Which brings me back to this thing…

Which is pretty unequivocally a tool. No motive, no agency. The behaviour is that of it’s evident, material properties.

Domestic pets, by contrast, are chock-full of behaviour, motive, agency. We have a model of what they want, and how they behave in certain contexts – as they do of us, we think.

We’ll never know, truly of course.

They can surprise us.

That’s part of why we love them.

But what about these things?

Even though we might give them names, and have an idea of their ‘motive’ and behaviour, they have little or no direct agency. They move around by getting us to move them around, by thriving or wilting…

And – this occurred to me while doing this talk – what are houseplants for?

Let’s leave that one hanging for a while…

And come back to design – or more specifically – some of the impulses beneath it. To make things, and to make sense of things. This is one of my favourite quotes about that. I found it in an exhibition explaining the engineering design of the Sydney Opera House.

Making models to understand is what we do as we design.

And, as we design for slightly-unpredictable, non-human-centred near-futures we need to make more of them, and share them so we can play with them, spin them round, pick them apart and talk about what we want them to be – together.

I’ll just quickly mention some of the things we talk about a lot in our work. The things we think are important in the models, and designs we make for connected products. The first one is legibility. That the product or service presents a readable, evident model of how it works to the world on it’s surface. That there is legible feedback, and you can quickly construct a theory how it works through that feedback.

One of the least useful notions you come up against, particularly in technology companies, is the stated ambition that the use of products and services should be ‘seamless experiences’.

Matthew Chalmers has stated (after Mark Weiser, one of the founding figures of ‘ubicomp’) that we need to design “seamful systems, with beautiful seams”

Beautiful seams attract us to the legible surfaces of a thing, and allow our imagination in – so that we start to build a model in our minds (and appreciate the craft at work, the values of the thing, the values of those that made it, and how we might adapt it to our values – but that’s another topic)

Finally – this guy – who pops up a lot on whiteboards in the studio, or when we’re working with clients.

B.A.S.A.A.P. is a bit of an internal manifesto at BERG, and stands for Be As Smart As A Puppy – and it’s something I’ve written about at length before.

It stems from something robotics and AI expert Rodney Brooks said… that if we put the fifty smartest people in a room for fifty years, we’d be luck if we make AIs as smart as a puppy.

We see this an opportunity rather than a problem!

We’ve made our goal to look to other models of intelligence and emotional response in products and services than emulating what we’d expect from humans.

Which is what this talk is about. Sort-of.

But before we move on, a quick example of how we express these three values in our work.

“Text Camera” is a very quick sketch of something that we think illustrates legibility, seamful-ness and BASAAP neatly.

Text Camera is about making the inputs and inferences the phone sees around it to ask a series of friendly questions that help to make clearer what it can sense and interpret. It kind of reports back on what it sees in text, rather through a video feed.

Let me explain one of the things it can do as an example. Your smartphone camera has a bunch of software to interpret the light it’s seeing around you – in order to adjust the exposure automatically.

So, we look to that and see if it’s reporting ‘tungsten light’ for instance, and can infer from that whether to ask the question “Am I indoors?”.

Through the dialog we feel the seams – the capabilities and affordances of the smartphone, and start to make a model of what it can do.

So next, I want to talk a little about a story you might be familiar with – that of…

I hope that last line doesn’t spoil it for anyone who hasn’t seen it yet…

But – over the last year I’ve been talking with lot to people about a short scene in the original 1977 Star Wars movie ‘A New Hope’ – where Luke and his Uncle Owen are attempting to buy some droids from the Jawas that have pulled up outside their farmstead.

I’ve become a little obsessed with this sequence – where the droids are presented like… Appliances? Livestock?

Or more troublingly, slaves?

Luke and Uncle Owen relate to them as all three – at the same time addressing them directly, aggressively and passive-aggressively. It’s such a rich mix of ways that ‘human and non-human actors’ might communicate.

Odd, and perhaps the most interesting slice of ‘science-fiction’ in what otherwise is squarely a fantasy film.

Of course Artoo and Threepio are really just…

Men in tin-suits, but our suspension of belief is powerful! Which brings me to the next thing we should quickly throw into the mix of the near-future…

This is the pedal of my Brompton bike. It’s also a yapping dog (to me at least)

Our brains are hard-wired to see faces, it’s part of a phenomena called ‘Pareidolia‘

It’s something we’ve talked about before on the BERGblog, particularly in connection with Schoolscope. I started a group on flickr called “Hello Little Fella” to catalogue my pareidolic-excesses (other facespotting groups are available).

This little fella is probably my favourite.

He’s a little bit ill, and has a temperature.

Anyway.

The reason for this particular digression is to point out that one of the prime materials we work with as interaction designers is human perception. We try to design things that work to take advantage of its particular capabilities and peculiarities.

I’m not sure if anyone here remembers the Apple Newton and the Palm Pilot?

The Newton was an incredible technological attainment for it’s time – recognising the user’s handwriting. The Palm instead forced us to learn a new type of writing (“Graffiti“).

We’re generally faster learners than our technology, as long as we are given something that can be easily approached and mastered. We’re more plastic and malleable – what we do changes our brains – so the ‘wily’ technology (and it’s designers) will sieze upon this and use it…

All of which leaves me wondering whether we are working towards Artificial Empathy, rather than Artificial Intelligence in the things we are designing…

If you’ve seen this video of ‘Big Dog’, an all-terrain robot by Boston Dynamics – and you’re anything like me – then you flinch when it’s tester kicks it.

To quote from our ‘Artificial Empathy’ post:

Big Dog’s movements and reactions – it’s behaviour in response to being kicked by one of it’s human testers (about 36 seconds into the video above) is not expressed in a designed face, or with sad ‘Dreamworks’ eyebrows – but in pure reaction – which uncannily resembles the evasion and unsteadiness of a just-abused animal.

Of course, before we get too carried away by artificial empathy, we shouldn’t forget what Big Dog is primarily designed for, and funded by…

Anyway – coming back to ‘wily’ tactics, here’s the often-referenced ‘Uncanny Valley’ diagram, showing the relationship between ever-more-realistic simulations of life, particularly humans and our ‘familiarity’ with them.

Basically, as we get ever closer to trying to create lifelike-simulations of humans, they start to creep us out.

It can perhaps be most neatly summed up as our reaction to things like the creepy, mocapped synthespians in the movie Polar Express…

The ‘wily’ tactic then would be to stay far away from the valley – aim to make technology behave with empathic qualities that aren’t human at all, and let us fill in the gaps as we do so well.

Which, brings us back to BASAAP, which as Rodney Brooks pointed out – is still really tough.

Bruno’s wild ancestors started to brute-force the problem of creating artificial empathy and a working companion-species relationship with humans through the long, complex process of domestication and selective-breeding…

…from that point the first time these kind of eyes were made towards scraps of meat held at the end of a campfire somewhere between 12-30,000 years ago…

Some robot designers have opted to stay on the non-human side of the uncanny valley, notably perhaps Sony with AIBO.

Here’s an interesting study from 2003 that hints a little at what the effects of designing for ‘artificial empathy’ might be.

We’re good at holding conflicting models of things in our heads at the same time it seems. That AIBO is a technology, but that it also has ‘an inner life’.

Take a look at this blog, where an AIBO owner posts it’s favourite places, and laments:

“[he] almost never – well, make it never – leaves his station these days. It’s not for lack on interest – he still is in front of me at the office – but for want of preservation. You know, if he breaks a leg come a day or a year, will Sony still be there to fix him up?”

(One questioner after my talk asked: “What did the 25% of people who didn’t think AIBO was a technological gadget report it to be?” – Good question!)

Some recommendations of things to look at around this area: the work of Donna Haraway, esp. The Companion Species Manifesto.

Also, the work of Cynthia Brezeal, Heather Knight and Kacie Kinzer – and the ongoing LIREC research project that our friend Alexandra Deschamps-Sonsino is working with, that’s looking to studies of canine behaviour and companionship to influence the design of bots and robots.

In science-fiction there’s a long, long list that could go here – but for now I’ll just point to the most-affecting recent thing I’ve read in the area, Ted Chiang’s novella “The Lifecycle of Software Objects” – which I took as my title for a talk partly on this subject at UX London earlier in the year.

In our own recent work I’d pick out Suwappu, a collaboration with Dentsu London as something where we’re looking to animate, literally, toys with an inner life through a computer-vision application that recognises each character and overlays dialogue and environments around them.

I wonder how this type of technology might develop hand-in-hand with storytelling to engage and delight – while leaving room for the imagination and empathy that we so easily project on things, especially when we are young.

Finally, I want to move away from the companion animal as a model, back to these things…

I said we’d come back to this! Have you ever thought about why we have pot plants? What we have them in the corners of our lives? How did they get there? What are they up to?!?

(Seriously – I haven’t managed yet to find research or a cultural history of how pot-plants became part of our home life. There are obvious routes through farming, gardening and cooking – but what about ornamental plants? If anyone reading this wants to point me at some they’d recommend in the comments to this post, I’d be most grateful!)

Take a look at this – one of the favourite finds of the studio in 2011 – Sticky Light.

It is very beautifully simple. It displays motive and behaviour. We find it fascinating and playful. Of course, part of it’s charm is that it can move around of its own volition – it has agency.

Pot-plants have motives (stay alive, reproduce) and behaviour (grow towards the light, shrivel when not watered) but they don’t have much agency. They rely on us to move them into the light, to water them.

Some recent projects have looked to augment domestic plants with some agency – Botanicalls by Kati London, Kate Hartman, Rebecca Bray and Rob Faludi equips a plant not only with a network connection, but a twitter account! Activated by sensors it can report to you (and its followers) whether it is getting enough water. Some voice, some agency.

(I didn’t have time to mention it in the talk, but I’d also point to James Chamber’s evolution of the idea with his ‘Has Needs’ project, where an abused potplant not only has a network connection, but the means to advertise for a new owner on freecycle…)

Here’s my botanical, which I chose to call Robert Plant…

So, much simpler systems that people or pets can find places in our lives as companions. Legible motives, limited behaviours and agency can illicit response, empathy and engagement from us.

We think this is rich territory for design as the things around us start to acquire means of context-awareness, computation and connectivity.

As we move from making inert tools – that we are unequivocally the users of – to companions, with behaviours that animate them – we wonder whether we should go straight from this…

…to this…

Namely, straight from things with predictable and legible properties and affordances, to things that try and have a peer-relationship, speaking with human voice and making great technological leaps to relate to us in that way, but perhaps with a danger of entering the uncanny valley.

What if there’s an interesting space to design somewhere in-between?

This in part is the inspiration behind some of the thinking in our new platform Berg Cloud, and its first product – Little Printer.

We like to think of Little Printer as something of a ‘Cloud Companion Species’ that mediates the internet and the domestic, that speaks with your smartphone, and digests the web into delightful little chunks that it dispenses when you want.

Little Printer is the beginning of our explorations into these cloud-companions, and BERG Cloud is the means we’re creating to explore them.

Ultimately we’re interested in the potential for new forms of companion species that extend us. A favourite project for us is Natalie Jeremijenko’s “Feral Robotic Dogs” – a fantastic example of legibility, seamful-ness and BASAAP.

Natalie went to communities near reclaimed-land that might still have harmful toxins present, and taught workshops where cheap (remember Argos?) robot dogs that could be bought for $30 or so where opened up and hacked to accommodate new sensors.

They were reprogrammed to seek the chemical traces associated with lingering toxins. Once release by the communities they ‘sniff’ them out, waddling towards the highest concentrations – an immediate tangible and legible visualisation of problem areas.

Perhaps most important was that the communities themselves were the ones taught to open the toys up, repurpose their motives and behaviour – giving them the agency over the technology and evidence they could build themselves.

In the coming world of bots – whether companions or not, we have to attempt to maintain this sort of open literacy. And it is partly the designer’s role to increase its legibility. Not only to beguile and create empathy – but to allow a dialogue.

As Kevin Slavin said about the world of algorithms growing around us – “We can write it but we can’t read it”

We need to engage with the complexity and make it open up to us.

To make evident, seamful surfaces through which we can engage with puppy-smart things.

As our friend Chris Heathcote has put so well:

Thanks for inviting me, and for your attention today.

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FOOTNOTE: Auger & Loizeau’s Domestic Robots.

I didn’t get the chance to reference the work of James Auger & Jimmy Loizeau in the talk, but their “Carnivorous Robots” project deserves study.

From the project website:

“For a robot to comfortably migrate into our homes, appearance is critical. We applied the concept of adaptation to move beyond the functional forms employed in laboratories and the stereotypical fictional forms often applied to robots. In effect creating a clean slate for designing robot form, then looking to the contemporary domestic landscape and the related areas of fashion and trends for inspiration. The result is that on the surface the CDER series more resemble items of contemporary furniture than traditional robots. This is intended to facilitate a seamless transition into the home through aesthetic adaptation, there are however, subtle anomalies or alien features that are intended to draw the viewer in and encourage further investigation into the object.”

And on robots performing as “Companion Species”

”In the home there are several established object categories each in some way justifying the products presence through the benefit or comfort they bring to the occupant, these include: utility; ornament; companionship; entertainment and combinations of the above, for example, pets can be entertaining and chairs can be ornamental. The simplest route for robots to enter the home would be to follow one of these existing paths but by necessity of definition, offering something above and beyond the products currently occupying those roles.”

James Auger is currently completing his Phd at the RCA on ‘Domestication of Robotics’ and I can’t wait to read it.

Last week, a series of talks on robots, AI, design and society began at London’s Royal Institution, with Alex Deschamps-Sonsino (late of Tinker and now of our friends RIG) giving a presentation on ‘Emotional Robots’, particularly the EU-funded research work of ‘LIREC‘ that she is involved with.

It was a thought-provoking talk, and as a result my notebook pages are filled with reactions and thoughts to follow-up rather than a recording of what she said.

LIREC‘s work is centred around a academic deconstruction of human emotional relations to each other, pets and objects – considering them as companions.

Very interesting!

These are themes dear to our hearts cf. Products Are People Too, Pullman-esque daemons and B.A.S.A.A.P.

With B.A.S.A.A.P. in mind, I was particularly struck by the animal behaviour studies that LIREC members are carrying out, looking into how dogs learn and adapt as companions with their human owners, and learn how to negotiate different contexts in a almost symbiotic relationship with their humans.

Alex pointed out that the dogs sometimes test their owners – taking their behaviour to the edge of transgression in order to build a model of how to behave.

Adaptive potentiation – serious play! Which lead me off onto thoughts of Brian Sutton-Smith and both his books ‘Ambiguity of Play’ and ‘Toys as Culture’. The LIREC work made me imagine the beginnings of a future literature of how robots play to adapt and learn.

Supertoys (last all summer long) as culture!

Which led me to my question to Alex at the end of her talk – which I formulated badly I think, and might stumble again here to write down clearly.

In essence – dogs and domesticated animals model our emotional states, and we model theirs – to come to an understanding. There’s no direct understanding there – just simulations running in both our minds of each other, which leads to a working relationship usually.

My question was whether LIREC’s approach of deconstruction and reconstruction of emotions would be less successful than the ‘brute-force’ approach of simulating the 17,000 years or so domestication of wild animals in companion robots.

Imagine genetic algorithms creating ‘hopeful monsters‘ that could be judged as more or less loveable and iterated upon…

Another friend, Kevin Slavin recently gave a great talk at LIFT11, about the algorithms that surround and control our lives – that ‘we can write but can’t read’ the complex behaviours they generate.

He gave the example of http://www.boxcar2d.com/ – that generates ‘hopeful monster’ wheeled devices that have to cross a landscape.

As Kevin says – it’s “Sometimes heartbreaking”.

Some succeed, some fail – we map personality and empathise with them when they get stuck.

I was also reminded of another favourite design-fiction of the studio – Bruce Sterling’s ‘Taklamakan‘

Pete stared at the dissected robots, a cooling mass of nerve-netting, batteries, veiny armor plates, and gelatin.
“Why do they look so crazy?”
“‘Cause they grew all by themselves. Nobody ever designed them.”
Katrinko glanced up.

Another question from the audience featured a wonderful term that I at least I had never heard used before – “Artificial Empathy”.

Artificial Empathy, in place of Artificial Intelligence.

Artificial Empathy is at the core of B.A.S.A.A.P. – it’s what powers Kacie Kinzer’s Tweenbots, and it’s what Byron and Nass were describing in The Media Equation to some extent, which of course brings us back to Clippy.

Clippy was referenced by Alex in her talk, and has been resurrected again as an auto-critique to current efforts to design and build agents and ‘things with behaviour’

One thing I recalled which I don’t think I’ve mentioned in previous discussions was that back in 1997, when Clippy was at the height of his powers – I did something that we’re told (quite rightly to some extent) no-one ever does – I changed the defaults.

You might not know, but there were several skins you could place on top of Clippy from his default paperclip avatar – a little cartoon Einstein, an ersatz Shakespeare… and a number of others.

I chose a dog, which promptly got named ‘Ajax’ by my friend Jane Black. I not only forgave Ajax every infraction, every interruption – but I welcomed his presence. I invited him to spend more and more time with me.

I played with him.

Sometimes we’re that easy to please.

I wonder if playing to that 17,000 years of cultural hardwiring is enough in some ways.

In the bar afterwards a few of us talked about this – and the conversation turned to ‘Big Dog’.

Big Dog doesn’t look like a dog, more like a massive crossbreed of ED-209, the bottom-half of a carousel horse and a black-and-decker workmate. However, if you’ve watched the video then you probably, like most of the people in the bar shouted at one point – “DON’T KICK BIG DOG!!!”.

Big Dog’s movements and reactions – it’s behaviour in response to being kicked by one of it’s human testers (about 36 seconds into the video above) is not expressed in a designed face, or with sad ‘Dreamworks’ eyebrows – but in pure reaction – which uncannily resembles the evasion and unsteadiness of a just-abused animal.

It’s heart-rending.

But, I imagine (I don’t know) it’s an emergent behaviour of it’s programming and design for other goals e.g. reacting to and traversing irregular terrain.

Again like Boxcar2d, we do the work, we ascribe hurt and pain to something that absolutely cannot be proven to experience it – and we are changed.

So – we are the emotional computing power in these relationships – as LIREC and Alex are exploring – and perhaps we should design our robotic companions accordingly.

Or perhaps we let this new nature condition us – and we head into a messy few decades of accelerated domestication and renegotiation of what we love – and what we think loves us back.

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P.S.: This post contains lost of images from our friend Matt Cottam’s wonderful “Dogs I Meet” set on Flickr, which makes me wonder about a future “Robots I Meet” set which might illicit such emotions…

The above is a post-it note, which as I recall is from a workshop at IDEO Palo Alto I attended while I was at Nokia.

And, as I recall, it was probably either Charlie Schick or Charles Warren who scribbled this down and stuck it on the wall as I was talking about what was a recurring theme for me back then.

Recently I’ve been thinking about it again.

B.A.S.A.A.P. is short for Be As Smart As A Puppy, which is my short-hand for a bunch of things I’ve been thinking about… Ooh… Since 2002 or so I think, and a conversation in a california car-park with Matt Webb.

It was my term for a bunch of things that encompass some 3rd rail issues for UI designers like proactive personalisation and interaction, examined in the work of Byron and Nass, exemplified by (and forever-after-vilified-as) Microsoft’s Bob and Clippy (RIP). A bunch of things about bots and daemons, conversational interface.

And lately, a bunch of things about machine learning – and for want of a better term, consumer-grade artificial intelligence.

BASAAP is my way of thinking about avoiding the ‘uncanny valley‘ in such things.

Making smart things that don’t try to be too smart and fail, and indeed, by design, make endearing failures in their attempts to learn and improve. Like puppies.

Cut forward a few years.

At Dopplr, Tom Insam and Matt B. used to astonish me with links and chat about where the leading-edge of hackable, commonly-employable machine learning was heading.

Startups like songkick and last.fm amongst others were full of smart cookies making use of machine learning, data-mining and a bunch of other techniques I’m not smart enough to remember (let-alone reference), to create reactive, anticipatory systems from large amounts of data in a certain domain.

Now, machine-learning is superhot.

The web has become a web-of-data, data-mining technology is becoming a common component of services, and processing power on tap in the cloud means that experimentation is cheap. The amount of data available makes things possible that were impossible a few years ago.

I was chatting with Matt B. again this weekend about writing this post, and he told me that the algorithms involved are old. It’s just that the data and the processing power is there now to actually get to results. Google’s Peter Norvig has been quoted as saying “All models are wrong, and increasingly you can succeed without them.“.

Things like Hunch are making an impression in the mainstream. Google Priority Inbox, launched recently, make the utility of such approaches clear.

BASAAP services are here.

BASAAP things are on the horizon.

As Mike Kuniavsky has pointed out – we are past the point of “Peak Mhz”:

driving ubiquitous computing, as their chips become more efficient, smaller and cheaper, thus making them increasingly easier to include into everyday objects.

This is ApriPoco by Toshiba. It’s a household robot.

It works by picking up signals from standard remote controls and asks you what you are doing, to which you are supposed to reply in a clear voice. Eventually it will know how to turn on your television, switch to a specific channel, or play a DVD simply by being told. This system solves the problem that conventional speech recognition technology has with some accents or words, since it is trained by each individual user. It can send signals from IR transmitters in its arms, and has cameras in its head with which it can identify specific users.

Not perhaps the most pressing need that you have in your house, but interesting none-the-less.

Imagine this not as a device, but as an actor in your home.

The face-recognition is particularly interesting.

My £100 camera has a ‘smile-detection’ mode, which is becoming common. It can also recognise more faces that a 6-month old human child. Imagine this then, mixed with ApriPoco, registering and remembering smiles and laughter.

Go further, plug it into the internet. Into big data.

As Tom suggested on our studio mailing list: recognising background chatter of people not paying attention. Plugged into something like Shownar, constantly updating the data of what people are paying attention to, and feeding back suggestions of surprising and interesting things to watch.

Imagine a household of hunchbots.

Each of them working across a little domain within your home. Each building up tiny caches of emotional intelligence about you, cross-referencing them with machine learning across big data from the internet. They would make small choices autonomously around you, for you, with you – and do it well. Surprisingly well. Endearingly well.

They would be as smart as puppies.

Hunch-Puppies…?

Ahem.

Of course, there’s the other side of domesticated intelligences.

Matt W.’s been tracking the bleed of AI into the Argos catalogue, particularly the toy pages for some time.

The above photo of toys from Argos he took was given the title: “They do their little swarming thing and have these incredibly obscure interactions”

That might be part of the near-future: being surrounded by things that are helping us, that we struggle to build a model of how they are doing it in our minds. That we can’t directly map to our own behaviour. A demon-haunted world. This is not so far from most people’s experience of computers (and we’re back to Byron and Nass) but we’re talking about things that change their behaviour based on their environment and their interactions with us, and that have a certain mobility and agency in our world.

I’m reminded of the work of Rodney Brooks and the BEAM approach to robotics, although hopefully more AIBO than Runaways.

Again, staying on the puppy side of the uncanny valley is a design strategy here – as is the guidance within Adam Greenfield’s “Everyware”: how to think of design for ubiquitous systems that behave as sensing, learning actors in contexts beyond the screen.

Adam’s book is written as a series of theses (to be nailed to the door of a corporation or two?), and thinking of his “Thesis #37″ in connection with BASAAP intelligences in the home of the near-future amuses me in this context:

“Everyday life presents designers of everyware with a particularly difficult case because so very much about it is tacit, unspoken, or defined with insufficient precision.”

This cuts both ways in a near-future world of domesticated intelligences, and that might be no bad thing. Think of the intuitions and patterns – the state machine – your pets build up of you, and vice-versa. You don’t understand pets as tools, even if they perform ‘job-like’ roles. They don’t really know what we are.

We’ll never really understand what we look like from the other side of the Uncanny Valley.

What is this going to feel like?

Non-human actors in our home, that we’ve selected personally and culturally. Designed and constructed but not finished. Learning and bonding. That intelligence can look as alien as staring into the eye of a bird (ever done that? Brrr.) or as warm as looking into the face of a puppy. New nature.

What is that going to feel like?

We’ll know very soon.