"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
This article would try to address the basic aspects of deep learning. Deep learning attempts to copy the working mechanism of the human brain by combining data inputs, weights, and biases. The basic mechanism of deep learning is to cluster data and make predictions with a high degree of accuracy. Deep learning involves layers that form a neural network. The layers help in improving accuracy and better prediction.
A group of researchers is using artificial intelligence techniques to calibrate some of NASA's images of the Sun, helping improve the data that scientists use for solar research. The new technique was published in the journal Astronomy & Astrophysics on April 13, 2021. A solar telescope has a tough job. Staring at the Sun takes a harsh toll, with a constant bombardment by a never-ending stream of solar particles and intense sunlight. Over time, the sensitive lenses and sensors of solar telescopes begin to degrade.
In a perfect world, what you see is what you get. If this were the case, the job of Artificial Intelligence systems would be refreshingly straightforward. Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action--steer right, steer left, or continue straight--to avoid hitting a pedestrian that its cameras see in the road. But what if there's a glitch in the cameras that slightly shifts an image by a few pixels? If the car blindly trusted so-called'adversarial inputs,' it might take unnecessary and potentially dangerous action.
Learn more about the book here and watch Shane's presentation on the book here. Learn more about the book here and watch Russel's presentation on the book here. Moving away from the more conceptual books listed, The Hundred-Page Machine Learning Book provides a concise and practical look at the most fundamental questions in ML. In this book, Interpretable Machine Learning Researcher and Ph.D, Christoph Molnar focuses on ML's biggest issue with adoption: that these systems seldom explain their inner-workings meaning a great deal of a machine's processes are hidden within a black box. A free online version of the book can be found here, and author's presentation of the main idea from this book can be found here.
MLOps follows a set of practices to deploy and maintain machine learning models in production efficiently and reliably. While the data science team has a deep understanding of the data, the operations team holds the business acumen. MLOps combines the expertise of each team, leveraging both data and operations skill sets to enhance ML efficiency. According to the Algorithmia report, nearly 22 percent of companies have had ML models in production for one to two years. With practice, MLOps professionals can enhance their skills, and develop a solid pipeline for developing machine learning models.
Artificial Intelligence (AI) and Machine Learning (ML) have transformed almost every sector. The testing industry is no longer an exception to this. It hasn't been long, we used to discuss the importance of "continuous testing" for "agile" and "DevOps". Undoubtedly, continuous testing provides the path for swiftly embedding quality assurance (QA) by ensuring that changes in the code can be integrated efficiently in the DevOps. However, continuous testing is not a walk in the park due to factors like siloed automation, lack of end-to-end visibility of requirements, high volume tests, etc.
AI or Artificial Intelligence is a buzzword across the world these days. Several industries are prospering with AI implementation, and many others are gearing up to adopt this latest technology to start a journey of steady progress. Accurate executions and quick operations with automated labor-intensive procedures are helping the companies to get their work done at low cost and in less time. Companies are using Artificial Intelligence to better understand their consumers and gauge their behavior and preferences by analyzing the available data. This allows them to optimize their offerings and prices accordingly.
The connectivity benefits of 5G are expected to make businesses more competitive and give consumers access to more information faster than ever before. Connected cars, smart communities, industrial IoT, healthcare, immersive education--they all will rely on unprecedented opportunities that 5G technology will create. The enterprise market opportunity is driving many telecoms operators' strategies for, and investments in, 5G. Companies are accelerating investment in core and emerging technologies such as cloud, internet of things, robotic process automation, artificial intelligence and machine learning. IoT (Internet of Things), as an example, improving connectivity and data sharing between devices, enabling biometric based transactions; with blockchain, enabling use cases, trade transactions, remittances, payments and investments; and with deep learning and artificial intelligence, utilization of advanced algorithms for high personalization.
When venturing into the field of chatbots and Conversational AI, usually the process starts with a search of what frameworks are available. Invariably this leads you to one of the big cloud Chatbot service providers. Most probably you will end up using IBM Watson Assistant, Microsoft LUIS/Bot Framework, Google Dialog Flow etc. There are advantages…these environments offer easy entry in terms of cost and a low-code or no-code approach. However, one big impediment you often run into with these environments, is the lack of diversity when it comes to language options. This changed 17 June 2021 when IBM introduced the Universal language model.
Artificial intelligence can be defined as "the ability of an artifact to imitate intelligent human behavior" or, more simply, the intelligence exhibited by a computer or machine that enables it to perform tasks that appear intelligent to human observers (Russell & Norvig 2010). AI can be broken down into two different categories: Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI), which are defined as follows: ANI refers to the ability of a machine or computer program to perform one particular task at an extremely high level or learn how to perform this task faster than any other machine. The most famous example of ANI is Deep Blue, which played chess against Garry Kasparov in 1997. AGI refers to the idea that a computer or machine would one day have the ability to exhibit intelligent behavior equal to that of humans across any given field such as language, motor skills, and social interaction; this would be similar in scope and complexity as natural intelligence. A typical example given for AGI is an educated seven-year-old child.