Role of Artificial Intelligence in the Digital Revolution

“We believe that if men have the talent to invent new machines that put men out of work, they have the talent to put those men back to work”.                                                                       John F. Kennedy

Abstract: Digital disruption means the rise of new technology and the effect that has on existing models and products. Artificial Intelligence will redefine and disrupt models and products in the coming years. Artificial Intelligence (AI) is the branch of computer systems that are able to perform tasks that require human intelligence. Digital disruption has an impact on businesses and economies, but AI takes it to next level with machine learning and big data which gives us insights for decision making with deep learning which we haven’t experienced in other disruptions.  The essay provides an overview of the definition of Artificial Intelligence and the role it plays in digital revolution. It also addresses the problems and outcomes of AI in digital distortion by analysing research papers and deriving conclusion from the overall aspects of AI.

Keywords: Machine Learning, Deep Learning, Robotics

I.     INTRODUCTION

AI and machine learning offer deeper insight and knowledge to predict things that has become impossible by human. It can identify patterns that cannot be recognised by human eye. It can develop new business models for customers and value propositions that cannot be done with mere human intelligence. Business model have a huge impact due to the coming of AI in a disruptive way than any other disruption that has occurred in the past. The number of companies mentioning AI in their earning cause have sky rocketed in recent years.

Get Help With Your Essay

If you need assistance with writing your essay, our professional essay writing service is here to help!
Find out more about our Essay Writing Service

The easiest way to think about artificial intelligence is in the context of a human. The goal of AI is to create systems work independently. AI can do speech recognition which is achieved by statistical learning. Humans can write and read text in a language. Through natural language processing (NLP).AI can write and read text in a language as humans do. Computer vision is a process in AI which enables it to see and process things which machines or algorithm see. It falls under the category of symbolic way of computer processed information. Image processing is the process used for computer vision. Similarly, Robotics in AI is typically the ability of human to understand the surroundings and move around. Pattern recognition is the ability to group objects and make conclusions, which is called machine learning in AI. In order to achieve cognitive capabilities, neural networks like in humans are used for teaching machines, these can be termed as deep learning in AI. Convolutional neural network (CNN) is used to recognise things and Recurrent Neural network (RNN) is used to recall the past in AI neural networks

The philosophy of AI to begins with Aristotle, who’s works can be regarded as the basis for modern science. Galileo, who contradicted the obvious truths of his age has applied mathematics as a source to test his findings and formulated that the world does not work as it appeared to us. Rene Descartes’ formulate the separation of body and mind, which states that mind has independent existence which follow its own rules. When it was established that mind and body were distinct, innovations were need to make connection between the two which was very influential to the formation of AI as thinking was considered as a way of computation. Mathematics & Logic are the next steps to implement AI. It involved the development of logic, Charles Babbage proposed that patterns of algebraic relationships can be studied to create a structed language of thought. The formal logic which consists of Boolean operations like AND, OR and NOT can be considered as the basis for computer automation. Mathematics was used to interpret logical probabilistic statements and decision-making problem to maximize outcome. Biology and life have also contributed to modern-day AI, with studies of neurons, working of human brain cognitive science and psychology. Finally engineering made contributions to AI by crating machines on which AI can run.

In machine learning we teach the machine lots of data and patterns so that it can learn from it. For example, we have lots of data for sales vs advertising spend you can plot the data to see some kind of pattern. If the machine can learn this pattern, it can make predictions on what it has learn. While, human can understand one, two, or three-dimensional data, machine can learn hundreds or thousands-dimensional data and determine patterns. We can use all these machine learning techniques to do Classification or Prediction. As an example, if we use some information of the customers to classify a group into young adults, it means classification. If we use data to predict what they are going to purchase it is prediction analysis.

II.   Literature Review and Analysis

As the technology evolves, digital disruption occurs and the actions need to be taken to survive at each stage and thrive in the digital age. There are lots of studies on digital disruption caused by AI and machine learning. A study by IBM on artificial intelligence (James L. McQuivey,2014) has found that people are aware of it coming but are not still prepared for it. People have skills but need to adapt to the changes that machine learning will bring to the organisation. For that purpose of quick adaptation organisations need to build the next immediate thing people needs, instead of building the future and let the future come on its own.

A recent study of SAP (Marc Teerlink,2018) has formulated that AI will automate the economies and can make people out of work by 94% of Accountants and Auditors,89% security guards,75% call centre employees,71% service technicians,66% legal associates and 48% computer programmers by 2035.Historically we can see that when new technology came in whether it is a electricity, steam engine ,car or the internet, at the beginning there was a dip and suddenly it created new jobs. Those new jobs are always driving economic growth. People can do things that machine learning cannot. Human creativity is something that machine can’t go over. The study argues that AI won’t replace managers but managers who are familiar with AI will replace those who are not aware of it. Further it points out that AI will impact employers first before employees so it is time for every organisation to start thinking about how AI and machine learning fit into a strategy for digital transformation.

A report of Constellation Research (Ray Wang,2014) has stated 52% of organisations in the Fortune 500 have either gone bankrupt, or stop to exist, starting from 2000 due to the reason that their business models has been disrupted or there was an increase in competition. One of the reasons is that a network which is isolated in the past has become connected with digital sensors based analytical ecosystem like Big Data, which creates new insights of hidden information. Cognitive computing which involves the help of artificial intelligence and hypothesis generation by learning, helps people to make better decisions that are machine guided.

A research paper on the impact of AI (Shruthi Anand Edited by Amber Sinha and Udbhav Tiwari,2017, The Centre for Internet and Society) suggest that the impact of AI on economy is mixed have positives and negatives. Demand of labour will be reduced on jobs that can be done using computerization and machine learning pattern recognitions, but there will increase in demand for jobs that cannot be computerised. Also, instead of replacing people, AI will lead to a replace how work is to be done. People will work for pleasure in the coming AI years and not for money which results in increase in productivity and social contributions. A study (PricewaterhouseCoopers,2016) indicates that the managers will be able to prioritize important issues and task and leaves the smaller ones to AI.

AI has been used all over enterprises whether it is weather forecasting, self driving car, speech recognition, agriculture fields like what type of crops should be grown in which time of the year, speech recognition, medical diagnosis, sales and marketing, logistics operations and production.

One area where technology especially artificial intelligence will be very important is electric grid,50 million electric devices worldwide will be connected to electric grid. These devices will inject electricity into the grid or take out of the grid and they will not notify prior to it. We need to analysis vast amount of data within seconds and take the right conclusions. Our homes are also going to change we might have a solar panel on the roof, an electric vehicle and homes will be more autonomous than ever since it will manage it for us based on data. The current energy business will be disrupted with AI.

AI can affect all aspects of human thinking and maybe it will one day surpass it. Go is a complex board game, the strategy is to round more territory than your opponent. The game is being played by humans for 2500 years. In 2016 google created algorithm, AlphaGo to play the game. It beat 18 times world champion Lee Sedol in 4 out of 5 games. It is more impressive than computers beating humans in chess or checker since Go cannot be predicted. There are 10 to the power 170 (10¹⁷⁰) moves possible in Go. AlphaGo was trained using real human Go games. It ran through millions of games and learnt the technique used and made new ones that no one has ever seen. Subsequently google released the next version AlphaGo Zero which beat the original AlphaGo in all the 100 games in a row. It also learnt how to play with zero human interactions. No data was given and no historical figures were given. It surpassed the previous model in 40 days of learning. In 40 days, it has surpassed 2500 years of strategy and learning. If we continue to develop.

AI can be crucial in retail business for example, Amazon learns and teaches himself our buying habits to suggest new products. Machine learning is a science that tries to get computers learn and think like humans do. By observations and interactions, we get more data. Machines not only cannot analysis data given to it but also learn from it and adapt a view on it. Which increases business for the organisation and service for the customers. The consultants who are advising customers that how they can make change to transform their businesses should be constantly up to date on what is happening on the market. The traditional approach has limitations like training on the subject matter, concepts and job shadowing and after making sure to good to go on by own takes a good amount of time frame which will nothing less than six to twelve months. So instead of training people on the subject we need to have a different approach like how do we train people to how to learn.AI comes here to make more contributions. For example, consider a company making 10 million of interactions every month with consumer base. It is expensive on one end and in the other end the company should be concerned about the consistency of services. Humans are not consistent. If we allocated some of the calls and interactions to a combination machines and human, it will increase the efficiency of customer care.

Disruptive innovations shift the character of warfare, from history we can find that tanks, aeroplanes, satellites and cyber weapons have caused changes in war. In war the winner is always the one which adapts disruptive change. In the world of information revolution that is changing warfare in profound ways. AI, robotics, autonomous systems create challenges and opportunities.AI can help military to analyse data for better decisions to physical systems cyber space or in airport security which eventually use game theory to allocate resources to deal with the fact that your enemy is also reasoning against you. As the industrial revolution, AI revolution is changing the human society, economics, politics and metrics of power. The volume and speed of information combined with AI possess new challenges for policy makers and increase speed of decision making. Technology opens possibilities but people are the agents of change. Policies, education and training are vital to creating a culture of innovation, prototyping, experimentation.

Machines are now able to recognise images, under human speech, translate languages and play complex strategy games, in sometimes better than human. We can assume AI as stack of technologies. The bottom stack is perception. It’s about data in senses and vision. In that layer machine learning is built in which a platform can predict the results of the outcome of the actions.  The next layer is acting layer where systems which plan to deal with the uncertainties or adversaries in making the strategic moves. And finally, the layer of autonomy where machines giving advice to human decision makers.

III. CONCLUSION

Artificial intelligence improves goods and services and increase the efficiency by making the automation of many tasks. It has a larger impact on the economy as a method of invention that can change the way people think and work.  From an organisation point of way it helps to bring flow of cash which helps to nourish the business and take strategic decisions. For the employees’ advantage is that they do not have to worry about monotonous work or manual and hard work, since algorithms can be trained to find solutions for customers. The jobs in an organisation will not be cut, or diminished by AI. Employees will be replaced by algorithms that can perform tasks. No jobs will be lost on a sudden but a gradual transition takes place which varies from industry to industry. The organisation can make use of AI to take over boring jobs and more time can be spent on higher level tasks by the human resources. Machine Learning will affect more areas of work than the previous automation types. Automation will only occur on task basis. ML can transform many jobs but and re organise some but fully automation cannot be achieved in many cases.AI will extend its reach to general intelligences but it will take a few decades to do so. But before that happens, with the technique that we currently know about, the decision-making tasks that are automated with AI can do better job than humans. There would be more consistency on the decision they make and won’t get tired. For example, driving a car with AI helps to reduce accidents due to inattention and opportunity to save live with AI. Similarly, image recognition techniques can be used for medical image analysis to eliminate the simple cases and send the difficult cases to radiologists or doctors so that they would be able to concentrate on difficult cases. It does not mean that a radiologist will lose his job. Automated systems will make the jobs interesting, a radiologist no longer need to sit in dark rooms and scan the images one by one to create some reports.AI systems will give the report by looking at the image patterns so that the radiologist can concentre on more difficult cases, so that they might not miss anything because of inattention.AI can help to increase human creativity and human to human communication more viable. Technology itself is not a determinant on how impact works, it is the way how it is implemented and put into place in the company makes a big difference as well. As the technology accelerates more and more people are left behind as their skills as not in phase with the skills that required in the economy, but the history says that the diffusion of technology in economy or society is limited by how fast people learn about it. For example computer technology appeared in 70s,but the increase in productivity was seen in 90s so there is a J curve where initially technology was disruptive and productivity went down and after few years it went up for a number of years. The same thing might occur with AI, the process that it can penetrate all economy will take few more years and it is also limited by how fast people and organisation can adapt the changes fast. But the concept of deep learning will not go away in near future.

IV.                REFERENCES

1. Gerlind, Blandine, Thibault Ulrich, Bormann Annemarie Guillermo, Soler, von Brauchitsch. (2017). Artificial Intelligence and Robotics and Their Impact on the Workplace, IBA Global Employment Institute
2. Shruthi Anand ,Amber Sinha and Udbhav Tiwari.(2016).Artificial Intelligence: The Centre for Internet and Society, India
3. R Wang (2014). Sneak Peeks From Constellation’s Futurist Framework And 2014 Outlook On Digital Disruption ,Constellation Research
4. Tom M. Mitchell.(2017).Machine Learning.
5. Russell.(2015)Artificial Intelligence : A Modern Approach.