Galvanize, Artificial Intelligence and the Big Boulder Initiative

There are few things as hot of a topic right now than Artificial Intelligence (AI). You will have two opportunities to hear Nir Kaldero, Director of Data Science for Galvanize, discuss: AI/Machine Learning Is Taking Over the World. Here’s How To Win and Not Get Shredded.

We are in the midst of the very beginnings of a new industrial revolution — the AI Revolution. Technology teams and companies that understand the power and opportunity of Artificial Intelligence and Machine Learning, and build their organizations around it, will survive and win. Those who don’t, are predicted to get shredded. In this session, we present compelling case studies from companies with whom we have worked and advised, who have successfully reshaped their organization to be a data-driven organization in this new era. In this session, we will offer a framework and tools so that you and your team can avoid common mistakes and be in a better position to adapt to the AI Revolution.

You can join Nir at the Galvanize Boulder Campus on Thursday, June 1 at 6 p.m. – but hurry, there are only a few seats left for this event. Or join Nir during the Big Boulder Initiative (tickets required) on Friday, June 2 at 12:00 p.m. – 1:00 p.m.

Data Science Immersive gets Department of Higher Education Approval

The Galvanize Boulder Campus got some exciting news recently when we were granted approval by the Department of Higher Education to host the three month Data Science Immersive on their campus starting Fall of 2017. To find out more about what it takes to become a Data Scientist and upcoming cohort dates, check out our website.

Visual is King

By Richard Lee, CEO of Netra, Inc. Richard will be speaking at Big Boulder 2017!

Consumers are taking more photos and videos than ever. With recent advances in technology, anyone can be a videographer or a photographer and feel like a professional.

As consumers, we love this. Documenting our daily lives on social media has become part of, or even the driving motivation, for seeking out new experiences. We have our photo albums on Facebook and take pride in our carefully-curated Instagram handles.

Followers and likes are the rewards for our efforts. We still use text-based captions and hashtags, but social media is becoming increasingly focused on the visual. It might be time to transition the old adage “content is king” to the more contemporary “visual is king.”

Images Capture Who We Are

The images we’re posting are chock full of information that goes beyond what we include in the captions or the hashtags. The context of the images and the objects and brands we engage with paint a much fuller picture of what we do and who we are. Our interests and our hobbies can be generalized into types of activities we enjoy, and that data is powerful.

This information has been traditionally found through things like Google searches and likes and follows on social media. This insight can be valuable too, but often doesn’t portray an up-to-date picture of a consumer’s interest. For instance, how many of us liked a brand on Facebook in college only to rarely follow them now?

This can be improved significantly by being able to “read” an image. For example: you’re a dog owner who loves to post about Fido. Your feed is filled with pictures of you and him at the dog park and going on hikes.

Sure, those images say you like animals and enjoy the outdoors, but what else does it say? It may show that you enjoy outdoor brands and value function over fashion. It may show that you’re interested in an all-natural dog food that focuses on its ingredients instead of fancy packaging.

All of this data is out there, but most brands aren’t aware of it and even fewer know how to pull such information.

The Power of Image Recognition

Companies and brands are in a constant battle for our attention and space on our screens. Intrusive advertisements are, well, intrusive, and the current technology behind the targeted ads embedded in our news feeds often ends up leaving consumers feeling creeped out, annoyed, or both.

But what if companies could tailor targeted ads in such a way that they feel like natural and useful suggestions for consumers

This is where Netra comes in. Our image recognition technology combs through the millions of photos that are openly shared online every day to harness valuable insights that bring brands closer to consumers.



Our technology gathers the valuable data in images that aren’t clearly communicated in captions, such as brand logos and objects, while also anonymously identifying age, gender and ethnicity of the people within the images. This allows for marketers to find deeper insights – such as when consumers are drinking Coke, and how and where they are enjoying it – without having to rely on traditional forms of tagging on social media, like hashtags.

With this “visual intelligence,” marketers can use image targeting to reach consumers who actually want to hear from the brand.

When looking to the future, I often tell friends and colleagues that the camera will replace the keyboard. Whether you look at social media, consumer electronics or your favorite news publication, it’s clear that more people prefer to consume information through images.

As platforms like Facebook, Instagram, Snapchat and Twitter continue to reign supreme, we’ll see more and more brands make their way onto these platforms to reach consumers, and Netra will be there to provide the insights that will create valuable content from marketers.

With over 3.5 billion photos being shared on social media every day, it’s no question that visual is king. But as recent years have shown, we’ve only just hit the tip of the iceberg.

Cutting through social data noise with machine learning

By Madeline Para, CEO and Co-Founder at Twizoo.  (Madeline will be speaking at Big Boulder 2017!)

All of us that work with social data know the signal to noise ratio is ever increasing, and not in our favor. As the irrelevant baby picture or the 1000th Trump meme pops up in our social, that micro-dopamine hit of ingesting content from social media may not fire like it used to, and you lose interest. In fact, it is this increasing noise that can cause businesses to become disillusioned or skeptical that they can use social data to achieve their goals.

As these noise levels increase, it’s not hard to recognize that businesses need ever more powerful tools to find those sparkly signal-diamonds in the social rough. The basics of attributing sentiment scores and broad topic categories are outgrowing their usefulness and competitive advantages to drive significant results.

So, what is the next step for reaching an even a higher level of value when tackling the signal to noise problem with your social data? After all, the more accurate you are at collecting contextually relevant social data for your use case, the better your outcome and analysis will be.

You may have thought about writing some of your own rules on top of your dataset to improve it’s relevancy and quality. We’ve all seen the complex Boolean search queries your teams may use in TweetDeck or similar tools. For example – perhaps you want to analyze all social posts that are about US President Donald Trump. You might create some search queries for his handle(s), as well as keywords like “Donald Trump” and “President”. However, the latter keyword term will bring back contextually irrelevant noise, compromising your dataset. You might then add some heuristics on top to reduce the noise, such as, must mention keyword “President” but NOT “Obama”. However, these rules can soon get out of control and unscalable, or give you misleading results.

Machine learning is a term coined in the 1950s, but has only recently become so prevalent in the tech press that it may leave you feeling left behind. Put simply, machine learning allows us to give the computer the ability to learn from data provided, and predict an outcome given incoming data—like predicting which social posts mentioning the keyword “President” are actually about Donald Trump versus someone else. With the massive increases in computing power at ever cheaper costs, improved tool sets and a growing abundance of data, we are living in a golden age for machine learning.

So, can machine learning solve all your complex signal to noise problems? Maybe. If you have enough data, patience and time – almost certainly. Here are the steps to get you started:

  • Define the desired outcome clearly. Take samples from your dataset and examine them carefully to really understand what is signal and what is noise. Have your team manually tag say 20 samples with what they think is the desired outcome. This may sound silly or you make think the desired outcome is obvious, but I promise you will find that your team (or even your customers) will differ in opinion at this step, especially with any noisy or complex dataset. Getting on the same page of what is actually signal and what is truly noise will save you a lot of pain down the road.
  • Evaluate if this is actually more than one problem. When you examine the data as part of step 1, does it seem like there are multiple different contributing factors that define noise? For example, are there certain types of accounts you don’t want in your dataset regardless of the content they contribute? If there are multiple different contributing factors, each sub-problem is usually better tackled separately. Define these problems here.
  • Don’t re-invent the wheel. Next, understand if any of your problems have already been solved. For example, if you think accounts with profile pictures that aren’t people is contributing to your noise, don’t go build a face detection machine learning model as this is a well-solved problem. Instead, use face detection output from existing technologies, and test it as a feature in your machine learning model unique to your problem.
  • Get technical. For any of your problems that are not already publicly solved, you now need to pick the best machine learning approach to apply. Unless you’re feeling completely wild, crazy and academic, you will likely not need to invent a new machine learning algorithm, you just need to find the most suitable one for your task to build a model unique to your problem. (You also may now need to gather a large labelled dataset using your clear desired outcome definition, but this is a separate blog post!). Microsoft has a great cheat-sheet to help you through this step:

We know how tempting it is to jump straight to step 4, especially for a team of smart engineers and scientists. At Twizoo, we’ve been solving signal to noise problems with machine learning for years, and have the battle wounds to show first-hand how diligently completing steps 1-4 may save you months of pain and ultimately drive higher precision and accuracy. If you want to talk more about your machine learning problem, or if you want to learn more about how Twizoo can help you mine social media for great user-generated content, feel free to reach out at madeline <at> twizoo <dot> com. See you at Big Boulder 2017!

Call for Big Boulder 2017 Sponsors!

rocky-mtWe’re excited to announce Big Boulder’s first ever sponsorship opportunities! Check out the details below to see how you can reach the influencers in social data.  If you’re interested in being one of our sponsors, please contact sponsorship@bigboulderinitiative.org.

  • Mt. Elbert: All Attendee Happy Hour Sponsor
  • Gray’s Peak: All Attendee Dinner Sponsor
  • Torreys Peak: Welcome Reception Sponsor
  • Mt Evans: Lunch or Breakfast Sponsor
  • Longs Peak: Round Table Sponsor
  • Pikes Peak: Break Sponsor

Mt. Elbert (elevation 14’443)

All Attendee Happy Hour Sponsor

  • Sponsor of Thursday evening all attendee happy hour
    • Opportunity to provide promotional signage with prior approval from BBI
    • Opportunity to provide promotional give-aways with prior approval from BBI
  • Branded sponsorship signage in prominent conference locations
  • Recognition and thank you during keynote by BBI board
  • Conference website recognition
    • Logo on sponsor section
    • 500 word company description
  • Conference brochure recognition
    • Logo on sponsorship section
    • 500 word description
  • Full page advertisement in conference brochure
  • Logo on footer of conference emails
  • Five full conference tickets
  • $200 discount code to apply to full conference passes for sponsor employees only

Gray’s Peak (elevation 14,270’)
All Attendee Dinner Sponsor

  • Sponsor of Thursday night all attendee dinner
    • Opportunity to provide promotional signage with prior approval from BBI
    • Opportunity to provide promotional give-aways with prior approval from BBI
  • Branded sponsorship signage in prominent conference locations
  • Recognition and thank you during keynote by BBI board
  • Conference website recognition
    • Logo on sponsor section
    • 500 word company description
  • Conference brochure recognition
    • Logo on sponsorship section
    • 500 word description
  • Full page advertisement in conference brochure
  • 
Logo on footer of conference emails
  • Five full conference tickets
  • $200 discount code to apply to full conference passes for sponsor employees only

Torreys Peak (elevation 14,267’)
Welcome Reception Sponsor

  • Sponsor of Wednesday night all attendee welcome reception
    • Opportunity to provide promotional signage with prior approval from BBI
    • Opportunity to provide promotional give-aways with prior approval from BBI
  • Branded sponsorship signage in prominent conference locations
  • 
Recognition and thank you during keynote by BBI board
  • Conference website recognition 
Logo on sponsor section
    • 500 word company description
Conference brochure recognition
    • 
Logo on sponsorship section 
500 word description
  • 
Full page advertisement in conference brochure
  • Logo on footer of conference emails
  • 
Five full conference tickets
  • $200 discount code to apply to full conference passes for sponsor employees only

Mt Evans (elevation 14,264’)
Lunch or Breakfast Sponsor

  • Sponsor of Thursday or Friday breakfast or lunch
    • 
Opportunity to provide promotional signage with prior approval from BBI
  • Conference website recognition
    • Logo on sponsor section
    • 
500 word company description
  • Conference brochure recognition
    • Logo on sponsorship section
    • 
500 word description
  • Half page advertisement in conference brochure
  • 
Logo on footer of conference emails
  • Four full conference tickets
  • $100 discount code to apply to full conference passes for sponsor employees only

Longs Peak (elevation 14,255’)
Round Table Sponsor

  • Sponsor of round table discussion for up to 20 self-selected attendees during lunch on Thursday or Friday
    • Opportunity to select a topic related to conference themes with prior approval from BBI
    • Opportunity to provide promotional signage with prior approval from BBI
    • BBI moderator will be provided to help facilitate the conversation
  • 
Conference website recognition
    • 
Logo on sponsor section
    • 500 word company description
  • Conference brochure recognition
    • Logo on sponsorship section
    • 500 word description
  • Quarter page advertisement in conference brochure
  • 
Logo on footer of conference emails
  • 
Four full conference tickets
  • $100 discount code to apply to full conference passes for sponsor employees only

Pikes Peak (elevation 14,110’)
Break Sponsor

  • Sponsor of Thursday or Friday morning or afternoon break
    • 
Opportunity to provide promotional signage with prior approval from BBI
  • Conference website recognition
    • 
Logo on sponsor section
    • 
500 word company description
  • 
Conference brochure recognition
    • Logo on sponsorship section
    • 500 word description
  • 
Quarter page advertisement in conference brochure 
Logo on footer of conference emails
  • 
Three full conference tickets

Call for Big Boulder 2017 Speakers

Big Boulder 2017 is just around the corner and we can’t wait to welcome you to Boulder in a few months. The board has been working hard to organize amazing content for this year’s conference on June 1st & 2nd. Along with our traditional set of compelling speakers/topics, we’re also looking for community contribution. As such, we’re opening a call for speakers! 

Themes

Every year, the BBI board works to determine what are the most relevant topics in the industry. After much deliberation, the themes for this year’s event are:

  • Abuse on Social Media
  • Messaging Apps Continued Rise
  • 
AI & Machine Learning
  • The Political Landscape of Data
  • Disruption: The New Norm

Do you have something important to share with the others in the industry? Are you passionate about one (or more) of the themes listed above? Well, here’s your chance. We’re using Pecha Kucha format to give you the opportunity to share it with the member community. So, craft up your best ideas in and send ’em in! If your Pecha Kucha is selected, you get a free ticket to the conference!

How to Submit

We invite provocative Pecha Kucha submissions related to our themes. Please send your 20-slide Pecha Kucha-compliant slide deck to mike@bbi.org by Friday, April 28, 2017. No product mentions or sales pitches please!

If you have any questions, please email mike@bbi.org

Big Boulder 2017 Registration is Open!

Register now!
Big Boulder 2017 will be June 1-2 and registration is now open! It’s hard to believe, but this is our 6th year and we’re excited to see everyone. Our themes for this year’s Big Boulder are:

  • Messaging Apps Continued Rise
  • AI & Machine Learning
  • The Political Landscape of Data
  • Abuse on Social Media
  • Disruption: The New Norm

In order to ensure the sustainability of our conference, we will be charging $1,000 for tickets this year. We are no longer able to provide comped tickets, unless your membership is current.  Please contact Michael at mike@bbi.org if you have any questions.

We’re also looking for sponsors! If you’re interested please contact Michael at mike@bbi.org.

Book Your Accommodations for the Conference
As with other years, Big Boulder will take place at the St Julien Hotel and Spa. If you’d like to reserve a room at the St Julien, please click here or call the St Julien at 720.406.9696 or 877.303.0900. If you make your reservations over the phone, please be sure to mention that you are part of the Big Boulder Conference in order to receive discounted pricing.

Again, if you have any questions, please contact mike@bbi.org..
Thank you,
Michael

Visualizing Changes in Meaning

By Eric Bell,Natural Language Processing Software Scientist at Pacific Northwest National Laboratory and BBI Board Member, Dr. Seuss, Jeggings, and Dill?

“So the writer who breeds
more words than he needs
is making a chore
for the reader who reads.”

-Dr. Seuss

Humans are social creatures, and social implies communication.  We communicate in many forms: words, pictures, gestures, etc., but language, and words, are at the center of all communication.

From Dr. Seuss to William Shakespeare, humans have been making up and playing with words for centuries.

Words can change meaning over long periods of time. For example, the word “awful”, used to mean worthy of awe. Other words change meaning in a matter of moments. Would your definition of “computer” include your phone?

While today’s millennial may be chillaxing in their jeggings, unfriending vestiges of a failed bromance, computers around the world are racing to understand what all of the new words in the first part of this sentence mean.  If computers are going to understand humans, they must understand what words mean, and that includes when words change their meaning.

Researchers at Pacific Northwest National Laboratory have been examining this phenomenon, known in academic circles as semantic shift.

Words, like technology, are evolving faster than before. Communication in modern society is characterized by an always-on, constant barrage of global conversations.  This is compounded by the recent transition from face-to-face and point-to-point telephone conversations to multiple consumer communications in the rise of social media and mobile messaging platforms.

These changes in communication styles have given rise to a number of phenomena including emojis, live video streaming from around the globe, and a world of shopping with reviews at your fingertips.

The approach to understanding the meaning of a new word was traditionally accomplished by examining its relationship or shared grammatical constructions with known words. This approach is called statistical semantics. For example, from the phrase “wear jeggings to work” a computer can infer that jeggings are most likely a piece of clothing, because a clothing-related word often follows the verb “wear”.  When this sort of reasoning is applied over billions of social objects every day, new words can quickly be assigned some meaning.

As that meaning slowly changes with the adoption of words and the natural process of semantic shifts, a backward look in time at a word’s meaning today can help explain its origins and historical relevance.

At PNNL, scientists have been exploring the use of new terms in a variety of social data platforms, focusing on techniques from statistical semantics that aren’t platform or language dependent.

We’ve taken a technique called ‘temporal embeddings’ and combined that with novel visualization techniques to produce visual summaries of a word’s semantic shift, focusing on some words that have rapidly changed meaning in recent events.  Below we present a visualization of the term ‘ukrop’ which during the Crimean Crisis went from being a term describing the spice dill to an insult for Ukrainians. The path illustrates how the meaning of ‘ukrop’ meanders through a concept space in a relatively short period of time.

Picture1.png

Here’s another example of how the word ‘fire’ shifted from ‘a natural disaster’ to ‘gunfire’.

Picture1.png

 

Both of these examples are based on examination of social data during the Crimean Crisis, gathered from VKontakte, Europe’s largest social networking service.

Like words themselves, our process is evolving. And we want you to help. Do these images help explain the origin of a term?   These depictions are snapshots of an interactive system that lets you look at multiple words in the same concept space or representation of word meanings.   We invite the industry to help take the next step, the exploration of a similar phenomenon in the significance and meaning of visual social content.

So you readers who read
more words than we need,
fear not these new words
computers can help explain the misdeed.

 

BBI November/December Events

BBI Community –

Only 17 days until Coca-Cola’s Data Summit! Registration is free and you can register here.  BBI is very fortunate to have Justin De Graaf of Coca-Cola on our board and because of our good fortune, you have access to this incredible event. We have confirmed the following amazing speakers.

The topics that will be covered are . . .

  • What’s My Name? A day in the life of data: What data is worth purchasing? So many vendors where does one start?
  • 99 Problems: Programmatic & Direct Advertising: How does the industry toe the line? Am I over or under invested?
  • The Symphony: Are you listening? Making Sense of Social Data
  • Paid in Full: Closing the loop for CPG companies: measuring marketing w/ dollars and sense
  • DMPs and the New World Order: How marketers from various industries are leveraging Data Management Platforms.
  • The Revolution Will Not Be Televised: Where to invest in linear, digital video or addressable?
  • Follow the Leader: Making the most of mobile (targeting, marketing and measurement)
  • A Good Day: Telling Better Stories with Data

The event will be held on November 17th, at The Roberto Goizueta Auditorium, Coca-Cola HQ. The address is 1 Coca-Cola Plaza, Atlanta, GA 30313. Please email me at mike@bbi.org for more information.

BBI Meetups

Our Social Data and Analytics Meetups have another round coming in early December.

  • New York: we are focusing on the Food and Beverage industry. Details are here
  • Washington DC: we are focused on leveraging Social Data for Social Good. Details are here
  • San Francisco: we are meeting up to discuss the details behind uncovering influencers in Social Data. Details are here

If you would like to participate – we would love to have you either host or present one of these well attended Meetups. Please contact  Patrick Callahan at Patrick@compassRed.com or me at mike@bbi.org.

 

Flip Back to the Future

By Tyler Singletary, BBI Board Member and VP & GM Klout and Consumer Data, Lithium (@harmophone, Klout, Lithium

Flipboard was the first killer app for the iPad — a beautiful new way to read content from across the web, without worrying about RSS feeds. It was an early innovator at leveraging social networks for personalization. And it just might be the canary in the coal mine in a number of ways.

While Facebook gets mired in a debate around its use of human curators, and again deeper in its use of algorithmic curation, Flipboard had already gotten us familiar with both — and without the backlash. It’s been awhile since one finds a completely irrelevant article, and it’s learned what sources you read, the topics you’re interested in, the amount of time you’re reading — nearly everything that optimizes a discovery and personalization system.

Flipboard’s native National Geographic experience, putting up the wall one brick at a time.

To accomplish this, we were lead through the gates of their walled garden. Don’t be afraid, they’re pretty good gardeners. Early Flipboard users remember a time when a decent amount of content wouldn’t render well. Limitations with the IOS browser, content crawling and classification, and inherent hardware problems presented some downright ugly content and application crashes. To drive a more consistent user experience, Flipboard introduced a few features that sound familiar in today’s world of OpenGraph tags, Instant Articles, Twitter Cards, and Pinterest optimization: custom Flipboard CSS tags and a content partnering platform with select publishers — to beautify and provide a native experience to users. Flipboard became a platform. It also gave them their first ad monetization opportunities.

Walled gardens aren’t usually made with bad intentions. Most people are just happy to see the foliage. It tends to rankle the open web purists though, and certainly gives engineers a lot more to consider. What if web standards are behind? What if users don’t actually care about a standards-compliant, utopian free web, and just want to see their content? Then there are the paywalled gardens, like the Wall Street Journal and The New York Times. While germinated from a more protectionist point of view, they’re fighting for survival. Users don’t want to see ads, but they want content for free. Open Web enthusiasts seem happy with simple RSS readers and ignore the window dressing that content creators have the right to be viewed. Flipboard remains one of the only platforms purely dedicated to aggregated content discovery and consumption, with Apple News taking their playbook since IOS9. In this game, the platform with the most eyeballs and the best relationships with publishers will win.

And this is where Flipboard may be an early warning system. We’ve seen a number of other “newsreader” tools fall by the wayside and be shut down, already. If Flipboard loses this battle, it isn’t because of their approach to personalization — it worked, and social data had one of its first proof of concepts (and corrections: Flipboard was smart enough to ask its users about their interests, too). It isn’t because of their deals with publishers — that was survival, good business sense, and a model Facebook and Apple adopted. But its peer-to-peer social features and curated magazines, perhaps its only unique features against Apple (with Nuzzelon its heels), aren’t essential to the experience. Apple has won these arms wars in the past through shrewd licensing. Ironically, Open Web concepts may be the saving grace that keeps Flipboard in the game.

Facebook is betting users will only care about the news as a passing interest amongst their friends, with Twitter focused on breaking the news, not just sharing it. LinkedIn thinks it’s part of an authored thought leadership forum. Apple will integrate it tighter with iMessage, but gave us the “just the facts, ma’m” version. If anyone dies in this mine, it’s not because there’s gas poisoning, it’s because there just isn’t enough clean air to breath. The users will read pretty, relevant content wherever and however it appears. The more seamless, the better.