If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Machine Learning (ML) models are designed for defined business goals. ML model productionizing refers to hosting, scaling, and running an ML Model on top of relevant datasets. ML models in production also need to be resilient and flexible for future changes and feedback. A recent study by Forrester states that improving customer experience, improving profitability & revenue growth as the key goals organizations plan to achieve specifically using ML initiatives. Though gaining worldwide acclaim, ML models are hard to be translated into active business gains. A plethora of engineering, data, and business concerns become bottlenecks while handling live data and putting ML models into production.
Cyber threats continue to escalate in both sophistication and volume. Traditional approaches to threat detection, however, are no longer sufficient to ensure protection. Correspondingly, machine learning (ML) has proven highly effective at identifying and warding off cyber attacks. Machine learning's power is the result of three factors: data, compute power and algorithms. Due to its very nature, the cyber field produces substantial amounts of data.
With part of the world dealing with the adverse effects of hurricanes and intense tropical cyclones, it has become imperative for researchers and scientists to develop a way to predict and analyse these hurricane patterns. Thus in an attempt to forecast future hurricane intensity, scientists at NASA's Jet Propulsion Laboratory in Southern California have proposed a machine learning model that claims to predict rapid-intensification events of the future accurately. The critical factor in understanding the intensity of a hurricane is the wind speed. Traditionally it has been a challenge to predict the severity of storms or hurricanes while it's brewing. However, NASA's new ML model can improve the accuracy of the prediction and provide better results.
You may hear about "no free lunch" (NFL) theorem, which indicates that there is no best algorithm for every data. One algorithm may perform well in one data but perform poorly in other data. That is why there are so many machine learning algorithms available to train data. How do we know which machine learning model is the best? We cannot know until we experiment and compare the performance of different models.
Machine learning sounds scary and fancy for most people. I always like to describe it as the capability to train computer software on how to handle a certain situation without coding for every single decision permutation that it can potentially make. Now that you know what ML is, it's time to start doing the dirty work and train your first ML model. It enables organizations to handle more customer queries even during times where employees are resting or taking holidays! However, one common attack that chatbot applications experience is the spamming of hateful speech and offensive language.
Hundreds of thousands of machine learning experiments are conducted globally every single day. Machine learning engineers and students conducting those experiments use a variety of frameworks like TensorFlow, Keras, PyTorch, and others. These models form the foundation of every AI-powered product. So where and how does the ONNX library fit into Machine Learning? What is it exactly, and why did big names like Microsoft and Facebook introduce this library?
Product designs are systematic, creative and iterative processes requiring physics or mathematical models and manipulated representations. Traditionally, mathematical models have been used to ensure that design fits real-world product usage. During such simulations, engineering models (CAD 3D/2D representations) are engaged, characterizing product and system behavior. Today's products are smart, equipped with sensors that continuously communicate about their health. Based on historical data trends and current feeds, modern simulation techniques (artificial intelligence methods) are also able to predict product/system behaviors.
Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for cardiologists. This paper builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. This paper aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases. Finally, the paper summarizes a list of reporting items as an itemized checklist that highlights steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.
This article is the 2nd in a series dedicated to Machine Learning platforms. It was supported by Digital Catapult and PAPIs. In the previous article, I presented an overview of ML development platforms, whose job is to help create and package ML models. Model building is just one capability, out of many, required in ML systems. I ended that article by mentioning other types of ML platforms, and limitations when building real-world ML systems.
Imagine the capabilities of your smartphone melded into every imaginable component. The Internet of Things describes a vision of the future as much as it implies a set of underlying technologies. It envisions a situation where everything that should be connected is connected. This currently includes Ring Doorbells, 'smart' appliances, and Apple's seamless connections between watch, iPhone, and AirPods. Where IoT describes connected devices, Machine Learning is the computer intelligence that draws insights from and controls these IoT devices.