Exploring Empathy in the Metaverse

As we emerge from the COVID-19 pandemic and are able to look back and reflect, we have found that after the initial sense of camaraderie and despite soundbites of "We're all in this Together," many experienced a profound sense of isolation and fear. Along with this came a decline in empathy and the ability to share and understand others feelings.At the same time, the demand for Augmented Reality and Virtual Reality has catapulted. Meeting virtually, working virtually, and presenting virtually have all become commonplace in the workplace. Retail, education, and entertainment all saw a huge rise in using these technologies during the pandemic.Keiken, a fem-collective of artists based in London and Berlin, recently launched its first solo show that includes an interactive CGI film series, The Life Game, and Bet(a) Bodies, a wearable technology that simulates the physical experience of being pregnant. They feel strongly that empathy is an essential component of the human experience.“It’s important to remember with mind and body that you can’t have one without the other,” they assert. “The relationship between the digital and the physical can’t sustain itself in the Metaverse if we ignore the body, or our organic matter. If the physical body can be supported and stimulated while we’re in the Metaverse, rather than just dislocated, we have the potential to emancipate ourselves from the physical limitations of our actual bodies.”Keiken's approach is in contrast to many gaming manufacturers where emotions are not central to the design. Their work echos Damasio principles regarding the connectivity of our emotions to motivation and creativity, and our ability to invent and create work that will benefit our culture and help us grow.Sources:https://www.designindaba.com/articles/creative-work/metaverse-multipleshttps://www.rewire.org/pandemic-empathy-deficit/https://www.forbes.com/sites/forbestechcouncil/2021/09/14/augmented-and-virtual-reality-after-covid-19/?sh=6037f30f2d97https://findingmastery.net/antonio-damasio/

3-D Printing Homes: Mexico or Mars!

It's amazing to think that 3D homes are being built for poverty stricken areas like Mexico, while the same technology is being used to build habitats for life on Mars. Two models that exist simultaneously, use the same technology, and have diametrically opposed outcomes: one to stay on the Earth and make it affordable and habitable, and the other to leave the earth and make another planet habitable, and it's highly doubtful that model will be affordable to most.NASA has been working with companies for several years on developing the technology to create sustainable homes for Mars. A recent article by Business Insider, reports that NASA is seeking volunteers to spend a year living in these homes pretending that they are on Mars.In San Francisco, the organization New Story is focused on providing housing solutions to those in extreme poverty. They claim they have built the world's first community of 3-D homes and have developed a micro-mortgage model to help finance them.One question that arises when considering the materials being used is whether or not these homes are sustainable and eco-friendly or are we adding to the waste stream on Earth and about to bring our bad habits to Mars? One article from a 3-D Building company, Build with Rise, states that by limiting construction waste and material transportation costs, 3-D printers drastically reduce the carbon footprint associated with building homes. If you companies use sustainable and renewable materials, the effect on the environment will be event better.Many feel the era of 3-D printing has arrived. As time goes on and printing methods are perfected, it will be interesting to see which model dominates. Will we use the technology to save our existing planet and populations, or use it to make an exit from our dying planet to somewhere new?Sources:https://www.nytimes.com/2021/09/28/business/3D-printing-homes.htmlhttps://www.businessinsider.com/nasa-mars-martian-habitat-icon-3d-printed-space-texas-2021-8https://www.iconbuild.com/updates/icon-3d-prints-the-first-simulated-mars-surface-habitat-for-nasahttps://thespaces.com/ai-space-factory-designs-homes-for-planet-mars/https://www.buildwithrise.com/stories/3d-printed-homes-sustainable-alternative

Cradlr: Helping Refugee Children with App Design

Designer Jiang Jian was chosen by Design Incubation as the 2020 Creative Works Award Winner for her UX/UI Design Project, Cradlr, that aims to create a global network to help displaced children all over the world.Wars, political persecution, famines, pandemics, natural disasters, and more have displaced nearly 80 million people, 26 million of them are registered refugees and half of them are under age of 18, and most without access to cell phones or other communication devices. The Cradlr Network is a place where temporary guardians, international and regional organizations, as well as volunteers, can collect these children's stories and data and store in a database which will become a collective digital memory, as well as a resource to connect lives on a global scale to rescue and nurture refugee children. With her design, Jiang Jian hopes to find a humanitarian solution for a complex social challenge.Learn more here:Cradlr: A Design Project for Refugee Children from Jing Zhou Studio on Vimeo.

Big Data—Big Responsibility

A recent article by FastCompany has declared that it's official, data visualization has gone mainstream.While data visualizations have been created by graphic designers for years, 2019 included fashion wear from information designer Giorgia Lupi, who created a super popular, data-driven fashion collection for Other Stories, a co-lab that turns data visualizations into wearable stories. The clothing line is so popular that many of the items have sold out. The designs reveal the amazing achievements of three trailblazing female scientists. The collection is an excellent example of how Lupi strives to find the human element in data-driven narratives.The article by FastCompany discusses other moments in 2019, including Donald Trump's use of a data visualization as well as the introduction of reflective data visualization with Michelle Rial's book, Am I Overthinking It?It's important to remember that while discussions and investigations into data bias are not new, a plethora of information that is being represented with them serves as a call to action to be mindful of the blank spots. A recent article by Meg Miller for Eye on Design, focuses on the work of artist Mimi Onuoha, "The Library of Missing Datasets." Onuoha's project is a mixed-media installation that shows how big the blank spots are from data that has been left out. File cabinets that feature empty file folders with titles like "Publicly available gun trace data" and "Accurate Birth Registration" point to how much misinformation we are likely being served. Onuoha says, "Spots that we've left blank reveal our hidden social biases and indifferences."The article features many other examples that speak to the problem of not just data bias, but the danger of data blank spots; about power, who has it and who does not.As we move forward in world filled with data visualizations, it's important for designers to be informed and aware of all the implications of the data they are using.https://www.fastcompany.com/90450827/its-official-data-visualization-has-gone-mainstreamhttps://eyeondesign.aiga.org/finding-the-blank-spots-in-big-data/

AI and Creativity—Is It Ethical; Will It Kill Creativity?

By Guest Author, Frank J. Martinez The use of Artificial Intelligence tools such as machine learning and predictive analytics will eventually become a part of the design process, it is inevitable. At first, AI will find its way into the decision-making process forming the design brief, because decision makers always seek a competitive advantage and a principal risk reduction strategy in business processes is the reduction of uncertainty. These decision makers will falsely believe that AI will grant a competitive advantage, a short cut to consumer acceptance and profit without investing research and testing. Why focus group or explore alternative designs, when the marketplace has spoken?The danger and promise of using AI in design decision-making is that AI will bake in the biases that currently exist in design practice, theory and design education. How? The processes underlying AI will rely on the use of data collections called data sets that will be made up of existing images of works of design and, when complete, may also contain consumer purchase patterns. The result will be the “baking in” of the biases of the choices made when creating the data sets. This means that the decisions with respect to style, wealth, race, gender, sexuality and cultural identities will be incorporated in the AI decision-making process. Contrary to common belief, an AI  computer can only work with the knowledge we give it and if AI is used in any design decision-making process, and it will be, such bias will form a silent but influential part of the design brief.Without a basic understanding of the processes underlying AI methods and how those processes generate a result, we can expect design decision makers will rely on a diet of culturally blind and biased design choices. This information will shape and guide the design brief, robbing it of the ineffable humanity that underlies good design. The role of the designer and design decision maker educated in the basics of AI is to be able to recognize when AI’s pernicious effects exist and to communicate the danger of overreliance on AI in the creative process. These design leaders will guide decision makers in understanding how the human-creative element in design can only be simulated by an algorithm, it can never be replaced.In a design context, a product manager may be tempted to use artificial intelligence to ask, what are the characteristics of a successful personal care product or packaging for a food product? An algorithm trained (machine learning) using existing data sets might have the “intelligence” to suggest the optimal package configuration for a product, design color way and type fonts and a retail price point in answer to the question. Furthermore, using data sets related to past purchasing patterns, buyer geodata and credit card use histories and other population data, the algorithm may even be able to suggest the optimal launch dates for such a product and provide  consumer profiles and contacts based upon purchasing histories for such goods from Amazon, Wal-Mart, and Google searches. Nowhere in that product development workflow description do the words design or designer occur.A product or brand manager could, in theory, develop a design brief that is almost entirely devoid of an inquiry about good design and how design communicates a product’s benefits or features or the relationship of the consumer to the product. If the data set is good and the algorithm is properly trained, the artificial intelligence engine will deliver an answer that will be accurate based upon the data it examines. However, that answer will also incorporate the limitations of the data sets and any biases that were incorporated into it.  What AI cannot deliver is a new design conceptualization based upon evolving trends flowing from consumer awareness, consumer behavior, buyer weariness or cultural changes. AI cannot incorporate design sensitivity to race, cultural, and gender concerns, if it is not present in the data sets. AI cannot do this because these trends, or more accurately information derived from these trends and concerns, are not, at this time, present in existing data sets.In addition to blindness as to race, culture, economic and gender issues another significant question is whether the growing use of AI reduce the decision-making power of the designer. Will the design brief devolve into mere instructions to create design based upon a narrow set of limitations, such as requirements to use defined colors, fonts and layouts? The corollary danger is that AI may reduce the designer’s ability to influence the growth of design or exploration in design. Stated simply, will AI reduce the chance for bravery in design? These are the principal challenges of the use of AI in design, in order to meet the challenge, a designer should acquire some understanding of AI.

How Does Artificial Intelligence Work and How will it Work in Design?

Artificial intelligence is the process of using mathematics to determine a best answer to a question. Such answers are reached by reviewing features and attributes that exist in a collection of information, generally called data. Stated with increased complexity, artificial intelligence is the process of using a mathematical algorithm to find the best answer to a question based upon (a) finding those features and attributes in a data set which (b) correlate to the question in a relevant manner.Artificial intelligence’s power is derived using complex mathematical analysis (algorithm) of information converted to numerical data and by this method, teaches itself to find the best answer to a question asked by the user. Using this process, a computer, will learn how to find the best answer by repeatedly applying the algorithm to the data set.  More importantly, the machine may have learned, by automatically refining the algorithm, how to apply that process to answer a question and more astonishingly, unrelated questions, with startling accuracy. On occasion, the ability of these algorithmic explorations seems to border on prescience. The important terms here are question and features. A question, is query posed by the user such as, what type of person will find me attractive based on my personality characteristics or how likely is a consumer to buy product X based upon its packages design features and/or their purchase of product Y? Stated another way, artificial intelligence is merely the seeking of an answer (or best answer) to a question using an analysis of relevant features in the data set to find the best answer. A feature in data is a character or element of the information in the data which is used as a guide to measure the “relatedness” of information in the data to the answer. Currently, design data sets are quite small and primarily limited to search libraries such as the Minst fashion data set, Imagenet (14 million images) and a painting image data set on Reddit. The Wikipedia listing for available machine learning data sets contains listings of images, sounds, twitter entries, handwriting, news, speech, and music, among others. The website Kaggle, has a data set devoted solely to the classification of artwork. Eventually, someone will create data sets devoted solely to works of design with subsets devoted to various design disciplines. In the next few years, the number and types of data sets devoted to art and design will grow and with them the temptation to use them in design decision-making. At this time AI technologies are not yet able to create works of art and design that pass the Turing test, but one day they will. Designers need to understand that AI tools will become more commonplace and their use will find their way into design decision-making. The challenge and goal for designers is to understand the basic principles of AI and to use that knowledge to help their client understand the difference between design by formula, imitations of another’s work, and genuine design that shows the thoughtful analysis of a client’s needs, their customers and the best design solution that serves all those parties.Frank J. Martinez is a former artist, designer and Patent Examiner. Frank earned a BFA from Pratt Institute and was the Production Director at Landor Associates in New York prior to attending law school. After serving as a Design Patent Examiner at the U.S. Patent and Trademark Office and an associate at several law firms, Frank founded The Martinez Group PLLC in 2008. Frank is admitted to practice law before the courts of the State of New York and the Federal District Courts for the Eastern and Southern Districts of New York as well as the Federal District Court for the Western District of Texas (Austin).Frank is also an Adjunct Professor at The School of Visual Art where he teaches Intellectual Property Law in the MFA Designer as Author and Entrepreneur Program. Frank is also a Mentor in the SVA GroundFloor Incubator Project where he counsels Incubator participants in IP Law. Frank earned an MBA in 2017 and studied advanced management at Harvard Business School’s HBX Program and is a member of the Board of the College Art Association Committee on Intellectual Property. For the past 2 years, He has studied Python Programming, AI and Machine learning at Code Academy and Coursera. 

Keeping UX Design Human

UX (User Experience) Design is one of the fastest growing fields in graphic design. The change in growth was more than 15% from 2010 to 2016, close to double the national average. The field is exciting, new, and still emerging. In 2016, UX/UI job postings comprised 28% of the list.Roles on a UX team change and overlap. It's also a place where today's graphic designers are likely to find work and a career. Eventually their role may fall under the category of visual designer, however the field is so new that we don't know yet what other job titles will emerge. The State of UX in 2018 by UX Trends discusses what some of the associated areas that designers are venturing into. AR (augmented reality), VR (virtual reality), motion design, prototyping, and product design are just some of the places. No matter what the job title, all involve engaging the end user and ultimately, creating their experience.Creating a user's experience is exciting, and full of responsibility. The line between authentic persuasion and manipulation is not a strong one. Ethical issues come into play in every aspect. How often a user waits, the imagery and colors used to engage them, the size of the elements along their path, are all design decisions that have ethical implications.In his Podcast, How Technology is Hijacking Your Mind, former Google ethicist Tristan Harris, discusses the ways in which tech designers use techniques like intermittent variable rewards, the number one psychological ingredient in slot machines. In the 1950s BF Skinner researched this concept and how effective unpredictable rewards are in keeping behavior going. FOMS (Fear of Missing Something) is another technique used by designers along with social Approval and social Reciprocity.Human-centered design expert Don Norman recently wrote an article for Fast Company, The Myth of Human-Centered Design, where he says that we design for "technology first" rather than putting the user first. Studies show us that users will adapt to these conditions and their behavior will be formed by technological advances, rather than a human-centered approach. Norman raises many questions about how experiences are created, including what defines the truth if anything can be simulated. Norman states that it's now time to produce a more sophisticated view of human-centered design, not just responding to what technology can do and what users crave.Graphic designers need to include what is in the best interest of the human race, rather than responding to technological advances. In this way, they will truly be participating in human-centered design.Sources:https://www.fastcompany.com/90208681/the-myth-of-human-centered-designhttps://medium.muz.li/2018s-ux-designer-salary-forecast-32ccc1dfcd5fhttps://www.bls.gov/ooh/arts-and-design/home.htmhttps://designation.io/blog/now-is-the-time-for-ux-uihttps://trends.uxdesign.cc/https://medium.com/thrive-global/how-technology-hijacks-peoples-minds-from-a-magician-and-google-s-design-ethicist-56d62ef5edf3