Some of the highlights from my notes follow:
Opening Remarks with Sandeep Sidhu and Stephen O’Connor (CEO of BCNET)
Disruption, security, leadership
New this year: mobile app, passport, ID the newbie, new venue for the night out
Research Innovation and Infrastructure track
Close to 200 new delegates
good slides, Convergence could learn from this
Every member institution is represented here
$250 000 raised from sponsors
Four platinum sponsors (AWS, DellEMC/Intel, Long View, Scalar)
Twelve Gold, Eleven Silver sponsors
Limited the number of sponsors (space tight)
Introducing program committee on slides with headshots
8 tracks
Next year at Sheraton Vancouver Airport Hotel Richmond
Opening Keynote - Shawn Kanungo (The 0 to 100 Effect: How to Navigate Through Disruption)
Everything is a remix, we all combine and steal
Many people have exactly the same idea as you, it’s almost impossible to come up with a truly original idea
He’s from Edmonton (Rented out South Edmonton Common Cineplex)
new challenges (new behaviours: trolls, memes)
Technology is not a competitive advantage, it is up to us to innovate on top of the technology
IT provides the “electricity” so learning can happen
The rising billion who have never transacted online
Most chaotic era in commerce (basement to mainstream)
Converging technology
Always: new challenges, new behaviours, new challenges
Sharing economy requires: mobile, GPS, commerce, and mobile payments
Basic Universal Income, rethink the idea of ownership (new opportunities)
Digital capturing and storage: mobile, cloud, etc. (where do we store all these pictures)
We throw away 99% of the pictures we take
New opportunities: Snap is the new Anti-Kodak
New language: Giphy (animated gifs)
Crowdsourcing: (e.g. Upwork, Fiver, 99 Designs)
“Do I need to do this, or can someone else do it cheaper and/or better”, becoming freelance managers
Traditionally: to grow you create more programs and services or hire more people
Today: scarcity to abundance: leverage third parties
Data.gove.sg (Singapore open data)
Anticipatory shipping: (Amazon patent in 2012) will send you broom without you asking (Amazon Prime wardrobe)
Future world: voice is the interface
Right drug to the right patient at the right moment
Automation, AI and ML: three AI research centres in Canada (overhyped)
Few organizations are deploying at scale
Tells organizations how to take out of business before someone else does
tell daughter future is not safe?
WillRobotsTakeYourJob
Separate the idea of jobs and tasks (tasks will be eliminated, jobs might be enhanced)
Robotic Process Automation (gateway drug to machine learning and artificial intelligence)
automating tasks, so humans don’t need to be involved in the process
eliminating the crappy parts of their job, so they can focus on what matters
Data are important (Benjamin Button Effect - organizations getting better over time)
The best organizations are self-learning (become more valuable with use)
We get that disruption is happening, but fear (kill, avoid, or regulate new innovations)
The improbable is the new normal (we can’t be fearful)
Nostalgia - married to what’s working now (same leaders, different era)
Leaders in digital era should have passion in digital era
if you don’t get nostalgic, you can evolve
There are no bad ideas in tech, just timing
Track the ecosystem, not the growth of individual technologies
Technologies may pop when the technology is ready
Chalk board in one-room schoolhouse vs universities (but took off when grade levels introduced in K-12)
How are we going to do things differently in 5-10 years when we have these technologies?
How do we experiment in the next six months?
The most established organizations are the most vulnerable to disruption. (even with best people)
nostalgia when things are working well, difficult to pivot when environment changes
Start with experimentation (small problems, small teams, small sprints), short terms
“If you double the number of experiments” - Bezos
Wework is looking to get into education space (data to build space-as-a-service)
Controlled experiments are the fundamental building blocks of decision-making at Airbnb
Domino’s pizza is biggest growth since 2010 (now is a technology company)
Tasty is the fastest growing media company in the world (40 second videos)
Observe human behaviour (at work, at home, etc.) Ethnographic research
why do people do what they do (in the context of their life)
Ask why (and ask stupid questions)
“Find someone who has time, I’d love to hire them”
Editing and publishing video is a fundamental skill in training and education
Start with small
add Shawn Kanungo on LinkedIn
Tell kids: Get a safe job “the ability to learn, be hungry, experiment”
Digital Humanities - Megan Meredith-Lobay (UBC Advanced Research Computing)
doesn’t like the term digital humanities (lots of baggage, doesn’t represent diversity)
but is using it for a shorthand for digital research in humanities and social sciences
marking up text to make it more machine readable
digitizing, crowdsourcing, digital exhibits and text encoding, text mining and mapping flow of letters, archiving and data repositories
http://tapor.ca for text analysis (UofA)
getting scholars to think more computationally about their data
Endless Possibilities with Data and AI - What are We Doing with it?
Teach the machine, don’t instruct it
assuming that the dataset is good
AI 100 project
near term: AI replace more tasks than jobs
long term: social safety nets?
Issues: biases, transparency, fair access, responsibility, potential for good and bad, technical expertise at all levels of decision-making
Agile Software Development Techniques - Henriette Koning (Senior Manager Software Delivery Boeing)
some process is a good thing (saves time, frustration, clarity, avoids panic)
MVP minimum viable process or product
argue against “we don’t have time for process”
sometimes finishing (closing) a project is hardest, how do you define “done”
https://hub.callysto.ca/jupyter/user-redirect/git-pull?repo=https://github.com/misterhay/Math&subPath=Pie Charts.ipynb
The Innovators: How educators are using open technology to create great learning experiences
@lucwrite @edtechfactotum CC:BY
BC Open Textbook Project is one of their big projects
Rights: Reuse Revise Remix Redistribute Retain
Different from free (also permissions)
Free, unfettered access (perpetual, irrevocable)
Vetted by faculty checkbox (LimeSurvey)
Co-created (students contribute)
Pillar 2: Open Pedagogy
a set of teaching and learning practices only possible in the context of the free access and 5R permissions (David Wiley 2013)
Pillar 3: Open Technology (i.e. open source)
Pockets of innovation often happen off the side of an educator’s desk
Rethinking IT Leadership 2.0 — A Science Based Approach to Team Engagement
Darren Eveleigh
off the grid, minimalist, sold car, hostels and airport floors
“People who lead with data are doomed for failure” Soledad O’Brien (we need narrative about data)
worked for Pallister School Division (with Maurice Hollingsworth)
minimal compliance vs highly engaged teams
Listen and make team successful
neuroleadership - transform leadership through neuroscience
resource challenges = creativity
change = adaptability (and comfort with change)
never been done before = insight and creativity
complex systems = focus and problem solving
we are hypersensitive to threat (survival instinct - limbic system)
two functional states: towards/reward or away/threat
perception (field of view), cognition (working memory), creativity, collaboration
social threat/distress are similar in the brain to physical pain
“Threat literally makes people less smart” - David Rock
scarf model - threat to status, certainty, autonomy, relatedness, fairness
status: mortality by “grade” among British civil servants (manager, support, etc.)
too much feedback (let people learn)
certainty: being able to predict the future (e.g. meeting with no description)
autonomy: sense of control over events
relatedness: sense of safety with others, friend or foe, ingroup or outgroup, trust
many new managers don’t connect with people on a human level, concerned about feeling too close
fairness: perception of being treated justly, try to introduce greater transparency
the ultimatum game: responders reject unfair offers (<20% of the total stake)
unfair activates “disgust” region
“Performance Management” is a bad word
Spin class is a good team building activity
Celebrations at any opportunity
We all have different strengths
Pro poker players will win with whatever hand hey are dealt
“Coworkers are not like family, they are family” Simon Sinek (you don’t replace your children)
In 50 years we will look back on our management styles and wonder what we were doing
Neuroleadership certification
The Robots are Coming
Neil Bunn CTO of Scalar
Machine learning changes everything
Infrastructure Security Cloud Digital Transformation
Machine learning is in all of these
Check out Prof Geoff Hinton
Classification (especially image) and outlier identification
Tensor is multidimensional matrix
Probabilistic
Training Models: Supervised (preclassified e.g. images), Unsupervised (clustering, normalcy, good for security/behaviour), Reinforcement (gaming: agent, environment, actions; trial and error and objective)
Research around trying to break ML languages
OpenSourced frameworks: tensorflow, pytorch, caffe, theano (and NVIDIA CUDA)
Exabeam for network security
Cylance endpoint protection
NetApp for help desk (IBM Watson) just ticketing system though, not end-user support
(Platinum sponsors get 30 minute keynote-style time slot)
Moving to Digital Assessments of Instruction
Judy Shandler
It’s all about reflective practice and moving forward in teaching practice
Decided on eXplorance Blue (most expensive but Canadian and secure)
Mythbusting in the Cloud (AWS)
Artur Rodrigues
Cloud definition (see photo)
Avoid over-provisioning (scale up or down)
HPC
Big data
Managed desktops - virtual desktop
PIA framework with BCNET
Don’t copy our data to different data centres (regions, clusters)
More security personnel than any other company
AWS Educate (credentialing)
Top Trends in the Big Data Era
Nora Young
Book: data and ourselves
Data boom
Designing for an age of continuous connection
Lectures in 14th century because books were expensive and scarce
Big Data by Viktor Mayer Schonberger and Kenneth Cukier
But big data are from us (human dimension, human-centred ways of handing our data)
Data trend 1: body tracking (generally fit people 35 or younger)
90% of top free apps and 60% of top paid apps have at least one embedded tracker
mental health tracking (started with paper diaries, but also ginger.io using phone metadata)
but what about data from one context that is being used in another context (transparency?)
Data trend 2: Tracking Experiences (travel, films on Netflix)
valuable in the aggregate
twitter sentiment analysis
increasingly visual (and automatically identified by ML/AI)
Data trend 3: Bottom-up, location-based mapping
democratization of mapping (e.g. openstreetmap.org) see Kibera in Africa
but still a need for professional map making
Data trend 4: quantified self
getnarrative.com (world’s most wearable camera, takes a picture every 30 seconds)
opt-in vs opt-out as the default
proteus digital health (proteus.com) ingestible: time and date of taking pill
super cool and very creepy
Google location history
notion of privacy includes subjective and hard to predict ways (context of data gathering and display)
case in Ohio: insurance fraud and arson (pacemaker data didn’t match alibi)
unintended trails of digital exhaust
Internet of Things: 31 billion by 2020
Data trend 5: Internet of Things
Data trend 6: AI and Big Data
enabled/exploded because of large data sets, technology developments, and different/better end uses
This stuff is useful individually and in the aggregate
interesting feedback loop with GPS navigation
smart city project with AlphaBet and area in Toronto
even in developing world
the public are not just sources of data, but are partners
ushahiti (syriatracker.crowdmap.com) to map incidences of violence on the map (Kenya)
how are we going to form these data partnerships (not data mining)
when is “good enough” good enough?
messy vs curated data
tyranny of algorithms (e.g. Facebook news feed)
black boxes may be too opaque for us to understand
even if you’re not doing anything “wrong”, you can still be caught by correlations
e.g. couples counselling correlated to decreased credit rating
Book: Black Box Society
Gigapixel (images stitched together from many crowd photos, e.g. rallys)
We are in a different world where we need to worry about enormous amounts of trivial data that may come together to have large privacy impacts.
Security: that thing that keeps you up at night
ransomware
messy, spammy, chaotic
IoT botnets
interesting times
Opportunities:
statistical literacy (and data science, etc.)
transparency
partnerships
smart design
data plus x (ethics + data, design + data, etc.)
data journalism
education for a world of AI
soft-skill jobs (emotional, creativity, etc.)
Data as Partnerships
tech sector needs to do a better job of respecting data
arrayofthings.github.io (City of Chicago, UofC, etc.)
sensors around downtown
free and openly available (by design)
if we get the technology right and the privacy right, we can do amazing things
think about yourself as having a public personality
there’s still a lot of social utility for Facebook
second machine age and centaurs
0wn your Inbox
Nav Bassi
Busy is not being productive
Cult of busyness
McKinsey study: 30% of our day is consumed by email, 61% of employees say they don’t have time
3 Evils of Email
- inbox overload
- Message management
- Constant checking
4D processing
- Delete or archive
- Delegate (it may be someone else’s job to respond or act)
- Defer (for a few minutes)
- Do (reply or add to list)
Filing is a waste of effort (because of search), instead just archive everything into a central folder
Big difference between focussed time on email vs checking email.
Check out quick steps in Outlook
Delayed delivery
Using Law Responsibly
Dr. Michael Geist
Great overview of Canadian laws related to technology and copyright.
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