Write people up for their design crimes with this ticket book


If so, Hoefler & Co. just released the perfect novelty product for you: the Typographic Ticket Book, an official-looking sheaf of common design crimes that you can use to write friends, co-workers, and total strangers up for. Ticketable infractions include “improper kerning,” “use of display font at text size,” and other designery in-jokes that will make any font nerd snicker. But the Ticket Book’s best jokes just might be on designers themselves.

[Photo: Hoefler & Co.]

First, though, let us praise Hoefler & Co.’s attention to detail. The Ticket Book nails all the design conventions of municipal meter-maid gear: “things set in ALL CAPS that would be easier to read in lowercase, searing colors that dazzle the eyes, and confounding administrative indicia like bar codes and form numbers,” says Jonathan Hoefler. “And Helvetica. If the state is dressing you down, it’s always in Helvetica. Helvetica means you’re in trouble.”

The delights don’t stop there. Individual citation codes run the gamut from dad-jokey (“poor typeface choice: 72-60-HUH”) to so-inside-baseball-it-hurts (“improper hyphenation/justification: 72-436-RVR“), with a few dashes of guffaw-inducing surrealism thrown in for kicks (“improper word spacing: 72-428-C/WLKN”… get it?). The ticket book includes 32 “common design infractions,” which Hoefler admits he had to edit down. “Space permitting, [it] could probably have run to at least 60,” he says.

[Photo: Hoefler & Co.]

This wry self-deprecation is the Ticket Book’s satirical secret weapon, and what makes it such a great piece of design in itself instead of a one-note sneer at supposed typographic rubes. Hoefler & Co. knows that anyone who’s picky enough to even get these jokes is at risk of becoming a walking, talking design crime themselves. Hoefler freely admits that taking the piss was his whole inspiration to begin with: “Earlier this summer, a friend got a stern ticket for some inscrutable traffic violation, which got us thinking about the absurdity of treating ridiculous topics with deadly earnest.”

[Photo: Hoefler & Co.]

Which brings me to my favorite gag in the Ticket Book: its cover. An officious-looking eagle clutches a banner emblazoned with the words “final_art_final9.ai”—which, if you make a living in design, will surely make your shoulders sag in resigned recognition of how little power you actually wield. (For those not in the know: that string of alphabet soup is a common file-naming convention in Adobe Illustrator, denoting the mind-numbing lengths that client-driven revisions regularly go to.) And below that, Hoefler can’t resist giving the knife one more twist: the Latin motto FACIET MAIOR LOGO means–wait for it—”make the logo bigger.” Oh, it hurts so good!

So, while anonymously issuing smirky tickets for “non-ironic use of a novelty typeface” may give certain design purists a trollish kind of thrill, be advised that Hoefler’s irony cuts both ways. “The next time you run into some pedantic ding-dong mansplaining the difference between ‘font’ and ‘typeface,’ you should write them up,” he says. “It’s right there on the ticket.”

from WebdesignerNews https://www.fastcompany.com/90251608/cite-people-for-design-crimes-with-this-ticket-book

Design Thinking isn’t design, it’s Design Thinking



Is one of the latest magic tools for businesses and consulting actually a good thing for Design and Designers?

Many people in the world and business still see ‘Design’ as that thing which makes things look good, function well, and perhaps have an emotive quality. Recently (although in reality it has been in the public arena for at least a decade now) there has been the emergence of the term, tool, discipline, and mindset, that is ‘Design Thinking’. ‘Created’ and coined by Tim Brown of IDEO, [see my other blog, Dangerous Times in Design, if you want the defintion/ know what it is] it was a nice power move to make; affording designers a whole new playground and application of their skills, and executives a seemingly original angle at which to approach their business problems with. Since then it has slowly gathered favour and momentum, but its use and users have constantly been changing…

The early adopters of Design Thinking were/are the consultancies — both design and management. Design consultancies originating in digital, brand, product and even advertising all realised it was a capability band wagon worth jumping on. After all, they had the designers, who had that intrinsic creativity. With a bit of education, and experience in applying design thinking, they could then potentially tackle whole problems and develop holistic solutions. No longer would be they be limited to applying ‘lipstick on a pig’, or just making the app, product, or marketing material/ad campaign. No, this was real power to shape… and with that, a fee that reflected it. Saying this, the sell was always going to be difficult. Why would companies pay their ‘design’ agency to solve their business problems? After all, they had been doing it themselves, with tried and tested processes for years and been successful(?)… and if it wasn’t they themselves, then surely it was the management consultancies, their go-to bed fellows that would be the next port-of-call?

… enter the Management Consultancies. Management Consultancies are brimming with clever, very hard working people who crack complex problems and projects at break-neck speed. These consultancies are there to provide, and by that implication, sell, the latest ways of thinking that give their clients a competitive advantage. One of the latest of these capabilities is of course Design Thinking (and/or Service design — the remit to more holistically solve problems, looking beyond experience design (front of stage) and into backstage workings, organisations, business & behaviour). This new fangled ‘human centred’ approach makes obvious sense. Of course we should ‘put the customer first’, and ‘have the customer at the heart of everything we do’. This isn’t new, but somehow business had forgotten this over the last few decades (perhaps its memory is cyclical) — hence it makes for a refreshing change and something everyone is adopting.

And what everyone (or most) want, consultancies are happy to provide. There have been several avenues to achieve this from training staff in the tools and methods (of Design Thinking) so it becomes part of their professional armoury, to whole acquisitions of design companies. Examples include Accenture-Fjord, EY-Seren, Deloitte-Market Gravity, McKinsey-Lunar. Each have had varying success, with perhaps the obvious comment to say that the road has been rocky. After all, for grass root designers, working in management consultancies was never going to be easy, but that’s for another blog.

What is really at the fore here is that Management consultancies (whose client book and relationships are much wider and deeper than design consultancies) started to sell a strategic level of design to their clients. Not necessarily as outright projects or capabilities, but as a new tool that their consultants could leverage to do even better work. This isn’t a bad thing. In fact, having management consultancies champion design thinking has been of great benefit to its cause, but has muddied the waters of what ‘Design’ is and who does it.

As discussed in my other blog post — Dangerous Times in Design the move to Design encompassing design thinking, has, overall, been of a net positive effect. The key here is, (I believe) the design application being done by management consultants, companies, and that too by many designers within management consulting & companies, is Design Thinking, not necessarily Design.

When we’ve abstracted and codified the approach so that it is just another tool, or at best a method, where anyone, after a brief period can ‘accomplish’ it, then it becomes difficult to label that a profession.

Bright consultants in these places are picking up the core of Design Thinking and its tools quickly and applying it to the plethora of projects they fly around the world solving. But this doesn’t make them ‘Designers’, it just makes them consultants with Design Thinking as a skill to their bow.

Am I just being precious? Is it because I, and many others I know, spent years training to be designers, and now it appears some people can pick up design thinking over a few weeks or months, (and I’m not just referencing job experience, but places like General Assembly (GA) that offer short 3 month courses to become UX designers) that I feel hard done by? 
Perhaps.

But I believe that Design is more… it is certainly more than ‘Design thinking’, that’s for sure.

Perhaps it’s really all about positioning. Let’s call things as they are more accurately.

Design Thinking is a mindset.

At its lowest common denominator it’s a bunch of tools that can be executed to illustrate a human centred point of view. This, even at this level, is a good thing that consultants can, and should adopt, blending it into their work. But this isn’t Design and doesn’t make them Designers.

Designers do of course exhibit Design Thinking, sometimes outright, like the management consultants do to solve problems more holistically, but many times they do this intrinsically — when they just go about designing stuff! This Design Thinking is more a part of Design. By itself it isn’t, but as a component of the greater process of creation; when combined with craft, creativity and a different level of empathy and understanding, then, it becomes Design.

It’s not all about the consultancies of course. Many businesses, especially larger organisations are empowering their staff with Design thinking workshops and Customer first/ centered initiatives. This again, is a valuable thing.

What people should remember is, that as they would never consider calling themselves fashion designers, interior designers or product designer, unless they had spent years mastering the craft, that same logic is true for those designers that bring years of true design experience and focus to tackling problems or opportunities in services and businesses. Due to it being less physical or aesthetic does not make it less true to design and easier to conquer and label.

I have seen designers use the components of Design Thinking and Service design in very different ways and to a very different effect as non-designers. The value is not just in knowing how to use a tool, but the population and application of that tool, and of course what you do with it!

Conclusion:

Design Thinking shouldn’t have the word design in it. It’s misleading.

The flavour of it that non-designers leverage is about a human-centered approach.

This is what it is about and should be called — a human-centered approach to problem-solving.

Designers (and others) doing real design, do design thinking, where a human-centered approach is intrinsic to the act of creation and craft.

Those of you that are ‘enlightened’ by Design thinking — you do not need to become service designers. Do what you do best, just add a human-centered approach to your skillset/the way you problem solve.

tl:dr
Design Thinking is only a component of Design and doing it does not make people designers.

I find the term misleading. A human-centred approach is what management consultancies, companies, and organizations really need. This can be provided expertly by designers but can be equally as effective through empowering their staff.

from UX Collective – Medium https://uxdesign.cc/design-thinking-isnt-design-it-s-design-thinking-b8a5f96f0294?source=rss—-138adf9c44c—4

Google AI claims 99% accuracy in metastatic breast cancer detection


Metastatic tumors — cancerous cells which break away from their tissue of origin, travel through the body through the circulatory or lymph systems, and form new tumors in other parts of the body — are notoriously difficult to detect. A 2009 study of 102 breast cancer patients at two Boston health centers found that one in four were affected by the “process of care” failures such as inadequate physical examinations and incomplete diagnostic tests.

That’s one of the reasons that of the half a million deaths worldwide caused by breast cancer, an estimated 90 percent are the result of metastasis. But researchers at the Naval Medical Center San Diego and Google AI, a division within Google dedicated to artificial intelligence (AI) research, have developed a promising solution employing cancer-detecting algorithms that autonomously evaluate lymph node biopsies.

Their AI system — dubbed Lymph Node Assistant, or LYNA — is described in a paper titled “Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection,” published in The American Journal of Surgical Pathology. In tests, it achieved an area under the receiver operating characteristic (AUC) — a measure of detection accuracy — of 99 percent. That’s superior to human pathologists, who according to one recent assessment miss small metastases on individual slides as much as 62 percent of the time when under time constraints.

“Artificial intelligence algorithms can exhaustively evaluate every tissue patch on a slide,” the authors of the paper wrote. “We provide a framework to aid practicing pathologists in assessing such algorithms for adoption into their workflow (akin to how a pathologist assesses immunohistochemistry results).”

LYNA is based on Inception-v3, an open source image recognition deep learning model that’s been shown to achieve greater than 78.1 percent accuracy on Stanford’s ImageNet dataset. As the researchers explained, it takes as input a 299-pixel image (Inception-v3’s default input size), outlines tumors at the pixel level, and, in the course of training, extracts labels — i.e., predictions — of the tissue patch (“benign” or “tumor”) and adjusts the model’s algorithmic weights to reduce error.

The team improved on previously published algorithms by exposing the LYNA to a 4:1 ratio of normal to tumor patches, and by increasing the “computational efficiency” of the training process, which in turn led to the algorithm “see[ing]” a greater diversity of tissues. Additionally, they normalized variations in the biopsy slide scans, which they say improved the model’s performance to a greater degree.

The researchers applied LYNA to the Cancer Metastases in Lymph Nodes 2016 challenge dataset (Camelyon16) — a collection of 399 whole-slide images of lymph node sections from Radboud University Medical Center (Nijmegen, the Netherlands) and University Medical Center Utrecht (Utrecht, the Netherlands) — as well as a separate set of 108 images from 20 patients. It trained on 270 of those slides (160 normal, 110 tumorous), and two evaluation sets — one consisting of 129 slides and another of 108 slides — were used to evaluate its performance.

In tests, LYNA achieved 99.3 percent slide-level accuracy. When the model’s sensitivity threshold was adjusted to detect all tumors on every slide, it exhibited 69 percent sensitivity, accurately identifying all 40 metastases in the evaluation dataset without any false positives. Moreover, it was unaffected by artifacts in the slides such as air bubbles, poor processing, hemorrhage, and overstaining.

LYNA wasn’t perfect — it occasionally misidentified giant cells, germinal cancers, and bone marrow-derived white blood cells known as histiocytes — but managed to perform better than a practicing pathologist tasked with evaluating the same slides. And in a second paper published by Google AI and Verily, Google parent company Alphabet’s life sciences subsidiary, the model halved the amount of time it took for a six-person team of board-certified pathologists to detect metastases in lymph nodes.

Future work will investigate whether the algorithm improves efficiency or diagnostic accuracy.

“[Lyna] achieves higher tumor-level sensitivity than, and comparable slide- level performance to, pathologists,” the researchers wrote. “These techniques may improve the pathologist’s productivity and reduce the number of false negatives associated with morphologic detection of tumor cells.”

Google has invested broadly in AI health care applications. This spring, the Mountain View company’s Medical Brain team claimed to have created an AI system that could predict the likelihood of hospital readmission and that they had used it in June to forecast mortality rates at two hospitals with 90 percent accuracy. And in February, scientists from Google and Verily created a machine learning network that could accurately deduce basic information about a person, including their age and blood pressure, and whether they were at risk of suffering a major cardiac event like a heart attack.

DeepMind, Google’s London-based AI research division, is involved in several health-related AI projects, including an ongoing trial at the U.S. Department of Veterans Affairs that seeks to predict when patients’ conditions will deteriorate during a hospital stay. Previously, it partnered with the U.K.’s National Health Service to develop an algorithm that could search for early signs of blindness. And in a paper presented at the Medical Image Computing & Computer Assisted Intervention conference earlier this year, DeepMind researchers said they’d developed an AI system capable of segmenting CT scans with “near-human performance.”

from VentureBeat https://venturebeat.com/2018/10/12/google-ai-claims-99-accuracy-in-metastatic-breast-cancer-detection/

LinkedIn launches Talent Hub to recruit applicants with predictive insights

Hiring new employees is neither easy nor inexpensive. Bringing on a worker costs the average small business about $56,770 per head, according to a report published by the Center for Economics and Business Research (CEBR). And in sectors like health services and financial services, it takes on average between 44 and 50 working days to find a qualified candidate.

LinkedIn introduced new tools during its Talent Connect conference in Anaheim, California that attempts to streamline the process: Talent Hub, an updated Recruiter platform, and skills insights in LinkedIn Learning.

It’s pitching Talent Hub — an applicant tracking system — as a sourcing and hiring platform that ties together many of its existing products, including LinkedIn Jobs and Apply with LinkedIn. Through it, teams of recruiters can manage talent, collect feedback, and extend promising applicants offers, all the while collaborating via note-taking tools integrated with the Talent Hub dashboard.

LinkedIn Talent Hub

Above: Sorting candidates within LinkedIn Talent Hub.

Image Credit: LinkedIn

In a briefing ahead of today’s announcements, Dan Reid, group product manager at LinkedIn, gave an overview.

Talent Hub is designed around a five-step flow, he said: (1) intake meetings, (2) source and distribute, (3) calibrate requirements, (4) interview candidates, and (5) offer. It starts with a job listing. Recruiters fill in key information about the role — i.e., salary range, hire date, and location — which LinkedIn uses to estimate which slice of its more than 550 million users might be a fit.

During the demo, Reid created a draft project for a “Media Marketing Manager” in the Greater Chicago Area. A breakout box to the left of the onboarding form showed the total number of LinkedIn members with a matching title, the subset of those who live within a specified geographic area (in this case Chicago), and the even smaller subset who met more specialized requirements.

To be clear, LinkedIn already offers an ATS — Recruiter System Connect (RSC) — that acts as a bridge between its candidate-sorting tools and third-party platforms like Greenhouse and Lever. But RSC is principally intended for customers with more complex tracking and management needs, Reid said, while Talent Hub was designed with small-to-mid-sized businesses in mind.

Charter Talent Hub customers include Kind, Avalara, and Infoblox.

Next on the list of announcements: a new version of Recruiter with a unified dashboard for sourced candidates, leads, and job applicants, and a tool within LinkedIn Learning Pro — a premium subscription education portal tailored to enterprises — that highlights skills gaps within organizations.

LinkedIn Learning Skills Insights

Above: LinkedIn Learning Skills Insights.

Image Credit: LinkedIn

Last but not least, LinkedIn said that it has infused gender diversity insights across its talent recruitment suite in a number of ways, starting with a gender representation visualization within its self-serve data product Talent Insights. Now, per-company gender breakdowns are easier to compare.

And within LinkedIn Recruiter, search results for a given talent pool are now more representative. Here’s how LinkedIn explained it: “If there are 6500 engineers (40% women, 60% men), the recruiter will see 40% of women in each page of the search results to more fairly represent the available pool.”

Finally, LinkedIn said it’s releasing new courses on LinkedIn Learning to “help hiring managers and leaders tackle unconscious bias.”

The Microsoft subsidiary’s announcements comes hot on the heels of its acquisition of employee engagement platform Glint, which uses predictive analytics and artificial intelligence to alert companies when certain departments or employees might be at risk of leaving.

Earlier this year, LinkedIn rolled out a feature that shows prospective job candidates what their commute time for a specific role would be before they apply, and a tool that enables job seekers to request referrals from someone they know at a company before applying to a role.

from Big Data – VentureBeat https://venturebeat.com/2018/10/10/linkedin-launches-talent-hub-to-recruit-applicants-with-predictive-insights/

The end of navel gazing

Earlier this year I gave a talk at UX London, and it’s no exaggeration to say that it felt like it had taken me my whole career to write it. The talk tackles the assumption among many designers and UX people that UX should be at the center of their company.

Watch the video of my talk below, or read the lightly edited transcript.

The talk you need to hear

In this talk, I’m going to challenge how you think about your profession, your industry and the value that user experience brings to a company. It might be somewhat controversial to people. It might not even be popular, but I think it’s the talk that people need to hear.

“I’m going to challenge how you think about the value that UX brings to a company”

At some point in the past, I was in your shoes. I was at the same stage in my career, in the same discipline, with the same feelings. So, you’re going to like parts of this, and other parts will possibly offend you. The more you feel offended, the more I think you need to hear this. I’m going to ask you to be open minded as I go through things.

This is a list of where I’ve worked and what I’ve done.

The reason I’m showing you this is to make you really see that I was in your shoes, and over the last 10 years, a lot has changed when it comes to how I think about research, about design, about product management, the difference between those things, but most importantly, the role of UX inside any company of any size.

When we first think about UX work, we think about things like commuting, going to work, writing on whiteboards, writing Post-it notes, sprints, workshops. We use Sketch and a whole host of other tools.

But these are not the things that build a great product. These are not the things that make a great product. This talk is about what it takes to make a great product.

It’s really a talk about seeing the forest, not the trees. We’ve a lot to cover, from things in our imagination, to our place in the universe, to some of the lies that we tell ourselves.

It’s high time we grew up

I’m going to start with somewhat of a rant, which is to say that it’s high time we grew up. This is the grand conclusion of the talk, by the way – that we UX people need to stop navel gazing and grow up.

When I say we, I mean everybody here. You can excuse yourself if you’ve never complained to a colleague about not being valued or not being understood. If you’ve never felt that way, you can exclude yourself.

Let’s look at exhibit A. This is Alan Cooper, who if you’re not familiar, is the forefather of our discipline. Alan tweeted this a few weeks ago, and it was the best set up I could ever imagine for the talk I’m about to give. It’s important to remember that Alan is a phenomenal individual and someone, as I said, who defined our craft and deserves a lot of respect and should be listened to.

This is what he tweeted: “There is no such thing as UX design.”

I thought this was a joke, and I thought it was pretty funny, but it turns out it was not a joke. A lot of people went a little bit nuts, and Allen came back on Twitter the next day and explained it.

When he posted this tweet, there were retweets, people agreeing and disagreeing, and rebuttals. There were Medium posts written, very long ones, and there were Medium posts written about those Medium posts.

“We’re having yet another existential crisis about who we are and what we do”

Meanwhile, in company leadership meetings everywhere, this is what’s going on. The CEO is saying, “Has anyone got any important updates from your team?” To which the UX leader says, “Yes, we’re having yet another existential crisis about who we are and what we do.”

You’ve got to remember that people fought to be in that meeting, and design, UX, however you want to describe it, is actually in that meeting. But, you’ve got to put yourself in the shoes of that CEO, and ask yourself whether you want that person having an existential crisis about the value they bring.

Do you actually want them in that meeting? I’ve since been in a lots of these meetings at Intercom, and they’ve taken different shapes over the years. There are leaders from all functions of the company, but no one else is having an existential crisis. No one else, just me.

So, we have to stop. We need to look up and look out.

Things that only exist in our imagination

There’s something much bigger going on. If we look up and look out, we realize that a lot of the things that we think about and value actually only exist in our imagination. This is paper and ink:


You may recognize it as money, but money only exists in our collective imagination. It doesn’t actually exist. It only exists because we’ve agreed in our minds that it has value. If I give you €10, we both agree that that is worth €10. Or, if you go into a shop and give £20 sterling or $10, we’ve agreed that it is worth the same thing, but that thing, money, only exists in our imagination. It doesn’t exist in the world of biology or chemistry or physics. It’s just paper and ink.

When we look at all these things – agile, design thinking, design sprints, lean UX, story points, jobs to be done, personas, user experience – you’ve got to remember that we made this shit up. None of this exists. It’s only in our imagination. Like money, these things have value, but you’ve got to remember that they don’t actually exist.


This is a design sprint in action, and if you look at this photo, you’ve got to ask yourself, what really exists in that room? Oxygen, carbon dioxide, plastic, glass, paper, people. These are the things from biology, from chemistry, from physics, which reminds me of this tweet:

When you look at all these things and realize that we just made this shit up, you’ve got to ask yourself how valuable it is, and how useful it is, and realize that if we made it all up, we can stop. We don’t have to use these things.

Our place in the universe

As you commute to work, write on whiteboards, use Post-it notes, have stand ups and use Sketch, I’m going to tell you that you don’t have to. I want you to free your mind of these things. If you free your mind of the default things that occupy it, you can consider your true place in the universe.


This is Earth, our planet.

This is a picture of Earth, but this is not actually Earth. This is just a picture of it.

This is another picture of Earth, and this picture is no less accurate than the first one. So, it’s important to know that your perspective of something, and your understanding of it, are deeply interlinked.


Only 500 years ago, it was universally understood that the earth was the center of the universe, and that everything rotated around the earth. So, if you lived in that time, which was not that long ago, this perspective would have constrained you. It constrained what our ancestors could learn. If you imagine being there and understanding that this was true, it sets your viewpoint on everything.

In the intervening few hundred years, we’ve learned that we’re actually on the edge of something, which is on the edge of something else, which is on the edge of something else, which is on the edge of something else. That is radically different to how people thought 500 years ago, and it totally changes what you think is possible, or what the human species even means.

“Your perspective of something, and your understanding of it, are deeply interlinked”

You’ve got to apply that thinking to where we are and ask yourself, what does a UX universe look like, and where are we in truly understanding it?

This is a Google image search for UX diagram. You might notice that UX is in the middle of all the pictures. I have drawn many Venn diagrams over the course of my career. I have been boring people to death with Venn diagrams all my life, with UX in the middle.

Here’s a classic example. UX in the middle, business down in the bottom left in red. Marketing, that other small thing, down there in purple, squeezed in between a bunch of other things. All these diagrams look the same.

UX in the middle, then business and technology, and information and content, and marketing, and all sorts of other stuff all around the edge, all flying around the UX thing. If you think this way, just like our ancestors, it sets your viewpoint and changes all your consequent behaviors.


The truth is that UX is not in the center. This is what reality looks like. You have things like research and design, which most people consider to be UX. Then, lots of other functions like product management and engineering, which can sit close to those disciplines. Analytics, biz ops, marketing, sales, customer support, recruiting, finance, HR, I can keep going. All these different functions have a specific thing to do, a specific job to do, and every single function right here is important and valuable, and they should all be viewed equally.

What we do is spend all our time talking about this bottom left piece. We spend all of our energy on it. When I saw Alan Cooper’s tweet, and the amount of energy people put into writing about it, I was like, “Oh my god,” it’s kind of depressing. You could be using that energy to talk to all these other people.


It’s self sabotage, and what’s really in the middle, in most companies, is a leadership team. All these functions have a leader, and those leaderships groups talk, connect one another, understand each other and then things flow back down.

Which raises some interesting questions – are these disciplines important, and are they equal?

The background of a leadership team will actually change this subtly. So for example, if you had a leadership team that had a sales background, sales might have more influence than others. If you work in a company where the leadership team has a marketing background, marketing might have more influence than others. If you work in a company where the leadership came form engineering backgrounds, they may have more influence than others, but in most companies, all these things still exist.

“All these things matter a great deal, and UX is not in the center”

It’s important to remember that all these things matter a great deal, and UX is not in the center. All these disciplines also talk to one another, you won’t be surprised to hear. Engineering talks to sales, and support, and recruiting and analytics. Design talks to a whole bunch of people without talking to leadership necessarily. I could extend this and this diagram could look insanely complicated.

Everyone is talking to everyone, and you can break into these little disciplines and ask yourself what’s inside. All of them will break up into lots of other sub-functions, like marketing will have events and communications, and public relations, and brand, and demand generation and a whole bunch of other things. So, in the same way that we have research analytics, interaction design, content strategy, visual design and all sorts of other stuff, all these functions have there own versions of that.

If you’re anything like I was for almost all of my career, you never really thought about all these other functions. You mapped them, maybe here and there, but you never really thought about them. We spent all our energy talking to ourselves. So, before I found myself in the leadership team, which is where I really got exposure to all these other functions, these are the types of things I thought.

I thought marketing would basically take what we build and announce it. We didn’t involve them in the process. They didn’t have a say or an opinion. We would give them something, and then they would take it to market.

I thought finance paid my expenses. I didn’t think that they might be thinking about the financial health of the whole company. That they might worry about how everyone’s salary might be paid next month or next year, or how much money we were spending.

Sales in my mind had bad practices but the best parties. Support worked in the other building – boring job. Again, I didn’t really think about what happened in that building, I just never went in. And then the last example, HR: “people problems, hope you never meet.”

When I think back about these things, I was naïve, I was biased, I was ignorant, and I was prejudiced. I was all of those things about all those other disciplines, and I never really thought too much about it. I didn’t think too much about it because I was too busy having existential crises with my colleagues about what the hell we even do, and why no one listens to us. I believed that our users should be at the center, and therefore, as a user experience person, we should be at the center. It made sense to me for most of my career.

The biggest lie we tell ourselves

Which leads me on the biggest lie that we tell ourselves. The biggest lie is that we are the voice of the user. That’s the biggest lie.

People in sales teams talk to customers every single day. Many designers might hear that and think “That’s different. They’re talking to sell, whereas when we talk to people, we’re talking to learn.” But that’s just wrong. Modern sales team talk to learn. If you go and talk to the best people in your sales team, in your company, you will discover that they talk to learn.

Sales is changing rapidly. The old way of sales was cold calls, selling people stuff they didn’t necessarily need, all those things. But, modern sales is changing, and the reason it’s changing is because many companies are moving their business model to subscription.

Subscription business means that you actually have to care about the long term. You have to care about relationships. You’ve got to build relationships with people. You can’t just sell and leave. That doesn’t work anymore. The companies with sales teams that still operate that way are not doing well, and the companies with sales team that don’t are doing fantastic. Hence, the rise of SaaS, for example.

It might surprise you that modern sales teams talk about farming a lot. This is what modern sales looks like, they use farming as a metaphor for selling. I can safely tell you that at Intercom, which is a proud product driven company with designers and UX folks as founders and leaders, our sales team know our customers better than our product team. Think about that. In a product driven company, our sales team in many ways, know our product and our customers better than our product team.

The same is true for our support team. Our customer support team talk to customers every single day, and they don’t talk to sell, they talk to understand, learn and find out the problems people have, the features that they’re missing and the ways in which the product is confusing. So, we’re only one voice of the user. An important one, but just one. Here’s a tweet from me, from the end of November:

“Many product people don’t talk to their sales team. This is a critical mistake. Your sales team knows your product failings better than you do. I learned this too late in my career, and I still don’t do it enough. Don’t be me.”

This is really, really important. Don’t be me. I learned this too late. I spent 12 years of my 15 year career not understanding all these other functions. I wasn’t in the real world.

A story about the real world

Here’s a story about the real world. We started a new product category – basically messaging and messengers for business – and now we’re in an increasingly competitive space. People see the success we’ve had to date and realize that there’s a huge market opportunity and huge business opportunity. Business messaging is huge. It’s quickly replacing email as the primary way that internet businesses and customers talk to one another. You won’t be surprised to learned that bots are a big part of that. We have a bot called Operator.

“People see the success we’ve had to date and realize that there’s a huge market opportunity”

We found ourselves in the position of a competitor having a bot feature that we do not have. They have something in their messenger that we do not. Our sales team is telling us that this feature matters. When they talked to customers to help them understand what Intercom can do for them, they quickly learned that this feature is solving a real problem, and we can’t provide the solution, so they don’t go with us, they’ll go with someone else.

But it turns out, this feature is extremely hard to design. It’s not straightforward. We thought and hoped it would be, but it’s not.

We have three options for the team.

  1. We could launch a solution in a month. We could build a hacked together solution, it would be pretty hard to understand and hard to use, but it would exist.
  2. We could launch in two months. It would be built well, but it’d still be very rough around the edges. We would have time to do some research, and that research would show us that our solution is still hard to use.
  3. We can launch in four months, design it really well, and release an easy to understand solution thanks to rounds of research.

So, the question is what should we do? And then, the second thing is, if you ask all the different people around the company what we should do, you get different answers. Research and design will say that we should do option three, it’s a no brainer. Why would you launch something that’s hard to understand and hard to use?

Product management and marketing will typically push for something more aggressive in terms of timeline. They’ll say that we can get something out sooner. We have an opportunity to talk to our potential customers about it. The competitive gap doesn’t exist anymore, and it’s not that bad. The solution is not that bad.

And sales, and most of leadership, would pick option one. The reason they’d pick option one is because they can sell it. If it exists and it’s out, we can sell it. It doesn’t matter if it’s hard to use, we can fix that later. Make it better later but sell it now.

“Do you want the thing that doesn’t exist, or do you want the thing that exists?”

The reason leadership will care about this is because they care about the financial health of the company. The reason that sales people care about this is because a lot of them are compensated on sales volume, so this will actually increase their salary.

No one here is wrong. This is only a recent revelation for me. No one here is wrong. Five years ago, I would have fought tooth and nail for option three. Fought, fought, fought and been extremely dogmatic about it. I’d go to town on people to say that we have to do option three. It is the only right option for our customers. But I’m changing. I err on the side of option two, but somebody could probably persuade me to go with option one.

The most interesting question to ask is, what would our customers want? If we present these three options to our customers and said, “Which one do you want?” They’d pick option one. Do you want the thing that doesn’t exist, or do you want the thing that exists?

“I’ll take the thing that exists.”

It’s an important insight. We say that we’re the voice of the customer, and yet, we’re pretty disconnected probably from what they might actually choose.

It turns out, we’re taking the middle option. We’re going to ship something that’s somewhat hard to use.

I’ve talked to our design team and our research team. We’re knowingly shipping something that’s going to be hard to understand. When customers see it, they’ll probably be a bit confused about how it works. The design problem is extremely hard, and we just didn’t have enough time to get it right.

The point is that we’re making a deliberate, conscientious choice. We’re making a multidisciplinary decision. We’re talking to everybody in the company to understand what the right decision for the company is, not what the right decision for the design team or even the product team.

Where do we go next?

If you recognize that everyone has something important to say, and you’re not the single voice of the user, you can actually make some intellectual breakthrough. Which leads me with where we go next.

Right at the start, I said that these are the things that we do. Commuting to work and writing on whiteboards and Post-its, standups, and workshops and Sketch. You can add to that list all day long, and yet, they don’t make a great product.

These are not the things that make a great product. I ask you to free your mind of these things. Free your mind of the defaults that are possibly dragging us all down and realize that UX is not in the center. We are not the only voice of the user.

It takes everyone to build a great product. The last question really is, what do we build, and how do we decide what to build?

“Everyone has a unique job, and everyone needs to understand everyone else’s unique job.”

At Intercom, everyone has input in this decision. Obviously we have a product management team, research team, design team and engineering team. I’m sure everyone here is familiar with this. I imagine that for almost everybody this is the way you work. All these people are involved in helping to decide what we build. We also have analytics helping us understand usage. They provide us with tons of quantitative insight into what people do and do not do, which helps us decide what to build.

We also have our sales team. What our sales team does is synthesize and summarize the hundreds and hundreds of conversations they have with prospective and existing customers. I was telling people this last night, and they were horrified at the idea that the sales team would effectively act as a research team. But we’ve helped them learn how to do it, and they’ve helped us help them. So, our sales team brings us lists of things that they think we should build next.

Our support team does exactly the same thing. Again, they’re having hundreds of conversations every day with existing customers. They know our product’s failings better than we do, and they bring us prioritized lists on what we should work on next. We also have marketing members embedded in product teams. Our marketing team are a key collaborator in the product teams. If we have a PM and a designer working on a specific thing, there is someone in product marketing also working on that thing in partnership with them.

This is what everyone does. This slide is a bit of a disaster, but I’m going to walk you through it. Everyone has a unique job, and everyone needs to understand everyone else’s unique job. It took us a long time to get to this place, by the way. Sales, starting at the top, is the voice of prospective customers. Support is the voice of our existing customers. Marketing is the voice of the market, the competitive landscape, how much things should be, pricing. Something again, I thought very little about for most of my career, which is, how much should our product cost? Turns out, that matters a lot, and I’d never really thought about it.

Marketing are the voice of the market. How we price, how we position, how we talk about things, driving awareness of the things that we make. Again, if you have an amazing product that no one’s ever heard of, do you actually have an amazing product? Or, if you have an amazing product that no one’s willing to buy or pay for, do you have an amazing product?

The analytics team looks at usage. Existing usage, finding patterns. And then, if you look at the bottom four, our PM team, our research team, or design team, and our engineering team all have a very specific job to do.

“When we were working with the sales team, we realized that we spoke different languages.”

Our product management team’s job is to understand the problems we need to solve. Deeply, deeply understand the problems. Talk to customers and talk to them again. Talk to the sales and support team. Synthesize all of their findings, really understand those problems and then prioritize them. It’s our job to take the lists from sales and support, and prioritize.

Then, our design and engineering team are paired, where our design team’s job is not to understand the problems, it’s to deliver solutions to those problems. Take the problems as we’ve understood them, design and help engineering build great solutions to those problems. Then, our engineering team’s job is to deliver those solutions with dates. So, estimates of when we can do something lies in engineering.

Everybody has a very, very specific job to do. However, it’s incredibly collaborative. And in truth, back to the earlier diagrams, everyone talks to everybody, and everyone helps everyone, because everyone respects each other, respects their disciplines and knows that everybody has something different, unique and valuable to bring to the conversation.

It works incredibly well, and it was insanely hard to get to this place. We started a few years ago helping PMs, researchers, designers and engineers disambiguate what they do. We said, “Okay, you do that, and you do that. You collaborate on both. If you’re accountable for anything, you’re accountable for that thing, and they’re accountable for that thing.”

One example is that when we’re working with the sales team, we realized that we spoke different languages. When they first started bringing these lists back to us, they were very specific feature requests.

Sometimes, the feature requests were insanely detailed, and sometimes they said things like, “Better analytics.” Customers need better analytics. They’re bringing these feature requests, and we when we spoke, we spoke in the world of jobs to be done.

“What are the problems our customers have? Then we can solve them for them”

Intercom historically has been a big proponent of jobs to be done, and that was the language that we spoke. Turns out, by the way, jobs to be done is one of the most difficult things to understand that I’ve ever come across. If you can understand it, internalize it and use it well, it’s an amazing tool, but it’s hard to understand.

And so, when we talked in the world of jobs to our sales team, they just didn’t understand what we were talking about, and so we were just talking over one another. So instead, we came to this middle ground, which is problems to be solved. It’s simple. What are the problems our customers have? Then we can solve them for them.

It takes everyone to build a great product, but as we know in life, no one gets everything they want.

Stop navel gazing and grow up

So remember this grand conclusion – it’s high time we UX people stop moaning. Stop navel gazing, and grow up. I really believe this.

This is a seminal moment in the history of this discipline. There are different ways this thing can go, and I think we need to stop doing this. Stop having this existential crisis about what we do and who cares, and who values what, and who talks to who, and instead go out and talk to other people. All these other people. Analytics, biz ops, marketing, sales, support, recruiting, finance, HR. Talk to these people.

The question I’ll leave you with, is what are you going to do? Are you going to get to your whiteboard, use your Post-its, have standups, hold workshops and use Sketch?

When you walk into work, before you breeze past six colleagues and sales, and then hit up Sketch, I want you to stop and just think about all this. I want you to look up, look out and realize that we’re not the center.

If you do that, and if we all do that, and start collaborating with all these other teams, and talking to them more than we talk to each other, I think that will be the next great leap for our industry.

Thank you.

 

The post The end of navel gazing appeared first on Inside Intercom.

from The Intercom Blog https://www.intercom.com/blog/the-end-of-navel-gazing/

Going, Going, Gone: Banksy Artwork Self-Destructs the Moment it’s Sold

[ By SA Rogers in Art & Drawing & Digital. ]

In a stunt that should surprise absolutely no one who knows anything about Banksy, the elusive street artist’s iconic work Girl with Balloon literally self-destructed the moment it was sold at auction for more than £1 million on Friday. “It appears we just got Banksy-ed,” said a Sotheby’s official afterward. Yep, it appears you did.

View this post on Instagram

Going, going, gone…

A post shared by Banksy (@banksy) on

The framed work, consisting of spray paint and acrylic on canvas, was the last piece to go up for sale that evening in London. The typical controlled chaos of the auction house was punctuated by the clap of the auctioneer’s gavel, and at that very second, the work slipped through its frame in shreds. Almost nobody noticed at first, but gasps from the crowd alerted the room to the situation.

Banksy himself released a video on Instagram that showed him building the shredder into the painting’s frame, “in case it ever sold at auction.” He deleted it soon afterward, but it had already circulated on the internet. The YouTube clip above edits this clip side-by-side with a video capturing the moment of the big reveal.

Ironically, the artwork might be worth even more now than it was at the moment it sold thanks to all the attention it got, but it seems likely that Banksy expected as much, and it’s part of the overall point. A perpetual critic of the commercialization of his own work, Banksy is no stranger to trolling the public. Even when he turns a profit from the sales, he often does so while essentially ridiculing the purchaser.

Learn more at WebUrbanist’s Banksy archive.

Share on Facebook





[ By SA Rogers in Art & Drawing & Digital. ]

[ WebUrbanist | Archives | Galleries | Privacy | TOS ]

from WebUrbanist | Urban Art, Architecture, Design & Travel https://weburbanist.com/2018/10/08/going-going-gone-banksy-artwork-self-destructs-the-moment-its-sold/

Mobile Moon Museum: A Massive Lunar Replica Exhibit is Circling the Globe

[ By WebUrbanist in Art & Sculpture & Craft. ]

This 23-foot scale model of the moon is making its way around the world, allowing viewers to get close and see the many facets of this amazing celestial object. Each centimeter on the replica represents 5 kilometers on the lunar surface.

Created by artist Luke Jerram, the Museum of the Moon is stopping in China, Finland, Australia and other countries to stop in exhibit spaces and outdoor venues.

The model was based on a 70-foot-wide image taken by NASA Lunar Reconnaissance Orbiter Camera satellites and compiled by NASA’s Astrogeology Science Center.

While the moon is remarkable to see in the sky, without technical assistance (like a telescope) it is hard to make out the details — this model aims to change that.

“As it travels from place to place, it will gather new musical compositions and an ongoing collection of personal responses, stories and mythologies, as well as highlighting the latest moon science.” (Images by Carl Milner, Gareth Jones, Leeds Living, Neil James, via Colossal)

Share on Facebook





[ By WebUrbanist in Art & Sculpture & Craft. ]

[ WebUrbanist | Archives | Galleries | Privacy | TOS ]

from WebUrbanist | Urban Art, Architecture, Design & Travel https://weburbanist.com/2018/10/06/mobile-moon-museum-a-massive-lunar-replica-exhibit-is-circling-the-globe/

A Two-Minute Guide To Artificial Intelligence


Colorful And Active Human BrainJohn Lund

If you keep hearing about artificial intelligence but aren’t quite sure what it means or how it works, you’re not alone. 

There’s been much confusion among the general public about the term, not helped by dramatic news stories about how “AI” will destroy jobs, or companies who overstate their abilities to “use AI.” 

A lot of that confusion comes from the misuse of terms like AI and machine learning. So here’s a short text-and-video guide to explain them:  

What’s the difference between AI and machine learning?

Think of it like the difference between economics and accounting. 

Economics is a field of study, but you wouldn’t hire a Nobel-Prize winning economist to do your taxes. Likewise, artificial intelligence is the field of science covering how computers can make decisions as well as humans. But machine-learning refers to the popular, modern-day technique for creating software that learns from data.   

The difference becomes important when money is at stake. Venture capital investors often dismiss AI as full of hype because they’ve got skin in the game. They prefer startups that make machine-learning software with a clear, commercial application, like a platform that can filter company emails with natural language processing, or track customers in a store with facial recognition (these are real businesses). 

On the other hand, universities and some large tech companies like Facebook and Google have large labs carrying out research that drives the wider field of AI forward. A lot of the tools they invent, like TensorFlow from Google, or Pytorch from Facebook, are freely available online.  

Why does the term “learning” (eg. deep learning) crop up everywhere? 

Because the most exciting application of AI today gives computers the ability to “learn” how to carry out a task from data, without being programmed to do that task. 

The terminology is confusing because this involves a mishmash of different techniques, many of which also have the word “learning” in their names. 

There are, for instance, three core types of machine learning, which can all be carried out in different ways: unsupervised, supervised and reinforcement, and they can also be used with statistical machine learning, Baeysean machine learning or symbolic machine learning.

You don’t really need to be clued up on these though, since the most-popular applications of machine learning use a neural network. 

What’s a neural network? 

It’s a computer system loosely inspired by the human brain that’s been going in and out of fashion for more than 70 years. 

So what is “deep learning?” 

That’s a specific approach to using a neural network – essentially, a (deep) neural network with lots of layers. The technique has led to popular services we use today, including speech-recognition on smartphones and Google’s automatic translation.   

In practice, each layer can represent increasingly abstract features. A social media company might, for instance, use a “deep neural network” to recognize faces. One of the first layers describes the dark edges around someone’s head, another describes the edges of a nose and mouth, and another describes blotches of shading. The layers become increasingly abstract, but put together they can represent an entire face.   

What does a neural network look like on a screen — a jumble of computer code? 

Basically yes. Engineers at Google’s AI subsidiary DeepMind write nearly all their code in Python, a general purpose programming language first released in 1991. 

Python has been used to develop all sorts of programs, both basic and highly complex, including some of the most popular services on the web today: YouTube, Instagram and Google. You can learn the basics of Python here

Does everyone agree that deep-learning neural networks is the best approach to AI? 

No. While neural networks combined with deep learning are seen as the most promising approach to AI today, that could all change in five years. 

———

This is the first in a series of guides to complicated but important new technology. Stay tuned for our next primer on quantum computing. Got a tip or suggestion for what we should cover next? Reach me by e-mail or on Twitter.

With thanks to Murray Shanahan, professor at Imperial College London and senior research scientist at DeepMind, and Luca Crnkovic-Friis, co-founder and CEO of machine-learning startup Peltarion.

from artificial intelligence – Google News https://www.forbes.com/sites/parmyolson/2018/10/03/a-two-minute-guide-to-artificial-intelligence/