Artificial Intelligence, Machine Learning and Neural Networks Defined
Using the Turing Test as a qualifier, Artificial Intelligence (AI) is defined as a software solution that performs a task on par with a human domain expert. When IBM’s Watson system played Jeopardy with former Jeopardy champions, much of the world saw the first real example of AI. Now, deep learning is enabling solutions that can interpret MRI images on par with doctors and operate buses on par with human drivers (e.g. Las Vegas Self Driving Shuttle).
Machine Learning (ML) is the basic foundation of AI comprised of the algorithms and data sets used to build an AI solution. In order to create a true AI system that can pass the Turing Test, the ML subset must be constantly improving with new sets of data and ongoing developments to the algorithms. While there are many different algorithms that have been in the ML toolbox for decades, it is only recently (circa. 2014) that the deep learning and neural network algorithms have taken a significant leap forward in performance due to the availability of large-labeled data sets for training and low-cost compute and storage.
Neural Networks are Accelerating Machine Learning
Thanks to the fast improvement of computation, storage and distributed computing infrastructure, ML has been evolving into more complex structured models like Deep Learning (DL), Generative Adversarial Network (GAN) and Reinforcement Learning (RL) – all using neural networks. Supervised neural networks are algorithms that can differentiate and make judgments based on image or pattern recognition, after being trained with labeled data. The concept of neural networks has been around for more than forty years, however, it was near 2014 that deep learning and neural networks began to disrupt different segments and bring us closer to passing the Turing Test. Thanks to today’s data gathering capabilities, and the sheer volume of said data, neural networking is one of the driving trends in successful ML execution.
Deep learning refers to a set of artificial neural network-based ML models that mimic the working mechanisms of neurons and the nerve network of the human brain. There are two kinds of popular neural network models: the Convolutional Neural Network (CNN) model, which is widely used in different image related applications like autonomous driving, robot, image search, etc., and the Recurrent Neural Network (RNN) model, which is empowering most of the Natural Language Processing-based (NLP) text or voice applications, such as chatbots, virtual home and office assistants and simultaneous interpreters.
Generative Adversarial Network (GAN) is a type of ML technique composed of two deep neural networks competing with each other in a zero-sum game framework. GAN runs typically in the unsupervised fashion; thus, it can help reduce the dependency of deep learning models on the amount of labeled training data.
NLP is another algorithmic trend that is driving ML advancement, particularly in the area of virtual home and office assistants. Similarly to neural networks, NLP is algorithmic based vocal- and word-based recognition. As more AI companies adopt these trends and execute on top of their ML foundation, they will be successful.
Key Considerations in Building an AI System
A solid data pipeline and a great data science toolbox are key to building an effective AI-driven system. We’ve only recently gained access to nearly unlimited compute power and storage in the cloud, which has, in turn, allowed for incredible data collection and analysis. With the right volume and quality of data, as well as the nurturing of data science programs, ML will advance quickly and bring companies closer to achieving true AI.
Almost any college graduate can build and train a deep learning model using tools such as Python, TensorFlow and Keras. To bring an AI solution to production, you need tools such as Spark, Kubernetes, and Docker to allow the collection and creation of large labeled datasets and data pipelines
There are many open source tools, like TensorFlow, Keras, and Mllib, which dramatically reduce the effort and knowledge required of building an ML – even DL – model, but bringing a solution to production requires the whole ecosystem of AI primitives, including data acquisition and labeling, data processing pipeline, model execution, post-deployment validation, and continuous model improvement.
In addition, there are other factors determining the success of an AI solution. These include how to leverage and integrate human knowledge and heuristics while developing machine intelligence; how to build human trust in the step-by-step process of automation, augmentation and autonomy; and how to accelerate knowledge learning and sharing across different customers without compromising individuals’ privacy information.