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Over the last 50 years, our world has turned digital at breakneck speed. No art form has captured this transitional time period - our time period - better than generative art. Generative art takes full advantage of everything that computing has to offer, producing elegant and compelling artworks that extend the same principles and goals artists have pursued from the inception of modern art.

Geometry, abstraction, and chance are important themes not just for generative art, but for all art of 20th Century. As an art historian and an amateur generative artist, I see a clear line of influence on generative art starting from Cézanne and shooting straight through to the:

Fracturing of geometry in Analytical Cubism

Emphasis on technology, machine aesthetic, and mechanized production from Futurism, Constructivism, and the Bauhaus

Introduction of autonomy and chance in Dada, Surrealism, Abstract Expressionism

Anti-figurative aesthetic, bold geometry, and intense color of Neoplasticism, Suprematism, Hard-edged Abstraction, and OpArt

Use of algorithms by Sol Lewitt and others

To my eyes, all of these influences and more are tied directly into early generative art through to its modern day practitioners. So I cringe when I hear the majority of my art-loving friends dismiss generative art as being irrelevant, not of interest to them, or even unworthy of being called art.

Every generation claims art is dead, questioning why it has no Michelangelos, no Picassos, only to have their grandchildren point out generations later that the geniuses were among us the whole time. We have a unique opportunity to embrace some of the most important artists of our generation while most of them are still living (and working). I hope to do my part in facilitating this through this article. Here we will explore why people so often struggle with generative art and:

Offer a simplified definition of generative art

Abolish ideas that credit machines for making generative art and not the artists themselves

Give some non-technical examples of how generative art works

Explore some of the history of generative art going back to the early 1960s

Highlight my favorite generative artists and share some of their work

Examine the world of generative AI art that is just now starting to get mainstream attention

Hopefully by the end of this post you will either share my love for generative art or you will at least be able to intelligently communicate your distaste for the genre.

What is Generative Art?

One overly simple but useful definition is that generative art is art programmed using a computer that intentionally introduces randomness as part of its creation process. This often brings up two common but misguided viewpoints that hold people back from appreciating the beauty and nuance of generative art.

Myth One: The artist has complete control and the code is always executed exactly as written. Therefore, generative art lacks the elements of chance, accident, discovery, and spontaneity that often makes art great, if not at least human and approachable.

Myth Two: The artist has zero control and the autonomous machine is randomly generating the designs. The computer is making the art and the human deserves no credit, as it is not really art.

The truth is that generative artists skillfully control both the magnitude and the locations of randomness introduced into the artwork.

Controlled randomness may sound contradictory, but if you are an artist or an art historian, you know that artists have always sought ways to introduce randomness into their work to stimulate their creativity. Thinking about the process of coding generative art as being similar to painting or sketching is actually spot on. In fact, we will see that the tool favored by most generative artists refers to the individual artworks produced as "sketches."

Let's Look at Some Early Examples of Generative Art

Let's look at Georg Nees' 1968 work Schotter (Gravel), one of the earliest and best-known pieces of generative art. Schotter starts with a standard row of 12 squares and gradually increases the magnitude of randomness in the rotation and location of the squares as you move down the rows.

Imagine for a second that you drew the image above yourself using a pen and a piece of paper and it took you one hour to produce. It would then take you ten hours if you wanted to add ten times the number of squares, right? A very cool and important characteristic of generative art is that Georg Nees could have added thousands more boxes, and it would only require a few small changes to the code.

Unlike analog art, where complexity and scale require exponentially more effort and time, computers excel at repeating processes near endlessly without exhaustion. As we will see, the ease with which computers can generate complex images contributes greatly to the aesthetic of generative art.

As with many innovations, there were several pioneers exploring the potential for generative art in its first few years. Frieder Nake and Michael Noll, along with Georg Nees, were all exploring the use of computers to generate art. Back then, computers typically had no monitors, and the work was shared by printing the art on plotters, large printers designed for vector graphics.

Pioneering Women of Generative Art

It was hard for anyone to be a generative artist in the '60s and '70s. Computers were primitive, filled entire rooms, and access to them was extremely limited. Today most people grew up with computers in their homes and now carry them in their pockets. The majority of people in those early decades of computing had little to no contact with computers or frame of reference outside of science fiction. Against this backdrop and in a time where women faced tremendous sexism in the workplace, a large number of female generative artists emerged, making key contributions to the craft and the community.

Vera Molnár is one of the more prolific generative artists (a personal favorite of mine), and her work spans several decades. Below we see Molnár's works from the '60s, '70s, and '80s.

Aware of the general perception of computers as cold, logical machines, Molnár spoke of the creative and humanistic gains it presented to her as an artist:

Without the aid of a computer, it would not be possible to materialize quite so faithfully an image that previously existed only in the artist's mind. This may sound paradoxical, but the machine, which is thought to be cold and inhuman, can help to realize what is most subjective, unattainable, and profound in a human being.

Generative artist and art researcher Lillian Schwartz worked as artist-in-residence at Bell Labs starting in 1968 for over 34 years. Her credentials are impressive. She was the first to have generative art acquired by the MoMA and is often credited, along with her collaborator Ken Knowlton, with being the first to exhibit animated digital work as fine art. In a 1982 interview in the Los Angeles Times, Lillian described the cool reception she received from her art world peers when she introduced the computer into her art making practice:

I had a reputation in the arts before I got involved in these areas, but when I started using computers, my fellow artists began to look on me as a prostitute. I haven't been able to find an artistic circle where I can discuss the aesthetics of my work. I've had to replace my artist friends with computer scientist friends.

Aside from her generative artwork, Lilian is one of my personal heroes for pioneering the use of computer databases in the analysis of art history (my life's passion). She shocked the world in 1984 when she used a computer to prove that Da Vinci himself was in fact the model for the Mona Lisa.

Other key female generative artists from the early days of generative art who made enormous contributions in popularizing the genre include Sonia Landy Sheridan, who founded the first generative systems department at the Art Institute of Chicago in 1970, and Grace Hertlein, who helped to popularize the first annual generative art competition when she became arts editor for Computers and Automation Magazine in 1974.

Though not known to be a programmer, Muriel Cooper had as much influence as anyone in establishing the aesthetics of the computing revolution. Cooper was trained in the design principles of the Bauhaus and influenced by her friend, master designer Paul Rand. Cooper imbued these principles at MIT, where she served as a long-time director of the MIT Press. She then founded MIT's Visual Language Workshop (VLW) in 1975, which moved to the MIT Media Lab in 1985 as " one of its founding sources." As we will see, the Media Lab went on to be more important to the evolution of generative art than any other singular institution.

A visionary who saw the need to reinvent design in the face of a shifting world, Cooper believed:

The shift from a mechanical to an information society demands new communication processes, new visual and verbal languages, and new relationships of education, practice, and production.

John Maeda and the MIT Media Lab

Many people may know John Maeda as the former president of the Rhode Island School of Design (RISD) or as the author of the book The Laws of Simplicity. They may not know that Maeda started as an engineering student at MIT where he was fascinated by the work of Murial Cooper and the VLW. After completing both his bachelor's and master's degrees in engineering at MIT, Maeda earned a Ph.D. in design at Tsukuba University's School of Art and Design in Japan.

Maeda returned to MIT and created the Aesthetics and Computation Group (ACG) within the Media Lab. As a group, ACG were heavily influenced by the prior work done by Murial Cooper's VLW group. Though Maeda is an accomplished generative artist with works in major museums, his greatest contribution to generative art was his invention of a platform for artists and designers to explore programing called "Design By Numbers."

In the late '90s Maeda recruited several brilliant and like-minded artists/technologists into the Media Lab to help work on "Design by Numbers," including Ben Fry and Casey Reas. Fry and Reas took Maeda's "Design by Numbers" into classrooms around the world and eventually built their own free platform that could be shared outside of universities and used by anyone with an interest learning to sketch with code. They called this platform " Processing."

Ben Fry, Casey Reas & The Birth of Processing

Processing made generative art accessible to anyone in the world with a computer. You no longer needed expensive hardware, and more importantly, you did not need to be a computer scientist to program sketches and create art. The language, the environment, and the community were all carefully crafted from the beginning make Processing accessible to as broad an audience as possible.

Processing is the key turning point in the production and proliferation of generative art. The massive growth is clear in the graph below.

Fry and Reas have worked on Processing over the last 17 years, and and it has long been the preferred platform for the best known generative artists. In 2012 they created the Processing Foundation to "expand our reach and to support the software development," and have added Daniel Shiffman and Lauren McCarthy as members of the foundation board. They have written an excellent post that details the evolution of the history of Processing that I highly recommend you read. Processing's impact on an entire generation of artists and programers is nearly impossible to overstate - revolutionizing data visualization, pop culture, and fine generative art.

As an artist trained in traditional painting and drawing, I was a slow convert to appreciating digital art. I was reluctantly working with computers to make a living in the early 2000s, and it was my personal opinion for a long time that generative art was too hard-edged, lacked nuance, and was generally inferior to analog art. Then around 2006, I discovered Processing and the work of Jared Tarbell, and everything changed.

Jared Tarbell was creating what I still consider to be the most cutting edge, aesthetically pleasing, and engaging generative art way back in 2003 using a relatively early version of Processing. Tarbell, who went on to co-found the wildly popular handmade goods site Etsy, studied computer science at New Mexico State University. He describes himself as "one part Magical Mystery Tour (Lennon/McCartney), one part Brandenburg Concerto (J.S. Bach), and one part Somnium (Robert Rich)".

For me, Tarbell's work is the perfect generative art. It is the poster child for the duality of chaos and control, and has an intense level of visual complexity that slowly emerges from simplicity, making it feel more like it grew out of the soil than coming from algorithms.

Tarbell's code-driven artworks have a pulse. He makes the digital feel as organic as any analog art I know of, but it is created through code and is comprised of pixels. His work was a step change for me in terms of believing computers can produce art at the highest level.

Tarbell's explanation of his process reinforces the concepts of controlled randomness in generative art that we have been discussing.

When you write a program, it's going to be executed the same way every single time. So if you define a system like this where things can happen at random, as the creator, you can be surprised by your own program, which is really great.

Before Processing, Tarbell was part of a group of generative artists developing work on the Flash platform from Macromedia (now owned by Adobe). His site Levitated.net served as an educational resource for a generation of artists seeking to better understand how to code in Flash. You can still access his open source code on the site.

Joshua Davis, Flash, and Praystation

One of the artists Jared Tarbell cites as a major influence on his work is Joshua Davis. Since 1995 Davis has been using programming to produce art. He is among the first and best known artists to use Flash to create generative art.

You likely remember Flash. We all had a plugin for it in the early 2000s that added animation and interaction to the web. Though Davis attended Pratt first for painting and then for design, his style is largely self-taught. He emerged from a life of hard partying to lead a movement of Flash artists through a "shit-load of hard work" and continues to create impressive work. As he describes it:

Around 1998 I bought my first domain, called praystation.com. I was stoked! I slowly progressed and became more aware of design; I sort of realized who I was and what I was doing, without even knowing I was doing it. There was just a moment when I thought, "I'm a computer artist now! When did that happen?" I had finally found the new form of expression I had been looking for: I was going to use technology to make art.

Davis' unusual blend of hard work and extreme generosity was critical in spreading Flash as a platform to other artists for generative art. He was one of the first generative artists to make it a point to share his code by going "open source" so others could learn from him.

I love giving stuff away. I like it because that's just what DIY culture tells us. It's about freedom of knowledge: I truly believe that, as humans, we have more to gain by sharing what we know than trying to profit and hoard it.

In 2001 Davis's "Praystation" won the Ars Prize Technica, and his work is now in the Cooper Hewitt museum.

Artificial Intelligence & Generative Art

Though you may notice that the fluid organic imagery is a departure from the geometric abstraction we have seen so far, AI art is a subgroup of generative art. Much of the new work in AI art is being created by GANs (generative adversarial networks). GANs are a concept based on neural networks that computer scientist Ian Goodfellow came up with back in 2014. If it sounds complicated, don't worry, we'll simplify it a bit.

First off, GANs are comprised of two neural networks, which are essentially programs designed to think like a human brain. In our case we can think of these neural networks as being like two people: first, a "generator," whom we will think of as an art forger, and second, as a "discriminator," whom we will think of as the art critic. Now imagine that we gave the art forger a book with 1,000 paintings by Picasso as training material that he could use to create a forgery to fool the critic. If the forger looked at only three or four of those Picasso paintings, he may not be very good at making a forgery, and the critic would likely figure it out pretty quickly. But after looking at enough of the material and trying over and over again, he may actually start producing paintings good enough to fool the critic, right?

This is precisely what happens with GANs in AI art. Artists like Robbie Barrat have been exploring the creative potential of these systems for image-making for several years.

Barrat did an excellent job of explaining this process to me in more detail (and his role as the artist within it) during our interview back in April, 2018.

But what happens is there is this thing called latent space that emerges after you train the GAN. All the possible paintings that are possible are laid out in highly dimensional space you are feeding into the generator. But the way they are laid out isn't random, it truly makes sense. So if you want to get a similar painting to your previous painting, you can pick a point that's very close to the point for your first picture. But some of the dimensions actually mean things like color scheme. So if I had a generation that I wanted to make more colorful, I could adjust one of the dimensions. So I do have some control, but only after the fact. I can't tell the GAN to produce a specific painting, but if I find a painting I like, I can then make adjustments to it.

This may sound a bit technical, but it should be clear that artists have a great deal of control over the process - in fact, that is where the artistry comes in. I would encourage you to read my full interview with Robbie Barrat, as I feel he does a great job of explaining the foundational information needed to understand GANs as part of appreciating AI art.

For his latest project, Robbie gathered images from the Balenciaga online fashion catalog and used them to train his AI model. The GANs have been producing radical new fashions and styles unlike anything a traditionally trained human fashion designer would ever come up with. Barrat particularly likes the absence of symmetry, the random placement of pockets, and the addition of non-functional adornments like handheld tassels.

Barrat explained to me that he is using Pix2Pix technology in combination with DensePose to map the new AI outfits to the models. DensePose tries to estimate human poses and "...aims at mapping all human pixels of an RGB image to the 3D surface of the human body." A simpler explanation of this is that Robbie is training the AI to not only recognize the clothing in the Balenciaga catalog of clothing, but also the poses of the fashion models, and is then mapping the new fashions on to AI-generated fashion models and postures. Barrat explained:

pix2pix is the same thing as GANs, except instead of taking in noise, the generator takes in one picture and tries to output another that goes along with it, and then the discriminator looks at both pictures and its task is no longer figuring out if the images are generated or not, but rather if they make up a good pair.

Based on the proliferation of news stories of late, there is no doubt that the use of AI in art is a topic of interst to the general public. A separate and maybe more important question is, "Is the art itself very interesting?" Robbie Barrat's art was the first to cross that bar for me. As AI art, it has that timeless quality found in much of the great art we find in our museums. A good test of high quality AI art is that you should be able to appreciate the end result even if you did not know it was created with AI (though the tools are fascinating, as well). Mario Klingemann is another artist who passes this test with flying colors.

Their styles are completely unique and distinct, but it does not surprise me that Barrat frequently credits Klingemann as inspiring his own techniques. Barrat recently said on Twitter:

I owe a lot of credit to @quasimondo for coming up with the DensePose + Pix2Pix method that I'm using for all this fashion stuff. Check out his work - he's got very compelling results from using this process with actual portraits as training data.

Barrat is spot on. Klingemann's recent Pix2Pix/DensePose works are aesthetically fresh and refined in a way that more hastily produced AI portraits that have been generated with AI are not. Klingemann's works are like revisionist art histories: they boil everything creepy about old paintings down into digital-surrealist masterpieces - you can almost smell the dust on the surface. They make me feel like I am a six year old on acid, lost in the "Art of the Americas" wing of the Boston MFA. Those scornful patriarchal, puritanical stares are made that much creepier by the melting abnormalities and deformations Klingemann introduces with his neural networks.

Klingemann knows his work is creepy, and that is how he (and I) like it. When the question of whether or not AI art is "attractive" recently came up on Twitter, Klingemann tellingly responded:

My goal is to make interesting images. Not sure if you know my work, but the average feedback is "nightmare fuel," "creepy," or "uncanny"... I personally prefer ugly over boring, conventional, normal, non-challenging or derivative.

To that I say amen. I engaged in the Twitter chat and asked Klingman his thoughts on who owns the artwork - the human or the machine - a question an art reporter friend of mine had asked me in an interview that morning. Klingemann responded:

Like with any other machine, the owner or the operator of the machine owns it. Ask any photographer or pianist.

I was not surprised by his reply, as it echoes that of so many other generative artists in this post who have had to answer the questions about what role, if any, they play in the work they create.

I am excited to see AI technology made more accessible to artists. Cristóbal Valenzuela has been designing tools that do just that, first, with his " Text 2 Image" tool that lets you type in words which the AI then tries to draw (with varying results). For example, I typed in "a man snorts bananas in a spaceship," pressed enter, and it produced the following image:

When you can produce an image or an effect with the push of a button, it usually gets old quick. So it is not hard to imagine that we will see something akin to the tsunami of images with Photoshop filters we were inundated with in the early '90s. In fact, Valenzuela is building a tool suite not unlike Photoshop, but leveraging AI to democratize the new tools for artists and designers. I'm not going to lie, I want to be first in line to play with it.

As AI technology becomes increasingly available, artistry and technical advancement will only become more important in separating the remarkable AI artists from those repurposing old tools built by others or simply pushing a button to achieve an overused visual paradigm. I remain fascinated by the space and will continue to watch artists like Robbie Barrat, Mario Klingeman, Tom White, Helena Sarin, Memo Atken, Gene Kogan, and others closely as their work evolves. I should also note at the time of this writing the traditional art world has started to take notice.

Summary and Conclusion

This post stems from me hearing too many people say they "do not like digital art." Saying this is like saying "I do not like paintings," as digital art is so broad a field with generative art just being one part of it. I thought surely if people better understood the genre, they would have a better appreciation for the skill of the artists and the art they produce.

My goal was to help you fall in love with generative art - or at least give you a better understanding of it - without having to talk about code and math. Your appreciation of this genre will only improve if you explore the algorithms and programming behind the work. But same as you do not need to know how to paint to appreciate paintings, I believe you can appreciate generative art without understanding programming.

Though it became quite lengthy, this post was also not intended as a comprehensive history of generative art. Many amazing artists and key practitioners were not included. This article is simply one path through the history of generative art that highlights many of the artists I admire. I'd be thrilled to write the full story if given a book advance (hint, hint).

In summary, let's take a look back at what we have learned:

Generative art is an extension of central themes from 20th Century art

The artists play a major role in the outcome of the work

The process is very similar to traditional artmaking

Generative art has its own rich history going back to 1960

Women have played and continue to play major roles in this genre

MIT has been an incubator for brilliant generative artists

In the last two decades the genre has exploded as a result of the open source movement, improved tools like Processing, and a supportive community

AI art using GANs, Pix to Pix, and DensePose is a subgenre of generative art

As with all generative art, AI art is largely driven by human guidance

I studied art history in undergrad and earned an MFA in digital media, so I feel I have some qualifications to write a blog post like this. But in many ways it is not my story to tell. There is a strong chance I have made mistakes, and if I have referenced your work here and have gotten anything wrong, please know that I included your work in this post out of great appreciation and respect for what you do. Feel free to email me any corrections at Jason@artnome.com.

Finally, some of you may have noticed that the feature image for this article is by artist Manolo (Manuel Gamboa Naon), but I did not mention him in the text. This was intentional, and his work is included as a teaser. Along with Jared Tarbell, Robbie Barrat, and Mario Klingemann, Manolo is one of my favorite generative artists. I was lucky enough to interview him a few months back for feature piece that is now available. I could not limit myself to just a few paragraphs and one or two of his works - I'm hope you enjoy the interview!

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Over the last 50 years, our world has turned digital at breakneck speed. No art form has captured this transitional time period - our time period - better than generative art. Generative art takes full advantage of everything that computing has to offer, producing elegant and compelling artworks tha

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WHY the CUCUMBER SANDWICH?

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Ux is the Key to Digital Marketing success

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Ux is the Key to Digital Marketing success

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Today's consumers are smart, savvy, time starved and want their needs delivered in an instant.
from The Wavin'Flags.

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Today's consumers are smart, savvy, time starved and want their needs delivered in an instant. Whether at a desk, on a bus or in a bank queue, people like to access the internet for a variety of reasons; be that to shop, find answers via search engines, book flights or check in with friends. In the execution on all of these tasks, the quality of an individual's online experience makes a huge difference.

A critical part of that experience is the ease with which they can find, access, review or buy products and services. For businesses wanting to attract and convert that interest into revenue, user experience (UX) is crucial to their success and in many cases survival.

When it comes to developing and executing modern digital marketing strategies, UX can often get forgotten as 96% of mobile customers experienced when they came across websites that weren't optimized for their device. More worryingly, 79% admit to searching for another site if the one they landed on does not live up to expectations.

The ideal online customer journey is a user-friendly website that combines seamless navigation; a clean cut purchasing journey; dynamic and digestible content; top notch customer support and mobile device compatibility. By combining these elements a business can create an online environment that can make a brand. So why do organizations so often neglect UX when it comes to engaging with customers?

User experience in a mobile world

The rise in smartphone usage has changed the way people view, buy and interact online. With mobile now accounting for 65% of digital media time, the desktop is becoming the secondary touch point for an increasing number of digital users.

Mobile is so ingrained into the psyche of today's user that mobile users are five times more likely to abandon their if a website isn't correctly optimized for their phone - and if you're running a business, this could mean a major loss in conversions, or even damaging to your brand's reputation.

That said, what's alarming is that 52% of users said a bad mobile experience made them less likely to engage with a company - and with 80% of local searches converting on mobile devices (if they are designed properly), UX is a fact that simply should not be ignored.

Although mobile is a dominant force in today's world, it doesn't mean that desktop is no longer relevant. The majority of e-commerce transactions still take place on the PC, and as such, a balanced multi-platform strategy is essential to the buyer's journey. Despite this, many marketers and brands still treat the desktop as their main area of focus, which is out of sync with today's consumer behaviour.

User experience is more than just usability

While many marketers shrug off user experience as an industry buzzword, this is not the case. In fact, the most successful marketers in the industry will tell you that user experience is the key to facilitating conversions and helping you to become that glistening needle in a giant (and oversubscribed) digital haystack.

According to American professor and statistician Edward Tufte, "Confusion and clutter are the failure of design, not the attributes of information." This rings true to user experience as a whole, and when you're trying to stand out from the crowd online, one of the best ways to do so is by being clear, concise and accessible to your target audience.

While usability is an essential component of user experience, it is only one piece of the overall pie as it focuses on things at surface level, while UX digs deeper into whether an individual page or piece of content will satisfy the needs of a user. As a whole, user experience determines how a brand speaks to its customers and delivers the message it is trying to convey, effectively.

Content is vital to user experience

One of the best ways to convey a message to your audience is by publishing content that they can truly engage with. According to Quick Sprout, 82% of people enjoy reading relevant content from company blogs while a whopping 71% of online publishers claim well-formed mobile content significantly boosted user engagement.

Well crafted digital copy that is optimized for search engines is a key ingredient to a good user experience. Not only will this help your web page to rank well for relevant search terms on Google, but SEO friendly copy will also give the consumer an informative answer to the question in an instant. This will ultimately result in conversions rather than website abandonment.

While it may seem obvious for online businesses to provide search optimized, engaging and consumer-focused content it is often overlooked by many.

Where UX fits into the five tiers of content marketing

A mind-blowing content strategy that yields incredible results is always best planned from the ground up. Essentially, there are five levels to a successful content marketing model, which are:

1. SEO: This is essential because if your site isn't optimized for search engines, your target customers will never find you in the first place.

2. User experience: This is where UX fits into the mix. By now, you are most likely aware of its importance, but let's put into perspective. According to a recent study by Adobe, given 15 minutes to consume content, two-thirds of people would rather read something beautifully designed than something plain. And that's just one piece of the overall UX puzzle.

3. Content strategy: The blueprint of your commercial direction as once your site is SEO optimised and your UX is tip top, you can use metrics such as visitor numbers and bounce rate to craft your content to meet the needs of your target audience, and start achieving the results you desire.

4. Content creation: With a strategy in place, it's time to start the fun part - creating content. Refresh existing content, create new blog articles and make sure each piece is formatted for digital consumption.

5. Content distribution: Once you have created the world's most dynamic content, it's time to start getting it noticed. Share it with the right people using your various social channels and any other glistening connections you may have.

As user experience is the second step in a robust content strategy, it has to be done right. Otherwise, the whole strategy will come toppling down before it's even taken off.

Apple's user experience makeover

Taking note of just how crowded the smartphone and mobile device market has become, Apple recently announced their plans to enhance their user experience in the near future. In a drive to create a seamless experience across all of its platforms and grow their business after a growth reduction in recent months, the software juggernaut is tying up threads and pulling the platforms even closer together.

Along with improving its already successful portable desktop - iCloud in which Apple's service will be available on Macs as well as across iPads and iPods - is also opening up its sensational digital assistant, Siri, to third parties. This means that developers can take advantage of Siri's sophisticated voice command technology outside Apple applications, a move the company hopes will drive use of Siri amongst new users.

It's plain to see from Apple's forward thinking just how crucial user experience is to today's consumer. Without constant improvements to the way companies interact with customers, any brand will fall by the wayside in the current fast changing climate.

The rise of visual searching

A star on the horizon, visual searching is expected to become huge in the not so distant future. For example, augmented reality style app Blippar is taking user experience to a whole new level with its intuitive style of brand interaction, which it plans to get people to use it for visual search, rather than searching through Google - and it seems a likely target too.

Jordan Harper, Chief Technology Officer at Iris Worldwide, explains "Andrew Ng of Baidu said (not that long ago) that in five years time half of all searches would be either voice or image based, and recent innovations by Amazon and Apple demonstrate that voice based search is definitely being prioritized. Visual search is potentially even more powerful, though, but outside the lab, it's only really Google who is advancing the tech in the mainstream. It's good to see another company revving up."

Food for thought for companies out there. The fact is that humans are intuitive creatures, and it seems that visual searching will cater to our ever growing need for instant gratification in a world that gets busier by the day.

UX is not an optional luxury for competitive businesses. To give user experience the attention it deserves, time, money and resources must be invested, which also means acquiring the skills required to ensure success. As our Co-Founder, Ian Dodson says: "Fresh ideas fuelled by clever interpretation of data and a good user experience deliver great results."

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Why User Experience is Key to Digital Marketing Success

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Today's consumers are smart, savvy, time starved and want their needs delivered in an instant. A critical part of that experience is the ease with which they can find, access, review or buy products and services. For businesses wanting to attract and convert that interest into revenue, user experience (UX) is crucial to their success and in many cases survival.

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Neauoie plays like a strange interactive real world, in an ever-changing world where you are not an avatar but a force acting upon places, and objects, and puns, words - moving around and into your other selves, threading chaos.

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We are studying which font gives more confidence to the user, the user must trust a service before entering their data.
from The Wavin'Flags.

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We are making research about the function of shapes and colours to create an amazing interaction with costumers.
from The Wavin'Flags.

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So at the end of the year 2017, I took the opportunity of the annual evaluation with my manager to take time to do it correctly and ask myself the right questions: What really gives you pleasure working on? What are your strengths? In which field are you good at? I realised by asking me theses questions that I had a strong interest in the designing part of my job. And actually all my previous managers agreed on this point. In all my professional evaluations they mentioned my aesthetics sensibilities and my adding value in creating beautiful cosmetics products.

Also I began to question my friends and families about how they perceived me, what was my qualities and weaknesses. Many of my friends told me I have a great ability of hearing people and put myself into their shoes. One of them made me do the 16 personalities test which made me understand myself a little bit better. I strongly recommend you to do it ! It is a valuable tool to know yourself better.

So I started looking for a profession where I would feel some pleasure creating products, find professional challenges, while maintaining a business and strategy dimension. I wanted to find a job that would enable me to use the skills that I had acquired in my past experiences in marketing strategy.

Once the idea was here, I began to talk about changing career with my friends. It became not only something I had in mind but something real on which I had to make progress. One of my friend was doing conferences about her work as a product manager in Silicon Valley startups. I assist to many of her meetups, it made me meet new people evolving in a completely different area than mine. I learned a lot about UX/UI Design just by talking about people's experiences in that field. That's also when I heard about Ironhack, a learning platform which provides intensive courses in Web Development, UX/UI Design & Data Analytics. I gather some money, applied for a loan and then went to see my manager and the HR team of my company. They agreed to let me take 2 months off on the period of their choice to assist the UX/UI Design Bootcamp. Four months later, I started the program!

Obviously, learning UX/UI design hasn't been without any obstacles. It was hard to go out of my comfort zone. I was used to do the same job for 6 years and as a senior in my field, I was good at it. So starting again and learning new software and new methods wasn't easy but so interesting! Little by little, I would feel more and more confident about my capabilities.

There is no magic trick, to become a good UX Designer you have to be curious, read a lot of material on UX trends, work on many many case studies and always be curious of new design trends and UX methodologies.

My big struggle: As a junior designer, I often feel overwhelmed and get a bit of impostor syndrome,which sounds a little bit like this:

"There is no way I am qualified to solve this problem, this is way beyond my experience and skillset" or "I am going to fail and everyone will realize I have no clue what I'm doing"

Let me let you in on a little secret: experienced designers feel the exact same way! It is completely normal to have this feeling and that's what make us good designers. The best way to overcome this feeling and provide good designs is to implement thoroughly each steps of the Design Thinking Process.

For me the most important advice I can give you, is not to rush into any company that provides any products just because you're scared of not getting rapidly a job in UX design. Take the time to choose a product that really motivates you. Otherwise you'll end up as frustrated as you were in your previous life.

I was lucky when I did my bootcamp at Ironhack. A former colleague of mine started her own company few months ago. She decided to create a brand of period-proof underwear: an ecological alternative to existing women hygienic protection. When she pitched me her project I instantly knew I wanted to help her building it. I knew I wanted to put into practice everything I learned on UX/UI Design on a product that would made women's lives better. So it was without hesitation that I chose this project as my final project at Ironhack. I conducted user research to create a prototype of a responsive e-commerce website to sell her products. After having finished my training program, I naturally joined her startup and created it! The name of the brand is Smoon Lingerie, here is the link to the e-commerce website: smoon-lingerie.com

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From Marketing to UX/UI Design: How did I successfully changed my life in 1 year?

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So at the end of the year 2017, I took the opportunity of the annual evaluation with my manager to take time to do it correctly and ask myself the right questions: What really gives you pleasure working on? What are your strengths? In which field are you good at?

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A discussion about the importance of the interaction between different screens from Adobe xd.It is based on my attempt to create a pseudo-app....however i need some suggestions about the use of fonts.

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Mapping the new journeys dictated by quarantine

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Mapping the new journeys dictated by quarantine

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We are trying to understand how user journeys changed with the introduction of quarantine and forced isolation. We would like to re-map common experiences that usually require a physical presence, like grocery shopping, but there is so much work to do that we would like a map ourselves!

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Which are the best YouTube tutorials and web tutors in order to improve our designing knowledge? In this period is difficult to order some books, so we only have the web sources...
For the future : which books do you suggest to learn from scratch?

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They really help visualize the layout and information shown in the app, and you can quickly pinpoint the elements that have been repeated.

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What makes an artist different from others?