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) …
AWS SageMaker, the machine learning brand of AWS, announced the release of SageMaker Studio, branded an "IDE for ML," on Tuesday. Machine-learning has been gaining traction and, with its compute-heavy training workloads, could prove a decisive factor in the growing battle over public cloud. So what does this new IDE mean for AWS and the public cloud market? First, the big picture (skip below for the feature by feature analysis of Studio): It's no secret that SageMaker's market share is minuscule (the Information put it around $11 million in July of 2019). SageMaker Studio attempts to solve important pain points for data scientists and machine-learning (ML) developers by streamlining model training and maintenance workloads.
Today, we're extremely happy to announce Amazon SageMaker Model Monitor, a new capability of Amazon SageMaker that automatically monitors machine learning (ML) models in production, and alerts you when data quality issues appear. The first thing I learned when I started working with data is that there is no such thing as paying too much attention to data quality. Raise your hand if you've spent hours hunting down problems caused by unexpected NULL values or by exotic character encodings that somehow ended up in one of your databases. As models are literally built from large amounts of data, it's easy to see why ML practitioners spend so much time caring for their data sets. In particular, they make sure that data samples in the training set (used to train the model) and in the validation set (used to measure its accuracy) have the same statistical properties.
When allied with domain knowledge, analytics can be key in finding the sources of uptime losses and margin leakage. However, results can prove sensitive to the context of the data, and sometimes data analysis can produce faulty outcomes. I would like to be able to tell you, (as a CTO of a startup machine learning company once told me), "just give me the data and I will sort out the problems." Unfortunately, it does not work like that. Data analysis techniques including machine learning are portable across industries, domain knowledge is not – and you need both to succeed.
Dataset distillation is a method for reducing dataset sizes by learning a small number of synthetic samples containing all the information of a large dataset. This has several benefits like speeding up model training, reducing energy consumption, and reducing required storage space. Currently, each synthetic sample is assigned a single hard' label, and also, dataset distillation can currently only be used with image data. We propose to simultaneously distill both images and their labels, thus assigning each synthetic sample a soft' label (a distribution of labels). Using soft' labels also enables distilled datasets to consist of fewer samples than there are classes as each sample can encode information for multiple classes.
AI for Longevity has more potential to increase healthy Longevity in the short term than any other sector. The application of AI for Longevity will bring the greatest real-world benefits and will be the main driver of progress in the widespread extension of healthy Longevity. The global spending power of people aged 60 and over is anticipated to reach $15 trillion annually by 2020. The Longevity industry will dwarf all other industries in both size and market capitalization, reshape the global financial system, and disrupt the business models of pension funds, insurance companies, investment banks, and entire national economies. Longevity has become a recurring topic in analytical reports from leading financial institutions such as CitiBank, UBS Group, Julius Baer, and Barclays.
Looking for a valuable way to use your data? With anomaly detection, you can use it to stop a minor issue from becoming a widespread, time-consuming problem. By proactively detecting abnormal behavior, your company can ensure the right people are alerted to unexpected changes and are able to make faster decisions about what actions need to be taken. Watch this on-demand "ask me anything" webinar to hear from a panel of data scientists on the basics of anomaly detection, common use cases, and some key techniques to keep in mind as you get started.
Image Classification helps us to classify what is contained in an image. The goal is to answer "is there a cat in this image?", Object Detection specifies the location of objects in the image. The goal is to identify "where is the cat in this image?", Image Segmentation creates a pixel-wise mask of each object in the images.
Artificial Intelligence (AI) has been the most buzz word in the media nowadays and this is rightly so because it is impacting our everyday lives and we are not even realizing it. Right from the google maps which takes you through the least traffic route to the personalized recommendation on e-commerce websites, our lives are driven greatly by artificial intelligence. As we now enter a new decade of the 2020s, a question arises, are we standing at the dawn of Artificial Intelligence! AI Revolution If we look back from the past industrial revolutions to today's digital revolution, we see that we have changed a lot in our work standard and we have changed the way of our lives so much. There have been phases in the last couple of centuries, where a disruptive technology emerges that completely changes the way people work and live.
An updated release of Baidu's deep learning framework includes a batch of new features ranging from inference capabilities for Internet of Things (IoT) applications to a natural language processing (NLP) framework for Mandarin. The latest version of PaddlePaddle released this week includes a streamlined toolkit dubbed Paddle Lite 2.0 aimed at inference for IoT, embedded and mobile devices. It works with PaddlePaddle as well as pre-trained models from other sources, Chinese Internet giant (NASDAQ: BIDU) said. Along with faster deployment of ResNet-50, used for image classification on convolutional neural networks, Paddle Lite 2.0 also supports edge-based FPGAs and other hardware. New development kits include ERNIE 2.0, and updated version of Baidu's natural language processing framework.
Last year Netflix announced that it signed on 135 million Paid customers worldwide. Netflix's US Users' demographics perfectly represent the overall US population in terms of different factors like wealth, age and education. With no ads, Netflix's Business model relies on customers who subscribe to their service in the long run. The happier the customers are, the longer they stay subscribed to the service. This is why it is central to Netflix's business to identify and analyze factors that impact the viewer's enjoyment.