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) …
Artificial Intelligence (AI) is the technology that allows machines to make intelligent decisions while applying the required skills. Global Artificial Intelligence (AI) in Construction Market was valued at US$ 449.7 million in the year 2018 and is expected to reach US$ 2,107.8 million by the year 2023, growing at a CAGR of 36.2%. The rising need for safety and security in the construction sector, coupled with an increasing demand for reducing the overall cost, is expected to drive the market. However, the complexity of the algorithms which has led to slower adoption of AI in the construction sector, is expected to hinder the growth of the market. The report overview includes studying the market scope, leading players like IBM (US), Alice Technologies (US), Aurora Computer Services (UK), Autodesk (US), Building System Planning (US), eSUB (US), etc., market segments and sub-segments, market analysis by type, application, geography.
FACT-Finder, a company that offers ecommerce companies tools to personalize their site with things like AI-driven recommendations, said it has acquired Loop54, a company that provides personalized search results. It's the latest in a trend of consolidation in the ecommerce world, where a host of companies arose to offer personalization with new technologies like AI, but now the bigger companies are gobbling up the smaller ones -- and specifically in the ecommerce software-as-a-service (SaaS) search market. On the smaller side, we reported last week on Coveo's acquisition of AI-powered personalization provider Qubit. On the much bigger side, yesterday, reports emerged that PayPal is making a $45 billion bid for e-commerce giant Pinterest. "With the expertise and unique approach that our new colleagues at Loop54 bring to the table, we will significantly expand our market leadership and push the bounds of what is possible in e-commerce," said Emile Bloemen, CEO of FACT-Finder.
In 1955 when John McCarthy and his colleagues proposed their first study of artificial intelligence, they suggested that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it". Whether that might ever be possible would depend on how we define intelligence, but what is indisputable is that new methods are needed to analyse and interpret the copious information provided by digital medical images, genomic databases, and biobanks. Technological advances have enabled applications of artificial intelligence (AI) including machine learning (ML) to be implemented into clinical practice, and their related scientific literature is exploding. Advocates argue enthusiastically that AI will transform many aspects of clinical cardiovascular medicine, while sceptics stress the importance of caution and the need for more evidence. This report summarises the main opposing arguments that were presented in a debate at the 2021 Congress of the European Society of Cardiology.
British researchers say they have created an artificial intelligence (AI) model that is highly effective at predicting rainfall within the next 90 minutes. The model was built by scientists at Google-owned research company DeepMind in London. The team says tests of the system showed it produced more accurate predictions, or forecasts, for short-term rainfall than other existing systems. The paper recently appeared in the publication Nature. The scientists centered on a kind of weather prediction known as "precipitation nowcasting."
Amazon is expanding its footprint in the Bay State, adding a 350,000-square-foot Westboro outpost that houses corporate offices, research and development labs, and a robotics manufacturing space. "This new innovation and manufacturing hub, along with its sister site up in North Reading, places Amazon robotics at the epicenter of robotics innovation here in Massachusetts for years to come," said Scott Dresser, vice president of robotics at Amazon. The site, once home to drugmaker AstraZeneca, has been open for a few months and was buzzing with activity Thursday afternoon during the official ribbon-cutting for the site, with Lt. Gov. Karyn Polito and Housing and Economic Development Secretary Mike Kennealy in attendance. Amazon began introducing robots into its facilities in 2012, and since then, has also added over a million employees to work alongside the more than 350,000 robots. Though Amazon Robotics has had a North Reading site for about 10 years, which hosts similar roles to the Westboro facility, the new one "is the first in terms of size and scale of operations for Amazon Robotics," according to an Amazon spokesperson.
In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib NumPy -- A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library. Pandas -- A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.
Apple's MacBook Pro Unleashed intro included enough performance multipliers to shame a pinball machine. We learned that the M1 Max has 4x faster GPU performance than the M1 -- and that the M1 Max's memory bandwidth is 2x that of the M1 Pro and 6x that of the M1. Furthermore, the M1 Max has 3.5x the transistor count and 4x the unified memory of the M1. Apple noted that the M1 Max's GPU performance is more than 2.5x that of a compact pro PC laptop when running on battery and over 3.3x faster than a high-end PC laptop on battery. Machine learning models perform on average of over 3x faster on the new chips than on Apple's fastest Core i9-based MacBook Pro, with some models performing over 20x faster!
This does not include optional dependencies such as depccg and PyTorch, which have to be installed separately. Warning: depccg is available only on MacOS and Linux. If you are using Windows, please install the base lambeq. This means that the DepCCGParser class will not be available on Windows, but you can still use all other compositional models from the reader module. Support for parsing on Windows will be added in a future version.
Akida NSoC and intellectual property enable a wide array of edge AI capabilities that include continuous learning and inference. BrainChip is offering two development kits both including the AKD1000 chip on a mini-PCI board: an X86 Shuttle PC development kit as well as an ARM-based Raspberry Pi development kit. "Offering development kits is not only a major step towards full commercialization, it's also an exciting opportunity to see how our partners and future customers will put Akida to work in environments and scenarios like consumer electronics, industrial applications, aerospace and defense systems, healthcare and medical devices, automotive technology, and more," said Anil Mankar, BrainChip co-founder and chief development officer. "We believe the AKD1000 silicon, or the licensing of Akida in a configurable IP format, will lead to major changes in industries using AI at the edge because of its performance, security, low power requirements, and mainly Akida's ability to perform AI training and learning on the device itself, without dependency on the cloud." Development kits for Akida-based applications and solutions evolving to production status are a step toward joining the neuromorphic revolution for edge AI applications.
Mark Cuban, the serial entrepreneur and investor, joins Scott to discuss Bitcoin, Ethereum, use-cases for digital assets, and where SEC regulations might come in. Mark also shares why he's bullish on AI, offers his thoughts on stimulus spending, and explains why Robinhood may not be as bad as Scott thinks. Follow Mark on Twitter, @mcuban. Scott opens with a prediction about the streaming wars.