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Machine Learning- Beyond the biases of the 4th Industrial Revolution

“Not merely the validity of experience, but the very existence of external reality was tacitly denied by their philosophy. The heresy of heresies was common sense.”- George Orwell, 1984.

I once wrote that knowledge was a common commodity and wisdom resided in an illiquid market, in the world of both man and machine, this statement is true. You can know something without understanding, but you can not understand without knowing, and this isn't a deep philosophical tandem that I am stating to open the mind of the reader, but this is merely just to explain why you and I both will exhibit throughout our lives poor decision making, frustration with people with differing ideologies and philosophies, and anger in part with sadness, when we are bestowed misfortune upon our own lives.

The quote above by George Orwell comes from a fictional book he wrote about a time period in which members of a society were censored, to the extent that the ruling government and their followers could be reduced to this single quote. Their purposeful denial of knowledge lead to an agreed upon absence of understanding, and therefore any common rationality that went against the ruling party and their followers belief was denounced and vilified. Unfortunately sometimes life imitates art, and this concept describes Politics and Social Media in the beginning of the 21st century. The information that we choose to acknowledge and how we understand what to accept and deny is based on our own personal biases the majority of the time, there's nothing wrong with that on a small scale when it only affects you as an individual, but when those biases affect everyone around you and the whole of society then it is clear we need a more abstract rational approach to how we operate our communities and nations. I don't believe one person is a reflection of their society, nor is a society a reflection of a person, we are each individuals with our own personal biases and need to be seen and judged as an individual not as the collective.


If you remove these biases when it comes to making decisions and judgements in our society, the question therefore posed is... what do you gain in return?


Klaus Schwab, the founder and executive chairman of the world economic forum, wrote in his book the fourth industrial revolution, different instances throughout humanity in which technological inceptions occurred that greatly influenced economic and societal change.


The first industrial revolution occurred in 1760 via the invention of the steam engine that powered agriculture and material manufacturing such as wool and cotton. It was the age of building machines.


By the early 20th century mankind moved into the second industrial revolution, the age of science and mass production from automotive vehicles created by henry ford, to airplanes to gasoline engines and nuclear power plants. This revolution lead to cities becoming heavily dense. Further creations in this period were the radio, telephones, and electric lighting. Perhaps it is worth noting that in this period man was able to reach the moon!


From the mid to late 20th century, we have seen the third industrial revolution, the age of digital technology. From Arpanet to the creation of TCP/IP, to the formation of the world wide web by Tim Berners-Lee. Due to this digital revolution, we have produced technologies such as gene sequencing in the early 2000s, 3D printing, gene editing with CRISPR, artificial intelligence. The digital revolution has lead to a change in our ability to communicate world wide i.e. via social media, and streaming services. We can now hold real time conversations from anywhere in the world.


Klaus Schwab and the world economic forum detail that there are six emerging technologies that are at the centre of the fourth industrial revolution. Artificial intelligence and machine learning; internet of things and urban transformation; blockchain and distributed ledger technology; Data Policy; autonomous and urban mobility; Drones and Tomorrow Airspace.


Artificial intelligence (A.I.) is essentially the ability of a computer to perform tasks associated with intelligent beings, in which an example would be the device you are perceiving these words on. A smart phone or a desktop. The automation of obtaining knowledge is not a new concept discovered in the last hundred years, it goes as far back as the 14th century. Forbes wrote a short article detailing the history of artificial intelligence which you can read here.


The concept however for Machine Learning is not old at all, and was conceived in 1949 based on a model of the brain. In the modern day, it is a application (software) of artificial intelligence that enhances a computer's system ability to automatically learn new pieces of information without being explicitly programmed with the prior knowledge, by enabling it with neural network data models (you can read the first section of my other blog explaining the company Oracle for better understanding of data models). The A.I. software is composed of algorithms (set of instructions) that use statistics to find patterns within ungodly amounts of data flowing through these created neural networks. The data can be anything and not limited to numbers, words and images. Examples of machine learning in action are YouTube; Spotify or Google using large amounts of data to best make recommendations to their users.


In 2006, Geoffrey Hinton co published a paper using the term deep to refer to a many layered network of nodes, titled "A fast learning algorithm for deep belief nets." In this paper he would describe an approach to training the layered networks.


"we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory."


Deep Learning is the evolution of artificial intelligence software, in which a technique is used to enhance a computer's ability to amplify and find small patterns. This technique is known as a deep neural network as it uses multiple layers of small computational nodes (specific points in a neural data model network, that is either the point of connecting information, redistributing data or communication endpoints). The data that flows through these nodes are analysed to produce a prediction of likely responses based on reoccurring patterns.


These evolutions and subsets of artificial intelligence have lead to the creation of self driving cars, in which automotive companies such as Tesla have stated they use deep learning technology to identify roads, cars, objects and people. Google funded the development of Alpha Zero, a computer programme that uses deep learning neural networks to master strategic board games such as Chess; Shogi and Go after acquiring the artificial intelligence research company DeepMind for $500 million in 2014. Apple since their purchase of the SIRI App in 2010 for $200 million have been utilising machine learning and A.I. in their products for features such as facial recognition; native sleep tracking for the apple watch; their translate app; library suggestions; sound and hand writing recognition.


Global Investments in A.I., machine learning and robotic process automation are projected to reach over $230 billion in a report by KPMG.



Perhaps it was inevitable that human existence would lead to the development of artificial intelligence as communication theorist Marshall McLuhan in his book, understanding media, conjured this quote.


"Man becomes, as it were, the sex organs of the machine world, as the bee of the plant world, enabling it to fecundate and to evolve ever new forms."


McLuhan later likened the development of man gathering information to that of the hunter gatherers of old... and maybe that's not such a bad thing. Artificial intelligence is maybe just another way for human beings to project higher intelligence so that we may take the next step in progressing humanity.


The world economic forum (a body of international government and institutional members) refer to Artificial intelligence and machine learning as the tools to shape the future of technology governance. They state it to be a key driver in the 4th industrial revolution in which robots will drive cars, stock warehouses and care for the young and elderly.


However through these advancements, what could it mean for us the individual?



In a report made by the world economic forum, 85 million people's jobs around the world will be replaced by machines utilising A.I. by the year 2025, however the technology possessed the potential to create a further 97 million new jobs. The issue with this sentiment of job creation is that as people become older they tend to be less mutable and enticed to develop career changing skills. It is not impossible but it becomes less likely. The new creation of jobs will likely be passed down to younger generations.


Sam Altman a co-founder (along with Elon Musk) of an artificial intelligence company known as OpenAI, has stated that as soon as 10 years (at the time of quote it was 2019) every adult in the united states could be paid $13,500 a year due to the amount of wealth generated by this fast growing technology.


"My work at OpenAI reminds me every day about the magnitude of the socioeconomic change that is coming sooner than most people believe. Software that can think and learn will do more and more of the work that people do now."


However Altman also notes that as phenomenal wealth is created, the price of labour will fall towards zero.


Perhaps in this recognition of potential economic development we as individuals should understand that machine learning and artificial intelligence isn't the distant future, it's our past and present.

Our aptitude to adapt to recognising this development will underline how successful we as individuals and A.I. technology will be in coexisting. The utilisation of A.I. and machine learning is a reflection of humans rather than the technology itself. Machine learning and artificial intelligence see through our biases and can very much improve the livelihood of those around us and our own, but the onus is on us to expand our knowledge and understanding of the subject matter just as machines are being built to understand us.


Our failure to understand machine learning and artificial intelligence will lead to the vilifying and denouncing of revolutionary technology when the problem at hand may lay with human beings.

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