Monthly Archives: January 2017

Artificial intelligence is a fast emerging technology, dealing with development and study of intelligent machines and software. This software is being used across various applications such as manufacturing (assembly line robots), medical research, and speech recognition systems. It also enables in-build software or machines to operate like human beings, thereby allowing devices to collect, analyze data, reason, talk, make decisions and act The global artificial intelligence market was valued at US$ 126.24 Bn in 2015 and is forecast to grow at a CAGR of 36.1% from 2016 to 2024 to reach a value of US$ 3,061.35 Bn in 2024.

The global artificial intelligence market is currently witnessing healthy growth as companies have started leveraging the benefits of such disruptive technologies for effective customer reach and positioning of their services/solutions. Market growth is also supported by an expanding application base of artificial intelligence solutions across various industries. However, factors such as low funding access or high upfront investment, and demand for skilled resources (workforce) are presently acting as major deterrents to market growth.

On the basis of types of artificial intelligence systems, the market is segmented into artificial neural network, digital assistance system, embedded system, expert system, and automated robotic system. Expert system was the most adopted or revenue generating segment in 2015. This was mainly due to the extensive use of artificial intelligence across various sectors including diagnosis, process control, design, monitoring, scheduling and planning.

Based on various applications of artificial intelligence systems, the market has been classified into deep learning, smart robots, image recognition, digital personal assistant, querying method, language processing, gesture control, video analysis, speech recognition, context aware processing, and cyber security. Image recognition is projected to be the fastest growing segment by application in the global artificial intelligence market. This is due to the growing demand for affective computing technology across various end-use sectors for better study of systems that can recognize, analyze, process, and simulate human effects.

North America was the leader in the global artificial intelligence market in 2015, holding approximately 38% of the global market revenue share, and is expected to remain dominant throughout the forecast period from 2016 to 2024. High government funding and a strong technological base have been some of the major factors responsible for the top position of the North America region in the artificial intelligence market over the past few years. Middle East and Africa is expected to grow at the highest CAGR of 38.2% throughout the forecast period. This is mainly attributed to enormous opportunities for artificial intelligence in the MEA region in terms of new airport developments and various technological innovations including robotic automation.

The key market players profiled in this report include QlikTech International AB, MicroStrategy Inc., IBM Corporation, Google, Inc., Brighterion Inc., Microsoft Corporation, IntelliResponse Systems Inc., Next IT Corporation, Nuance Communications, and eGain Corporation.

Chapter 1 Preface 1.1 Research Scope 1.2 Market Segmentation 1.3 Research Methodology

Chapter 2 Executive Summary 2.1 Market Snapshot: Global Artificial Intelligence Market, 2015 & 2024 2.2 Global Artificial Intelligence Market Revenue, 2014 2024 (US$ Bn) and CAGR (%)

Chapter 3 Global Artificial Intelligence Market Analysis 3.1 Key Trends Analysis 3.2 Market Dynamics 3.2.1 Drivers 3.2.2 Restraints 3.2.3 Opportunities 3.3 Value Chain Analysis 3.4 Global Artificial Intelligence Market Analysis, By Types 3.4.1 Overview 3.4.2 Artificial Neural Network 3.4.3 Digital Assistance System 3.4.4 Embedded System 3.4.5 Expert System 3.4.6 Automated Robotic System 3.5 Global Artificial Intelligence Market Analysis, By Application 3.5.1 Overview 3.5.2 Deep Learning 3.5.3 Smart Robots 3.5.4 Image Recognition 3.5.5 Digital Personal Assistant 3.5.6 Querying Method 3.5.7 Language Processing 3.5.8 Gesture Control 3.5.9 Video Analysis 3.5.10 Speech Recognition 3.5.11 Context Aware Processing 3.5.12 Cyber Security 3.6 Competitive Landscape 3.6.1 Market Positioning of Key Players in Artificial Intelligence Market (2015) 3.6.2 Competitive Strategies Adopted by Leading Players

Chapter 4 North America Artificial Intelligence Market Analysis 4.1 Overview 4.3 North America Artificial Intelligence Market Analysis, by Types 4.3.1 North America Artificial Intelligence Market Share Analysis, by Types, 2015 & 2024 (%) 4.4 North America Artificial Intelligence Market Analysis, By Application 4.4.1 North America Artificial Intelligence Market Share Analysis, by Application, 2015 & 2024 (%) 4.5 North America Artificial Intelligence Market Analysis, by Region 4.5.1 North America Artificial Intelligence Market Share Analysis, by Region, 2015 & 2024 (%)

Chapter 5 Europe Artificial Intelligence Market Analysis 5.1 Overview 5.3 Europe Artificial Intelligence Market Analysis, by Types 5.3.1 Europe Artificial Intelligence Market Share Analysis, by Types, 2015 & 2024 (%) 5.4 Europe Artificial Intelligence Market Analysis, By Application 5.4.1 Europe Artificial Intelligence Market Share Analysis, by Application, 2015 & 2024 (%) 5.5 Europe Artificial Intelligence Market Analysis, by Region 5.5.1 Europe Artificial Intelligence Market Share Analysis, by Region, 2015 & 2024 (%)

Chapter 6 Asia Pacific Artificial Intelligence Market Analysis 6.1 Overview 6.3 Asia Pacific Artificial Intelligence Market Analysis, by Types 6.3.1 Asia Pacific Artificial Intelligence Market Share Analysis, by Types, 2015 & 2024 (%) 6.4 Asia Pacific Artificial Intelligence Market Analysis, By Application 6.4.1 Asia Pacific Artificial Intelligence Market Share Analysis, by Application, 2015 & 2024 (%) 6.5 Asia Pacific Artificial Intelligence Market Analysis, by Region 6.5.1 Asia Pacific Artificial Intelligence Market Share Analysis, by Region, 2015 & 2024 (%)

Chapter 7 Middle East and Africa (MEA) Artificial Intelligence Market Analysis 7.1 Overview 7.3 MEA Artificial Intelligence Market Analysis, by Types 7.3.1 MEA Artificial Intelligence Market Share Analysis, by Types, 2015 & 2024 (%) 7.4 MEA Artificial Intelligence Market Analysis, By Application 7.4.1 MEA Artificial Intelligence Market Share Analysis, by Application, 2015 & 2024 (%) 7.5 MEA Artificial Intelligence Market Analysis, by Region 7.5.1 MEA Artificial Intelligence Market Share Analysis, by Region, 2015 & 2024 (%)

Chapter 8 Latin America Artificial Intelligence Market Analysis 8.1 Overview 8.3 Latin America Artificial Intelligence Market Analysis, by Types 8.3.1 Latin America Artificial Intelligence Market Share Analysis, by Types, 2015 & 2024 (%) 8.4 Latin America Artificial Intelligence Market Analysis, By Application 8.4.1 Latin America Artificial Intelligence Market Share Analysis, by Application, 2015 & 2024 (%) 8.5 Latin America Artificial Intelligence Market Analysis, by Region 8.5.1 Latin America Artificial Intelligence Market Share Analysis, by Region, 2015 & 2024 (%)

Chapter 9 Company Profiles 9.1 QlikTech International AB 9.2 MicroStrategy, Inc. 9.3 IBM Corporation 9.4 Google, Inc. 9.5 Brighterion, Inc. 9.6 Microsoft Corporation 9.7 IntelliResponse Systems Inc. 9.8 Next IT Corporation 9.9 Nuance Communications 9.10 eGain Corporation

The Artificial Intelligence Market report provides analysis of the global artificial intelligence market for the period 20142024, wherein the years from 2016 to 2024 is the forecast period and 2015 is considered as the base year. The report precisely covers all the major trends and technologies playing a major role in the artificial intelligence markets growth over the forecast period. It also highlights the drivers, restraints, and opportunities expected to influence the market growth during this period. The study provides a holistic perspective on the markets growth in terms of revenue (in US$ Bn), across different geographies, which includes Asia Pacific (APAC), Latin America (LATAM), North America, Europe, and Middle East & Africa (MEA).

The market overview section of the report showcases the markets dynamics and trends such as the drivers, restraints, and opportunities that influence the current nature and future status of this market. Moreover, the report provides the overview of various strategies and the winning imperatives of the key players in the artificial intelligence market and analyzes their behavior in the prevailing market dynamics.

The report segments the global artificial intelligence market on the types of artificial intelligence systems into artificial neural network, digital assistance system, embedded system, expert system, and automated robotic system. By application, the market has been classified into deep learning, smart robots, image recognition, digital personal assistant, querying method, language processing, gesture control, video analysis, speech recognition, context aware processing, and cyber security. Thus, the report provides in-depth cross-segment analysis for the artificial intelligence market and classifies it into various levels, thereby providing valuable insights on macro as well as micro level.

The report also provides the competitive landscape for the artificial intelligence market, thereby positioning all the major players according to their geographic presence, market attractiveness and recent key developments. The complete artificial intelligence market estimates are the result of our in-depth secondary research, primary interviews, and in-house expert panel reviews. These market estimates have been analyzed by taking into account the impact of different political, social, economic, technological, and legal factors along with the current market dynamics affecting the artificial intelligence markets growth.

QlikTech International AB, MicroStrategy Inc., IBM Corporation, Google, Inc., Brighterion Inc., Microsoft Corporation, IntelliResponse Systems Inc., Next IT Corporation, Nuance Communications, and eGain Corporation are some of the major players which have been profiled in this study. Details such as financials, business strategies, recent developments, and other such strategic information pertaining to these players has been provided as part of company profiling.

See the original post here:

Artificial Intelligence Market Size and Forecast by 2024

We use ad-blockers as well, you know. We gotta keep those servers running though. Did you know that we publish useful books and run friendly conferences crafted for pros like yourself? E.g. upcoming SmashingConf San Francisco, dedicated to smart front-end techniques and design patterns.

Ive been following the idea of algorithm-driven design for several years now and have collected some practical examples. The tools of the approach can help us to construct a UI, prepare assets and content, and personalize the user experience. The information, though, has always been scarce and hasnt been systematic.

However, in 2016, the technological foundations of these tools became easily accessible, and the design community got interested in algorithms, neural networks and artificial intelligence (AI). Now is the time to rethink the modern role of the designer.

One of the most impressive promises of algorithm-driven design was given by the infamous CMS The Grid3. It chooses templates and content-presentation styles, and it retouches and crops photos all by itself. Moreover, the system runs A/B tests to choose the most suitable pattern. However, the product is still in private beta, so we can judge it only by its publications and ads.

The Designer News community found real-world examples of websites created with The Grid, and they had a mixed reaction4 people criticized the design and code quality. Many skeptics opened a champagne bottle on that day.

The idea to fully replace a designer with an algorithm sounds futuristic, but the whole point is wrong. Product designers help to translate a raw product idea into a well-thought-out user interface, with solid interaction principles and a sound information architecture and visual style, while helping a company to achieve its business goals and strengthen its brand.

Designers make a lot of big and small decisions; many of them are hardly described by clear processes. Moreover, incoming requirements are not 100% clear and consistent, so designers help product managers solve these collisions making for a better product. Its much more than about choosing a suitable template and filling it with content.

However, if we talk about creative collaboration, when designers work in pair with algorithms to solve product tasks, we see a lot of good examples and clear potential. Its especially interesting how algorithms can improve our day-to-day work on websites and mobile apps.

Designers have learned to juggle many tools and skills to near perfection, and as a result, a new term emerged, product designer7. Product designers are proactive members of a product team; they understand how user research works, they can do interaction design and information architecture, they can create a visual style, enliven it with motion design, and make simple changes in the code for it. These people are invaluable to any product team.

However, balancing so many skills is hard you cant dedicate enough time to every aspect of product work. Of course, a recent boon of new design tools has shortened the time we need to create deliverables and has expanded our capabilities. However, its still not enough. There is still too much routine, and new responsibilities eat up all of the time weve saved. We need to automate and simplify our work processes even more. I see three key directions for this:

Ill show you some examples and propose a new approach for this future work process.

Publishing tools such as Medium, Readymag and Squarespace have already simplified the authors work countless high-quality templates will give the author a pretty design without having to pay for a designer. There is an opportunity to make these templates smarter, so that the barrier to entry gets even lower.

For example, while The Grid is still in beta, a hugely successful website constructor, Wix, has started including algorithm-driven features. The company announced Advanced Design Intelligence8, which looks similar to The Grids semi-automated way of enabling non-professionals to create a website. Wix teaches the algorithm by feeding it many examples of high-quality modern websites. Moreover, it tries to make style suggestions relevant to the clients industry. Its not easy for non-professionals to choose a suitable template, and products like Wix and The Grid could serve as a design expert.

Surely, as in the case of The Grid, rejecting designers from the creative process leads to clichd and mediocre results (even if it improves overall quality). However, if we consider this process more like paired design with a computer, then we can offload many routine tasks; for example, designers could create a moodboard on Dribbble or Pinterest, then an algorithm could quickly apply these styles to mockups and propose a suitable template. Designers would become art directors to their new apprentices, computers.

Of course, we cant create a revolutionary product in this way, but we could free some time to create one. Moreover, many everyday tasks are utilitarian and dont require a revolution. If a company is mature enough and has a design system9, then algorithms could make it more powerful.

For example, the designer and developer could define the logic that considers content, context and user data; then, a platform would compile a design using principles and patterns. This would allow us to fine-tune the tiniest details for specific usage scenarios, without drawing and coding dozens of screen states by hand. Florian Schulz shows how you can use the idea of interpolation10 to create many states of components.

My interest in algorithm-driven design sprung up around 2012, when my design team at Mail.Ru Group required an automated magazine layout. Existing content had a poor semantic structure, and updating it by hand was too expensive. How could we get modern designs, especially when the editors werent designers?

Well, a special script would parse an article. Then, depending on the articles content (the number of paragraphs and words in each, the number of photos and their formats, the presence of inserts with quotes and tables, etc.), the script would choose the most suitable pattern to present this part of the article. The script also tried to mix patterns, so that the final design had variety. It would save the editors time in reworking old content, and the designer would just have to add new presentation modules. Flipboard launched a very similar model13 a few years ago.

Vox Media made a home page generator14 using similar ideas. The algorithm finds every possible layout that is valid, combining different examples from a pattern library. Next, each layout is examined and scored based on certain traits. Finally, the generator selects the best layout basically, the one with the highest score. Its more efficient than picking the best links by hand, as proven by recommendation engines such as Relap.io15.

Creating cookie-cutter graphic assets in many variations is one of the most boring parts of a designers work. It takes so much time and is demotivating, when designers could be spending this time on more valuable product work.

Algorithms could take on simple tasks such as color matching. For example, Yandex.Launcher uses an algorithm to automatically set up colors for app cards, based on app icons18. Other variables could be automatically set, such as changing text color according to the background color19, highlighting eyes in a photo to emphasize emotion20, and implementing parametric typography21.

Algorithms can create an entire composition. Yandex.Market uses a promotional image generator for e-commerce product lists (in Russian24). A marketer fills a simple form with a title and an image, and then the generator proposes an endless number of variations, all of which conform to design guidelines. Netflix went even further25 its script crops movie characters for posters, then applies a stylized and localized movie title, then runs automatic experiments on a subset of users. Real magic! Engadget has nurtured a robot apprentice to write simple news articles about new gadgets26. Whew!

Truly dark magic happens in neural networks. A fresh example, the Prisma app29, stylizes photos to look like works of famous artists. Artisto30 can process video in a similar way (even streaming video).

However, all of this is still at an early stage. Sure, you could download an app on your phone and get a result in a couple of seconds, rather than struggle with some library on GitHub (as we had to last year); but its still impossible to upload your own reference style and get a good result without teaching a neural network. However, when that happens at last, will it make illustrators obsolete? I doubt it will for those artists with a solid and unique style. But it will lower the barrier to entry when you need decent illustrations for an article or website but dont need a unique approach. No more boring stock photos!

For a really unique style, it might help to have a quick stylized sketch based on a question like, What if we did an illustration of a building in our unified style? For example, the Pixar artists of the animated movie Ratatouille tried to apply several different styles to the movies scenes and characters; what if a neural network made these sketches? We could also create storyboards and describe scenarios with comics (photos can be easily converted to sketches). The list can get very long.

Finally, there is live identity, too. Animation has become hugely popular in branding recently, but some companies are going even further. For example, Wolff Olins presented a live identity for Brazilian telecom Oi33, which reacts to sound. You just cant create crazy stuff like this without some creative collaboration with algorithms.

One way to get a clear and well-developed strategy is to personalize a product for a narrow audience segment or even specific users. We see it every day in Facebook newsfeeds, Google search results, Netflix and Spotify recommendations, and many other products. Besides the fact that it relieves the burden of filtering information from users, the users connection to the brand becomes more emotional when the product seems to care so much about them.

However, the key question here is about the role of designer in these solutions. We rarely have the skill to create algorithms like these engineers and big data analysts are the ones to do it. Giles Colborne of CX Partners sees a great example in Spotifys Discover Weekly feature: The only element of classic UX design here is the track list, whereas the distinctive work is done by a recommendation system that fills this design template with valuable music.

Colborne offers advice to designers35 about how to continue being useful in this new era and how to use various data sources to build and teach algorithms. Its important to learn how to work with big data and to cluster it into actionable insights. For example, Airbnb learned how to answer the question, What will the booked price of a listing be on any given day in the future? so that its hosts could set competitive prices36. There are also endless stories about Netflixs recommendation engine.

A relatively new term, anticipatory design38 takes a broader view of UX personalization and anticipation of user wishes. We already have these types of things on our phones: Google Now automatically proposes a way home from work using location history data; Siri proposes similar ideas. However, the key factor here is trust. To execute anticipatory experiences, people have to give large companies permission to gather personal usage data in the background.

I already mentioned some examples of automatic testing of design variations used by Netflix, Vox Media and The Grid. This is one more way to personalize UX that could be put onto the shoulders of algorithms. Liam Spradlin describes the interesting concept of mutative design39; its a well-though-out model of adaptive interfaces that considers many variables to fit particular users.

Ive covered several examples of algorithm-driven design in practice. What tools do modern designers need for this? If we look back to the middle of the last century, computers were envisioned as a way to extend human capabilities. Roelof Pieters and Samim Winiger have analyzed computing history and the idea of augmentation of human ability40 in detail. They see three levels of maturity for design tools:

Algorithm-driven design should be something like an exoskeleton for product designers increasing the number and depth of decisions we can get through. How might designers and computers collaborate?

The working process of digital product designers could potentially look like this:

These tasks are of two types: the analysis of implicitly expressed information and already working solutions, and the synthesis of requirements and solutions for them. Which tools and working methods do we need for each of them?

Analysis of implicitly expressed information about users that can be studied with qualitative research is hard to automate. However, exploring the usage patterns of users of existing products is a suitable task. We could extract behavioral patterns and audience segments, and then optimize the UX for them. Its already happening in ad targeting, where algorithms can cluster a user using implicit and explicit behavior patterns (within either a particular product or an ad network).

To train algorithms to optimize interfaces and content for these user clusters, designers should look into machine learning43. Jon Bruner gives44 a good example: A genetic algorithm starts with a fundamental description of the desired outcome say, an airlines timetable that is optimized for fuel savings and passenger convenience. It adds in the various constraints: the number of planes the airline owns, the airports it operates in, and the number of seats on each plane. It loads what you might think of as independent variables: details on thousands of flights from an existing timetable, or perhaps randomly generated dummy information. Over thousands, millions or billions of iterations, the timetable gradually improves to become more efficient and more convenient. The algorithm also gains an understanding of how each element of the timetable the take-off time of Flight 37 from OHare, for instance affects the dependent variables of fuel efficiency and passenger convenience.

In this scenario, humans curate an algorithm and can add or remove limitations and variables. The results can be tested and refined with experiments on real users. With a constant feedback loop, the algorithm improves the UX, too. Although the complexity of this work suggests that analysts will be doing it, designers should be aware of the basic principles of machine learning. OReilly published45 a great mini-book on the topic recently.

Two years ago, a tool for industrial designers named Autodesk Dreamcatcher46 made a lot of noise and prompted several publications from UX gurus47. Its based on the idea of generative design, which has been used in performance, industrial design, fashion and architecture for many years now. Many of you know Zaha Hadid Architects; its office calls this approach parametric design48.

Logojoy51 is a product to replace freelancers for a simple logo design. You choose favorite styles, pick a color and voila, Logojoy generates endless ideas. You can refine a particular logo, see an example of a corporate style based on it, and order a branding package with business cards, envelopes, etc. Its the perfect example of an algorithm-driven design tool in the real world! Dawson Whitfield, the founder, described machine learning principles behind it52.

However, its not yet established in digital product design, because it doesnt help to solve utilitarian tasks. Of course, the work of architects and industrial designers has enough limitations and specificities of its own, but user interfaces arent static their usage patterns, content and features change over time, often many times. However, if we consider the overall generative process a designer defines rules, which are used by an algorithm to create the final object theres a lot of inspiration. The working process of digital product designers could potentially look like this:

Its yet unknown how can we filter a huge number of concepts in digital product design, in which usage scenarios are so varied. If algorithms could also help to filter generated objects, our job would be even more productive and creative. However, as product designers, we use generative design every day in brainstorming sessions where we propose dozens of ideas, or when we iterate on screen mockups and prototypes. Why cant we offload a part of these activities to algorithms?

The experimental tool Rene55 by Jon Gold, who worked at The Grid, is an example of this approach in action. Gold taught a computer to make meaningful typographic decisions56. Gold thinks that its not far from how human designers are taught, so he broke this learning process into several steps:

His idea is similar to what Roelof and Samim say: Tools should be creative partners for designers, not just dumb executants.

Golds experimental tool Rene is built on these principles58. He also talks about imperative and declarative approaches to programming and says that modern design tools should choose the latter focusing on what we want to calculate, not how. Jon uses vivid formulas to show how this applies to design and has already made a couple of low-level demos. You can try out the tool59 for yourself. Its a very early concept but enough to give you the idea.

While Jon jokingly calls this approach brute-force design and multiplicative design, he emphasizes the importance of a professional being in control. Notably, he left The Grid team earlier this year.

Unfortunately, there are no tools for product design for web and mobile that could help with analysis and synthesis on the same level as Autodesk Dreamcatcher does. However, The Grid and Wix could be considered more or less mass-level and straightforward solutions. Adobe is constantly adding features that could be considered intelligent: The latest release of Photoshop has a content-aware feature60 that intelligently fills in the gaps when you use the cropping tool to rotate an image or expand the canvas beyond the images original size.

There is another experiment by Adobe and University of Toronto. DesignScape61 automatically refines a design layout for you. It can also propose an entirely new composition.

You should definitely follow Adobe in its developments, because the company announced a smart platform named Sensei62 at the MAX 2016 conference. Sensei uses Adobes deep expertise in AI and machine learning, and it will be the foundation for future algorithm-driven design features in Adobes consumer and enterprise products. In its announcement63, the company refers to things such as semantic image segmentation (showing each region in an image, labeled by type for example, building or sky), font recognition (i.e. recognizing a font from a creative asset and recommending similar fonts, even from handwriting), and intelligent audience segmentation.

However, as John McCarthy, the late computer scientist who coined the term artificial intelligence, famously said, As soon as it works, no one calls it AI anymore. What was once cutting-edge AI is now considered standard behavior for computers. Here are a couple of experimental ideas and tools64 that could become a part of the digital product designers day-to-day toolkit:

But these are rare and patchy glimpses of the future. Right now, its more about individual companies building custom solutions for their own tasks. One of the best approaches is to integrate these algorithms into a companys design system. The goals are similar: to automate a significant number of tasks in support of the product line; to achieve and sustain a unified design; to simplify launches; and to support current products more easily.

Modern design systems started as front-end style guidelines, but thats just a first step (integrating design into code used by developers). The developers are still creating pages by hand. The next step is half-automatic page creation and testing using predefined rules.

Platform Thinking by Yury Vetrov (Source67)

Should your company follow this approach?

If we look in the near term, the value of this approach is more or less clear:

Altogether, this frees the designer from the routines of both development support and the creative process, but core decisions are still made by them. A neat side effect is that we will better understand our work, because we will be analyzing it in an attempt to automate parts of it. It will make us more productive and will enable us to better explain the essence of our work to non-designers. As a result, the overall design culture within a company will grow.

However, all of these benefits are not so easy to implement or have limitations:

There are also ethical questions: Is design produced by an algorithm valuable and distinct? Who is the author of the design? Wouldnt generative results be limited by a local maximum? Oliver Roeder says68 that computer art isnt any more provocative than paint art or piano art. The algorithmic software is written by humans, after all, using theories thought up by humans, using a computer built by humans, using specifications written by humans, using materials gathered by humans, in a company staffed by humans, using tools built by humans, and so on. Computer art is human art a subset, rather than a distinction. The revolution is already happening, so why dont we lead it?

This is a story of a beautiful future, but we should remember the limits of algorithms theyre built on rules defined by humans, even if the rules are being supercharged now with machine learning. The power of the designer is that they can make and break rules; so, in a year from now, we might define beautiful as something totally different. Our industry has both high- and low-skilled designers, and it will be easy for algorithms to replace the latter. However, those who can follow and break rules when necessary will find magical new tools and possibilities.

Moreover, digital products are getting more and more complex: We need to support more platforms, tweak usage scenarios for more user segments, and hypothesize more. As Frogs Harry West says, human-centered design has expanded from the design of objects (industrial design) to the design of experiences (encompassing interaction design, visual design and the design of spaces). The next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systems. Rather than hire more and more designers, offload routine tasks to a computer. Let it play with the fonts.

(vf, il, al)

Back to top Tweet itShare on Facebook

Yury leads a team comprising UX and visual designers at one of the largest Russian Internet companies, Mail.Ru Group. His team works on communications, content-centric, and mobile products, as well as cross-portal user experiences. Both Yury and his team are doing a lot to grow their professional community in Russia.

See more here:

Algorithm-Driven Design: How Artificial Intelligence Is ...