It’s great to see that communication science/media studies tackle IoT and human-computer interfaces as a field of research. I was impressed with the level of thinking and questions from the group. The discussion was lively, on point, and there were none of the obvious questions. Instead, the students probed the pretty complex issues surrounding IoT, AI, and algorithmic decision making in the context of communications and communication science.
It’s part of the master program, and of Prof. Engesser’s new role as professor there, to also set up a lab to study how smart home assistants and other voice-enabled connected devices impact the way we communicate at home—both with other people and with machines.
It’ll be interesting to watch the lab’s progress and findings, and I hope we’ll find ways to collaborate on some of these questions.
At their Pixel 2 event at the beginning of the month, Google released a whole slew of new products. Besides new phones there were updated version of their smart home hub, Google Home, and some new types of product altogether.
I don’t usually write about product launches, but this event has me excited about new tech for the first time in a long time. Why? Because some aspects stood out as they stand for a larger shift in the industry: The new role of artificial intelligence (AI) as it seeps into consumer goods.
Google have been reframing themselves from a mobile first to an AI first company for the last year or so. (For full transparency I should add that I’ve worked with Google occasionally in the recent past, but everything discussed here is of course publicly available.)
We now see this shift of focus play out as it manifests in products.
Here’s Google CEO Sundar Pichai at the opening of Google’s Pixel 2 event:
We’re excited by the shift from a mobile-first to an AI-first world. It is not just about applying machine learning in our products, but it’s radically re-thinking how computing should work. (…) We’re really excited by this shift, and that’s why we’re here today. We’ve been working on software and hardware together because that’s the best way to drive the shifts in computing forward. But we think we’re in the unique moment in time where we can bring the unique combination of AI, and software, and hardware to bring the different perspective to solving problems for users. We’re very confident about our approach here because we’re at the forefront of driving the shifts with AI.
AI as a platform: Google has it.
First things first: I fully agree – there’s currently no other company that’s in as well positioned to drive the development of AI, or to benefit from it. In fact, back in May 2017 I wrote that “Google just won the next 10 years.” That was when Google just hinted at their capabilities in terms of new features, but also announced building AI infrastructure for third parties to use. AI as a platform: Google has it.
Before diving into some structural thoughts, let’s look at two specific products they launched:
Google Clips are a camera you can clip somewhere, and it’ll automatically take photos when some conditions are met: A certain person’s face is in the picture, or they are smiling. It’s an odd product for sure, but here’s the thing: It’s fully machine learning powered facial recognition, and the computing happens on the device. This is remarkable for its incredible technical achievement, and for its approach. Google has become a company of high centralization—the bane of cloud computing, I’d lament. Google Clips works at the edge, decentralized. This is powerful, and I hope it inspires a new generation of IoT products that embrace decentralization.
Google’s new in-ear headphones offer live translation. That’s right: These headphones should be able to allow for multi-language human-to-human live conversations. (This happens in the cloud, not locally.) Now how well this works in practice remains to be seen, and surely you wouldn’t want to run a work meeting through them. But even if it eases travel related helplessness just a bit it’d be a big deal.
So as we see these new products roll out, the actual potential becomes much more graspable. There’s a shape emerging from the fog: Google may not really be AI first just yet, but they certainly have made good progress on AI-leveraged services.
The mental model I’m using for how Apple and Google compare is this:
Apple’s ecosystem focuses on an integration: Hardware (phones, laptops) and software (OSX, iOS) are both highly integrated, and services are built on top. This allows for consistent service delivery and for pushing the limits of hardware and software alike, and most importantly for Apple’s bottom line allows to sell hardware that’s differentiated by software and services: Nobody else is allowed to make an iPhone.
Google started at the opposite side, with software (web search, then Android). Today, Google looks something like this:
Based on software (search/discovery, plus Android) now there’s also hardware that’s more integrated. Note that Android is still the biggest smartphone platform as well as basis for lots of connected products, so Google’s hardware isn’t the only game in town. How this works out with partners over time remains to be seen. That said, this new structure means Google can push its software capabilities to the limits through their own hardware (phones, smart home hubs, headphones, etc.) and then aim for the stars with AI-leveraged services in a way I don’t think we’ll see from competitors anytime soon.
What we’ve seen so far is the very tip of the iceberg: As Google keeps investing in AI and exploring the applications enabled by machine learning, this top layer should become exponentially more interesting: They develop not just the concrete services we see in action, but also use AI to build their new models, and open up AI as a service for other organizations. It’s a triple AI ecosystem play that should reinforce itself and hence gather more steam the more it’s used.
AI-driven automation will continue to create wealth and expand the American economy in the coming years, but, while many will benefit, that growth will not be costless and will be accompanied by changes in the skills that workers need to succeed in the economy, and structural changes in the economy. Aggressive policy action will be needed to help Americans who are disadvantaged by these changes and to ensure that the enormous benefits of AI and automation are developed by and available to all.
This cuts right to the chase: Artificial intelligence (AI) will create wealth, and it will replace jobs. AI will change the future of work, and the economy.
AI will change the future of work, and the economy.
For the record: In other areas, Germany is making good progress. Take autonomous driving, for example. Germany just adopted an action plan on automated driving that regulates key points of how autonomous vehicles should behave on the street—and regulates it well! Key points include that autonomous driving is worth promoting because it causes fewer accidents, dictates that damage to property must take precedence over personal injury (aka life has priority), and that in unavoidable accident situations there may not be any discrimination between individuals based on age, gender, etc. It even includes data sovereignty for drivers. Well done!
On the other hand, for the Internet of Things (IoT) Germany squandered opportunities in that IoT is framed almost exclusively as industrial IoT under the banner of Industrie 4.0. This is understandable given Germany’s manufacturing-focused economy, but it excludes a huge amount of super interesting and promising IoT. It’s clearly the result of successful lobbying but at the expense at a more inclusive, diverse portfolio of opportunities.
So where do we stand with artificial intelligence in Germany? Honestly, in terms of policy I cannot tell.
So where do we stand with artificial intelligence in Germany? Honestly, in terms of policy I cannot tell.
Update: The Federal Ministry of Education and Research recently announced an initiative to explore AI: Plattform Lernende Systeme (“platform living systems”). Thanks to Christian Katzenbach for the pointer!
AI & the future of work
The White House AI report talks a lot about the future of work, and of employment specifically. This makes sense: It’s one of the key aspects of AI. (Some others are, I’d say, opportunity for the creation of wealth on one side and algorithmic discrimination on the other.)
How AI will impact the work force, the economy, and the role of the individual is something we can only speculate about today.
In a recent workshop with stipendiaries of the Heinrich-Böll-Foundation on the future of work we explored how digital, AI, IoT and adjacent technologies impact how we work, and how we think about work. It was super interesting to see this diverse group of very, very capable students and young professionals bang their heads against the complexities in this space. Their findings mirrored what experts across the field also have been finding: That there are no simple answers, and most likely we’ll see huge gains in some areas and huge losses in others.
Like all automation before, depending on the context we’ll see AI either displace human workers or increase their productivity.
The one thing I’d say is a safe bet is this: Like all automation before, depending on the context we’ll see AI either displace human workers or increase their productivity. In other words, some human workers will be super-powered by AI (and related technologies), whereas others will fall by the wayside.
Over on Ribbonfarm, Venkatesh Rao phrases this very elegantly: Future jobs will either be placed above or below the API: “You either tell robots what to do, or are told by robots what to do.” Which of course conjures to mind images of roboticized warehouses, like this one:
Just to be clear, this is a contemporary warehouse in China. Amazon runs similar operations. This isn’t the future, this is the well-established present.
Future jobs will either be placed above or below the API: “You either tell robots what to do, or are told by robots what to do.”
I’d like to stress that I don’t think a robot warehouse is inherently good or bad. It depends on the policies that make sure the humans in the picture do well.
Education is key
So where are we in Europe again? In Germany, we still try to define what IoT and AI means. In China it’s been happening for years.
This picture shows a smart lamp in Shenzhen that we found in a maker space:
What does the lamp do? It tracks if users are nearby, so it can switch itself off when nobody’s around. It automatically adjusts light the temperature depending on the light in the room. As smart lamps go, these features are okay: Not horrible, not interesting. If it came out of Samsung or LG or Amazon I wouldn’t be surprised.
So what makes it special? This smart lamp was built by a group of fifth graders. That’s right: Ten and eleven year olds designed, programmed, and built this. Because the curriculum for local students includes the skills that enable them to do this. In Europe, this is unheard of.
I think the gap in skills regarding artificial intelligence is most likely quite similar. And I’m not just talking about the average individual: I’m talking about readiness at the government level, too. Our governments aren’t ready for AI.
Our governments aren’t ready for AI.
It’s about time we start getting ready for AI, IoT, and robotics. Always a fast mover, Estonia considers a law to legalize AI, and they smartly kick off this process with a multi-stakeholder process.
What to do?
In Germany, the whole discussion is still in its earliest stages. Let’s not fall into the same trap as we did for IoT: Both IoT and AI are more than just industry. They are both broader and deeper than the adjective industrial implies.
The White House report can provide some inspiration, especially around education policy.
We need to invest in what OECD calls the active labor market policies, i.e. training and skill development for adults. We need to update our school curricula to get youths ready for the future with both hands-on applicable skills (coding, data analysis, etc.) and with the larger contextual meta skills to make smart decisions (think humanities, history, deep learning).
We need to reform immigration to allow for the best talent to come to Europe more easily (and allow for voting rights, too, because nobody feels at home where they pay taxes with no representation).
Without capacity building, we’ll never see the digital transformation we need to get ready for the 21st century.
Zooming out to the really big picture, we need to start completely reforming our social security systems for an AI world that might not deliver full employment ever again. This could include Universal Basic Income, or maybe rather Universal Basic Services, or a different approach altogether.
This requires capacity building on the side of our government. Without capacity building, we’ll never see the digital transformation we need to get ready for the 21st century.
But I know one thing: We need to kick off this process today.
First things first: You’ll have noticed that we completely relaunched this website. It’s much more clearly structured, and visually built more around text than images. This should make it a lot easier to find the content you’re looking for, and make load times a lot faster.
Most importantly, the front page is now a lot better structured, and we also optimized the navigation:
Front page: Client services and in-house projects are more visibly separated, recent projects are more visible, and text highlights allow for extra quick skimming.
Not everything needed to change, though. For example, the MEDIA and SPEAKING pages work well, and provide the most comprehensive log of my speaking engagements as well as media mentions and contributions. (By the way, I’m keeping the dual BLOG structure of COMPANY blog and PERSONAL blog—mostly the occasion travel log—, which exists for purely historical/archival reasons. I simply didn’t want to move it to another server or domain.)
While we were at it we also cleaned up a ton of copy and got a spanking new SSL certificate.
Curious to hear what you think!
A Trustmark for IoT
For Mozilla, we explored the potentials and challenges of a trustmark for the Internet of Things (IoT). That research is now publicly available. You can find more background and all the relevant links at thewavingcat.com/iot-trustmark
If you follow our work both over at ThingsCon and here at The Waving Cat, you know that we see lots of potential for the Internet of Things (IoT) to create value and improve lives, but also some serious challenges. One of the core challenges is that it’s hard for consumers to figure out which IoT products and services are good—which ones are designed responsibly, which ones deserve their trust. After all, too often IoT devices are essentially black boxes that are hard interrogate and that might change with the next over-the-air software update.
So, what to do? One concept I’ve grown increasingly fond of is consumer labeling as we know from food, textiles, and other areas. But for IoT, that’s not simple. The networked, data-driven, and dynamic nature of IoT means that the complexity is high, and even seemingly simple questions can lead to surprisingly complex answers. Still, I think there’s huge potential there to make huge impact.
I was very happy when Mozilla picked up on that idea and commissioned us to explore the potential of consumer labels. Mozilla just made that report publicly available:
Due to technical issues with the video projection, my slides weren’t shown for the first few minutes. Apologies. On the plus side, the organizers had kindly put a waving cat on the podium for me.
It’s a rare talk in that I gave it in German, something I’m hardly used to these days, so it was extra fun. In the talk, I argue that IoT poses a number of particular challenges that we need to address (incl. the level of complexity and blurred lines across disciplines and expertise; power dynamics; and transparency). I outline inherent tensions and propose a few approaches on how to tackle them, especially around increasing transparency and legibility of IoT products.
I conclude with a call for Europe to actively take a global leadership role in the area of consumer and data protection, analog to Silicon Valley’s (claimed/perceived) leadership in disruptive innovation as well as funding/scaling of digital products, and to Shenzhen’s hardware manufacturing leadership.
You can find the slides, a video, and links to more extensive German write-ups in this blog post.
I also have a few talks coming up:
In October, I’ll be speaking at a lecture on communications and IoT at Dresden University, where if logistics work out I’ll be chatting a bit about the practitioner’s side of IoT. (Details TBD).
On 9 November, also in Berlin, I’ll be at SimplySecure‘s conference Underexposed (program). My talk there is called The Internet of Sneaky Things. I’ll be exploring how IoT security, funding and business models, centralization and data mining, and some larger challenges around the language we use to consider the impact of data-driven systems combined all form a substantial challenge for all things related to IoT. But it’s not all bleak. There are measures we can—and through ThingsCon, we do—take.
Wrapping up ODINE
For the last two years, I was part of the pool of evaluators for ODINE, the Open Data Incubator Network Europe. The program just ended, after funding around 57 companies doing interesting work with and around open data in Europe. And so now the list of evaluators is being made publicly available. As far as I can tell from the more-or-less outside, it was a successful project. Congratulations to the team and all the participants, and best of luck with the next steps.
ThingsCon has too much to go on right now to include everything here, so I’ll point to the ThingsCon blog where we now also have monthnotes. When I opened last night’s ThingsCon Salon Berlin with a quick community update about what’s been happening across the ThingsCon network my mind was blown by the level of activity there.
The super short version is this: 15 events, 3 publications, and 2 ongoing newsletters so far this year alone, with much more coming up. (See thingscon.com/resources for links.)
The “coming up” section includes highlights like the biggest ThingsCon event to date with the annual ThingsCon Amsterdam, a world premiere in Antwerp in that it’s a new chapter, the first event in Flemish, and a comedy special to boot! It also looks like new events or chapters are in the making in Nairobi and Manila, which is very exciting. All that and much more at thingscon.com/events
Over at Zephyr Berlin, we’re preparing for the second small batch production. In the meantime, there are still a few pairs in some sizes available!
What’s on the horizon?
The next few weeks will go into wrapping up/advancing the Trustmarks for IoT project, as well as planning for the rest of the year. We’re starting a new client project in the space of evaluation of education programs in South Africa. ??
We strongly believe that good ethics mean good business. This isn’t just an empty phrase, either: We know from our own experience that often it pays great dividends to go the extra step and taking into account the implications of business decisions.
This is especially true in areas that employ new technologies, simply because there are more unknowns in emerging tech. And more unknowns = higher risks.
Our field of operation is at the intersection of emerging tech, strategy, and good business ethics.
Take, for example, the global tech company’s VP who adapted community-driven guidelines for data ownership in IoT: He knew that this particular pioneer community had a deeper understanding than most of the issues at stake. Even though these data ownership guidelines meant possibly losing some short term revenue gains, he trusted in their long-term positive side effects. Now, and at the time unexpectedly, his organization is in a better position than most to comply with the new EU data protection regulation (GDPR). Even before that, these guidelines likely inspired user trust and confidence.
Other companies lose their best talents because of sketchy business tactics—to those who are honest and trustworthy, and have a credible and powerful mission.
If you pay attention you’ll find these examples everywhere: Good ethics aren’t a buzzword, nor are they rocket science. They’re 100% compatible with good business. They might just be a requisite.
Here at The Waving Cat, we’re in the business of analyzing the impact of emerging technologies and finding ways to harness their opportunities. This is why our services include both research & foresight and strategy: First we need to develop a deep understanding, then we can apply it. Analyze first, act second.
Over the last few years, my work has mostly homed in on the Internet of Things (#IoT). This is no coincidence: IoT is where a lot of emerging technologies converge. Hence, IoT has been a massive driver of digital transformation.
IoT has been a massive driver of digital transformation.
However, increasingly the lines between IoT and other emerging technologies are becoming ever-more blurry. Concretely, data-driven and algorithmic decision-making is taking on a life on its own, both within the confines of IoT and outside of them. Under the labels of machine learning (#ML), artificial intelligence (#AI), or the (now strangely old school moniker) big data we’ve seen tremendous development over the last few years.
The physical world is already suffused with data, sensors, and connected devices/systems, and we’re only at the beginning of this development. Years ago I curated a track at NEXT Conference called the Data Layer, on the premise that the physical world will be covered in a data layer. Now, 5 years or so later, this reality has absolutely come to pass.
IoT with its connected devices, smart cities, connected homes, and connected mobility is part of that global infrastructure. No matter if the data crunching happens in the cloud or at the edge (i.e. close to where the data is captured/used), more and more has to happen implicitly and autonomously. Machine learning and AI play an essential role in this.
Increasingly, artificial intelligence is becoming a driver of digital transformation
Most organizations will need to develop an approach to harnessing artificial intelligence, and so increasingly artificial intelligence is becoming a driver of digital transformation.
As of today, Internet of Things, artificial intelligence & machine learning, and digital transformation are intimately connected. You can’t really get far in one without understanding the others.
These are exciting, interesting times, and they offer lots of opportunities. We’re here to help you figure out how to harness them.
In Germany, like most industrial nations, there’s a lot of talk about digital transformation. This holds especially true for the public sector, for citizen service delivery.
While in the UK, the Government Digital Services team (GDS) has been doing tremendous pioneering work that’s also echoed in the USDS, and Estonia has gone fully digital a while ago, most countries struggle.
A recent example from Berlin exemplifies this almost perfectly (Morgenpost, in German): In Berlin’s Charlottenburg district, the application for parking permits has been digitized—kind of.
The digital service delivery is worse than before.
According to this article, citizens used to be able to get their parking permits by mail. After the service was put online, they could apply online. So far so good, but the implementation was so lacking that payments couldn’t be processed online. First, government employees manually processed that last bit for every application, but because that was obviously unsustainable the district switched the system. Citizens now apply online, but then to pay have to come in personally to pay on the spot. The payment process was, so the article, forgotten.
The implementation was so bad that putting the service online means that citizens require more efforts to get something simple done. The digital service delivery is worse than before.
This is insanity. On the one hand, process by process is digitized. On the other, it’s done so clumsily that all parties are worse off.
It seems that every step in the process is going wrong.
It seems that every step in the process is going wrong. How can this happen? There’s a simple answer, and a more complex one.
The simple answer is this: The administration doesn’t have the capacity for building digital services yet.
Building digital services requires digital transformation, which requires the institution and all its workflows and org charts to be updated and transformed.
The more complex answer is: Building digital services requires digital transformation, which requires the institution and all its workflows and org charts to be updated and transformed. Otherwise, digitization might bring incremental change at best, or at worst actively create damage.
Germany is a prosperous country with an economy built on high tech. It’s a driving force behind industrial IoT (here summarized under the label “Industry 4.0”). And yet, when it comes to digital delivery of citizen services, the country is woefully behind. This goes all the way from broadband access, where Germany is among the worst in Europe, all the way to how local, state, and federal administrations deliver their services online—or rather don’t, as it were.
Germany is woefully behind in digital services.
In order to even start fixing this malady, we don’t need yet another paper-based workflow to be digitized half-bakedly. Instead, we need a strong mandate from the top, backup up with the necessary budgets, to rethink and truly transform our institutions and administration. Only through true digital transformation can Germany get ready for the 21st century. Until then, citizens and companies alike will have to find workarounds to make things work. We need to aim higher, and we can afford to aim higher.