Posts

Manfred Klaus

Facebook Messenger chatbots: a communication channel for brands?

For six months, chatbots have existed in Facebook Messenger and there are now more than 30,000 available for users. The initial hype has calmed down and now companies are wondering if bots actually have the potential to become relevant communication and distribution channels for their content.

All chatbots essentially work in the same way. Users ask the bot a question and the bot searches through its stored database in accordance with certain rules, in order to respond with a suitable answer. The greater the database, the greater the knowledge which the chatbot can revert back to.

Mobile driven user behaviour and technical developments smooth the way

The requirements needed for the success of chatbots certainly exist: On one hand, internet usage is extending increasingly to mobile devices and here communication occurs primarily through instant messengers. The approach to text messaging has finally become seen as an everyday matter and the users have got accustomed to this reduced communication form.

On the other hand, all major tech companies are investing massively in the development of artificial intelligence, machine learning and in the understanding and processing of natural language through algorithms. Bot providers can relatively easily incorporate offers and services of interested companies into chatbots via standardised interfaces.

Unpredictable human communication

It will remain some time before a conversation with a chatbot is indistinguishable from a talk with a real person, as many chatbots currently reach the limits of their communication rather quickly. Either they fail in the correct processing of human communication, including all unpredictable factors such as slang, dialect or typos, or their repertoire of responses is rapidly used up. Initial reactions of early adopters were sobering. Among other factors, this was due to the fact that Facebook opened the chatbot platform for developers only a few weeks before the official launch.

Facebooks vice president of messaging products, Davis Marcus, admitted that this time frame was possibly too short to develop a good chatbot. Since the launch, Facebook has made many APIs and much guidance available to developers. We can therefore look forward to seeing how the second generation of bots will turn out.

For long term success, however, two central requirements must be fulfilled, above all:

Discovery: There is currently no easy way to find chatbots for Facebook Messenger, as we are still waiting for the launch of the announced bot store. The user must therefore know the name of the bot and integrate it via the search function of Messenger. Other messengers like Kik, Telegram or Skype already offer overview catalogues.

Added value: So that users don’t delete a chatbot after trying it out just once, from the first use on, the bot must offer real added value. This can include various aspects:

  • Reducing complexity and information: shopping bots, such as Tommy Hilfiger’s chatbot, help users when looking for suitable products, by giving them a pre-selection of products through targeted questions. The added value of news bots like the one of CNN also depends upon a reduction in information. Users indicate which content they are interested in and then receive suitable contributions in return through push messages.
  • Time efficiency and problem solving: The airline KLM emphasises special service for their customers: if you want to change your seating place, for example, you don’t need to open the app. You can simply send a quick message to the KLM bot.
  • Additional offers: In several US cities, through the Absolut Vodka chatbot, users can find bars in which the product is available. The added value here is that the user receives a voucher for a free drink as well.

If these points are further optimised in the new generation of chatbots and the problem of discovering bots is resolved, there is much to suggest that these services will establish themselves as communication channels for brands. With a sufficient amount of offers, in Europe and in North America Facebook Messeger could become a mobile central service point for users, just as weChat, LINE and Kik have done in many Asian markets.

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Stephan Enders

A view into the future. The Sample City Lab 2016.

The Sample City Lab shows us where we’re headed

Organised by Trend One, the Sample City Lab shows us the upcoming trends that will keep us busy next year. The event is focused on topics such as virtual reality, augmented reality, artificial intelligence, robotics and the internet of things in particular. The Plan.Net Mobile team were there in Innsbruck, where they weren’t just thrilled by the view from the ski jump (Bergiselschanze), but also by the content there.

Sample City Lab 1

Nils Müller, co-organiser of City Labs and founder of Trend One, introduced the innovations that would be shown at the exhibition. One of the exhibits at the show was the NAO robot. A completely programmable, autonomously acting humanoid robot that can supposedly help with issues such as programming, robotics and steering and control technology, as well as creativity, problem solving and working as a team.

Sample City Lab 2

The scanning robot NavVis measures room interiors quickly and cost-effectively. 3-dimensional diagrams of interior spaces can be called up via a browser based app to realise virtual tours.

Sample City Lab 3

The highlight of the show was the Microsoft HoloLens. The augmented reality glasses allow to display the user information and interactive 3D projections on the direct environment. The HoloLens works without a computer or smartphone, and can be used independently.

Sample City Lab 4

A few people at the Sample City Lab were allowed to test the HoloLens themselves. Games and videos right up to Office Programs can be controlled with hand gestures.

Sample City Lab 5

The fitness device ICAROS connects workouts with virtual reality. A virtual reality flight simulation is shown while you balance on the device, creating the believable illusion that you’re actually flying through a VR world. The positive side effect: training is fun this way.

Sample City Lab 6

An additional controller on the fitness device ensures that every movement of the device is measured precisely. Furthermore, it can control the virtual reality glasses and trigger specific actions.

Sample City Lab 7

Barbie has also arrived in the digital age. With artificial intelligence, she patiently answers all questions, and will gladly get into conversations. Sometimes, Barbie herself asks for advice, or wants to know more about her counterpart. The answers are surprisingly complicated, and some conversations take a rather interesting course. Childhood dreams come true here.

Sample City Lab 8

A holographic display was an eye-catching highlight. Video projections are reflected in a glass pyramid that conveys a 3-dimensional feeling, and brings the content to life. Additionally, the projections can be examined from three sides, and the scenery can be perceived from various angles.

Sample City Lab 9

The Sample City Lab shows us where we’re headed: virtual reality, augmented reality, artificial intelligence, robotics and the internet of things. These are the themes that drive us, and determine what our world will look like in the future.
Almost everything is fitted out with intelligence, is mobile networked and can react to environmental stimulus. With virtual reality, anyone can quickly immerse themselves in a unknown world, and experience new things. Mobile internet connects (almost) everything, and robots undertake tasks that previously only humans could do. The development is faster than ever before, and one thing’s for sure: it remains exciting!

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Christian Polzenhagen

Deep Learning – Machines are learning how to learn

Deep Learning is a sub-discipline of artificial intelligence (AI), whose basic idea harks back up to the 1950s. Although, mass suitability has as of now not been reached. With sinking costs for computer chips and thus also for networks, as well as the constantly growing amount of digitally available data, machine learning has been undergoing an impressive renaissance over the last few years. Deep Learning enables computer systems to recognise certain patterns in volumes of data through iteration, in this case the repeated execution of commands, and to further and further refine these. In short, Deep Learning machines are learning how to learn. The range of applications is sheer endless with only one requirement to be met: data in digital form should be available in large amounts to extract useful patterns.

Especially companies in the Silicon Valley are currently betting on this reawakened trend. The pace of evolution with which new insights can be won from the now massive amount of available data is enormous: In 2009, a team around Geoffrey Hinton from the University of Toronto delved into the topic of speech recognition. After intensive training, the software was in a much better position to convert spoken words into written text than all of its predecessors combined. Two years later, Google applied Deep Learning to data of its service YouTube and let it separate the data into several categories. The result saw next to categories such as ‘human faces’ also the category ‘cat’ appear, which led to a considerable degree of amusement.

Deep Learning has evolved enormously since that time. Only a few weeks ago, the Google computer programme AlphaGo beat the until then dominating champion Lee Sedol in the strategy game Go. Many consider this a milestone of AI, even if such excursions by Google should be seen rather as a gimmick. Google’s actual fields of application lie in the areas of search and the presentation of search results. For the company, the so-called Rank-Brain – which leads to even better search results – is much more important, because it is supposed to guarantee future domination on the search engine market.

Deep Learning is booming

The list of other current examples is already a long one – and it will grow even further in the future.

  • Facebook‘s new messenger M for instance, is being fed Deep Learning insights, which can result in entirely new services. Via machine-led interactions, the user can for example comfortably create a digital assistant, who facilitates everyday life through interactive calendar and reminder functions. As recently presented during the yearly conference, Facebook’s chatbots are becoming more powerful due to machine learning. Until a full-fledged assistant, able to make travel arrangements and administer an account, comes into existence, not that much more is needed.
  • IBM, Oracle and eBay are working on new solutions that are only possible because of Deep Learning. The goal is to make technology even more efficient, to customise search results or lists of suggestions according to the needs of the user.
  • Siri, Majel and Cortana are speech input systems, designed to facilitate input and search in smartphones of the platforms iOS, Android and Microsoft. The vision are devices that can be operated by only using one’s voice. These applications do not only revolve around a results list driven by an algorithm, but also around recognising semantic connections faster and better to further and further increase the programme’s intelligence.

It is also conceivable, that Amazon uses this technology to further refine the flow of goods. In doing so, the online merchant can get closer to its dream of delivering goods virtually in real-time. Should Amazon be capable of developing new prediction models to store goods in the respective warehouse before the customer orders his goods, the merchant must not stock the entire inventory in each warehouse. While this is still a dream of the future, it is already certain now that Amazon is working on speech input devices like Alexa, that are connected to the internet and as such, are supposed to facilitate everyday life.

The world will see lasting change because of Deep Learning within the next five to ten years. These innovations will also have consequences for job development. We will gain new insights through Deep Learning that would not be possible without it. In particular, data protection represents a big challenge, because not everything that is possible is being applied to the advantage of the consumer. The challenges consist of finding the correct norms. This is because there are no technical limits or industries, in which Deep Learning could not be used. As soon as – in whatever field – certain patterns have been identified, a huge potential for optimisation exists. These new insights will then be used in the most diverse fields, to exhaust its complete potential, increase reliability and to design the technology in an easier and easier way.

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