Artificial Intelligence: your key to success :)
The potential problem posed by Artificial Intelligence (AI) has nothing to do with crazy robots or sane robots taking over jobs. It has to do with people and products becoming 'outdated'. The good news is that this can be remedied by understanding what AI is and what areas it will impact with a view to creating a mindset in accordance with this near term inevitability.
background in AI, colleagues and friends have been asking me if AI means
the end of the world as we know it. They ask about software writing software,
singularities, what Stephen Hawking said and more. Most express feeling outdated
when reading about or discussing Al. So far, professionally, they had
managed with various new technologies like email, learning to Google, shopping on eBay, uploading
personal material on Facebook for all to see, Snapchatting and so on.
But Artificial Intelligence? How do you handle that? Time to check out retirement
here to stay. AI in essence is the science of Knowledge. It’s all about
Knowledge and Learning. Technology is knowledge and
at some level everything in existence is technology, including humans.
So, what is AI?
To make a long story short, AI can be broken
down into various focus areas, the most important of which are Machine Learning
and Natural Language Processing. A quick and simple explanation of these
Machine Learning is based on Neural Networks
which model biological brain function. A Neural Network is a net of
neurons (brain cells) that are linked to each other through
connections called synapses (think of
a fishing net)
These connections have a certain strength (positive or
negative) and the combination of these will result in a neuron being turned on
(firing), or not, according to a certain threshold value (think of a light bulb
with some power cords attached: if the cumulative power is enough the light
bulb will turn on). Human thinking is the
effect of firing (or not) of neurons (although this may be an understatement).
Now, take one side of a
Neural Net and turn on some neurons. Then take the other side and again turn on
some neurons. You have given the net an example of a given input and an
expected output (think of an input of 1 plus 1, and an output of 2 so as to
teach it addition). Next, give it lots of examples and then use a specific
standardized learning algorithm through which it can learn by adjusting the
power of its connections and when neurons will fire. Once it stabilizes through
many iterations, ask it to add numbers that you haven’t taught it: it will respond with a high probability of
accuracy based on what is has learned. In this way, given an input dataset
(e.g. images) and an output data set (e.g.
a description of these images), based on the patterns it ‘sees’ it can very
accurately answer what a new picture contains even though it has never seen it before.
A practical example of
this is the Google Vision API which was trained on a huge image dataset and can
understand the content of an image.
Language Processing (NLP)
essence our knowledge of the world is an internal Ontology. An Ontology is
a hierarchical set of concepts and
categories along with their properties and the relations between them. Babies
begin building this ontology on day 1 of their career on earth, starting with
the concept of ‘hunger’ and the related sub concept of ‘milk’ (properties:
color: white, taste: pretty good).
A communication medium called a ‘language’
is then introduced which is quickly accepted as very useful by all parties
involved. Language is directly related to the internal ontology (knowledge
base) which is continuously being expanded.
At the beginning an Example-Based Learning Paradigm is used to teach
this new communication technology but eventually a set of formal grammar rules
is introduced at school.
Language Processing models the above in order to understand text, syntactically
/ semantically (as defined by the language) and contextually (as related to circumstances and ontological knowledge).
To make a
long story short again, a practical example of AI in practice using NLP is
which makes a long story short of your emails by summarizing each one
using NLP and presenting them optionally in summary, bullet point or one-pager
How to stay in the race
cannot stop a river flowing by standing in the middle of it with outstretched
hands and legs. Depending on your profession, the best and only way to deal
with AI is to think of how it can help you
the value, accuracy and efficiency of what you do
. If you are a business
owner, you should be looking
AI can help you become more competitive and keep your products from being quickly outdated.
than providing a ‘Top ten ways to…’ list, below
is a reference by profession of areas of potential impact of AI in the very
AI in Finance
ML through analyses of recurrent stock chart patterns to
assessing potential price movement
Network based applications that make better informed lending decisions based on
case history data sets
AI in Education
Using NLP as a
to syntactically and conceptually
analyze and summarize texts
Using ML to predict learning outcomes given
Using NLP to assess and grade essays
AI in Law
Forecasting court verdicts based historical case
data and outcomes
Intelligently searching case and decision texts based
on a contextual level
Managing email campaigns using NLP and ML
to read and understand email responses
Intelligent Ads: How to win Buyers and Influence sales
AI in Health
to support patients with chronic conditions
as decision support
for treatment plan selections
AI in News (Fake or not)
Using ML in
conjunction with NLP
to differentiate Fake from
Real news given historic data sets