Digital Design Forecast: Cloudy with a Chance of Dropping Jaws

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Tomorrow’s services are designed, developed and run on the cloud. What once was science fiction is now readily available for anyone. But technology itself is of little value unless it solves a relevant problem. For designers, this is both a challenge and an opportunity.

Better Services Live on the Cloud

Hyperscale public cloud platforms solve problems related to managing servers, developing software, and scaling services. They provide organisations with benefits such as lower costs and faster time to market.

But the benefits of modern cloud technology are not only about making existing stuff cheaper and faster. They’re about making better stuff.

Going into the cloud is not just about refactoring — it’s about reimagining.

I’m talking about new service concepts, intuitive human interaction, and simpler ways for you to serve your customers.

But technology alone won’t do any of those things. Not without some help.

And that’s where design comes in.

Cloud-Powered Designer Emerges

Design is about conceptualising and creating new things; designers imagine and designers make. Design differs from art in that it solves problems by delivering what people need.

Consider an industrial designer set to design a chair. The designer would have to understand who will use the chair, for what purpose, and where.

For example, watching TV with the family requires a different kind of ‘sitting solution’ than, say, manning an information desk at a shopping mall.

To design a chair that can be physically built, the industrial designer must also understand the properties of materials, such as wood, metal, plastic, and fabrics.

The same principles apply to digital design in the cloud.

For a new breed of cloud-powered designer, the beginning of the design process is the same. They too must understand who is going to use the digital service, for what purpose, and in what context.

But when it comes to the materials, the two designers deviate. Where the industrial designer uses tangible substances, the cloud-powered designer’s materials are in the cloud.

It’s not Science Fiction Anymore

All hyper-scale cloud platforms feature pre-made components, e.g. image recognition, language processing, intelligent search, decision making, machine learning…

There’s a new one almost every week. For the cloud-powered designer, those are the building blocks of services that help users reach their goals easier and faster.

For example, Uber is using Microsoft Azure’s Cognitive Services to offer real-time ID checks. Drivers verify their identity using selfies before they are able to accept rides.

Uniqlo uses Google Cloud Platform’s Dialogflow to offer a new type of shopping experience through a messaging interface, and responses are constantly improved through machine learning.

Tinder uses Amazon Webservices SageMaker to simplify machine learning and build models for predictions that create new connections that otherwise might have never been possible.

These are but a few examples of cloud-enabled building blocks that today’s digital designers have at their disposal.

Yesterday’s science fiction is today’s pre-made component.

And with those building blocks, we can create some jaw-dropping stuff.

Reimagining the Future, Together

The future of digital services is in the cloud, and those services are being imagined now. Going into the cloud is not just about refactoring — it’s about reimagining. As Abraham Lincoln said, the best way to predict the future is to create it.

For organisations, this means learning and working together.

If you’re a technologist, you must understand that design is the only thing that will differentiate you from the rest. Design makes for better business. Design is not only what it looks like; it’s about how it works.

If you’re a digital designer, you must become comfortable with technology. Become comfortable with the cloud; learn all about the building blocks of great digital services. It will be a challenge, but to design better things, you must know how to make them.

If you’re anyone involved in creating digital services, say hello to new team members outside the obvious realms of business, design or technology. Say hello to data scientists, futurists, anthropologists, social scientists, ethicists, philosophers… or people beyond the comfortable set of disciplines.

The future will be imagined and built by diverse groups of skilled people working together as teams toward common goals. For this to work well, everyone must be curious beyond one discipline.

By nature, designers have a massive opportunity to be the glue that binds everyone together.

Let’s use the best technologies to solve relevant problems. And let’s work together to create a more humane digital future.

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    Kari Koskikallio joined to lead our digital design studio Intergalactico

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    Kari Koskikallio, 43, has joined Nordcloud to lead our designers to engage the human superpower of creation at Intergalactico, Nordcloud digital design studio.

     

    Watch Kari talk about the magic in creative work on this video

     

     

    Kari Koskikallio,43

    Background:

    • Finnish and Australian citizen
    • Begun his career as a graphic designer in an advertising agency
    • Business consultant at Accenture (Finland and Australia)
    • COO at Fjord (Finland and Australia)

    At Nordcloud 

    • I will grow and develop the design business in all our 10 countries in Europe and sync the design offering with Nordcloud´s offering.

    When I have free time

    • I write science fiction under alias Rock Forsberg
    • I exercise and run
    • I am a football manager of my son´s football team

     

    Read more about Intergalactico digital design services here

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      Nordcloud launches new design studio: Intergalactico

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      Europe’s leading cloud-services provider Nordcloud has launched a new design brand and studio that will work across the company’s main markets. Intergalactico will focus on securing Nordcloud’s leadership in the design of cloud-first services, user interfaces and development strategies – ensuring Nordcloud stays at the forefront of high-end design for future cloud-based applications and experiences.

      “Smart, high-quality, user-centric design for the cloud that brings our customers consistently great user-experiences at reasonable investment – that’s what Intergalactico is all about,” says Mikko Rajala, Head of Design at Intergalactico. 

      “We see that the demand for excellent design is increasingly moving to upper-management level in organisations,” says Rajala. “Design is now seen as a competitive advantage, especially when companies are looking at new business areas and new solutions for their existing customers.” 

      The design experts at the core of Intergalactico have a solid track record in designing digital services and experiences, focusing on sustainable and high-quality designs that delight users. The team has been developing its service portfolio since 2006, working both directly with Nordcloud’s customers and as part of multi-vendor teams.

      “At the product level, our design operations have grown from doing single implementations to creating full-blown design systems,” says Rajala. “This means we’ve been designing validated, high-quality modular design patterns to serve multiple different teams within an organisation. Intergalactico is at the cutting-edge of cloud-first service design.”

      By creating Intergalactico as a brand, Nordcloud is highlighting the importance of service design as a business advantage for its clients. This enhances Nordcloud’s ability to offer its customers full life-cycle support for their cloud-based applications – from inception to continuous development and maintenance.

      Read more about Intergalactico and our design studio offering at www.intergalactico.io

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        Four UI Design Guidelines for Creating Machine Learning Applications

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        Previously, I’ve introduced three underlying general capacities of machine learning that are exploited in applications. However, they are not enough for designers to actually start building applications. This is why this particular post introduces four general design guidelines that can help on the way.

        How can we and will we communicate machine intelligence to users, and what kinds of new interfaces will machine learning call for?

        Machine learning under the hood entails both opportunities to do things in a new way, as well as requirements for new designs. To me, this means we will need a rise in importance of several design patterns or, rather, abstract design features, which will become important as services get smarter. They include:

        1. suggested features,
        2. personalization,
        3. shortcuts vs. granular controls,
        4. graceful failure.

        Suggested features

        Text and speech prediction has opened up new opportunities for interaction with smart devices. Conversational interfaces are the most prominent example of this development, but definitely not the only one. As we try to hide the interface and underlying complexity from users, we are balancing between what we hide and what we reveal. Suggested features help users to discover what the invisible UI is capable of.

        Graphical user interfaces (GUIs) have made computing accessible for the better part of the human race that enjoys normal vision. GUIs provided a huge usability improvement in terms of feature discovery. Icon and menus were the first useful metaphors for direct manipulation of digital objects using a mouse and keyboard. With multi-touch screens, we have gained the new power of pinching, dragging and swiping to interact. Visual clues aren’t going anywhere, but they are not going to be enough when interaction modalities expand.

        How does a user find out what your service can do?

        Haptic interaction in the first consumer generation of wearables and in the foremost conversational interfaces presents a new challenge for feature discovery. Non-visual cues must be used that facilitate the interaction, particularly at the very onset of the interactive relationship. Feature suggestions — the machine exposing its features and informing the user what it is capable of — are one solution to this riddle.

        In the case of a chatbot employed for car rentals, this could be, “Please ask me about available vehicles, upgrades, and your past reservations.”

        Specific application areas come with specific, detailed patterns. For instance, Patrick Hebron’s recent ebook from O’Reilly contains a great discussion of the solutions for conversational interfaces.

        Personalisation

        Once a computer gets to know you and to predict your desires and preferences, it can start to serve you in new, more effective ways. This is personalization, the automated optimization of a service. Responsive website layouts are a crude way of doing this.

        The utilization of machine learning features with interfaces could lead to highly personalized user experiences. Akin to giving everyone a unique desktop and home-screen, services and apps will start to adapt to people’s preferences as well. This new degree of personalization presents opportunities as well as forces designers to flex their thoughts on how to create truly adaptive interfaces that are largely controlled by the logic of machine learning. If you succeed in this, you will reward users with a superior experience and will impart a feeling of being understood.

        Amazon.com’s front page has been personalised for a long time. The selection offered to me looks somewhat relevant, if not attractive.

        Currently, personalisation is foremost applied in order to curate content. For instance, Amazon carefully considers which products would appeal to potential buyers on its front page. But it will not end with that. Personalisation will likely lead to much bigger changes across UIs — for instance, even in the presentation of the types of interactive elements a user likes to use.

        Shortcuts versus granularity

        Photoshop is an excellent example of a tool with a steep learning curve and a great deal of granularity in controlling what can be done. Most of the time, you work on small operations, each of which has a very specific influence. The creative combination of many small things allows for interesting patterns to emerge on a larger scale. Holistic, black-box operations such as transformative filters and automatic corrections are not really the reason why professionals use Photoshop.

        What will happen when machines learn to predict what we are doing repeatedly? For instance, I frequently perform certain actions in Photoshop before uploading my photos to a blog. While I could manually automate this, creating yet another user-defined feature among thousands already in the product, Photoshop might learn to predict my intentions and offer a more prominent shortcut, or a highway, to fast-forward me to my intended destination. As Adobe currently puts effort into bringing AI into Creative Cloud, we’ll likely see something even more clever than this very soon. It is up to you to let the machine figure out the appropriate shortcuts in your application.

        Mockup of a possible implementation of “predictive history” in Photoshop CC. The application suggests a possible future state for the user based on the user’s history and preceding actions and on the current image.

         

        A funny illustration of a similar train of thought comes from Cristopher Hesse’s machine-learning-based image-to-image translation, which provides interesting content-specific filling of doodles. Similar to Photoshop’s content-aware fill, it creates most hilarious visualisations of building facades, cats, shoes, and bags based on minimal user input.

        The edges2cats algorithm employs machine learning to finish your cat doodle as a photorealistic cat monster.

        Graceful failure

        I call the final pattern graceful failure. It means saying “sorry, I can’t do what you want because…” in an understandable way.

        This is by no means unique to machine learning applications. It is innately human, but something that computers have been notoriously bad at since the time that syntax errors were repeatedly echoed by Commodore computers in the 1980s. But with machine learning, it’s slightly different. Because machine learning takes a fuzzy-logic approach to computing, there are new ways that the computer could produce unexpected results — that is, things could go very bad, and that has to be designed for. Nobody seriously blames the car in question for the death that occurred in the Tesla autopilot accident in 2016.

        The other part is that building applications that rely on modern machine learning are still in its infancy. Classic software development has been around for so long that we’ve learned to deal with its insufficiencies better. As Peter Norvig, famous AI researcher and Google’s research director puts it like this:

        The problem here is the methodology for scaling this up to a whole industry is still in progress.… We don’t have the decades of experience that we have in developing and verifying regular software.

        The nature of learning is such that computers learn from what is given to them. If the algorithm has to deal with something else, then the results will not be to your liking. For example, if you’ve trained a system to detect animal species from pet photos and then start using it to classify plants, there will be trouble. This is more or less why Microsoft’s Twitterbot Tay had to be silenced after it picked up the wrong examples from malicious users when exposed to real-world conditions.

        The uncertainty in detection and prediction should be taken into consideration. How this is done depends on the application. Consider Google Search. No one is offended or truly hurt but merely amused or frustrated, by bad search results. Of course, bad results will eventually be bad for business. However, if your bank started using a chatbot that suddenly could not figure out your checking account’s balance, you would be rightfully worried and should be offered a quick way to resolve your trouble.

        To deal with failure, interfaces would do well to help both parties adjust. Users can tolerate one or two “I didn’t get that, please say that again” prompts (but no more) if that’s what it takes to advance the dialogue. For services that include machine learning, extensive testing is best. Next comes informing users about the probability and consequences of failure, and instructions on what the user might do to avoid it. The good practices are still emerging.

         

        This text is from an article originally appearing in Smashing Magazine:
        https://www.smashingmagazine.com/2017/04/applications-machine-learning-designers/ 

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