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Artificial Intelligence, Machine Learning, and Neural Networks

(MIT Dome, Yu-Chih Ko)


Artificial Intelligence: Fueling the Next Wave of the Digital Era


Artificial Intelligence (AI) and Machine Learning (ML) principles have been around for decades. AI's recent surge in popularity is a direct result of two factors. First, AI/ML algorithms are computationally intensive. The availability of cloud computing has made it feasible to run these algorithms practically. Second, training AI/ML models requires massive amounts of data. The availability of big data platforms and digital data have improved the effectiveness of AI/ML, making them better in many applications than humans.


What is Artificial Intelligence (AI)?

Artificial Intelligence: Knowledge Representation, Machine Learning, Data Mining, and Causal Discovery  

Artificial intelligence (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. 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), 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.

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. 

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 Goal of Artificial Intelligence (AI)


Rather than increasingly complex calculations, work in the field of AI concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways. The goal of AI is to simulate natural intelligence to solve complex problems and increase the chance of success. AI will try and find the most optimal solution. It will use machine learning to reflect on the outcomes and optimize decision making based on observing its surrounding environment. 

"The goal of AI is to understand intelligence by constructing computational models of intelligent behavior. This entails developing and testing falsifiable algorithmic theories of (aspects of) intelligent behavior, including sensing, representation, reasoning, learning, decision-making, communication, coordination, action, and interaction. AI is also concerned with the engineering of systems that exhibit intelligence." -- (The Pennsylvania State University


The Foundation of AI

(AI Algorithm Flowchart, Karen Hao, MIT)


Artificial Intelligences (AI) – devices designed to act intelligently – are often classified into one of two fundamental groups – applied or general. 

  • Applied AI is far more common – systems designed to intelligently trade stocks and shares, or maneuver an autonomous vehicle would fall into this category. 
  • Generalized AIs – systems or devices which can in theory handle any task – are less common, but this is where some of the most exciting advancements are happening today. It is also the area that has led to the development of Machine Learning. Often referred to as a subset of AI, it’s really more accurate to think of it as the current state-of-the-art. 

The grand idea is to develop something resembling human intelligence, which is often referred to as “artificial general intelligence,” or “AGI.” Some experts believe that machine learning and deep learning will eventually get us to AGI with enough data, but most would agree there are big missing pieces and it’s still a long way off. AI may have mastered Go, but in other ways it is still much dumber than a toddler.

In that sense, AI is also aspirational, and its definition is constantly evolving. What would have been considered AI in the past may not be considered AI today. Because of this, the boundaries of AI can get really confusing, and the term often gets mangled to include any kind of algorithm or computer program.  To clear things up, you may use the flowchart on the right to  work out whether something is using AI or not.


The Rise of Machine Learning (ML)


Machine Learning is a current application of AI. The technology is based on the idea that that we should really just be able to give machines access to data, and let them learn for themselves. Machine learning is a technique in which we train a software model using data. The model learns from the training cases and then we can use the trained model to make predictions for new data cases. 

Machine learning provides the foundation for Artificial Intelligence (AI). Two important breakthroughs led to the emergence of Machine Learning as the vehicle which is driving AI development forward with the speed it currently has. One of these was the realization that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves. The second was the emergence of the Internet, and the huge increase in the amount of digital information being generated, stored, and made available for analysis.
Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the Internet to give them access to all of the information in the world. 

Machine learning is concerned with the scientific study, exploration, design, analysis, and applications of algorithms that learn concepts, predictive models, behaviors, action policies, etc. from observation, inference, and experimentation and the characterization of the precise conditions under which classes of concepts and behaviors are learnable. Learning algorithms can also be used to model aspects of human and animal learning. Machine learning integrates and builds on advances in algorithms and data structures, statistical inference, information theory, signal processing as well as insights drawn from neural, behavioral, and cognitive sciences. 


Deep Learning and Deep Neural Networks


Deep Learning is a subset of Machine Learning which deals with deep neural networks. It is based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. 

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.



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