Personal tools
You are here: Home Research Trends & Opportunities New Media and New Digital Economy Artificial Intelligence, Machine Learning, and Neural Networks

Artificial Intelligence, Machine Learning, and Neural Networks

MIT Stata Center_051118
(MIT Ray and Maria Stata Center, Jenny Fowter)


Artificial Intelligence: Fueling the Next Wave of the Digital Era


- The Relationship Between AI, ML, DL, and Neural Networks

  • Artificial Intelligence (AI): Mimicking the intelligence or behavioral pattern of humans or any other living being entity.
  • Machine Learning (ML): A technique by which a computer can "learn" from data, without using a common set of different rules. This approach is mainly based on training a model from data sets.
  • Deep Learning (DL): A technique to perform machine learning inspired by our "brain's own network of neurons" - network capable of adapting itself to new data.
  • Neural Networks: A beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data. Deep learning, a powerful set of techniques for learning in neural networks. Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. 

- The Future of Artificial Intelligence (AI)

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.

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.  

AI is evolving all by itself. 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. 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. 

AI is the goal; AI is the planet we’re headed to. ML is the rocket that’s going to get us there. And Big Data is the fuel. AI is impacting the future of virtually every industry and every human being. AI has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future.


Artificial Intelligence System_112720A
[Artificial Intelligence System - Deloitte]

- The Rise of Machine Learning (ML)

Machine Learning (ML) is an interdisciplinary field that uses statistics, probability, algorithms to learn from data and provide insights which can be used to build intelligent applications.

ML is a current application of AI. The technology is based on the idea 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.  

ML provides the foundation for Artificial Intelligence (AI). Two important breakthroughs led to the emergence of ML 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. 

ML 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 (DL)

Machine learning (ML) is a subfield of AI and gives machines the skills to ‘learn’ from examples without being explicitly programmed to do so. Deep learning (DL) is a specialized ML technique that mimics the behavior of the human brain and enables machines to train themselves to perform tasks.

DL teaches computers to do what comes naturally to humans: learn by example. DL is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. 

DL is the hot topic of the day as it aims to simulate human thinking. It is getting lots of attention lately and for good reason. It’s achieving results that were not possible before. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. DL models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

DL basically is ML on steroids and allows the crunching of vast amounts of data with improved accuracy. As it’s more powerful it also requires considerably more computing power. Algorithms can determine on their own (without intervention of an engineer) whether a prediction is accurate or not. Think for example of providing an algorithm with thousands of images and videos of cats and dogs. It can look at whether the animal has whiskers, paws or a furry tail, and use learnings to predict whether new data fed into the system is more likely to be a cat or a dog.


- Neural Networks

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.

Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.

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



[More to come ...]

Document Actions