An Overview of Artificial Intelligence, Big Data and Their Impact on Our Society

Introduction

The speed of technology’s increases day after day, leaving the analog world that was here before as a far memory, even for those who are more experienced and have lived the past stages of this evolution. Modern life is changing and Artificial Intelligence is shaping this metamorphosis. Machine speed decision making is much faster than human decision making, and this is what has brought us to surprisingly smart assistants, augmented security and new sorts of comforts. AI will greatly benefit all industries, achieving technological objectives in the fields of big data, algorithmic development, cloud computing and connectivity, made accessible by the recent highly performant cost-effective computational power available.

Questions start to arise when we wonder if is human beings driving this evolution or the technology itself, making us just a tool for this new, unpredictable, being. This paper will explore the history of modern artificial intelligence, considering the intrinsic ethics and responsibilities of such sensitive topic.

The Origins of Artificial Intelligence The term Artificial Intelligence was invented by a Stanford computer science professor named John McCarthy, in 1956, to describe the contents of his academic conference at Darthmouth College, occured on the summer of that year.

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Since then, and due to a lack of the right technologies, AI has gone through a series of up and downs, characterized by technology breakthroughs that brought excitement and activity about the topic, followed by periods of disillusionment and disinterest, also known as “AI Winters”, as technical limitations were discovered. Nonetheless, Artificial Intelligence is now living an “AI Spring”, again.

Figure 1 Gartner's Hype Cycle of Artificial Intelligence 5

The rise of AI AI can be define as human intelligence exhibited by machines; systems that approximate, mimic, replicate, automate, and eventually improve on human thinking. The key components essential for Artificial Intelligence are: perception, understanding, reasoning and problem solving. And two terms are important in tackling the ethical aspects of AI: deep learning, which is a subset of Machine Learning, and Big Data, the new science of understanding and predicting human behaviour by studying large volumes of unstructured data.

Big Data Today's data comes from a huge number of sources, which makes it difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or data can quickly spiral out of control, and Machine Learning algorithms allow us to add knowledge and new insights off of this chaotic stream of data. Deep Learning is a widely used type of Machine Learning based on Neural Networks where machines learn how to solve problems without explicitly programming them to do so. Instead, they learn by example using data.

Figure 2 Showing how through AI we can achieve new insights Deep Learning AI driven systems learn how to solve a problem by identifying statistical patterns in data. In essence, with Deep learning, we use existing data to learn a function that can make a prediction given new data. For example, if we want to create a neuron to determine whether a photo contains an image of a cat or not. First, we would feed it a dataset that contains images with cats, and images without cats, after a human being will label each photo, indicating whether it contained a cat or not. Applying a machine learning algorithm to the data set of images the neuron will learn whether an image contains a cat or not. Now, while this is a vastly oversimplified explanation of machine learning, it captures the essence of what it is attempting to accomplish. There are numerous machine learning techniques and algorithms in existence today, the most important ones are:

• Supervised learning
• Clustering
• Dimensionality reduction
• Structured prediction
• Anomaly detection
• Artificial neural networks

Reinforcement learning IoT

IoT (Internet of Things) is also, transforming the world, together with Artificial Intelligence, producing data off of practically anything that can be equipped with a sensor and made “smart” ,from a smart-watch that monitors your steps, heart rate, and blood sugar levels to a connected factory that oversees every stage of the production process. IoT can be imagined as the machine’s senses covering vision, hearing and touch and it has impacted modern Artificial Intelligence evolution. From the public sector, to manufacturing, transportation, and even healthcare, it provides an opportunity to extract new data and improve existing business processes, gather new information on people’s trends and preferences and allow users to monitor and ease some repetitive life’s tasks.

An example of use of IoT devices is in the automotive industry, IoT has helped manufacturers make connected, autonomous, shared, and electric (CASE) vehicles a safe reality, which now are being taught to become autonomous and operate driver-less.

Ethical Issues

AI is a system or device intended to act with intelligence, machine learning is a more specific term that refers to systems that are designed to take in information, usually within a specific domain, and learn from what they have been given, evaluating and categorizing received data and output an insight, a decision or a conclusion off of this process. Negative aspects and Major threats As the modern industrial economy depends on computers and particularly selected AI algorithm, many tasks are now automated. As stated by Russell (2010), some negative aspects that can arise by the implementation of evolute Artificial Intelligence are listed as follows:

• People might lose their jobs to automation
• People might have too much leisure time

  People might lose their sense of being unique

• AI systems might be used toward undesirable ends
• The use of AI systems might result in a loss of accountability
• The success of AI might mean the end of the human race

For example credit card transactions, charge approvals and fraud detection are now done by AI programs, supporting the argument that thousands of workers have been displaced by these AI programs, but, by actually taking away the AI these jobs would not exist, because human labour would add an unacceptable cost to the transactions. So far, automation through information technology and AI has created more jobs than it has eliminated, and has created more interesting, higher-paying jobs. Tim Ferriss (2007) recommends using automation and outsourcing to achieve a four-hour work week. Another problem that AI brings is the loss of accountability and, as we know, advanced technologies have often posed a threat to humanity, when used by the powerful to suppress their rivals.

Autonomous AI systems are now commonplace on the battlefield. There is one moral theory that states that military robots are like medieval armor taken to its logical extreme: no one would have moral objections to a soldier wanting to wear a helmet when being attacked by large, angry, axe-wielding enemies, and a teleoperated robot is like a very safe form of armor. On the other hand, robotic weapons pose additional risks. To the extent that human decision making is taken out of the firing loop, robots may end up making decisions that lead to the killing of innocent civilians or the end of the human race itself. Privacy issues In Ethics, privacy is seen as a “Negative right”, which means that Privacy should be allowed without interference, however, this has become more and more hard to accomplish since AI-driven systems programmed by commercial entities, that want to know everything about and profile their customers, are out tracking everything about us. Furthermore, with the risk of attacks related to terrorism, people seems to care less about giving out their right to privacy. CCTV and sensor-based surveillance systems are part of our daily lives now in this modern society due to the advances in telecommunications technology and the demand for better security.

Speech recognition technology is also widely available, and could lead to widespread wire-tapping, and hence to a loss of civil liberties. Future Developments The following are just some of the industries that are going to rely on AI to improve their efficiency, costs and flexibility :

Logistics: with the advent of delivery drones, AI optimizes logistics processes so that they are able to adapt independently and dynamically to environmental changes. Also, autonomous robots pick up items stored inside the warehouse, conveying it to the human packers, avoiding safety related issues for employees.

Finance: with fraud detection, transactions tracking and automation  Automotive Industry: with the rapid progress made in machine learning and deep neural networks is possible to achieve the development of autonomous vehicles,which require no human intervention even in complex situations.

Security Industry and Surveillance: otherwise known as smart CCTVs, use deep learning and facial recognition to help law enforcement improve situational awareness as well as obtain additional sources of data for analysis or investigation.  Healthcare: using simple machine learning techniques is possible to predict the health status of a patient, of whom data comes from the many sources available today, like: fitness trackers, electronic health records, smartphones or insurance claims data.

Space Exploration: using AI to understand data collected from active space missions and positive outcomes in the research for the development of space robots.

Conclusions

AI is set to thrive and unlike past waves of hype and disillusionment, today’s current technology, business, and societal conditions have never been more favorable to widespread use and adoption of AI. In the consumer oriented industry, AI is already there to stay. Tech, finance and mobility industries are well into their AI journey, As big data from operation, public, and private sources becomes exposed to and processed by, computer vision and language-focus AI, machines will be capable to understand, and interact with the world in novel, more efficient ways than before.

These same AI technologies will give rise to a new class of intelligent asset that augment human capabilities. As Russel (2010) mentions “One threat in particular is worthy of further consideration: that ultra-intelligent machines might lead to a future that is very different from today and such considerations lead inevitably to the conclusion that we must weigh carefully, and soon, the possible consequences of AI research”.

Business, society and government bodies will need to develop standards and regulations to ensure the continued progress of AI for the benefit of humanity. Ultimately, AI will place a premium on human intuition, interaction, and connection allowing people to contribute to more meaningful developments. IoT, Big Data and AI are ultimately shaping the future, opening possible dystopian scenarios, while surely defeating some of the limitations humans have ever had a chance to overcome. 6.Glossary AI (artificial intelligence) - computers being able to perform specific tasks as well as or better than human intelligence. In the context of video surveillance, AI is used in the field of computer vision to classify visual images and patterns within them.

Big data – huge amounts of different information being stored, organized and analyzed by computers to identify trends, patterns, and relationships. Cloud computing – instead of using a local server to store or manage video surveillance data, using a network of internet-connected remote servers. Generally this network has 9 the ability to provide additional resource if and when required from a larger available pool. The available resource may be clustered into a data centre or network of datacenters. These may be private (entirely or partly owned for exclusive use by specific organization/s) or public (resource accessible to multiple separate users). Deep learning – a branch of machine learning and subset in the field of AI. Deep learning makes use of algorithms to structure high-level abstractions in data by processing multiple layers of information, emulating the workings of a human brain (a neural network).

Face recognition – when a video system can automatically match a person’s face against a database of individuals. IoT (the Internet of things) – IoT is not a specific device or technology – it is a conceptual framework, driven by the idea of embedding connectivity and intelligence in a wide range of devices. It has embedded connectivity that allows it to be directly connected to the internet, or allows it to connect (tether) to an IP addressable device.

References

1. Russell, S. J., Norvig, P. and Davis, E. (2010) Artificial intelligence: a modern approach. 3rd ed. Upper Saddle River: Prentice Hall (Prentice Hall series in artificial intelligence).
2. Baase, S. (2013) A gift of fire: social, legal, and ethical issues for computing technology. 4th ed. Upper Saddle River, NJ: Pearson.
3. Ramaswamy, P., Jeude, J., Smith, J. and Solutions, C. T. (2018)
4. ‘Making AI Responsible and Effective’, p. 28. Alex Smola, S. V. N. V. (2008) Introduction to Machine Learning. Cambridge ; New