SOGETI UK BLOG

Anyone 5 years ago who would have predicted that the CEO of Microsoft will announce that (ro) bots on messenger platforms is at the core of their new strategy, would be declared insane. But that’s exactly what Satya Nadella did on March 31, 2016. Followed by Mark Zuckerberg two weeks later, announcing the exact same strategy. What’s behind this, and how should we interpret this robot development? The end of the app era dawns.

“The Bot Effect: Friending a Brand ‘is the second report in the series on Machine Intelligence Sogeti’s Exploration Institute New Technology.

Breakthrough of machine intelligence

On April 12, 2016 Mark Zuckerberg, CEO of Facebook, shared his vision on the digital future. During the developer conference ‘F8’ he declared that that ‘bots’ – clever pieces of software – will be provided widely accessible to Facebook Messenger.  People are able to add organizations to their friend list and start a conversation. Because it is impossible for an organization to engage in a real conversation with everyone, bots will do that for them. These bots not only listen, but they can also find out things, book a flight or buy or suggest something. That this is all possible has everything to do with the emergence of machine intelligence. Thanks to the ability to interpret natural language (natural language processing) and to respond in a comprehensive manner (natural language generation), robots, like humans, have a conversation. At least, they are becoming more capable.

Friending your brand

KLM recently launched a virtual assistant that you can add to your Facebook Messenger or WeChat list. That’s convenient, because changes on the departure time of your flight will be automatically sent and you can change your seat in a conversation on the messaging platform. You will normally need a separate app to do these kind of things. But in this case you do not have to leave the chat window while talking with your children. You swap your seat and continue to chat with others at the same time. You can ask questions in your own words, and the bot will arrange it for you. A service with a smile and a ‘brand as a friend’ is the dream of every marketer. Basically, it provides a wealth of new possibilities: the postman-bot that lets you know that he is near your home with a parcel, or dietitian who comes on the line if you do not move enough. Using location data, social media updates, lists of friends, restaurants where you’ve been, the agenda of the next week, and so on, the smart agent creates a perfect digital image of us to assist us where possible.

Microsoft opened its botstore

In late March Microsoft’s CEO, Satya Nadella, announced at the Build conference that the new strategy of the company is based by robots and chat platforms. “Conversation as a platform Nadella calls it.  “We are on the cusp of a new frontier that pairs the power of natural human language with advanced machine intelligence.” he said. Nadella explained that artificial intelligence in the form of chat bots are “The next big thing” and added that it’s just as important as the introduction of the web browser, the graphical user interface and the touchscreen. In the Business Week article “Clippy’s Back: The Future of Microsoft Chatbots” is explained in detail why Microsoft thinks every company – from hairdressing to car manufacturer – are going to make a bot. To achieve this kind of business, Microsoft recently opened a Bot Framework. This enables you to create and manage your own bots. The so called Bot Connector plays an important role, as it connects your bot to all the different chat platforms. Botstores replace the app stores. Do you see the compelling advantages? Finally freed from questions about a button in an app; should it be left or right, and should the background color be red or orange? In one fell swoop, all of these design issues are history. From now on, the interface is the conversation, not an app. It also provides new design issues. How do you design a good conversation for instance, what character should you give your bots? Not for nothing Microsoft hires experts from Hollywood to give the AI flagship Cortana an interesting personality.

Conversations popular than apps

The figures are well known and impressive: every day three billion people are using a messaging platform; one billion use WhatsApp, Messenger counts 900 million, Wechat 650 million and Line 215 million. Conversations dominate the Internet, and doing business while chatting is the new emerging trend. The new word for that is ‘conversational commerce’. The creators of chat bots bring their products to the market as’ Artificial Intelligence for Conversational Commerce’. The term “conversational commerce” was coined by Chris Messina. He was surprised in 2015 by integrating Uber within Facebook Messenger. This change in the way companies can approach customers directly, he describes as follows:

“Conversational Commerce :

Utilizing chat, messaging, or other natural language interfaces (i.e., voice) to interact with people, brands, or services and bots, therefore have had no real place in the bidirectional, asynchronous messaging context.”

If we look at the figures, we see that chat has been more important than apps. Around 42 billion messages are sent daily via WhatsApp and 1.6 billion photos and 250 million videos are shared on that platform. “Messaging is one of the few things that people do more than social networking.” – Mark Zuckerberg said in a public interview in November 2014. By messaging you create an alternative to the worldwide web and the various app stores. Messaging as a platform where bots are included make apps obsolete. Bots are the new apps.

Asian paradigm

The reason two of the main players in information technology have so much faith in this bot-future, is due to China’s WeChat. This messenger platform already opened its doors to bots. The Chinese can pay a bill directly from the messaging environment WeChat, transfer money, book a restaurant or order a taxi. On New Year’s Eve 2016 alone, there were over 8.08 billion WeChat ‘red envelopes’ (a bot that sends money), while PayPal for the full year 2015 ‘only’ had 4.9 billion transactions processed. Of the 700 million Chinese who are daily on WeChat, there are 300 million that have such digital pay-bot. It elicited David Marcus, VP Messaging Products of Facebook and former CEO of PayPal, to make the following statement: “Messaging is really, truly the next frontier … The Asian paradigm has shown there’s a there there.” There is a ‘there there’, in other words, ‘we see a bright future’. If 300 million Chinese people are paying through a chat program, the rest of the world is following these developments with great interest. WeChat now has 10 million services on their platform. “If you want to start a business in China, you are not the first to start a website, but you open an official WeChat account.” The number of officially registered users is more than 1 billion. You can plan a doctor’s visit in China via WeChat. First select the bot that you need, in this case one that makes an appointment with the doctor. Then choose the hospital, the department and the doctor, and finally plan the visit in the calendar of the specific doctor. Of course, this functionality depends on the processes and business rules, which vary by country.

The bot effect

We are at the beginning of the post-app era. There’s a shift from apps to conversation, from apps to messaging platforms and from app-stores to bot-stores. This all is triggered by machine intelligence, also known as artificial intelligence. The fact that machines are now (more and more) capable of understanding natural language like voice and speech, and talk back is something that we’ve seen happening in science fiction movies. Now fiction becomes a fact. Due to better hardware, new neuromorphic chips and Big Data, these machines are able to learn and improve. The impact of this shift in possibilities has implications for businesses and human beings, for efficiency and psychology. We foresee a playground of the following four elements.

1  People-fication of IT

Properties that were previously reserved for men, such as conducting a conversation, listening to arguments or anticipating certain intentions, now come within the scope of information technology. As these human capabilities of machines are getting better, it’s likely that we will treat computers more and more as if it were a real human. Some proof of that is already found in chatbots like the chines ‘Little Bing’. This people-fication, as we like to call it (or antropomorphic personification) could have a possible backfire on human to human conversations. When bots are 24/7 willing to listen, when they are more knowledgeable and always serve us with a smile, will people still accept other people not to be interested or friendly anymore? Bots will lift the bar for a new standard.

2  The emergence of the ‘butler economy’

The ability to engage in a conversation with a bot, provide creates a master-butler relationship. A servant who is always ready to put on a polite and discreet way to be of service to us, means a huge improvement of the service in most situations. Little things that we need to do daily will be done by bots. One of the side effects is that ‘search’ as we know it will come to an end. No need to go to Google any more, if the conversation and a butler are leading to the desired outcome.

3  A battle on micro moments is about to begin

Conversations are commonplace and conversational commerce stresses that many ‘trade’ is done in lost moments. These are the times when people pack their smartphone and do their things on the spot. These micro moments are crucial to the strategy of companies. Conversational capabilities will support that strategy.

4  Bot conversations on the work floor

The same principles of conversational commerce can be used in the workplace, and this has become known by the term ‘conversational office’. The bot as a colleague or boss is a very likely future. In Japan, McCann, an advertising and marketing organization, has just assigned its first Chief Creative Director in their board. Doctors and Lawyers will work together with their machine mates, and call center agents already are getting replaced by robots.

Sogeti Labs AUTHOR:
SogetiLabs gathers distinguished technology leaders from around the Sogeti world. It is an initiative explaining not how IT works, but what IT means for business.

Posted in: Behaviour Driven Development, Big data, Business Intelligence, communication, Developers, Digital strategy, Infrastructure, Innovation, integration tests, IT Security, IT strategy, Microsoft, Robotics, Software Development, Technology Outlook, User Experience, User Interface      
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Sogeti launches a new book today called the “IoTMap – Testing in an IoT environment” and has been written by Sogeti Labs authors Tom van de Ven (Sogeti NL),  Jaap Bloem and Jean-Pascal Duniau (Sogeti High Tech).

The Internet of Things (IoT) gives us solutions made of a mix of expertise such as High Tech “Things”, mobile solutions and business intelligence. Not only do we need a wide range of test expertise for IoT testing but with “Things” we introduce sensors, actors, electronics and other hardware to the test scope. “IoTMap” gives insight in testing an Internet of Things solution.
Setting up an IoT test approach is explained in five clear steps. Using a simple IoT model, each step is described and put into IoT context. The authors put existing building blocks from the TMap Suite in IoT perspective and added some new ones. The book gives you all the handles you need to cope with the trend that less functional testing is asked and more “IoT-experience-testing” needs to be put in place.

An exciting future
Mapping out the Internet of Things is essentially a matter of testing. Take Google’s self-driving car. The vehicles have travelled a few million miles since the project started in 2009 but every day they drive twice as far in the lab. Before the rubber of new software hits the road, every single change is thoroughly tested in the simulator by virtually driving the total mileage history of the fleet. Autonomously and manually that is!

You may think this is typical of today’s nascent state the Internet of Things is in but continuously checking behavior and delivering software updates is already the norm during the entire life cycle of systems. From thermostats to smartwatches, turbines, toothbrushes, connected cars, and complete production plants. IoT simply means automation to the max.
The road to success for Internet of Things applications and systems is paved with continuous testing. That much is clear. IDC estimates there are currently 13 billion connected things. Over forty percent of worldwide IoT revenue currently comes from manufacturing, transportation, smart cities, and consumer applications. This is gradually changing.

Ongoing disruption
The ongoing disruption of value chains via machine-to-machine communication is forcing organizations to completely rethink and retool their business. The Internet of Things and the Industrial Internet of Things have started to reshape product design, customer engagement, decision making, marketing strategies and after sales.
Within five years all industries will have IoTified their business models. The transformation towards mature IoT ecosystems delivering secure services will consist of connectivity, platforms, applications, and devices, blended together in so-called “cyber-physical systems of systems.”

Analytics, which is at the core of continuous testing, will drive intelligent services related to consumer, government, and enterprise oriented domains, ranging from shopping and vehicles to healthcare, energy, manufacturing, entertainment, and more.The enabler for increased growth is pervasive wireless connectivity to the Internet from every location. As much as ninety percent of all IoT data will eventually be hosted on cloud based service platforms. The question is not whether companies will be ready – there is no way of escaping this Innovation of Technology that is already developing.

Cover IoTMap-red-1200x900

The future belongs to those who create it, so start testing now! Sogeti’s IoTMap will help you map out your journey.
The book can be downloaded in our global online bookstore starting April 21st.

 

Sogeti Labs AUTHOR:
SogetiLabs gathers distinguished technology leaders from around the Sogeti world. It is an initiative explaining not how IT works, but what IT means for business.

Posted in: Application Lifecycle Management, Automation Testing, Business Intelligence, Cloud, communication, High Tech, Innovation, Internet of Things, Marketing, Quality Assurance, Research, Smart, Software Development, Sogeti books, SogetiLabs, Uncategorized, User Experience      
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Tom van de Ven and Chris den Arend speak at the biggest European IoT seminar: IoT TechDay 2016.

They speak about the smart miniature living room that demonstrates the full capabilities of Sogeti filling in an IoT solution from electronically engineering up to cloud solutions and UI. It also demonstrates multiple IoT technologies used with one “Thing” (IBM Bluemix, Microsoft Azure and Oracle Cloud). Watch the video  to find out more about their session.

https://www.youtube.com/watch?v=wr1jcnZ7bAw

After a successful session, Tom also speaks about his upcoming book IoTMap : Testing in an IoT Environment which is scheduled to be launched on the 21st of April in the Netherlands. Stay tuned!

 

Sogeti Labs AUTHOR:
SogetiLabs gathers distinguished technology leaders from around the Sogeti world. It is an initiative explaining not how IT works, but what IT means for business.

Posted in: Cloud, Data structure, Digital strategy, Infrastructure, Innovation, Internet of Things, IT strategy, Microsoft, Open Innovation, Quality Assurance, Requirements, Research, Smart, Software Development, Testing and innovation, User Experience, User Interface      
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education

Our world as we know it is running on artificial intelligence. Siri manages our calendars. Facebook suggests our friends. Computers trade our stocks. We have cars that park themselves, and air traffic control is almost fully automated. Virtually every field has benefited from advances in artificial intelligence, from the military to medicine to manufacturing.

However, almost none of the recent advancements in artificial intelligence have advanced the education industry. Why is education lagging behind? Why has the momentum for artificial intelligence in education seemed to have largely faded in the past few years?

Woolf, et al., (2013) proposed some “grand challenges” that artificial intelligence in education should work to address, including:

  • Virtual mentors for every learner: Omnipresent support that integrates user modeling, social simulation and knowledge representation.
  • Addressing 21st century skills: Assist learners with self-direction, self-assessment, teamwork and more.
  • Analysis of interaction data: Bring together the vast amounts of data about individual learning, social contexts, learning contexts and personal interests.
  • Provide opportunities for global classrooms: Increase the interconnectedness and accessibility of classrooms worldwide.
  • Lifelong and lifewide technologies: Taking learning outside of the classroom and into the learner’s life outside of school.

Over the last decade, applications of artificial intelligence have addressed several challenges of learning, including language processing, reasoning, planning, and cognitive modeling (Woolf, 2009). Known asIntelligent Tutor Systems, computer software is able track the “mental steps” of the learner during problem-solving tasks to diagnose misconceptions and estimate the learner’s understanding of the domain. Intelligent Tutor Systems also can provide timely guidance, feedback and explanations to the learner and can promote productive learning behaviors, such as self-regulation, self-monitoring, and self-explanation.  Furthermore, Intelligent Tutor Systems can also prescribe learning activities at the level of difficulty and with the content most appropriate for the learner (Azevedo & Hadwin, 2005;Shute, 2008;VanLehn, 2006). These systems are also able to mimic the benefits of one-to-one tutoring, and some of these systems outperform untrained tutors in specific topics and can approach the effectiveness of expert tutors (VanLehn, 2011). Noteworthy examples of these intelligent tutor systems include Tabtor,Carnegie Learning and Front Row. A meta-analysis comparing learner outcomes using Intelligent Tutor Systems to learner outcomes using other instructional methods found that over a wide aware of conditions, learning from Intelligent Tutor Systems led to higher outcome scores (Ma et al., 2014).

In another application to learning, artificial intelligence  can help organize and synthesize content to support content delivery. Known as deep learning systems, technology can read, write and emulate human behavior.  For example, Dr. Scott R. Parfitt’s Content Technologies, Inc. (CTI) enables educators to assemble custom textbooks. Educators import a syllabus and CTI’s engine populates a textbook with the core content.

Progress in artificial intelligence and machine learning has been impressive, but there is still much work to be done to advance learning science. While some progress is being made to bring artificial intelligence to the education space as described above, these efforts pale in comparison to advancements in the non-education space. Most of the exciting breakthroughs in 2015 were in fields outside of education. For example, companies such as Amazon and UPS have been piloting the use of drones to deliver packages and other goods to customers. Google recently purchased an AI software company, DeepMind, from a British startup for half a billion dollars. Google has dedicated more than 140 computer scientists to DeepMind, and the software recently taught itself how to play 49 retro video games so well that it consistently outperforms human players. Google has also been testing its driverless cars. PR2, a robot from Cornell University, learned how to perform various small tasks, and then “taught” Baxter, another robot from Brown University, how to perform the same tasks in an alternate setting. Another robot, ConceptNet 4, took an IQ test with tasks in vocabulary, comparisons and comprehension and was found to have the intelligence of a 4-year-old.

I believe that artificial intelligence could play a role in the growing field of learning analytics, evaluating the quality of curricular materials, and in adaptive learning and recommendation engines. There is also the potential for artificial intelligence to create unique learning pathways for individual learners in MOOCs and blended and online learning (Chaudhry, et al., 2013). (For more information about the potential for artificial intelligence in these areas, check out a recent special issue of AI Magazine.)

The possibilities for artificial intelligence to make significant contributions in any field are tremendous, and education shouldn’t be left behind.

This article was written by Barbara Kurshan from Forbes and was legally licensed through the NewsCred publisher network.

 

Sogeti Labs AUTHOR:
SogetiLabs gathers distinguished technology leaders from around the Sogeti world. It is an initiative explaining not how IT works, but what IT means for business.

Posted in: Application Lifecycle Management, communication, Digital, Digital strategy, Human Behaviour, Human Interaction Testing, Innovation, Quality Assurance, Research      
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The other week I was discussing some Internet of Things (IoT) related topics with my colleagues. Part of the discussion was on how to name the elements that make up an IoT solution. Not the specifics on functionality of the elements but how to name the set. Platform is something that comes by a lot, but mainly as one of the elements (for example a data storage platform). Stack is the other word we used to describe the set of elements: the IoT stack.

A lot of people in the discussion including me were in favour of the word “stack”. Reading articles here and there we find “stack” and “platform” being used in all kinds of contexts. Sometimes even in the same sentence: “Microsoft Azure Stack is a new hybrid cloud platform product” or we find questions as “What is a good/scalable tech stack to use for cross platform messaging service?”. In the Business Intelligence world we see the Hadoop stack from Sprak on a IBM Big Data and Analytics platform. So we see stacks running on platforms but also the term “cross-platform” makes an appearance. Apparently we can use a stack on different platforms. This brief research gives the impression that a stack is not a platform.

Let’s take a look at definitions before we proceed:

stack-1Stack

Originates from the data stack this term is used to describe a Last In First Out (LIFO) data storage principle. In this context we must actually refer to “solution stack” or “software stack”. According to the wikipedia definition the stack is a set of software subsystems or components needed to create a complete platform such that no additional software is needed to support applications. Applications are said to “run on” or “run on top of” the resulting platform. Some definitions of a platform overlap with what is known as system software.

stack-2Platform

A computing platform is, in the most general sense, whatever a pre-existing piece of computer software or code object is designed to run within, obeying its constraints, and making use of its facilities.

The term computing platform can refer to different abstraction levels, including a certain hardware architecture, an operating system (OS), and runtime libraries. In total it can be said to be the stage on which computer programs can run.

Since an application should be able to run on a software stack and the platform being the stage this application can run on, the terms “stack” and “platform” seem interchangeable. After looking at the definitions it is safe to say that stacks are needed to build your platform. Maybe we can restate the definition to a stack being the term for a platform under development (if there is such a thing as finished platform?!).

Coming back to the initial discussion on IoT stack or IoT platform I think we now must go with IoT stack, it being a nice generic term and only when choosing a specific implementation (i.e. Microsoft IoTOracle IoTIBM Bluemix) we refer to it as the IoT platform we work with.

For now “Stack” is not the new “Platform” but it is preceding platform until it becomes one.

Related Posts:

 

Sogeti Labs AUTHOR:
SogetiLabs gathers distinguished technology leaders from around the Sogeti world. It is an initiative explaining not how IT works, but what IT means for business.

Posted in: architecture, Big data, Business Intelligence, Cloud, Developers, Digital strategy, IBM, Innovation, Internet of Things, Software Development, test framework      
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