It's no secret that automation, machine learning, artificial intelligence and maybe even robots are going to completely change the world of work in the next decade. And it's already playing a role in how Google works.
So how can we humble homo-sapiens prepare ourselves for our new robot overloads?
By learning from the past. I'll take you through stories of success and failures of interactions between the robots and the humans and leave you with a practical approach to avoid search marketing extinction.
10. @kelvinnewman
Because I organise an SEO
conference I spend more time
than is healthy thinking
about what SEO is really
about
* this isn’t actually me thinking about the
future of search, I’m reading my emails, but it
makes me look moody and enigmatic
18. @kelvinnewman
If you spend fifteen minutes
doing this you’ll get seventy
billion links
“
“
It’s more about getting you to think differently about the future.
And the impact technology has on people.
33. @kelvinnewman
Mark Carney
Governor of the Bank of
England
He’s not a robot you should
be scared of.
But his organisation is behind
a report that scared me.
34. @kelvinnewman
Taking the probabilities of automation, and multiplying them by
the numbers employed, gives a broad brush estimate of the
number of jobs potentially automatable.
For the UK, that would suggest up to 15 million jobs could be at
risk of automation. In the US, the corresponding figure would be
80 million jobs.
“
“
Andy Haldane
Chief Economist at the Bank of England
44. @kelvinnewman
Three types of task
Perception &
Manipulation
Robots are still unable to
match the depth and
breadth of human
perception.
Tasks that relate to an
unstructured work
environment can make
jobs less susceptible to
computerisation.
Creative
Intelligence
The psychological
processes underlying
human creativity are
difficult to specify.
The principal obstacle to
computerising creativity is
stating our creative values
sufficiently clearly that
they can be encoded in
an program.
Social
Intelligence
Human social intelligence
is important in a wide
range of work tasks, such
as those involving
negotiation, persuasion
and care.
The realtime recognition
of natural human emotion
remains a challenging
problem.
45. @kelvinnewman
Things people are good at…
Tasks requiring perception
in unstructured
environments.
Creative tasks which rely
both on novelty and
value.
Non-routine social tasks
which rely on hard to
articulate “common
sense”
Things machines are good at…
Routine tasks in structured
environments.
Tasks which require novelty
and experimentation.
Analysis at a speed and
scale.
59. @kelvinnewman
I studied media studies at Uni not
computer science or anything like it…
Now feels like a good time to share
@kelvinnewman
60. @kelvinnewman
Computer systems able to perform tasks normally requiring human
intelligence, such as visual perception, speech recognition, decision-making,
and translation between languages.
What is artificial intelligence?
@kelvinnewman
61. @kelvinnewman
The wide availability of GPUs that make parallel processing ever faster,
cheaper, and more powerful.
All aided by practically infinite storage and a flood of data sources.
Why AI has exploded?
@kelvinnewman
62. @kelvinnewman
There’s three levels of artificial intelligence in rising levels of nightmarish-ness
Difference between narrow, general and
super intelligence
63. @kelvinnewman
AI that specializes in one area. There’s AI that can beat the world
chess champion in chess, but that’s the only thing it does.
Ask it to figure out a better way to store data on a hard drive, and
it’ll look at you blankly.
Artificial Narrow Intelligence
(ANI)
64. @kelvinnewman
A computer that is as smart as a human across the board—a
machine that can perform any intellectual task that a human
being can. Creating AGI is a much harder task than creating ANI,
and we’re yet to do it.
Artificial General Intelligence
(AGI)
66. @kelvinnewman
Artificial Superintelligence ranges from a computer that’s just a
little smarter than a human to one that’s trillions of times
smarter—across the board.
Artificial Superintelligence
(ASI)
68. @kelvinnewman
The 24k words on AI on
this blog will literally
blow your mind
http://waitbutwhy.com/
2015/01/artificial-
intelligence-
revolution-1.html
70. @kelvinnewman
Machine Learning
Machine Learning at its most basic is the practice of using algorithms to
parse data, learn from it, and then make a determination or prediction
about something in the world.
So rather than hand-coding software routines with a specific set of
instructions to accomplish a particular task, the machine is “trained” using
large amounts of data and algorithms that give it the ability to learn how to
perform the task.
71. @kelvinnewman
Deep Learning
A branch of machine learning based on a set of algorithms that attempt to
model high level abstractions in data by using a deep graph with multiple
processing layers, composed of multiple linear and non-linear
transformations.
74. @kelvinnewman
That previous slide was
made by slidebot.io
25,352,683 images combine with 10,000+ design rules and 237 unique
layout styles to create one powerful presentation.
84. @kelvinnewman
Newspaper sports reports are often
generated by machines
Friona fell 10-8 to Boys Ranch in five innings on Monday at
Friona despite racking up seven hits and eight runs. Friona
was led by a flawless day at the dish by Hunter Sundre,
who went 2-2 against Boys Ranch pitching. Sundre singled
in the third inning and tripled in the fourth inning … Friona
piled up the steals, swiping eight bags in all …
https://www.wired.com/2012/04/can-an-algorithm-write-a-better-news-story-than-a-human-reporter/
85. @kelvinnewman
The software also generate natural
language Google Analytics Reports.
https://www.narrativescience.com/quill-engage
86. @kelvinnewman
IBM Watson and cancer
treatments
http://www.nytimes.com/2016/10/17/technology/ibm-is-
counting-on-its-bet-on-watson-and-paying-big-money-for-
it.html?_r=0
87. @kelvinnewman
At the University of North Carolina School of Medicine,
Watson was tested on 1,000 cancer diagnoses made by
human experts. In 99 percent of them, Watson
recommended the same treatment as the oncologists.
In 30 percent of the cases, Watson also found a treatment
option the human doctors missed. Some treatments were
based on research papers that the doctors had not read —
more than 160,000 cancer research papers are published a
year.
88. @kelvinnewman
Eliza simulated conversation by using a 'pattern matching' and substitution
methodology that gave users an illusion of understanding on the part of the
program, but had no built in framework for contextualizing events.
https://en.wikipedia.org/wiki/ELIZA
Eliza the 1960s rubbish robot
people loved
91. When 30 of the US’s biggest companies lost 9% of their value for no
apparently caused by algorithmic trading
May 2010 Flash Crash
I did a whole deck about the the implications of the flashcrash on how we think about SEO
http://bit.ly/flashcrashlessons
94. @kelvinnewman
There’s software used across
the country to predict future
criminals.
And it’s racist.
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-
sentencing
98. @kelvinnewman
Anything that is in the world when you’re
born is normal and ordinary and is just a
natural part of the way the world works.
Douglas Adams
@kelvinnewman
99. @kelvinnewman
Anything that’s invented between when
you’re fifteen and thirty-five is new and
exciting and revolutionary and you can
probably get a career in it.
Douglas Adams
@kelvinnewman
117. @kelvinnewman
image recognition open source
GoogleBrain have released their image captioning system
available as an open source model in TensorFlow.
https://research.googleblog.com/2016/09/show-and-tell-image-captioning-open.html
127. @kelvinnewman
Altify
Uses deep learning to caption
images in an HTML file and fills
out its alternative text attributes
with the related caption
https://github.com/ParhamP/altify
@kelvinnewman
131. @kelvinnewman
Tutorials on use Machine
Learning on over 1M hotel
reviews finds interesting insights
https://blog.monkeylearn.com/machine-learning-1m-hotel-reviews-finds-interesting-insights