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) …
You can choose from a variety of customizable clock faces to match your home's décor. Amazon's Echo Show 5 may be small, but it's loaded with many of the same features as bigger and more expensive models like the 2nd-generation Echo Show and Echo Show 8. The Echo Show 5 is great for tight spaces like desks, nightstands, and other books of the home. It comes with a front-facing camera so you can video chat with friends and family. The screen is bright and the resolution, while lower than other small smart displays, is pretty sharp given its petite size.
Text generation is under the branch of data science that is natural language generation or commonly referred to as NLG. Whereas natural language processing, or NLP, uses libraries to clean, convert, transform, and ultimately manipulate text, NLG strives to create new text from past data. One of the prominent examples is the chatbot. It uses a variety of different types of algorithms that model off of previous text to produce a response where the user thinks (somewhat) that they are talking to a person instead of a machine. This generation of text is not only awesome, but it is useful as well because it can automate otherwise manual processes.
Kotlin is one of the most preferred languages among professional developers these days, because of some of its great features as it is very much concise than other languages and is easy to learn. With these features, this language could be used from creating data pipelines to the development of machine learning models. As per the official documentation, Kotlin claims to be a preferred choice for working with data. While making the code legible and intuitive, this library provides extension functions for exploratory and production statistics. It also provides a few clustering algorithms which attempt to group objects based on their proximity, that are closely connected on a 2-dimensional plot.
The National Oceanic and Atmospheric Administration wants to leverage machine learning to improve its ability to verify marine species. NOAA's Fisheries Sampling Branch is responsible for monitoring and observing fishing in the Northeast and mid-Atlantic. Over the last 25 years, FSB has worked to make this identification easier for its observers -- who started out taking pictures of samples of frozen fish with film cameras, noting the location and focusing on the unique characteristics that allow differentiation of each fish species, such as whole body, gills or fins. The images were submitted within 48 hours and verified by other observers. Currently, images -- which still vary greatly in quality due to different cameras, photographic conditions and characteristics of the species -- are upload to the Fish House, a web-based user interface for a Oracle relational database that is used for species verification.
To develop a deep learning approach to bone age assessment based on a training set of developmentally normal pediatric hand radiographs and to compare this approach with automated and manual bone age assessment methods based on Greulich and Pyle (GP). In this retrospective study, a convolutional neural network (trauma hand radiograph–trained deep learning bone age assessment method [TDL-BAAM]) was trained on 15 129 frontal view pediatric trauma hand radiographs obtained between December 14, 2009, and May 31, 2017, from Children's Hospital of New York, to predict chronological age. A total of 214 trauma hand radiographs from Hasbro Children's Hospital were used as an independent test set. The test set was rated by the TDL-BAAM model as well as a GP-based deep learning model (GPDL-BAAM) and two pediatric radiologists (radiologists 1 and 2) using the GP method. All ratings were compared with chronological age using mean absolute error (MAE), and standard concordance analyses were performed.
Published under a CC BY 4.0 license. Supplemental material is available for this article. This dataset provides vertebral segmentation masks for spine CT images and annotations of vertebral fractures or abnormalities per vertebral level; it is available from https://osf.io/nqjyw/ This public CT dataset holds 160 image series of 141 patients including segmentation masks of 1725 fully visualized vertebrae; it is split into a training dataset (80 image series, 862 vertebrae), a public validation dataset (40 image series, 434 vertebrae), and a secret test dataset (40 image series, 429 vertebrae, to be released in December 2020). Metadata include annotations of vertebral fractures using the semiquantitative method by Genant and of instances of foreign material per vertebral level, as well as opportunistic measurements of lumbar bone mineral density per patient.
Harro has over 20 years of experience in management consulting and early stage investments in disruptive products, processes and services. In many of the conversations with our (potential) customers, we discuss the power of artificial intelligence. In many publications the usage of AI is almost promoted as "the land of milk and honey" -- but those with a bit of experience will be able to tell you that using AI is not always the answer, and it's not as easy to implement as many try to make you believe. But with the right use-cases defined, it can help your company -- or you as a person -- make life easier or create specific added value. I'd like to tell you about how AI improved my personal life in five examples.
The Quick Python Book, Second Edition, is a clear, concise introduction to Python 3, aimed at programmers new to Python. This updated edition includes all the changes in Python 3, itself a significant shift from earlier versions of Python. The book begins with basic but useful programs that teach the core features of syntax, control flow, and data structures. It then moves to larger applications involving code management, object-oriented programming, web development, and converting code from earlier versions of Python. True to his audience of experienced developers, the author covers common programming language features concisely, while giving more detail to those features unique to Python.
We are very familiar with the single-label classification problems. We mostly come across binary and multiclass classifications. But, with the increasing applications of machine learning, we face different problems like movie genre classifications, medical report classification, and text classification according to some given topics. These problems can't be addressed using single-label classifiers, as an instance may belong to several classes or labels at the same time. For instance, a movie can be of Action and Adventure genre at the same time.
Editor's note: This story was updated Aug. 13 to correct the spelling of AI startup Synthesia. The past few years have seen artificial intelligence reach a tipping point, fueled by record levels of investment and the growing availability of training data. Yet what we see today is still only the tip of the iceberg when it comes to AI's creative potential. Various incarnations of synthetic media -- content generated or manipulated by AI, often through machine learning and deep learning -- have already started to be adopted for commercial purposes. These include virtual assistants, fashion models, and chatbots that synthesize text and speech, and we've also witnessed a wave of virtual beings, including computer-generated Instagram influencer Lil Miquela, KFC's virtual Colonel Sanders, and Shudu, the first digital supermodel.