Josh Clark's Blog, page 10

How Machines Are Taking Over the World���s Stock Markets

Time magazine interviewed Marcos L��pez de Prado, a specialist in using machine learning for investment and finance. This quote caught my eye:

���Machine learning should be used as a research tool, not as a forecasting tool. It should be used to identify new theories, and once you identify a new theory, you throw the machine away, you don���t want the machine.���

���Marcos L��pez de Prado

A caveat: L��pez de Prado is speaking specifically about machine learning for market predictions, and he notes that markets resist prediction. “Markets evolve,” he said. “You are an investor and when you extract money from the market, the market learns to prevent you from extracting profits next year.”

Still, this resonates with a philosophy that has deepened for me the more I’ve worked with AI and machine learning: machine learning is better at signals than answers.

The first generation of mainstream AI applications has over-dialed on presenting just-the-facts answers. A one-true-answer mentality has created a whole raft of problems, some of them dangerous. Here’s the thing: the machines are flaky, with narrow and literal interpretations of the world. That means they’re brittle for decision-making. Instead of replacing human judgment, AI should amplify it. Machine learning is a mediocre substitute for human judgment and individual agency, but it’s an excellent signal booster for both.

I love the way L��pez de Prado frames it: use the machines to surface patterns, signals, and suggestions to develop a theory for action���and let humans make the decisions from there.

Time | How Machines Are Taking Over the World's Stock Markets

How Machines Are Taking Over the World's Stock Markets

Time magazine interviewed Marcos L��pez de Prado, a specialist in using machine learning for investment and finance. This quote caught my eye:

���Machine learning should be used as a research tool, not as a forecasting tool. It should be used to identify new theories, and once you identify a new theory, you throw the machine away, you don���t want the machine.���

���Marcos L��pez de Prado

A caveat: L��pez de Prado is speaking specifically about machine learning for market predictions, and he notes that markets resist prediction. “Markets evolve,” he said. “You are an investor and when you extract money from the market, the market learns to prevent you from extracting profits next year.”

Still, this resonates with a strong philosophy I’ve developed since I started working with AI and machine learning: machine learning is better used for signals than for answers.

The first generation of mainstream AI applications has focused too much on providing just-the-facts answers. The machines are flaky, with narrow and literal interpretations of the world. That means they’re brittle for decision-making. Instead of replacing human judgment, AI should amplify it. Machine learning is a crummy substitute for human judgment and individual agency, but it’s an excellent signal booster for both.

I love the way L��pez de Prado frames it: use the machines to surface patterns, signals, and suggestions to develop a theory for action���and then humans make the decisions from there.

Time | How Machines Are Taking Over the World's Stock Markets

Slack and the Decline of Bots

Because most chatbots understand only a very limited vocabulary, using them can become a guessing game to arrive at the precise incantation to make them do your bidding. The more we talk to robots, the more we talk like robots.

Will Oremus wrote this report in October about Slack’s expansion of support for third-party plugins. Those plugins were previously limited to text-only chatbots���via either conversational UI or specific “slash commands”���but can now offer more traditional GUI elements like windows, buttons, forms, and so on.

It seems Slack’s users found the chat-only UI too challenging because of its rigid command-line syntax. Discoverability was a challenge, and users found it hard to remember the precise words to make the bots go, or even which bots were installed. ���Nobody should have to be a specialist in the dozens of apps they interact with on a daily or weekly basis,��� said Andy Pflaum, Slack���s head of platform, in an interview.

Will writes:

Bots will ���continue to exist and have their role in
Slack,��� Pflaum said. But the company���s research has
found that ���the typical user isn���t as comfortable with
those, or forgets how to use those methods.��� Testing
of more graphical interfaces has generated ���so much
positive response,��� he added, and should make apps
accessible to ���a much broader base of users.���

Slack’s investment in feature expansion at once suggests the success of the plugins (1800 third-party apps and counting), but also the limiting nature of plain-text UI at a moment when bots still have very narrow language understanding. This will get better as natural language processing (NLP) improves and bots get more flexible in what they can understand. We’re already seeing that happen in the latest generation of NLP (see AI Dungeon for a fun example).

In the meantime: when you can take advantage of the full range of UI on a specific platform, you should���and that’s exactly what Slack is doing here. The future of interaction is increasingly multi-modal (and multi-platform for that matter). Enabling people to move nimbly among modes and platforms is as important as the ability to move among services, the very point of third-party plugins in the first place.

OneZero | Slack and the Decline of Bots

���Pair Curiosity with Generosity���

I really enjoyed my recent conversation with Phil Burgess for his IT Career Energizer podcast, because Phil takes a personal slant on tech conversation. While UX, design, and artificial intelligence inevitably came up, most of the focus was on the personal and the human, rather than the technical. The overarching theme: how to craft a healthy and fulfilling career in this industry. I can���t say that I���ve cracked that one, but I did share what I���ve learned so far. And things got a little touchy-feely.

Listen to the 33-minute episode here: Keep Your Work Life in Perspective, and Pair Curiosity with Generosity with Josh Clark.

We talked about:

How designers should be customer advocates without falling into the trap of becoming adversarial with the very business they work for
The challenge of separating one���s work from one���s sense of self. ���Success doesn���t make you a better person, and failure doesn���t make you a worse one. ��� There���s no rest or satisfaction in thinking that you will finally be happy if only your work is a success.���
Lessons learned from my career low, the failure of my first business���and my long unwillingness to let it simply fail so that I could move on
The future of careers in technology, and the role we can all play in crafting a data-driven future that is also responsible and respectful
The best career advice I ever received: ���There is no big time. You never arrive. Every time you reach a goal, there���s always something new that���s just beyond your reach. ��� You���ve got to, in your work, draw satisfaction from today, and draw pleasure from the thing you���re making right now.���
My number-one non-technical skill: active listening and working collaboratively
How to pair curiosity with generosity in your career in a way that lets you be proud of what you do and learn���but with humility and care for others

I found the conversation to be a useful bit of reflection on my own career and values, and perhaps you���ll find it useful, too. Listen here.

The Decade of Design

In a wide-ranging essay for Figma, Carmel DeAmicis chronicles the rise of design in the last decade of product and business: The Decade of Design: How the last 10 years transformed design���s role in tech. She asked a dozen people, including me, about the themes that raised design���s profile and shifted its focus since 2010.

���Mobile normalized the idea of computing beyond the screen��� is a tidbit I offered. Not only did mobile make touch mainstream as an input alternative to keyboard and mouse, it also made sensor-based computing utterly normal. The camera, the microphone, the accelerometer, GPS���all became expected, everyday surfaces for interaction in the first full decade of the smartphone. For that matter, ���invisible��� interactions became commonplace, too, with notification-based interfaces driven by data-backed predictive services.

At an even more fundamental level, mobile changed consumer expectations of what software should be���in quality, ease of use, and even personality. ���Applications used to be gray, bland, functional affairs imposed upon us to do the mundane tasks of the day,��� I suggested to Carmel. ���Mobile really blew that up.���

Carmel���s central theme is not only that mobile was the key driver for digital product design in the last decade, but that this sea change also had several follow-on (and follow-on-follow-on effects). ���Mobile sped up the pace of everything, design included,��� she writes. A few of her call-outs:

The collection, storage, and use of personal data exploded.
Data-driven design and A/B testing became part of the standard toolkit for designers (for better and for worse).
As more companies built on top of identical infrastructure (AWS, cloud services, open source software), design became the distinguishing factor for products, not the code.
Big companies started hiring big design staffs, instead of outsourcing design work to agencies.
Educational programs, bootcamps, and self-serve courses about design have proliferated to meet the need for more designers.
Design tools exploded to meet the growing, varied, and dynamic needs of interaction design.


What comes next?

Carmel closes her essay by noting that designers are now charged with figuring out how to use their new and growing influence to focus tech on meaningful opportunities���and limit tech���s potential and demonstrated dangers. ���Many people we interviewed mentioned the moral responsibilities that lie ahead,��� she writes.

For me, that will be all about how we choose to feed the algorithms, present their results, and limit their risks. Carmel and I talked about this in our interview for the article, though it didn���t make the final cut. Here are a few of my comments from that conversation:

If mobile defined the last decade of digital product design, machine learning is already defining the next.

Algorithmic interfaces already drive so many of the digital products all of us use every day. For better and for worse, algorithms determine the news we see, the movies we watch, the products that are surfaced, even the way we drive home from work. For designers, the next decade is all about understanding our roles and responsibilities in using and shaping the algorithm as a design material. How do we use data and sensors and machine learning in ways that are meaningful, personal, useful���and most of all, respectful and responsible? That���s the opportunity and challenge that will be mobile���s legacy���and the work of design in the coming years.

Are you or your team wrestling with how to adopt and design for machine learning and AI? Big Medium can help���with executive sessions, workshops, or full-blown engagements for product design and development. Get in touch.

Figma | The Decade of Design: How 10 years transformed design���s role in tech

The Damaging Fiction of the Wellness Industrial Complex���

My astonishing wife Liza Kindred posted an elegant and thoughtful takedown of ���the wellness industrial complex,��� the manipulative mashup of wellness and capitalism. (Even if wellness isn���t your thing, stick with me���this has an important tie-in to design.)

In The Damaging Fiction of the Wellness Industrial Complex���, Liza writes:

There is a lie���a simple but damaging fiction���that seeps under the doors of our yoga studios and burrows into our meditation cushions. It dresses up in cute quotes on Pinterest and has hacked its way into countless Instagram accounts. It smells weird; like someone tried to burn sage over a garbage can. But if we wrinkle up our noses and ask if anyone else smells that weird smell, all we get in response is nervous laughter. Our ears ache from the shrillness of the hollow words; we���re choking on the dust of toxic positivity; and through the haze we think we can see a place where the air is clean���but standing between us and that clearing stands a group of people wearing t-shirts with ancient holy symbols on them, which were purchased from a big box store.

The lie, Liza writes, is the insidious suggestion that you���re not good enough. If only you would hustle harder, do more yoga, keep up your Headspace streaks, and just ���work on yourself��� more… you could finally be the better person you were meant to be. ���Anyone who wants to fix you thinks that you are broken,��� Liza warns:

This idea���that we are broken and need to be fixed���is what I call The Golden Cut. It is self aggression masquerading as a journey towards self acceptance. It is a damaging fiction that has invaded the lives and hearts of so many of us, an unchecked moral imperative that has cloaked itself in the language of wellness and well-being.

Constant striving has become the planned obsolescence of the wellness world. It���s precisely how capitalism has invaded wellness.

Liza puts her finger on something that has always felt off-putting to me about aspects of mindfulness as an industry. While mindfulness (and yoga and meditation and its other many cousins) have real and evident benefits, they���re often wrapped in a superficial commercial culture that broadcasts a smug kind of judgment, superiority, and shame to those ���not in the club.��� For a movement that promises personal growth, that���s just gross.

Liza is explicit about separating out the practices and services of wellness from their toxic alter ego, the wellness industrial complex. It���s not wellness or wellness-related businesses that are bad, she writes; it���s the cynical and manipulative marketing that suggests you are broken and need to be ���improved.��� It���s healthy to seek and cultivate personal development, and of course to acknowledge our flaws, ���but none of this means that we are broken; it means that we are human.���

So what���s this got to do with design?

The very purpose of interaction design is to shape behavior. It guides users through an intentional path to a desired outcome. Commercial design seeks to shape that behavior to the benefit of the company. When done well, of course, it also benefits the customer; both get what they need. All too often, though, that power dynamic becomes lopsided, and the interests of the company steamroll those of the individual.

That���s when you get abusive anti-patterns that incite false urgency or FOMO or shame in order to get that purchase. This one in particular reminds me of the ���you���re broken��� message of the wellness industrial complex. “Nope, I don’t care enough”:

“Nope, I don’t care enough.” via confirmshaming.tumblr.com.

This kind of confirmshaming is a familiar tactic that fronts as tongue-in-cheek playfulness even as it forces you to say you���re a jerk for not buying the product. (And whattya know, that one���s for a wellness service to boot.)

Every time you design to stir this kind of guilt or false urgency (���75 people are looking at this hotel room���) or interruption (popover ads and notifications) or other unease, you take advantage of the customer. The trouble, of course, is that the tactics work, at least in the short term. If you���re just measuring conversions, you���ll see those metrics rise. But at what cost? Cynical sales tactics sully the very product they aim to sell; they erode trust. How are you measuring that? And how does that result fit with the brand you���re trying to build, or the effect your business is trying to have for your customers?

In the wellness world at least, Liza shows us that the wellness industrial complex deepens jittery unease and dissatisfaction, instead of the calm and peace it promises on the surface. We can all do and demand better.

EFF THIS! Meditation | The Damaging Fiction of the Wellness Industrial Complex���

Simple Plan Gets Millions Running

Writing for Hong Kong���s South China Morning Press, Jack Lau tells the story of Couch to 5K, the running schedule I created in 1996. Get the scoop on how the thing came to be and, um, compare photos of 25-year-old Josh running in 1996 to 49-year-old Josh running now.

I created the schedule well before I became a designer, but looking back, I consider Couch to 5K (C25K) to be my first big UX project. The ���brief���: onboard skeptical would-be runners to a regular, sustainable running habit. Twenty-four years later, many millions of runners have used C25K to do exactly that.

The secret behind the nine-week plan is to offer kind encouragement to go (very) slow and advance (very) gently. Many come to C25K after defeating experiences with fitness: pain, self-flagellation, boredom, or ���failure.��� The program turns that around by delivering attainable victories. As Jack calls out in the article, the process has deeper effects than the merely physical. I���ve received a truly remarkable number of notes from people who tell me that completing the program revealed new confidence in what they might achieve.

���If a depressed 33-year-old with bad knees who hates cardio can do it, enjoy it, and start to thrive, anyone can,��� one C25K alum told Jack. And that���s pretty cool.

South China Morning Post | Simple Plan Gets Millions Running

How Dotdash, Formerly About.com, Is Taking over the Internet

Fast Company���s Aaron Cohen shares the story of Dotdash, the network formerly known as About.com. Big Medium had a big role in this tale, and it may be the most successful design- and business-turnaround story we���ve ever been involved with.

Three years ago, About.com���s audience and ad revenue were plummeting, and CEO Neil Vogel told us the company was ���circling the drain��� and needed drastic change. We helped the company develop a new vertical strategy, carving out the content from the main network into branded premium experiences. The new network, Dotdash, relaunched its vast archive of content with a collection of great-looking, fast, and premium websites, powered by a single CMS and a themed design system. Big Medium led the design of three of those early properties���Verywell, The Balance, and The Spruce���and the network has since grown to nearly a dozen.

We tell our bit of the story here, and Fast Company shares what���s happened since:

Maybe you���ve never even heard of Dotdash, but its service content reaches about 90 million Americans a month. … Collectively, Dotdash���s sites have increased traffic by 44% year over year in Q3 2019. Driven by advertising and e-commerce, the company���s annual revenue grew by 44% in 2018 and 34% as reported in Q3 2019 earnings.

A big part of this success boils down to some very intentional design and technology bets that we made together:

Make more money… by showing fewer ads
Create a respectful UX that celebrates content instead of desperate revenue grabs
Create a front-end architecture that is modular and nimble
Make the sites fast

It���s worth noting that all of these choices are counter to what most media companies are doing. Most are pouring on more ads, imposing design that abuses readers and content with popovers etc, slowing their sites with heavy scripts and trackers. No kidding, it was a seriously brave and non-obvious choice to reject those paths. Fast Company describes the impact of Dotdash���s industry-bucking choices:

While other independent media companies were engineering their coverage around social media, video, and trending topics, Dotdash doubled down on text-based articles about enduring topics and avoided cluttering them with ads. ��� Dotdash sites run fewer ads, with no pop-ups or takeovers, and because the ads are relevant to each article, they perform better. At a time when digital ad rates have continued to crater for most online publishers, Vogel says the company���s ad rates have increased nearly 20 percent each year since 2016, and 25 percent of 2019 revenue came from affiliate marketing fees (bonuses paid to the publisher after Dotdash visitors made purchases via ads on the sites.)

The sites load very quickly, and the company���s proprietary content management system is designed for efficiency: Designers and editors can choose from fast-loading templates that include images, video, and interactive applications. And there���s an emphasis on creating the kinds of detailed, informative articles that turn up in search results. At Verywell, for example, each article is updated at least once every nine months and reviewed by medical professionals.

Dotdash has not only turned itself around, it���s been expanding as other media companies have contracted, selling themselves off piece by piece. Big congrats to our friends at Dotdash: they���ve demonstrated that ad-supported websites can be presented in ways that are both respectful and (very) profitable.

Fast Company | How Dotdash, Formerly About.com, Is Taking over the Internet

You���re Not Late to Machine Learning

Algorithms have gotten into everything, right? For better or worse, machine learning determines the news we see, the movies we watch, the products we buy, and the way we drive home. In fact, it might seem like AI is everywhere you look, except um… your own company���s app or website?

Never fear, you���re not late to this, and you���re not behind. Even as AI has become pervasive in our individual lives, it���s not yet widespread in product organizations. Only a select few companies have adopted machine learning as an ordinary part of doing business and building products, but that set is growing faster than you might think.

An IBM report released this week underscores the speed of change. In a survey of 4500 companies around the world, 34% say they���ve adopted AI (for large companies with 1000+ employees, it���s 45%). Another 39% are ramping up in exploratory phases. ���If you look forward to the next 18 to 24 months, that���s probably going to change to 80% to 90% adoption of AI across the board,��� said IBM VP Ritika Gunnar at CES yesterday.

An IBM survey found that 3 of 4 companies have adopted or are experimenting with AI.

For many of the companies already working with this new design material, sprinkling AI into a product or business process has become just another part of everyday software creation. For the managers, designers, and developers at these companies, it���s already second nature to collect data around a product and apply machine learning to make that product better or more personalized.

These organizations may be in the vanguard, but we���ll all join them soon. There���s nothing magic about these companies or their underlying technologies. Machine-learning models are readily available, even as plug-and-play services that you can start using within the hour. You don���t have to build the whole thing yourself. The tools are already available to all���and yes, that includes you, your team, your organization. Here at Big Medium, a growing portion of our product work is bringing AI and machine learning to products whose companies are using it for the first time. It���s starting to happen for everyone now.

Just as mobile defined the last decade of digital product design, machine learning is already defining the next. The arrival of mainstream machine learning is as transformative as the advent of relational databases in the 1980s, the web in the 1990s, or mobile in the 2000s. And like those technologies, designing with AI is bound to become the everyday norm. Before long, pretty much everything will have a little machine learning inside, and that will be no big deal. This technology is not only within the reach of all, it���s soon to be expected of all. We���ll all be making software with casual intelligence.

You���re not behind, but it���s time to get after it. The good news is that this is not a winner-take-all race. The big technology companies may be slurping up all the data they can, but no matter how large their hoard becomes, it doesn���t mean that they ���win��� at AI. A company may dominate a specific domain, but data is highly specific to any application. No matter how many photos Facebook collects to become the best at face recognition, that has no bearing on the system you create to recommend products to customers, or plan your delivery logistics, or identify new customer behaviors. Machine learning is only the enabling infrastructure layer. The data you bring to it and the way you use it is completely your own���and outside the reach of the data-thirsty tech giants.

AI has not so much come of age as reached an awkward adolescence. We���re still inventing this together; you’re right on time.

What matters much more, and is still very much in play, is how to best put these powerful new tools to use. The last couple of years have shown that even the big tech companies are still sorting that out. If anything, the first generation of mainstream AI products has shown us as much about what not to do as what we should. AI has not so much come of age as reached an awkward adolescence. At Big Medium, we���re doing what we can to help it grow up. Much of our recent product work focuses on helping clients create design patterns that put the algorithm to work in meaningful, respectful, and responsible ways. We���re still inventing this together. You���re right on time.

Are you or your team wrestling with how to adopt and design for machine learning and AI? Big Medium can help���with executive sessions, workshops, or full-blown engagements for product design and development. Get in touch.

You���re Not Late

Algorithms have gotten into everything, right? For better or worse, machine learning determines the news we see, the movies we watch, the products we buy, and the way we drive home. In fact, it might seem like AI is everywhere you look, except um… your own company���s app or website?

Never fear, you���re not late to this, and you���re not behind. Even as AI has become pervasive in our individual lives, it���s not yet widespread in product organizations. Only a select few companies have adopted machine learning as an ordinary part of doing business and building products, but that set is growing faster than you might think.

An IBM report released this week underscores the speed of change. In a survey of 4500 companies around the world, 34% say they���ve adopted AI (for large companies with 1000+ employees, it���s 45%). Another 39% are ramping up in exploratory phases. ���If you look forward to the next 18 to 24 months, that���s probably going to change to 80% to 90% adoption of AI across the board,��� said IBM VP Ritika Gunnar at CES yesterday.

An IBM survey found that 3 of 4 companies have adopted or are experimenting with AI.

For many of the companies already working with this new design material, sprinkling AI into a product or business process has become just another part of everyday software creation. For the managers, designers, and developers at these companies, it���s already second nature to collect data around a product and apply machine learning to make that product better or more personalized.

These organizations may be in the vanguard, but we���ll all join them soon. There���s nothing magic about these companies or their underlying technologies. Machine-learning models are readily available, even as plug-and-play services that you can start using within the hour. You don���t have to build the whole thing yourself. The tools are already available to all���and yes, that includes you, your team, your organization. Here at Big Medium, a growing portion of our product work is bringing AI and machine learning to products whose companies are using it for the first time. It���s starting to happen for everyone now.

Just as mobile defined the last decade of digital product design, machine learning is already defining the next. The arrival of mainstream machine learning is as transformative as the advent of relational databases in the 1980s, the web in the 1990s, or mobile in the 2000s. And like those technologies, designing with AI is bound to become the everyday norm. Before long, pretty much everything will have a little machine learning inside, and that will be no big deal. This technology is not only within the reach of all, it���s soon to be expected of all. We���ll all be making software with casual intelligence.

You���re not behind, but it���s time to get after it. The good news is that this is not a winner-take-all race. The big technology companies may be slurping up all the data they can, but no matter how large their hoard becomes, it doesn���t mean that they ���win��� at AI. A company may dominate a specific domain, but data is highly specific to any application. No matter how many photos Facebook collects to become the best at face recognition, that has no bearing on the system you create to recommend products to customers, or plan your delivery logistics, or identify new customer behaviors. Machine learning is only the enabling infrastructure layer. The data you bring to it and the way you use it is completely your own���and outside the reach of the data-thirsty tech giants.

AI has not so much come of age as reached an awkward adolescence. We���re still inventing this together; you’re right on time.

What matters much more, and is still very much in play, is how to best put these powerful new tools to use. The last couple of years have shown that even the big tech companies are still sorting that out. If anything, the first generation of mainstream AI products has shown us as much about what not to do as what we should. AI has not so much come of age as reached an awkward adolescence. At Big Medium, we���re doing what we can to help it grow up. Much of our recent product work focuses on helping clients create design patterns that put the algorithm to work in meaningful, respectful, and responsible ways. We���re still inventing this together. You���re right on time.

Are you or your team wrestling with how to adopt and design for machine learning and AI? Big Medium can help���with executive sessions, workshops, or full-blown engagements for product design and development. Get in touch.