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AI Research and Applications

[Sydney Harbor Bridge and Opera House, Sydney, Australia - Photologic]


The Industrial Revolution freed up a lot of Humanity from Physical Drudgery, 

Artificial Intelligence has the potential to free up Humanity from a lot of the Mental Drudgery.




- Overview

Every few decades, a technological development leads us to believe that artificial general intelligence (strong AI) , the brand of AI that can think and decide like humans, is just around the corner. However, every time we thought we were closing in on strong AI, we have been disappointed. We are currently in the full heat of one such cycle, thanks to machine learning (and deep learning), the technologies that have been at the heart of AI developments in recent years. 

With the digitization of all records and processes, and prevalence of cloud‐driven services and the Internet of Things, today's era can truly be considered as an era of data. Machine learning (ML) and AI skills are among the most sought‐after skills today. AI is proving to be one of the most disruptive forces in technology in decades. Much like the introduction of electricity in the early 20th century and the more recent advent of the Internet and mobile technologies, AI offers broad technological capabilities that can be applied to all industries, profoundly transforming the world around us. 

AI has various applications in today's society. It is becoming essential for today's time because it can solve complex problems with an efficient way in multiple industries, such as Healthcare, entertainment, finance, education, etc. If you use an automated assistant, make a simple Google search, get recommendations on Netflix or Amazon, or find a great deal in your inbox, then you will have interacted with AI. 

Indeed, it seems that every company and service today is incorporating AI in some way or another. AI is making our daily life more comfortable and fast. AI enabled technologies are already shifting how we communicate, how we work and play, and how we shop and care for our health. For businesses, AI has become an absolute imperative for creating and maintaining a competitive edge. 


- What Is Artificial Intelligence (AI)?

What is AI, exactly? The question may seem basic, but the answer is kind of complicated. The definition of AI is constantly evolving. What would have been considered AI in the past may not be considered AI today. In basic terms, AI can be defined as: a broad area of computer science that makes machines seem like they have human intelligence. AI is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. 

Essentially, AI is the wider concept of machines being able to carry out tasks in a way that could be considered “smart”. In the broadest sense, AI refers to machines that can learn, reason, and act for themselves. They can make their own decisions when faced with new situations, in the same way that humans and animals can. If a machine can solve problems, complete a task, or exhibit other cognitive functions that humans can, then we refer to it as having artificial intelligence.

AI makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.


- AI Is Constantly Evolving All By Itself

AI is evolving - literally. Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input. The program replicated decades of AI research in a matter of days, and its designers think that one day, it could discover new approaches to AI. While most people were taking baby steps, they took a giant leap into the unknown. 

Building an AI algorithm takes time. Take neural networks, a common type of machine learning used for translating languages and driving cars. These networks loosely mimic the structure of the brain and learn from training data by altering the strength of connections between artificial neurons. Smaller subcircuits of neurons carry out specific tasks - for instance spotting road signs—and researchers can spend months working out how to connect them so they work together seamlessly.

AI is probably the most complex and astounding creations of humanity yet. And that is disregarding the fact that the field remains largely unexplored, which means that every amazing AI application that we see today represents merely the tip of the AI iceberg, as it were. While this fact may have been stated and restated numerous times, it is still hard to comprehensively gain perspective on the potential impact of AI in the future. The reason for this is the revolutionary impact that AI is having on society, even at such a relatively early stage in its evolution.


(Maggie, Jeffrey M. Wang)

- From Science Fiction to Reality: The Evolution of AI

Artificial Intelligence (AI) has undoubtedly been the technology story of the 2010s, and it doesn't look like the excitement is going to wear off as a new decade dawns. The past decade will be remembered as the time when machines that can truly be thought of as “intelligent” - as in capable of thinking, and learning, like we do - started to become a reality outside of science fiction.

As it currently stands, the vast majority of the AI advancements and applications you hear about refer to a category of algorithms known as machine learning. Machine learning - as well as deep learning, natural language processing and cognitive computing - are driving innovations in identifying images, personalizing marketing campaigns, genomics, and navigating the self-driving car. Machine learning is the basis of many major breakthroughs, including facial recognition, hyper-realistic photo and voice synthesis, and AlphaGo (the program that beat the best human player in the complex game of Go)

Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) - images, text, transactions, mapping data, you name it. 


- The Difference Between AI, ML, and DL

AI is an umbrella discipline that covers everything related to making machines smarter. Machine Learning (ML) is commonly used along with AI but it is a subset of AI. ML refers to an AI system that can self-learn based on the algorithm. Systems that get smarter and smarter over time without human intervention is ML. Deep Learning (DL) is a machine learning (ML) applied to large data sets. Most AI work involves ML because intelligent behaviour requires considerable knowledge. 

AI is defined as the study of intelligent agents, which can perceive the environment and intelligently act just as humans do. AI can philosophically be categorized as strong AI or weak AI. Machines that can act in a way as though intelligent (simulated thinking) are said to possess weak AI, and machines that are intelligent and can actually think are said to possess strong AI. In today's applications, most AI researchers are engaged in implementing weak AI to automate specific task(s). 

Machine learning (ML) techniques are commonly used to learn from data and achieve weak AI. ML involves the scientific study of statistical models and algorithms that can progressively learn from data and achieve desired performance on a specific task. The knowledge/rules/findings inferred from the data using ML are expected to be nontrivial. Therefore, ML can be used in many tasks that need automation, and especially in scenarios where humans cannot manually develop a set of instructions to automate the desired tasks. Deep learning (DL) is a subfield of ML, which focuses on learning data representations with computational models composed of multiple processing layers.


- Beyond the AI Hype Cycle: Trust and the Future of AI

At the heart of digital transformation is the commitment to building trust and data stewardship into our AI development projects and organizations. 

There’s no shortage of promises when it comes to AI. Some say it will solve all problems while others warn it will bring about the end of the world as we know it. Both positions regularly play out in Hollywood plotlines like Westworld, Carbon Black, Minority Report, Her, and Ex Machina. Those stories are compelling because they require us as creators and consumers of AI technology to decide whether we trust an AI system or, more precisely, trust what the system is doing with the information it has been given.

Investment and interest in AI is expected to increase in the long run since major AI use cases (e.g. autonomous driving, AI-powered medical diagnosis) that will unlock significant economic value are within reach. These use cases are likely to materialize since improvements are expected in the 3 building blocks of AI: availability of more data, better algorithms and computing. 

Short term changes are hard to predict and we could experience another AI winter however, it would likely be short-lived! 


[More to come ...]

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