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) …
Clustering of large-scale data is key to implementing segmentation-based algorithms. Segmentation can include identifying customer groups to facilitate targeted marketing, identifying prescriber groups to allow health care players to reach out to them with the right messaging, and identifying patterns or abnormal values in the data. K-Means is the most popular clustering algorithm adopted across different problem areas, mostly owing to its computational efficiency and ease of understanding the algorithm. K-Means relies on identifying cluster centers from the data. It alternates between assigning points to these cluster centers using the Euclidean distance metric and recomputes the cluster centers till a convergence criterion is achieved.
When we talk about Computer vision products, most of them have required the configuration of multiple things including the configuration of GPU and Operating System for the implementation of different problems. This sometimes causes issues for customers and even for the development team. Keeping these things in mind, Nvidia released Jetson Nano, which has its own GPU, CPU, and SDKs, that help to overcome problems like multiple framework development, and multiple configurations. Jetson Nano is good in all perspectives, except memory, because it has limited memory of 2GB/4GB, which is shared between GPU and CPU. Due to this, training of custom Computer Vision models on Jetson Nano is not possible.
Neuromorphic computing aims to realize artificial intelligence (AI) by mimicking the mechanisms of neurons and synapses that make up the human brain. Inspired by the cognitive functions of the human brain that current computers cannot provide, neuromorphic devices have been widely investigated. However, current Complementary Metal-Oxide Semiconductor (CMOS)-based neuromorphic circuits simply connect artificial neurons and synapses without synergistic interactions, and the concomitant implementation of neurons and synapses still remains a challenge. To address these issues, a research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering implemented the biological working mechanisms of humans by introducing the neuron-synapse interactions in a single memory cell, rather than the conventional approach of electrically connecting artificial neuronal and synaptic devices. Similar to commercial graphics cards, the artificial synaptic devices previously studied often used to accelerate parallel computations, which shows clear differences from the operational mechanisms of the human brain.
Artificial intelligence for IT operations, mainly acknowledged as AIOps, is the talk of the town these days, but people talk less about the way to implement AIOps. However, to implement AIOps successfully, businesses must know the process and tools needed at each stage. And, yes, AIOps will help businesses optimize their IT operations. Today, IT companies operate in complicated and extensive environments, often while connecting on-premises and private and public clouds legacy setups. IT leaders, managers, and teams are usually under pressure to serve the business with their end-to-end IT operations and services. The enterprise's core focus is to prevent the most significant instances and any downtime.
Carrot ("Carrot" or the "Company") and Luxrobo announced today the establishment of a Joint Venture (JV) that brings together two premier tech companies from the field of digital insurance and IoT-technology. Together they operate as Lucky Box Solutions Inc. to deliver the most optimized IoT systems and telematics devices while positioning itself as the leader of InsurTech, industry that has become the noise of the town in recent years. Both companies have established proven track records of providing innovation in emerging sectors and are poised to offer cutting-edge solutions in the areas of Artificial Intelligence, Machine Learning, Data Interoperability, Analytics and Processing, and Internet of Things while providing risk prevention and insurance protection services. Carrot has made a name for itself over the past few years by providing modernized insurance service to the consumers of South Korea with its customer-centered design of Pay-Per-Mile auto insurance product. As seen with its top-tier customer retention, customers are highly praising for its fair and transparent pricing, month-end payment term, IoT-driven emergency and other value-added services, and cash incentive program that rewards customers based on their safe-driving scores.
This article was published as a part of the Data Science Blogathon. The MNIST dataset classification is considered the hello world program in the domain of computer vision. The MNIST dataset helps beginners to understand the concept and the implementation of Convolutional Neural Networks. Many think of images as just a normal matrix but in reality, this is not the case. Images possess what is known as spatial information.
Over the past 5 months, I had been reading the book Probability Essentials by Jean Jacod and Philip Protter, and the more time I spent on it, more I started to treat every encounter with Probability with a rigorous perspective. Recently, I was reading a paper in Deep Learning and the authors were talking about Stochastic Gradient Descent (SGD), which got me thinking, why is it called "stochastic"? Where is the randomness in it? Disclaimer: I won't be trying to explain any mathematical bits in this article solely because it is a pain to add equations. I hope the reader has some familiarity with the mathematical bits of the Gradient Descent algorithm and its variants. I'll provide a brief introduction where necessary, but won't be going into much detail.
Machine learning (ML) has empowered businesses to scale up to modern business demands. From training artificial Intelligence (AI) to answering customer concerns, optimizing processes to detecting and analyzing fraud, the advent of technology in business has been exquisite. While the full impact of machine learning is yet unknown, ethical issues are becoming more prevalent. ML has already experienced some unexpected catastrophic events. Therefore, debates over ML and AI ethics and risk assessments are far from over.
Artificial intelligence in general, and more specifically Deep Learning and neural networks, open the door to a new era in image processing. Why should companies look into this technology, what is important to know and how easy is it actually to set up a new project? After participation, you will have a better grasp of this new technology and be familiar with the essential know-how concerning this field. We also show you that it is actually really easy to set-up your individual, deep learning-based vision solutions, even if you have no prior knowledge.
Chris Matyszczyk is an award-winning creative director who now runs the consultancy Howard Raucous LLC. I've come with my lawyer, just in case. I'll admit I'm out of practice. The last time I was being interviewed for a job was certainly when America was still sane. Yet I never really prepared for particular questions to be asked. I merely feared that the first question would be: "Tell me a little about yourself."