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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 It has to do with people and products becoming 'outdated'.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.

Given my 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 Most express feeling outdated when reading about or discussing Al.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 options? No.


AI is 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 follows.


1.     Machine learning (ML)


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.


2.     Natural Language Processing (NLP)


In 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.


Natural 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 Email 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 mode. 


  How to stay in the race


You 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 increase the value, accuracy and efficiency of what you do . If you are a business owner, you should be looking at how embedded AI can help you become more competitive and keep your products from being quickly outdated. .


Rather than providing a ‘Top ten ways to…’ list, below is a reference by profession of areas of potential impact of AI in the very near future.


AI in Productivity


·        Creating an all-inclusive AI Assistant


AI in Finance


·        Decision support using ML through analyses of recurrent stock chart patterns to assessing potential price movement

·        Neural Network based applications that make better informed lending decisions based on case history data sets


AI in Education


·       Using NLP as a learning tool to syntactically and conceptually analyze and summarize texts

·       Using ML to predict learning outcomes given historical datasets

·       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


AI in Sales


·       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


·        Using ML to support patients with chronic conditions

·       Using ML 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

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