In the series The inside story x 3, experts from the Plan.Net group regularly explain a current topic from the digital world from different perspectives. What does it mean for your grandma or your agency colleague? And what does the customer – in other words, a company – get out of it?

In the western media, China’s social credit system is often compared to the Nosedive episode of Black Mirror and described as an Orwellian nightmare. Based on online search requests, shopping history, education, criminal record, social media behaviour and many other factors, every citizen is to be evaluated according to a point system. If the three-digit score is too low, there are far-reaching consequences. some jobs will be blocked for you, your children will not be able to attend good schools, travel will be denied, and you’ll also be unable to get a loan. The picture of the social credit system painted by the western media looks very disturbing. But fortunately, the reality is not quite that bad.

China’s social credit system is an ecosystem composed of various initiatives

In 2014, the Chinese government announced a plan that provided for setting up an extensive social credit system by 2020. The aim is to improve governance and create order in a country that often has to combat fraud. As China is in the process of becoming a market economy, it still does not have properly functioning institutions, such as courts, to deal with these issues. For this reason, the Chinese government is trying to establish a kind of reward and punishment system to promote trustworthiness and integrity. The central Joint Punishment System puts citizens on a blacklist if they violate certain rules. For example, if a citizen is ordered by a court to pay a fine and does not pay, they are put on a blacklist. After that, the person concerned cannot book flights, travel first class by train, or purchase luxury items on TMALL or Taobao until they have paid the fine. Furthermore, they are denied access to loans and government jobs.

However, this Joint Punishment System does not assign scores to citizens. The basis for this mistaken idea is related to Alibaba. The Chinese government is not alone in working on a social credit system – private-sector companies have also launched initiatives. In the western media, these are often lumped together and confused with each other.

Like Amazon, Alibaba is an online retailer that provides a platform for merchants to sell their products to consumers. At the time when Alibaba set up its e-commerce business, China was largely a cash country in which few people had credit cards. To be able to implement their business model, Alibaba had to secure payment transactions between buyers and sellers. As there was no provider like Visa or MasterCard in China that could handle this task, Alibaba had to set up its own payment infrastructure. Alibaba’s subsidiary Ant Financial was established for that purpose. As most people in China cannot show a documented payment history, Alibaba needed other factors to enable them to assess the creditworthiness of consumers and build trust between merchants and purchasers. That was the origin of the Sesame Credit Score system.

The score can range from 350 to 950 points and consists of several factors: the amount of revenue at Alibaba, whether purchased products, as well as electricity and phone bills, are paid on time, the completeness of personal details, and social contacts.

In addition, the Public Bank of China (PBoC) plans to develop a national creditworthiness check comparable to the SCHUFA Report in Germany. However, they lack the data necessary for this, so in 2015 the PBoC contracted eight companies on a trial basis to develop an official credit scoring system. Sesame Credit was one of those companies. Due to privacy concerns and conflicts of interest, in the end none of these companies received an official licence for their rating system. Instead, a joint venture composed of the eight companies and the China Internet Finance Association was founded. This joint venture is called Baihang Credit, and it is the first uniform credit information agency in China.

The Sesame Credit score offers my grandma lots of advantages

Participation in the Sesame Credit point system is currently voluntary and does not have any downside for its users. Actually the score resembles a loyalty programme, like collecting air miles. Ant Financial cooperates with many external partners that reward customers who have high scores and offer them many advantages. For example, my grandma with her high score does not have to pay deposits for hotel bookings, car rentals or bike rentals. She is directed to the fast lane at airport security, and her visa application for Luxembourg or Singapore gets priority treatment. Quite a few singles also post their Sesame Credit score on Baihe, Alibaba’s online dating service, in the hope of increasing their chances.

The score is intended to be a means of building up mutual trust. However, its additional use outside the Alibaba platform and the immediate financial context as a criterion for government tasks, such as airport security or issuing a visa, is a questionable mix of different sectors.

Impact of the score on companies: are product categories evaluated differently?

In a press interview, Li Yingyun, Technology Director at Sesame Credit, indicated that the type of product purchased affects the score. For example, buying nappies would increase your score because the system thinks you are a responsible parent. By contrast, if you buy a lot of video games, you are seen as less trustworthy, with a negative impact on your score. Although Ant Financial later denied this statement, doubts remain. For companies that market their products on the Alibaba platform, this represents a great uncertainty. If their products are in a category that is weighted negatively by the algorithm, that could lead to declining sales of these products in future because consumers are afraid of losing points.

Do the scores of my colleagues affect my own score?

One thing that aroused interest in the western media was the rumour that the online behaviour of your friends could be considered in the calculation of your own score. Alibaba has denied this. According to their statements, what matters is the size of your social network, not the online behaviour of your contacts. That’s because the more verified friends you have, the less likely it is that your account is fake.

We should follow the developments in China with a critical eye

It remains to be seen how the social credit system will develop up to 2020. Nevertheless, presently there is not (yet) any overarching AI-based super-system that evaluates the Chinese population according to a rating system and affects all aspects of their lives.

When it comes to China and technology, we quickly assume the worst and can easily imagine nightmare scenarios. However, the developments are often a bit more complex, and a critical attitude to news from the Far East is worthwhile. Especially for companies that are active in the Chinese market, it is essential to do your own research and keep a close eye on the market. The following websites that report on technological, economic and cultural developments in China can serve as a starting point:

  • Tech In Asia and Technode are blogs that discuss technology trends and the latest news on start-ups and large companies in China. Technode posts short daily briefs that explain what is happening and why the news is relevant. Their China Tech Talk podcasts are also recommended.
  • The South China Morning Post has a good business section as well as extensive technology coverage. If you’re looking for the latest headlines on China’s Internet heavyweights Alibaba, Tencent or JD.com, that’s the right place. However, you should bear in mind that Alibaba bought the newspaper in 2015.
  • Radii China primarily deals with the cultural aspects of present-day China, and Magpie Digest gives good insights into China’s youth culture.

There’s no longer any doubt that artificial intelligence (AI) can be creative. The question now is exactly what role AI is capable of playing in the creative process. Will this role be limited to that of any other tool, like a paintbrush or a camera? Or could AI become the muse, or even the independent originator of new creations? Could it even be responsible for the extinction of artistic directors as a species? If so, when?

For the time being at least, I can reassure my colleagues that their jobs are safe. Nonetheless, it might be wise for them to start getting on the right side of this new co-worker. Even though the beginnings of AI development go all the way back to the 1950s, it’s only today that exponential development of the three “ABC factors” is enabling it to really gather pace (for the uninitiated: A is for algorithms, B is for big data, and C is for computer chips). That’s why the time has now come for every sector and every company to ask itself how artificial intelligence should be transposed and integrated into its everyday activities.

Within marketing, applications for AI have so far been concentrated primarily in the areas of predictive analytics (for example, for providing recommendations in online shops), personalisation (for example, for individually-tailored newsletters), linguistic assistance, and automation (for example, in media planning). Another important area of marketing, which has so far been almost entirely ignored, is creativity. This is often entrusted only to human hands, and portrayed as an unassailable fortress. With sophisticated puns, poetry, sentimental melodies, magnificent graphics, and everything else that stirs our emotions, there’s surely no way that the processors of a cold machine could ever dream up creative content – is there?

Perhaps we shouldn’t be so sure. For there are already numerous examples today of how artificial intelligence can support, expand, or even imitate human creativity – and the numbers keep growing.

AI can write

How many journalists relish the prospect of laboriously scrolling through the same stock market updates, sporting results, and weather forecasts every day? No problem: the responsibility for texts like these that follow a fixed format can now be shouldered by AI – and without the reader being able to tell the difference. Who knows when robo-journalism will lead to the first advertising texts written by machines, or copy-CADs, as they’ve already been dubbed?

AI can speak

Adobe hasn’t only created the world’s most important program for image editing in the form of Photoshop, but has been hard at work on human speech as well: Adobe VoCo is Photoshop for voice files. After only 20 minutes of listening to a person talk, the program’s AI is capable of fully imitating their voice. VoCo doesn’t simply stitch together snippets of words already spoken by its human subject either, but is instead capable of pronouncing entirely new words as they are typed in.

KI can compose

A team from the University of Toronto has succeeded in programming AI to be able to compose and write catchy and memorable songs. The program, Neural Karaoke, was fed on more than 100 hours of music, based on which it produced an entire Christmas song complete with lyrics and cover graphics.

KI can construct images and grafics

So-called generative adversarial networks are capable of producing astonishingly realistic images from descriptions written by people. Simply put, they function by using a “generator” to randomly create pictures which are then evaluated by a “discriminator” that has learned to recognise objects with the help of real images. This process can turn the words “a small bird with a short, pointy, orange beak” into a photorealistic image.

KI can paint

AI program Vincent from product design specialists Cambridge Consultants, which is also based on generative adversarial networks, has extensively studied the style of the most important painters of the 19th and 20th centuries, and can now make any sketch drawn on a tablet resemble the work of a specific Renaissance artist.

KI can do product design

Intelligent CAD system Dreamcatcher from Autodesk can generate thousands of design options for metal, plastic, and other components, all of which provide the same specified functionality. The designs also have an astonishingly organic look which couldn’t be described as “mechanical” or “logical” at all.

KI can produce videos

Working together with MIT’s Computer Science and Artificial Intelligence Laboratory, Canadian company Nvidia has developed a technology that can synthetically produce entire high-resolution video sequences. The videos, which have a 2K resolution, are up to 30 seconds long and can be made to include complete street scenes with cars, houses, trees, and other features.

KI as the Art Director

Advertising agency McCann Japan has already been “employing” AI in the role of Creative Director for some time. AI-CD ß has been fed a diet of award-winning advertising for the last 10 years, and has already produced its own TV ad based on these data.

Big changes begin with small steps

What does all of this mean for us? Although we may still chuckle at the shortcomings of such AI applications today, development is now moving at an exponential rate – and the progress being made is impressive. This is why now is the time to start getting over the prejudices and fears, and to give proper thought to how we will construct creative processes in the future, together with the role that we want artificial intelligence to play in them. Big changes can’t be made at a single stroke, and are instead better implemented in many small steps. Barriers are best removed by being prepared to play around with new technologies in order to test them out and gather experience. True, doing this takes up a certain amount of a company’s time and resources. But those of us who begin with a small project and then slowly feel our way forward have much higher chances of achieving long-term success, and maybe even of helping to shape a new development in the AI world.

As Christmas trade slowly gathers pace, this year too it’s mainly prettily wrapped electronics that we can expect to see under German Christmas trees. November’s instalment of SEO News examines why we should keep a critical mind when it comes to technology, and also considers the possibility of Google’s homepage relaunch going awry.

Google is becoming a long quiet river

So, it’s finally happened. The last 20 years have seen Google not only set the standard for web-based search engines, but also lead the way with the minimalism and efficiency of its homepage design. During the Internet boom of the early naughties, Google’s simple search field with just its logo – or doodle – and two buttons underneath was the welcome antithesis of labyrinthine jumbles of links and tedious Flash intros. Much has happened since 1998, however, and the market leader from Mountain View is now finally bowing to the trend for constant and personalised stimulation. “Discover feed” is the name of a new feature which has been in the process of a progressive worldwide roll-out on desktop and mobile devices, including search apps, since the end of October. The first of several new functions announced by Google to celebrate its 20th birthday, Discover feed marks the first step towards an individualised response engine that delivers results without even needing to be asked questions (see our report). Although Google has experimented in the past with new homepage features that allow users to enter into popular subject areas, and with its assistant service “Now”, this is the first time that relevant content in the context of personal search histories is being presented in endless stream form. And, just like on YouTube, the whole experience is also available in a Night Mode which comes in special muted colours.

This design overhaul – the most comprehensive since Google’s very beginnings – has clearly been a difficult step for the decision-makers in Mountain View, even though the competition at Microsoft have taken a different visual tack from the start with their search engine Bing. With a striking new image to greet visitors to its homepage every day and the latest news, Bing has always provided more points of entry for its users than the market leader. It’s also interesting to compare Google with Amazon: for the Seattle-based retail search engine, content personalisation is the obvious starting point when it comes to homepage design. Perpetual upsell with the help of the A9 algorithm means that users are presented with countless individually-tailored offers. On the other hand, recent integration of increasing numbers of new features and placements has resulted in user experience and usability of design suffering significantly. The consequence seems to be that Amazon’s homepage design is devolving back into the confusing times of fragmented front page websites. Neither does user experience appear to be too great a sacrifice as long as takings are good. And for Google too, integrating paid ads into the Discover stream is naturally providing new forms of monetisation.

That said, the homepage itself may ultimately turn out to be a doomed model. Voice and visual search capabilities are now providing countless touchpoints for search engines, which may soon enough ditch classic web or app-based presentation formats to offer users a tailor-made answers and solutions package in their place. Until that time comes, SEOs will need to wait and see whether the new Google stream gains acceptance among its users, and what criteria Google’s Discover feed uses to generate its responses. This new, larger stage certainly shouldn’t go unused.

Led around by the nose

Technological progress is a function of modernity – it’s both its cause and its consequence. One of the clearest examples of just how deeply technology has embedded itself into our lives is the phenomenon of the search engine. Whether it’s Google’s vision of an invisible companion for the challenges of the unplannable outside world, or Amazon’s promise of immediate consumer satisfaction, neither project would be conceivable without the technology that functions as its beating heart. It was no different with the steam engine or with internal combustion. The difference is that the machinery driving the present chapter of modernisation is far harder to see inside. If the new diesel generator was something that you could take apart with your own hands, the same can hardly be said of algorithms and artificial intelligence, which exist only in distant clouds of data. And sometimes it’s difficult to shake the impression that the bombastic promises and visions of the high-tech industry are little more than a glitzy marketing show for a helplessly naive public.

This is why it’s always reassuring to catch the technological elite showing a more fallible side. To this end, the SEO Signals Lab Group announced a competition which challenged contestants to achieve a high ranking among responses to the search term “Rhinoplasty Plano” within the space of 30 days. The term was one that users might enter in order to locate plastic surgeons in the greater Dallas area of Texas who specialise in sculpting noses. This was a query that had not formerly been the subject of a great deal of competition, and which had high local relevance. The small-scale challenge delivered some unexpected results, however. Google’s mantra for success in organic searches can be broken down into three key points: relevant content, a friendly user experience, and clean technical compatibility with all platforms. That’s why it’s more than surprising that the winning website of the Signals Lab competition is written entirely in Latin – right down to its URLs, headings and footers. The use of Latin dummy text in website production is nothing unusual; in this case, however, the ancient language wasn’t just found in a forgotten placeholder for content in production, but throughout the site, as part of a strategy to reveal the fallibility of search engine algorithms. On top of that, the website was also packed with made-up local information, forged reviews, and substandard backlinks. That Google allowed what is clearly a fake website to rank second among responses to the search term in question can only be explained either as an anomaly, or as a blind spot in the omniscient Googleverse.

Two lessons can be taken away from this little experiment. The first is that it’s a comfort for the search engine sector to know that, even with the supposedly mature level of its technology, Google can still be caught out with classic old-school fake spam SEO. The second is that users need to stay vigilant, and try to establish how far they can trust technological progress before letting themselves get swept up in all the excitement. Although search engines are certainly extremely practical, they will never become part of human reality. Whether it’s Google or Bing, at the end of the day, search engines are no more than database-supported ways of selling advertising which offer a compact and free version of real life to tempt users in. By the way: if you’re looking for Latin-speaking surgeons to operate on your nose, apparently Florida has what you need as well.

As with every trend, many see voice interfaces as a magic bullet. Yet their application is not relevant to every situation. So, for which services do they offer a genuine incremental value? What characterises a good dialogue and how do we guarantee that a customer’s data is handled securely? Let us show you what you should be paying attention to.

In theory, voice interfaces should be perfectly integrated into our everyday lives. We are accustomed to packing information into language and expressing our wishes verbally. However, this is not our only means of communicating information. Information is also passed on non-verbally, often through gestures, mimicry and tone. In online chats, we attempt to balance out the scant possibilities of non-verbal communication with the help of numerous emojis. When describing superlatives, most of us will turn to wild gesticulation. For example, we use sweeping gestures to explain the size or width of something. If we see something extraordinary and want to describe it, as with a phone call, email or letter, we can only do so verbally; this often feels limiting and explains why we gladly rely on sending pictures. With countless gadgets available online, when we come across a great one and tell a friend about it, we tend to enumerate only a few of its attributes. We do so not only because we are limited with our time, but also because we know that our counterparts might find different features exciting. Our experience tells us that it is much better to simply send friends a link to the product so that they can see for themselves what they like most about the gadget.

Verbal communication in everyday life reflects verbal communication with voice interfaces. Not every application has the potential to generate added value through the use of a voice interface. An example of this is Amazon Alexa’s Skill Store. There are a lot of so-called ‘useless skills’, poorly rated skills that nobody uses. Voice interface skills are the equivalent to apps in the mobile world. But what characterises these useless skills? They have no incremental value for the user. Either they are simply not designed for voice interfaces or they are not well designed for dialogues and thus cause user frustration. But why is that? What can be done better and how can useless skills be avoided

Find a meaningful application

We often use everyday phrases like “Can you just…?”, “I need a quick…” or “What was that again…?”. This is especially true when we are short on time or have our hands full. Especially in these situations, we do not have the opportunity to sit in front of a computer or to get our mobile phones out. And this is exactly where the ideal scenarios for practical voice interface usage are found.
It is possible to provide all kinds of information, from the control of connected systems such as smart homes, or the use of services such as rental car bookings. All ‘hands free’ scenarios are also predestined for voice interfaces. From the mechatronics engineer who is working on an engine with oily hands and needs some info on a spare part, to the amateur cook who wants to know the next step of a recipe while kneading dough.
In such situations, software serves to make our everyday lives easier and more pleasant. And that’s exactly what counts when using voice interfaces. It’s a question of short questions, logical support and fast results. Pragmatism is key. It is therefore important to consider exactly which service or application you want to offer with a voice interface and whether it will really help the user in their private or professional life.

Remember to always think in terms of dialogue and never in visual concepts

When the smartphone and mobile app revolution flooded the market, already existing concepts were simply scaled down and taken over. It was only over the course of time that these adapted concepts were refined and adapted for the mobile format. However, the way in which people process visual information is very selective. The subconscious mind acts like a filter, directing our attention to the things that are important to us. Additional information will only come to us later. By contrast, auditory perception works quite differently. In this case, the subconscious mind cannot decide which information to absorb first. Instead, we process everything we hear in a predetermined order.

And this is where the first big mistake arises: When designing a skill for a voice interface, it is often falsely assumed that all it takes is a simple adaptation of an already functioning visual concept. Yet visual concepts contain too much information for a voice interface. If you use all this content, the user is flooded with long texts and an endless amount of information. The result is both exhausting and unpleasant. For this reason, Amazon has already launched the ‘one-breath rule’. It states that the text Alexa should communicate in an interaction with the user must be no longer than a slow breath. To ensure the user does not feel overwhelmed and the voice interface adapts better, it is important to look at the information to be communicated in detail and take into account text lengths and information restrictions. 

Avoid long dialogues: A second big mistake in terms of dialogue, which is also based on the adaptation of visual concepts, are overly long stretches of dialogue. Especially when it comes to e-commerce, we are used to being led through a process page by page so that by the end of the process, the system contains all the information needed to make a purchase. These processes are stable and, in most cases, lead to success. With a voice interface, the situation is different. A simple, multi-step, question-answer dialogue that can be executed quickly by the interface can still take several minutes. If the user takes too long to answer, the dialogue is usually simply ended. If something is incorrect or misunderstood, it can lead to errors. In addition, some well-known interfaces simply drop dialogue, even for no apparent reason. This is all the more annoying the more advanced this sluggish dialogue is.

In order to avoid this, when using a voice interface for the first time, certain basic user information can be queried and then assumed during further use. If necessary, you can also access this default data through another source. For example, if a user wants to book a trip to Munich, the voice interface needs the following data: Place of departure, final destination, date, time, preferred method of travel and payment type. The user has previously stated that he lives in Hamburg, mostly travels by train and often pays by credit card. The next possible time is selected as the default departure time. The interface would therefore be able to make a valid booking by asking just one question, namely the destination. And all this without a long and possibly error-prone and repetitive question-answer game with a poor dynamic. The user should always be able to make subsequent changes to the existing data. 

Different phrases employed at the right time with a pleasant dynamic: Language gives us the opportunity to express a specific statement in many different ways. Linguistic variation is an expression of intelligence. So why shouldn’t voice interfaces also vary in their formulations? Through enhanced dynamics and numerous phrases, the process and overall interaction are rendered much more natural. The interface adapts to the user, instead of the other way around. These linguistic adjustments also correspond to repeated use of the interface. If the interface explains everything in detail the first time you use it, further repetition of usage instructions should be avoided unless the user asks for them.

In situations where the user needs help, there is also a lot to take into account. It is not always clear how to use voice interfaces. Therefore, there is the option of asking for help. The interface can take into account the situation in which the user finds themselves. Finally, it recognises whether the user is currently in a shopping cart or specifying a date for a trip. This ensures that it is easy to provide the user with a shopping cart-related help request specifically when the user is dealing with the shopping cart. This knowledge should definitely be harnessed to provide the best possible in-situ support.

Ensuring secure dialogues

As with any software development, data security is a key issue when developing voice interfaces. So, what must be considered during analysis and conception? In the article ‘Voice Interfaces – The Here and Now‘, the big players were put under the magnifying glass. The interfaces that it describes are all cloud-based. Thus, the language analysis and processing does not take place locally on the user’s computer, but in the respective data centres of the provider. Within the framework of the GDPR, these providers not only have to provide information about where their processing servers are located, but also comply with applicable basic regulations. However, the question arises, why would a financial service provider or health insurance company want to store highly sensitive customer data in the cloud of a foreign company? Amazon, for example, offers a high level of security when accessing their services through encrypted transmission or authentication via OAUTH2, yet everything else in their infrastructure is invisible to users and developers. It is almost impossible to anonymise a voice interface that works with sensitive data in such a way that prevents knowledge about the user being tapped from the cloud side. Everything that is said is processed in the cloud, as is everything that the interface communicates to the user. Therefore, it is only possible to use voice interfaces in situations where no sensitive data is handled.

Why the cloud? The blessing and curse of current voice interfaces is that sentence transcription and analysis is based on machine-learning technology. Once a dialogue model has been developed, the system must learn this model so that it can then understand similar sentence variants. This ‘learning’ is a computationally intensive process that is performed on the hardware of a server. From this perspective, these cloud solutions are both pragmatic and, seemingly, essential. However there are a few solutions in the field of voice interfaces that can run on local machines or servers. For example, with its speech recognition software Dragon, software manufacturer ‘Nuance’ offers a tool that enables transcription via local hardware.

What needs to be considered when dealing with pins and passwords? Another aspect of data security is the type of interface in question. While it is easy to quickly glance at a visual interface and check if anyone is paying attention when entering our password, with spoken language it is far more problematic. The tapping of security-sensitive data is therefore easy game. Pins and passwords should therefore never be part of a voice interface. Here, connection with a visual component is more advisable. The user is authenticated via the visual component, while additional operations are carried out using the auditory component.

Conclusion

The handling of sensitive data still represents one of the biggest challenges when using voice interfaces. Here, it is important to work with a particularly critical eye and design dialogues accordingly. Security questions should never be part of a voice interface dialogue. While it may be tempting, visual concepts should never be transferred directly to a voice interface. This results in the user being overwhelmed and dialogues being interrupted for being too long or due to errors. If all of these points are taken into consideration, the user will find working with a voice interface pleasant, natural and helpful. Of course, whether the interface makes sense overall largely depends on the concept and field of application.

This is the third contribution of a four-part series on the subject of voice interfaces:

Until now, companies looking to advertise their products online have done so using ads on search engines, social networks or media websites. Now, there is yet another option: an increasing number of retail websites are offering space for companies to place ads. For the retail platforms, such advertising represents a lucrative source of income. The platforms offer a decisive advantage as well: first-hand access to data about what consumers want. And companies are seeing plenty of other benefits, too – for one thing, people visiting these websites are already in a buying mood.

   1. Top players such as Amazon, Zalando, Otto and Alibaba

Based on such data, being able to advertise and sell where consumer interest is at its peak is exciting. Top players such as Amazon, Zalando, Otto and Alibaba have long been aware of the marketing potential to be found here. These marketplaces are set to bring in significantly more income in the future, with increasing numbers of users (more than 50% on Amazon) not only wanting to search for products, but also to buy sooner or later in most cases.

Ad placement, budgets and costs have a significantly closer relationship to sales on marketplaces than they do in other channels or on other platforms. The logistical aspects of marketplace operators (availability, scheduled delivery, shipping costs, special consumer benefits, etc.) are already rated by many buyers as highly relevant to their purchases – alongside price comparability or coordinated, similar product ranges.

    2. What’s changing where strategies are concerned?

In order to appeal to consumers at the end of the consumer decision journey, it is essential that, in addition to performance marketing on search engines, companies also seek to make contact on marketplaces. Deploying user data on integrated retail media platforms in particular enables companies to make even more efficient use of different advertising formats. In addition, the advertising effect of brand messages beyond platforms like these should not be neglected, irrespective of the focus on conversions and sales. With often extensive coverage and direct placement within the competitive environment, retail media offer many options for generating additional income beyond simply optimising the cost-turnover ratio (CTR).

   3. What about consumer spending behaviour? What’s up and what’s down?

According to the latest eMarketer Report, spending on Amazon in the USA is set to almost double in the next two years – primarily at the expense of Google and Facebook. Other channels and platforms on the market will continue to develop at a stable rate, however. A new and exciting development will be represented by marketplaces springing up which offer their advertising clients even more options for placement and/or cooperation, and, if chosen correctly from a strategic point of view, provide at least one thematically relevant alternative to Amazon or price comparison websites.

Until the release of Amazon’s Echo, aka Alexa, the big players had paid little attention to voice technologies. In the meantime, there are numerous other variants, but which are the best known and which voice interface is the most suitable?

Today’s voice interfaces are a combination of two components, namely transcription and natural language processing (NLP). A spoken sentence is transcribed into text. This is analysed using artificial intelligence, based on which a reaction is generated and converted back to analogue speech via a speech synthesis (see also part 1).

Different classifications

Conversational interfaces are differentiated by whether they use so-called knowledge domains or not. Knowledge domains are digital structures that map knowledge around a given subject area.

1) Conversational interfaces with knowledge domains 

Conversational interfaces with knowledge domains are not just about parsing phrases, but about understanding the actual meaning behind a sentence. These types of interfaces are called smart assistants. Consider this sentence, which is simple for us humans: “Reserve two seats at a two-star restaurant in Hamburg!” – it is very easy for us to understand. We know that a restaurant can be given ‘stars’, that Hamburg is a city and that you can reserve seats in a restaurant. However, without this prior knowledge, it is difficult to make sense of the sentence. ‘Two Stars’ could just as well be the name of a specific restaurant. What two seats are and how to reserve them is then completely unclear. That a restaurant with certain characteristics in Hamburg is to be searched for, is also unclear. However, Smart Assistants should be able to precisely understand these concepts and therefore require special basic knowledge in respective domains such as gastronomy, events, weather and travel.

2) Conversational Interfaces without knowledge domains

Conversational interfaces without domain knowledge, such as Alexa, do not have this skill. Instead, they use a different approach. For a possible dialogue, sentence structures are specified during implementation in which variable parts, so-called slots, are defined. The spoken sentence is then analysed and assigned with a sentence structure. Subsequently, the component which generates the response to what has been said is informed of which sentence structure has been recognised by the given variable parts. The fact that this does not require any basic knowledge is clarified by the following sentence: ‘I would like to buy a red shirt’. At this point, the system does not need to know anything about clothes or colours because it just compares the phrase with given phrases related to buying a shirt. For this purpose, it is defined in the interface dialogue model that there is a sentence structure with an ID called, for example, ‘shirt purchase’. It is then subsequently determined that the sentence structure may have the following characteristics: “I want to buy a <colour> shirt”, “I want to buy a shirt in the colour <colour>” and “I want to buy a shirt in <colour>”. In this way, it also defines that there is a variable phrase (slot) named ‘colour’. The desired possibilities for this slot are indicated, e.g. ‘red’, ‘green’ and ‘yellow’. If the user utters the above sentence, the analysis shows that it has the ‘shirt purchase’ sentence structure with the value ‘red’ for the slot ‘colour’. In a correspondingly structured form, a back-end system can already begin to build something with this information.

The current key stakeholders

Until the release of Amazon’s Echo, aka Alexa, most IT companies had paid little attention to voice technologies. Although Siri was released with a bang, it was perceived more as a helpful tool rather than a whole new class of interfaces. However, the advantages of hands-free features for mobile devices were not to be dismissed and today each big player develops their own language solution. Here is a brief introduction to the current key stakeholders:

Amazon‘s Alexa

If you look at the Amazon product range, it is clear that Alexa is a logical development from already existing technologies. The Fire Tablets (launched 2013), Fire Phone (2014) and first Fire TVs (2014) were already equipped with voice control. However, Alexa’s ‘Voice Interface as a Service’ or ‘Alexa Voice Service’ technology is still not considered a Smart Assistant. Instead of analysing the meaning of sentences, they are simply compared in the background. When asked more complex questions, Alexa quickly bails out. The reason for this is that it only handles superficial knowledge domains that are not open to the developer. In addition, requests that can be expressed to an Echo must be very concise and not overly complex in their formulation. For example, films can be searched for using the name of an actor, or restaurants can be searched for by indicating the area. However, it does not get much more complex than this.

Google Assistant

Google Now was originally part of Google Search and was only searchable on the web. Later it was spun off to expand domain knowledge, making it more competitive with wizards like Apple’s Siri or Samsung’s S Voice. Last year, Google Now was replaced by Google Assistant. The extent to which the various knowledge domains in the Google Assistant are interlinked was impressively demonstrated at the Google Developer Conference with the ‘Google Duplex’ product. As a component of the assistant, Google Duplex can make phone calls to real people and make appointments for the hairdresser, for example, or even book a table. In doing so, the assistant not only accesses the appointment calendar, but must also have appropriate domain knowledge.

Apple‘s Siri

The story of Siri is a bit different. The Smart Assistant was developed by the Siri Inc. company and from the outset took the approach of analysing language by means of domain knowledge. Siri Inc. is a spin-off of the Stanford Research Institute (SRI). Fifteen years ago, SRI collaborated with these institutions on the CALO (Cognitive Assistant that Learns and Organizes) project, the experience of which influenced the development of Siri. Siri was released in the App Store in 2010 and Siri Inc. was promptly bought by Apple. A year later, Apple then officially announced that Siri is now an integral part of iOS. It has since been unrolled across all platforms. Most recently, the HomePod was released as a smart loudspeaker that reflects the current trend in voice interfaces and is comparable to Amazon’s competing product, Echo.

Microsoft’s Cortana

Microsoft’s Cortana was presented to the public for the first time in 2014 at a conference. Also designed as a Smart Assistant, Cortana features interesting reality-based adaptations. For example, a real assistant usually takes notes about their supervisor or client in order to get to know the person better and remember their habits. This is where Cortana uses a virtual notebook. For example, when being used for the first time, Cortana asks a few preferences in order to be able to provide personalised answers at an early stage. This functionality can also be prompted as needed. The key element of Cortana is Bing; Bing-based services allow you to make informal queries with the search engine.

Samsung’s Viv

Samsung has also been trying to establish intelligent software for their devices for quite some time, which naturally must also include a voice interface. In 2016 Samsung bought the company of Siri’s developers, Viv Labs. Viv Lab’s system fully relies on domain knowledge. Unlike its competitors, however, Viv is able to extend the knowledge base of external developers into new domains. As a result, the system should become more intelligent and be able to understand more and more. For example, imagine a whiskey distillery. With the help of experts, the Viv is provided with knowledge about the domain of whiskey and its products. In addition, a distillery shares all of its knowledge concerning wooden barrels and their production. The Viv domain knowledge now provides valuable expertise on which wooden barrels influence the taste of certain types of alcohol. For example, oak barrels provide whiskey with a vanilla flavour. If I now ask Viv what results in the vanilla note of a particular whiskey from said factory, Viv can answer that this taste is most likely due to oak barrel aging. Thus, Viv has merged both domains.

IBM’s Watson

To clear up any misunderstandings, IBM Watson should also be mentioned here. There is no ‘Artificial Intelligence Watson’ that understands everything and continuously accumulates knowledge. Instead, Watson is a collection of various artificial intelligence tools brought together under a common concept that can be used to realise a wide variety of projects. In addition, there are projects that serve to build up a large knowledge base. However, one should not labour under the illusion that each Watson project provides access to this knowledge. If you want to implement a project with Watson, you need to provide your own database – just as with any other machine learning toolkit. Among other features, Watson provides transcription (The IBM® Speech to Text Service) and text analysis (Natural Language Understanding Service) tools. If you want to implement a project together with Watson, you build on these two tools when implementing voice interfaces.

From analysing the problem to finding the right voice interface

Of course, there are many additional solutions, some of which are very specialised, but which also aim to break through the restrictions of the big players in order to offer more development opportunities. Now, the question naturally arises: But why all the different voice interfaces? As with many complex problems, there is no single universal solution. There is no ‘good’ or ‘bad’ interface. There are only ‘right’ or ‘wrong’ applications for the different technologies. Alexa is not good for complex sentence structures, but is great for fast conversions and is already widely used. On the other hand, while Viv has not been able to assert itself yet, it has the potential to understand random and complex sentences.

The selection of the right voice interface therefore involves choosing certain criteria, such as the application, focus, problem definition, needs of the target group and how open an interface is for integration into your own projects.

This is the second contribution of a four-part series on the subject of voice interfaces:

These days, it’s hard to shake the feeling that everything is changing. Unfortunately, we cannot provide much more stability – because things are about change. This edition of SEO News for the month of October asks the question of whether the Internet as we know it will still exist in ten years, and explores what Google has planned for the next 20 years.

1) The Brave New World of Google

Major birthdays are a welcome occasion to take stock and look ahead. It’s no different for companies and institutions. The search engine Google is currently celebrating its 20th anniversary. Consequently, the Head of Search, Ben Gomes, who was promoted just a few months ago, has attempted to construct a grand narrative in the form of a blog post. Gomes’ story begins with his childhood in India, when his only access to information was a public library, a remnant of Britain’s long-vanished colonial power, and finishes with the modern search engine. Gomes suggests that personalisation, automation and relevance are the cornerstones of a quality product that, according to him, still follows the original vision: “To organize the world’s information and make it universally accessible and useful”. But is this goal being sacrificed globally on the altar of proportionality? SEO news will take up this question again below, with regard to the double standards in dealing with China.

An interesting issue for everyday SEO work, however, is a paradigm shift which Gomes believes will be groundbreaking for Google over the next 20 years. The Head of Search confirms the vision of an invisible and omnipresent information, solutions and convenience machine. According to Google, the transformation to this ubiquitous service is to be followed by three fundamental processes of change. First, it’s about even stronger personalisation. At this level, Google wants to try to evolve from a situation-dependent provider of answers, into a constant companion. According to Gomes, users’ recurring information deficits and ongoing research projects will be recognised, taken up and handled. This is to be achieved, above all, by a restructuring of the user experience on the Google results page. All sorts of personalised elements will be found here in the near future to help users make their journey through the infinite information universe more efficient. The user not only gets to know themself in this process, more importantly, the search engine gets to know the user – that goes without saying.

But before any criticisms can arise, we move swiftly on to the second paradigm shift: The answer before the question.

Google has set out to identify and prepare information relevant to the individual user, even before they have formulated a search query at all. The key element here is technological. Following “Artificial Intelligence” and “Deep Learning”, a technique called “Neural Matching” should be especially helpful: It links the representation expressed by text, language or image with the higher-level object or concept. This represents the continuation of the concept of semantic searches and entities with new technological concepts, and is exceptionally consistent from a business perspective.

The third pillar of the change should be a greater openness to visual information in the search systems. The visual search has great potential for users and advertisers, as we have already discussed several times before. Google is immediately taking action, introducing a complete overhaul of its image search, as well as the integration of its AI-driven image recognition technology “Lens” into the new generation of in-house “Pixel” smartphones. The interesting thing about Google’s anniversary publication is what it doesn’t mention: The voice assistant Google Home. This is a good sign that, despite all market constraints, Google is not distancing itself from its technological DNA and allowing itself to be pushed into a competition with the voice market leader Amazon. Contrary to the publicised hype, voice software is yet to create a huge stir in the search world.

2) The end of the networked world

Oh, how everything is connected: The individual, the world, technology and democracy. More and more aspects of our existence are digitised or transmitted via digital channels. In this process, it always comes back to bias. The well-known tech companies are acting as the pacesetters of this upheaval with their platforms. It may not be too long before Facebook, Amazon or Google establish themselves as the quasi-institutionalised cornerstones of our social and economic systems. Even today, the real creative power of these companies often exceeds the capabilities of existing state regulations. And search engines are at the centre of this development as a human-machine interface and mediation platform. The most relevant shopping search engine Amazon, for example, is changing not only our personal consumption habits but also the appearance of our cities and landscapes, with its radical change in the retail sector. The convenience for the consumer has resulted in empty shops in the inner cities and miles of faceless logistics loading bays in the provinces. Meanwhile, global populism has cleverly used social and informational search systems to accurately position and reinforce its messages. Facebook and Google have contributed at least partially to the sudden and massive political upheaval in one of the largest democracies in the world. Maintaining their self-image as pure technology companies, Google, Facebook and the like, however, have so far persistently refused to accept responsibility for the consequences of their actions. Apart from public repentance and the vague announcement that they are looking for “technical solutions”, they have shown little openness to adapting their strategies to the intrinsic systemic dangers. So the interesting question is: do global technology companies have to represent those values of freedom and democracy that have laid the foundation for their own rise and success in the US and Western Europe? Or can companies such as Google or Facebook be flexible depending on the market situation, and utilise their technological advantage in dubious cases in the context of censorship and repression? Currently, the state of this debate can be seen in Google’s project “Dragonfly”. Since Mountain View has refused to censor its product content, the global leader has been denied access to the world’s largest and fastest-growing market. When Google ceased all activities in China in 2010, the People’s Republic was forced to do without it, and managed pretty well. China has managed just fine without competition for its own flagships Baidu, Tencent and Alibaba. According to consistent media reports, Google has been working for several months to restart involvement in the Middle Kingdom, with the blessing of the government in Beijing. Under the working title “Dragonfly”, Google is reportedly planning to launch a Search app and a Maps app. Working closely with the Chinese authorities, and under state control and censorship, these apps are expected to pave the way for future, widespread activities for Mountain View in the People’s Republic. It just goes to show that Google is prepared to play the game, if the price is right. This approach can be seen as pragmatically and economically motivated. Particularly in light of the fact that the Chinese authorities recently granted Google’s competitor Facebook company approval, then withdrew it after only one day. Rampant discord in the West and cooperative subordination in Asia: former Google CEO Eric Schmidt outlined the consequences of this double standard a few days ago in San Francisco. Schmidt told US news channel CNBC that he expects the Internet to divide over the next decade. He predicts a split into a Chinese-dominated and a US-dominated Internet by 2028 at the latest. Apparently, Silicon Valley has already given up on the vision of a global and open network for the world. However, the consequences of this development will be felt by every individual.

Until 2015, voice interfaces were perceived by most as a nice gimmick that was limited to smartphones and navigation systems. But with Amazon Echo, this technology entered the living rooms of many consumers around the world virtually overnight. Amazon is holding back its exact sales figures and has not released any other details yet, but according to news portal Business Insider in 2015 alone, 2.4 million Amazon Echos were sold worldwide, while in 2016, sales rose to 5.2 million. As a result, Apple also revamped the previously neglected Siri and, after six years of silence concerning its speech recognition programme, in June 2017 announced a very unique device: the HomePod. Other companies were subsequently forced to follow this trend, even if they were unsure how to handle it.

Back to the roots

At the same time, voice and conversational interfaces are not an entirely new concept. Voice interfaces are essentially conversational interfaces with a special input channel, namely for analogue language. The development stages of the past decades may even be known to many market observers. If you look at the technology behind a voice interface today, you will find two different components: One is responsible for transcribing analogue speech into text. The other analyses the text and reacts accordingly. This part is carried out by natural language processing and other artificial intelligence (AI) technologies. Both components have existed as separate technologies for a very long time:

1) Transcription

Transcribing simply means transforming spoken text or even sign language into a written form. Corresponding software has been available since 1982 when Dragon System launched its software. Somewhat rudimentary, it was developed for the former DOS (x86) and was called DragonDictate. Continuous transcribing was not yet possible, however 15 years later the same company launched Dragon NaturallySpeaking 1.0. The software already understood natural language so well that it was mainly used for computer dictation. However, the former systems had to be heavily voice trained, or the vocabulary used had to be limited in order to improve the recognition accuracy. Therefore, there were already corresponding prefabricated language packs for lawyers or medical practitioners, for example, whose language is highly specialised. Once optimised, these early systems delivered amazingly good results. In addition, Dragon already offered the option to control a Windows system with voice commands.

2) Natural Language Processing

After the language has been transcribed, the text can then be further processed. When considering a technology that can work with a natural-sounding input text, and that is also capable of reacting coherently to it, one quickly thinks of chatbots. These are a subclass of autonomous programmes called bots that can carry out certain tasks on their own. Chatbots simulate conversation partners and often act according to topics. Although these have enjoyed increasing popularity in recent years, it should also be described as a renaissance; The first chatbot was born 52 years ago. Computer scientist Joseph Weizenbaum developed ‘ELIZA’, which successfully demonstrated the processing of natural language and today is considered the prototype of modern chatbots.

3) Artificial Intelligence

The development of ELIZA showed that simple means are sufficient to achieve good results in the Turing artificial intelligence (AI) test, which concerns the subjective evaluation of a conversation. In spite of the bot’s simple mechanisms, test subjects have begun to build a personal bond and even write about private matters. Experiences with this first conversational Interface attracted a lot of attention and continuously improved chatbot technologies.

For example, in 1981, BITNET (Because It’s There NETwork) was launched, a network that links US research and teaching institutions. One component of this network was Bitnet Relay, a chat client that later became the Internet Relay Chat (IRC). Over the years, students and nerds have developed countless, more or less simple, chatbots for these chat systems, including ICQ. Like ELIZA, they were based on the simple recognition of sentences and not on the evaluation of knowledge.

In 2003, another important development was sparked, banking on a new class of chatbots, Smart Assistants such as Siri. CALO, the ‘Cognitive Assistant that Learns and Organizes’, was a development initiated by the Defense Advanced Research Projects Agency, involving many American universities. The system should help the user to interact with information more effectively and provide assistance by constantly improving their ability to interpret the wishes of the user correctly. The concept is based on digital knowledge representation. In this way, knowledge can be captured in a digital system and made usable. Semantic networks allow objects and their capabilities to be mapped in relation to other objects that enable the Smart Assistant to understand what a user wants to express with a given utterance. For example, if a customer wants to order a ‘dry wine’ through their Smart Assistant, then it needs to understand the connection between the terms ‘dry’ and ‘wine’, depending on the context. Only then does it understand that this term refers to a taste sensation and not the absence of fluid.

Learning

The simple recognition and comparison of texts, also called matching, and the intelligent analysis by means of knowledge representation are two different technologies that have evolved independently of each other. With the help of the matching approach, most applications can be implemented with straightforward resources. For more complex queries, however, a Smart Assistant is much better. However, in turn, this technology is more involved in terms of development and implementation because it requires a broad knowledge base.

Currently, the chatbots that one usually comes across are based on matching technology and can be trained with the help of machine learning (ML). With this method, the system is given as many text variants as possible to a certain statement, which it learns in order to then recognise other similar sentences in its subsequent application, without the need for any special knowledge.

Today we can choose between two technologies that can be used in a conversational interface. Depending on the requirements, one must ask the question whether a system that compares what has been said with learned sentence structures is sufficient or is a system needed that understands the meaning of what has been said and reacts accordingly?

This is the first contribution of a multi-part series on the subject of voice interfaces:

I used to love them as a child. Books where I could determine how the plot unfolded. How the book ended depended on the decisions that I made.

My childhood days are over and choose your own adventure stories have lost their popularity – or at least that’s what I thought! Recently, however, they have been cropping up again. And in digital form. And the best part is that these new story formats are not just for children.
Below is a summary of which channels these formats are available on and why they might be of particular interest for digital communication and therefore for corporate digital marketing.

1. Choose your own adventure TV story

In the HBO series “Mosaic“, viewers can decide for themselves from which perspective they want to view the story from next. In the app linked to the show, users can put together the half-hour sequences on their own like a mosaic or a puzzle and in this way influence the chronological order of the narrative thread.

If you listen to rumours from Bloomberg, Netflix is also planning interactive episodes for the fifth season of Black Mirror, where the audience will be able to determine the dystopian ending. It will certainly be interesting!

2. Choose your own adventure Instagram Story

Fans of the Netflix series Stranger Things have the opportunity to control what Steve and Dustin, two of the main characters, talk about and do. Poll stickers on Instagram stories will be used in a creative way to decide how the story unfolds.

3. Choose your own adventure Story via Voice-assistant

At the end of 2017, the BBC’s research and development department launched an interactive audio drama for voice assistants, such as Google Home and Amazon’s Alexa. The experience starts with the command “Alexa, open The Inspection Chamber”. What follows is a radio play that depends on the interaction with the listener. Sections of the 20-minute story change depending on the spoken responses from the user. There are three different endings in total.

Why are these formats so interesting for digital marketing and communication?

Choose your own adventure books drew me in so much when I was younger that I would read them several times over just to find out how the story would have changed if I had made a different decision at a certain point in the book.

With exciting content, this high level of attention and commitment leads to long periods of time taken up with it. A creative way for brands and advertisers to draw in their customers and build loyalty.

A review of the long-awaited Magic Leap One augmented reality platform

Hovering in front of me is a virtual shelf with various objects and categories for me to choose from. I use the controller to grab a small rocket, take it off the shelf and place it in the room. Thwack! The small spacecraft is rooted in front of me like something out of a comic book or video game. I walk around it and look at it from all sides.

To my colleagues, I look a bit like Vin Diesel in Riddick. Not because of the muscles, unfortunately, but because of the futuristic glasses I am wearing. This is the headset of the new AR platform Magic Leap One. I had the opportunity to test the new system in the “Creator Edition” just a few days after its release in our Plan.Net Innovation Studio in Munich.

The augmented image viewed through the glasses is slightly transparent, I can still see the pattern of the carpet behind the rocket – at least if I consciously try. The colours are bright and cheerful, as they should be – for a brief moment I have the desire to reach out a finger and poke the rocket. Immersion Level 1 is quickly achieved.

Originally it was said that the glasses would be able to project images on six different levels in order to represent objects in different degrees of sharpness in connection with the tracking of the eye movement. Experts have already complained that the hardware only features two of these levels, which significantly reduces the effect of the experience. I cannot understand that, because, as I see it, the overall performance of the graphics still does not come close to being able to capitalise on these six levels. Everything still looks very video game-like and the resolution is not so high that this level of detail would play a crucial role in the sensory experience.

The rocket is still stuck there. “Was that it?” I ask myself. I click on the rocket and it whizzes off with a “woosh!” No idea where it’s gone. Then I put a cowboy hat on one of my colleagues and give him a moustache from the objects library, which is still floating behind me in the room. At least until he leaves, and his hat and moustache are left hovering in space. Spoilsport.

Then I grab a few things from the next category in the library, to quickly set up a kind of marble run. I can work on my design from all angles, while I talk to my colleagues and look them in the eye. It’s like I’m just wearing sunglasses, all the while working on a setup that has no regard for the laws of physics, but is otherwise quite realistic to me. Anyway, the whole thing quite quickly becomes pretty routine and natural. Then I grab the marble from the library and listen to it rumble through my tubing system, fall onto a trampoline, fly across the room and bounce off a real table leg in the room thanks to the real-world tracking.

I look at my colleagues, beaming like a little kid and say: “That’s awesome, right?”. My three colleagues look at me, their hands in their pockets, and almost simultaneously shrug their shoulders. Immersion Level 2! Only I can see my marble track – I had forgotten that. This is one minor disadvantage – sharing the experiences is not quite so easy.

The second app that I can test is about the Icelandic band Sigur Rós. They spent five years learning how to create or compose music in mixed reality, in collaboration with Magic Leap. Tónandi is the name of the app and it has exceptional gesture control – this app does not require a controller. The task is to move through different levels or worlds that come to life with gestures. For example, you can collect stones, prowl through virtual grass and prod virtual jellyfish. When you have more or less completed a scene, it creates a kind of wormhole, through which you plunge into the next scene. Impressive!

The variety of the gestures you can perform using your own hand movements is a huge advantage. There are eight different gestures that the system recognises. Developers can design and add even more gestures. From my perspective, this is a huge advantage of the Magic Leap system. Microsoft currently only offers two defined gestures. I am someone who develops interfaces and products for the digital world and is always driven by the desire for the best possible user experiences and relevant products. So, the various possibilities for implementing different gestures in this context are obviously of great interest to me. I am convinced that the usage of a product must be regarded as having a direct impact on branding. This means that our work should be focussed not only on user satisfaction, but also brand image. With details such as meaningful gesture control, offers are accepted much faster and can become much more immersive experiences much sooner.

The gestures are often still very unnatural in practice. It can of course still be difficult to reach for a virtual stone, and high latencies leave you uncertain about whether you have grabbed it successfully or not. However, allowing developers to develop their own gestures enables testing and experimentation, ensuring faster development of user experiences and making the operation of extended realities as natural as possible.

In conclusion, all in all the first version of the Magic Leap One offers a mega-experience and is an important step in the direction of consumer-facing AR for the living room. The hardware is rock solid, but Magic Leap still has some details to work on. The massive hype built up in recent years by the communications is the major factor negatively impacting on the overall impression. Because the Creator Edition cannot really fulfil this dream just yet. Just like with all modern platforms, they are now asking for the opinions of the third-party development community. This will quickly reveal the actual potential of the platform and provide Magic Leap with good support for further development of hardware and software.

And what does it cost? If you want to buy one of these coveted devices, you will have to get it from the USA, and it will cost you the equivalent of just under EUR 2,000. Not to mention the customs duty for bringing it back to Germany.