Global Nerdy

Here’s a word that you should add to your vocabulary: Scenius. It was coined by musician, music producer, and visual artist Brian Eno to describe the extreme creativity that groups, places, or “scenes” can generate.

Eno came up with the term as a way of countering the pervasive myth of the Lone Genius — the idea that innovation comes from a small, select set of Chosen Ones.

Here’s an expanded definition, courtesy of Eno:

Creative Commons photo by Algemene Vereniging Radio Omroep (AVRO).
Tap the image to see its source.

“Scenius stands for the intelligence and the intuition of a whole cultural scene. It is the communal form of the concept of the genius.”

…I thought that originally those few individuals who’d survived in history – in the sort-of “Great Man” theory of history – they were called “geniuses”. But what I thought was interesting was the fact that they all came out of a scene that was very fertile and very intelligent.

So I came up with this word “scenius” – and scenius is the intelligence of a whole… operation or group of people. And I think that’s a more useful way to think about culture, actually. I think that – let’s forget the idea of “genius” for a little while, let’s think about the whole ecology of ideas that give rise to good new thoughts and good new work.”

“Genius/EGOsystem vs. Scienus/ECOsystem” graphic by Austin Kleon.

Here are some examples of scenius, where the collective smarts, creativity, and passion of a group of people coming together to do great things is greater than the sum of its parts:

• The Lunar Society of Birmingham: a dinner club run between 1765 and 1813 in Birmingham, England, and attended by industrialists, scientists, and thinkers who changed science and engineering forever. Their regulars included Boulton and Watt (steam engines and their applications to manufacturing), Erasmus Darwin (biology, inventions, and grandfather of Charles Darwin), Keir (industrialist, chemistry, inventions), Priestly (chemistry, philosophy), Small (Thomas Jefferson’s professor at the College of William and Mary), Stokes and Withering (early heart medicine), Wedgewood (industrialized pottery, pretty much invented modern marketing, including the concepts of direct mail, money-back guarantees, self-service, free delivery, buy one get one free, and illustrated catalogs), Whitehurst (geology).
• The Sex Pistols’ 1976 gig. It was attended by a mere 42 people, but those people went on to revolutionize music through their work in British alt-rock: Howard Devoto and Pete Shelley (The Buzzcocks) organized, Tom Wilson and Martin Hannett (Factory Records and the Hacienda), Morrissey, Mark E. Smith (The Fall), Paul Morley (NME magazine, Frankie Goes to Hollywood, The Art of Noise), Mick Hucknall (Simply Red), and Ian Curtis, Bernard Sumner, and Peter Hook (Joy Division, New Order).
• Silicon Valley: Your iPhone and Android are direct descendants of the scenius that was born when the “Traitorous Eight” left Shockley Semiconductor to form their own company, Fairchild, and the “Fairchildren” who then left Fairchild to form their own companies, and so on, creating a cross-pollenating scene that we now know as “The Valley”.

Global Entrepreneurship Week Tampa’s Happy Hour + Florida Innovation Discussion, November 12, 2018.

Kevin Kelly, founding editor of Wired and former editor and publisher of the Whole Earth Review, wrote that the geography of scenius is nurtured by several factors:

• Mutual appreciation — Risky moves are applauded by the group, subtlety is appreciated, and friendly competition goads the shy. Scenius can be thought of as the best of peer pressure.
• Rapid exchange of tools and techniques — As soon as something is invented, it is flaunted and then shared. Ideas flow quickly because they are flowing inside a common language and sensibility.
• Network effects of success — When a record is broken, a hit happens, or breakthrough erupts, the success is claimed by the entire scene. This empowers the scene to further success.
• Local tolerance for the novelties — The local “outside” does not push back too hard against the transgressions of the scene. The renegades and mavericks are protected by this buffer zone.

BarCamp Tampa Bay, November 10, 2018.

Here’s what Austin Kleon, a writer and artist whose ideas have been adopted by the tech community, has to say about scenius:

Under this model, great ideas are often birthed by a group of creative individuals—artists, curators, thinkers, theorists, and other tastemakers—who make up an “ecology of talent.” If you look back closely at history, many of the people who we think of as lone geniuses were actually part of “a whole scene of people who were supporting each other, looking at each other’s work, copying from each other, stealing ideas, and contributing ideas.” Scenius doesn’t take away from the achievements of those great individuals: it just acknowledges that good work isn’t created in a vacuum, and that creativity is always, in some sense, a collaboration, the result of a mind connected to other minds.

What I love about the idea of scenius is that it makes room in the story of creativity for the rest of us: the people who don’t consider ourselves geniuses. Being a valuable part of a scenius is not necessarily about how smart or talented you are, but about what you have to contribute—the ideas you share, the quality of the connections you make, and the conversations you start. If we forget about genius and think more about how we can nurture and contribute to a scenius, we can adjust our own expectations and the expectations of the worlds we want to accept us. We can stop asking what others can do for us, and start asking what we can do for others.

Tampa Community Connect, October 20, 2018.

The time is ripe to build Tampa Bay’s tech scenius. Consider these recent developments in the area…

• Massive $3 billion development will accelerate Tampa, Florida’s growth (April 2017)
• Why Have So Many Millennials Become Tampreneurs? (April 2017)
• Tampa Ranks as Top Spot Where Millennials are Moving (June 2017)
• 5 Reasons Why People Are Moving to Tampa Bay Area (December 2017)
• Jeff Vinik hires Chicago startup specialist Lakshmi Shenoy to run Tampa innovation hub at Channelside Bay Plaza (December 2017)
• Tampa Bay area’s population projected to grow to 3.1 million this year (February 2018)
• Evidence of Tampa Bay Area’s growing tech hub: Clearwater counts 50 tech companies, 2K jobs (March 2018)
• Global study shows economic growth in Tampa Bay (June 2018)
• Tampa metro’s job growth is second highest in the state (June 2018)
• Young talent helps Tampa Bay stand out in tech, new group says (August 2018)
• David Straz unveils job growth strategy for Tampa (October 2018)
• Tampa Bay looking to brand itself as technology hub of the state, Southeast (October 2018)

…as well as Tampa’s place as one of the new “18-hour cities” or “new boomtowns” referenced in this Washington Post article.

hack</hospitality> Hackathon, August 2017.

Over the past little while, the elements of an interesting, vital technology scene have been gathering in Tampa Bay and the surrounding area (I like to think of Tampa Bay as the western end of the “Orlampa” corridor, with Orlando — who have a lively tech community of their own — at the eastern end). This scene will be boosted by the arrival of that startup hub and gathering place Embarc Collective in March:

Click the image to see it at full size.

Click the image to see it at full size.

Click the image to see it at full size.

While the elements of scenius are in place for Tampa Bay’s tech scene, there’s still some way to go before Tampa can match places like Nashville — whose tech scene is bigger than you might think — never mind places like Austin, Charlotte, Indianapolis, and Raleigh.

The success or failure of Tampa’s tech scenius depends on us, the Tampeños who work in tech, creative, and related industries.

I’m originally from Toronto. While it has one of the hottest tech scenes in North America today, it wasn’t that way 15 years ago. While the city did launch some initiatives to change this, what truly made the difference was Toronto’s own tech community stepping up and organizing. We held events of all sizes, from regular meetups and user group meetings at pubs and lecture halls to independent conferences like Mesh, RubyFringe and FutureRuby to tech “camp” events to big corporate gatherings put on by the likes of the Canadian subsidiaries of IBM and Microsoft. We built places to get together, from hackerspaces such as Hacklab and Site3 coLaboratory to the MaRS Centre. In my work as a developer evangelist for Microsoft, I’ve met many students at Toronto’s fine universities and colleges, and they’re eager to crank out the ‘wares, both hard and soft, and they’re bright as all get-out. We built a great community bound together by cooperation, a strong social media scene and good old-fashioned face-to-face meetings. We got stuff done, and the stuff we did traveled far and wide. We built Toronto’s tech scenius, and it put the city on the map.

Can Tampa do the same? I believe so — it’s just up to us.

Worth reading/watching

• 12 Tips to Building a Successful Startup Community Where You Live (April 2012)
• This is how you build a tech community (September 2012)
• How to build a tech ecosystem: The essential building blocks for your city (April 2014)
• How Cities Build Vibrant Tech Scenes (September 2014)
• The Recipe For Building A Startup Scene In Any City (October 2014)
• How to Create a Tech Startup Scene If You’re Not in Silicon Valley (December 2014)
• Building Tech Communities (March 2017)
• The No Bullshit Way To Grow The Phoenix Tech Scene (April 2017)
• How to Build a Vibrant Tech Community: 4 Lessons from Cincinnati (May 2018)
• How To Build A Civic Tech Community (June 2018)
• Seedcamp’s “How to build a tech ecosystem: The essential building blocks revisited by Carlos Espinal” page
• How to Build Your Startup Community

To build trust in data science, work together

From the Cornell Chronicle:

As data science systems become more widespread, effectively governing and managing them has become a top priority for practitioners and researchers. While data science allows researchers to chart new frontiers, it requires varied forms of discretion and interpretation to ensure its credibility. Central to this is the notion of trust – how do we reliably know the trustworthiness of data, algorithms and models?

The kinds of data scientist

From Harvard Business Review:

In 2012, HBR dubbed data scientist “the sexiest job of the 21st century”. It is also, arguably, the vaguest. To hire the right people for the right roles, it’s important to distinguish between different types of data scientist. There are plenty of different distinctions that one can draw, of course, and any attempt to group data scientists into different buckets is by necessity an oversimplification. Nonetheless, I find it helpful to distinguish between the deliverables they create. One type of data scientist creates output for humans to consume, in the form of product and strategy recommendations. They are decision scientists. The other creates output for machines to consume like models, training data, and algorithms. They are modeling scientists.

Three reasons why mothers should consider a career in data science

From LinkedIn:

For several women, the time during their pregnancy is one of overwhelming happiness, and at times, worry. We worry about things like childbirth and not knowing what to do with our baby after he or she is born. Women with careers have an added worry; we think about how this adorable new addition to our family will impact our careers.

One thing that I’ve discovered over the past four years is that having certain skills can reduce uncertainty around our careers. I’m a mom of two little girls and have a career in data that has provided me with the more flexibility and less stress. Below, I outline the three reasons why mothers should consider a career in data science.

Why you shouldn’t be a data science generalist

From Towards Data Science:

I work at a data science mentorship startup, and I’ve found there’s a single piece of advice that I catch myself giving over and over again to aspiring mentees. And it’s really not what I would have expected it to be.

Rather than suggesting a new library or tool, or some resume hack, I find myself recommending that they first think about what kind of data scientist they want to be.

The reason this is crucial is that data science isn’t a single, well-defined field, and companies don’t hire generic, jack-of-all-trades “data scientists”, but rather individuals with very specialized skill sets.

To see why, just imagine that you’re a company trying to hire a data scientist. You almost certainly have a fairly well-defined problem in mind that you need help with, and that problem is going to require some fairly specific technical know-how and subject matter expertise. For example, some companies apply simple models to large datasets, some apply complex models to small ones, some need to train their models on the fly, and some don’t use (conventional) models at all.

Each of these calls for a completely different skill set, so it’s especially odd that the advice that aspiring data scientists receive tends to be so generic: “learn how to use Python, build some classification/regression/clustering projects, and start applying for jobs.”

 

Click the image to see it at full size.

(They also should’ve done a better job vetting the one IT guy they hired.)

With Student Interest Soaring, Berkeley Creates New Data-Sciences Division

From Chronicle of Higher Education:

Berkeley’s move follows MIT’s announcement last month that it was investing $1 billion in a new college of artificial intelligence. But leaders at Berkeley say their disclosure of the division today was driven by an imminent international search for a director, who will hold the title of associate provost, putting the program on an institutional par with Berkeley’s colleges and schools. They explain that in creating a division rather than a new college, they are reflecting the way data science has become woven into every discipline.

Berkeley has been planning the division for four years, said David Culler, interim dean for data sciences, and has been rolling it out incrementally through a new data-sciences major approved last year, and corresponding growth in data-science courses. Enrollment in “Foundations of Data Science” has soared from 100 in 2015 to 1,300 in 2018. Enrollment in the upper-level “Principles and Techniques of Data Science” has grown from 100 in 2016 to 800 students. The emerging program has served as a “pilot” for the division, which is now set to evolve under a new director.

…

The core of the data-science curriculum, said Culler, is computer science and statistics, with additional depth courses in optimization and visualization. But students will also be required to have a “domain emphasis” that would most likely synthesize material from various other departments. For instance, a data-science student’s exploration of social inequality might include courses in sociology, ethnic studies, economics, and philosophy.

‘With a basic degree, you can learn data science on the job’

From SiliconRepublic:

Next week at the National Analytics Conference, [Jennifer Cruise from the Aon Centre for Innovation and Analytics] will be on a panel where she expects to discuss several aspects and challenges that businesses face relating to data, including how to deal with the abundance of information that is now available and, of course, the key issues of skills and resources.

“You can only truly exploit the data if you get the right people in that space, and there’s a double whammy,” she said. “On the one hand, you have a lack of hands-on resources. Skilled data scientists are hard to come by and things are changing quickly, so people who are qualified need to stay on top of things. Then, you also have a gap in the leadership space – the people who can advise you how to turn [data] into revenue for your company, or how to use your data to become more operationally efficient.”

8 common questions from aspiring data scientists, answered

From Tech in Asia:

So, you want to be become a data scientist? Great. But you have zero experience and have no clue how to get started in this field. I get it. I’ve been there and I definitely feel you. This is why this post is for you.

All the questions below came from the community through my LinkedIn post, email, and other channels. I hope that by sharing my experience, you will be enlightened on how to pursue a data science career and make your learning journey fun.

OPINION: How to craft effective data science job descriptions

From ComputerWorldHK:

In today’s data science job market, demand far outstrips supply, said Chris Nicholson, co-founder and CEO of artificial intelligence and deep learning company Skymind, and co-creator of the open source framework Deeplearning4j. That means organizations must resist the temptation to seek candidates with every last required data science skill in favor of hiring for potential and then training on the job, he said.

“A lot of data science has to do with statistics, math and experimentation—so you’re not necessarily looking for someone with a computer science or software engineering background, though they should have some programming experience,” Nicholson said. “You want folks from physical science, math, physics, natural sciences backgrounds; people who are trained to think about statistical ideas and use computational tools. They need to have the ability to look at data and use tools to manipulate it, explore correlations and produce data models that make predictions.”

Because a data scientist’s job isn’t to engineer entire systems, minimal programming experience is fine, Nicholson said. After all, most organizations can rely on software engineering, DevOps, or IT teams to build, manage and maintain infrastructure in support of data science efforts. Instead, strong data science candidates often have a background in science and should be proficient with data science tools in one or more different stacks.