Information Technology

C# Machine Learning Projects [PDF] - Programmer Books


Machine Learning is applied in almost all kinds of real-world surroundings and industries, right from medicine to advertising, finance and scientific research. This book help you learn how to choose a model for your problem, how to evaluate the performance of your models, and how you can use C# to build machine learning models for your future projects. You will get an overview of the machine learning systems and how C#, Net users can apply your existing knowledge to the wide gamut of intelligent applications through a project-based approach. You will start by setting up your C# environment for machine learning with required packages, Accord.NET, LiveCharts, Deedle. We will then take you right from classification models for spam email filtering, NLP techniques for Twitter sentiment analysis, time-series data for forecasting foreign exchange rates to drawing insights from Customer segmentation in E-commerce.

Ford's Argo AI will release its HD maps for free to autonomy researchers


It's not like academic researchers have the time or money to command a fleet of self-driving mapping vehicles, which is why Argoverse is so important to that community. A self-driving car is only as good as its maps. Automakers around the world have made efforts to create high-definition maps of as many roads as possible as they ramp up AV development, so that their cars can have the best idea possible of the surrounding world. But while most groups don't seem too keen on the idea of giving those maps away, Ford's Argo AI is taking a different approach. Argo AI announced on Wednesday that it has created a public repository for its self-driving-car development data, including high-definition maps.

Building a modern data and analytics architecture


We expect the landscape to be an integrated edge-to-core-to-cloud solution enabling what today is called IoT, Big Data, Fast Data and AI. Each time a promising new technology emerges, we seem to go through a period where it is proposed to be the solution to everything--until we reconcile how that technology fits into the bigger picture. Such is the case with artificial intelligence (AI). Clearly the advancements in deep learning will create new classes of solutions but rather than being a standalone solution, we are just now beginning to see how it fits into our IT landscape. AI emerges at a time when several other shifts in analytics technology are occurring.

The Guardian view on female voice assistants: not OK, Google Editorial


Within two years there will be more voice assistants on the internet than there are people on the planet. Another, possibly more helpful, way of looking at these statistics is to say that there will still be only half a dozen assistants that matter: Apple's Siri, Google's Assistant, and Amazon's Alexa in the west, along with their Chinese equivalents, but these will have billions of microphones at their disposal, listening patiently for sounds they can use. Voice is going to become the chief way that we make our wants known to computers – and when they respond, they will do so with female voices. This detail may seem trivial, but it goes to the heart of the way in which the spread of digital technologies can amplify and extend social prejudice. The companies that program these assistants want them to be used, of course, and this requires making them appear helpful.

Empowering the Manufacturing Industry Through Decentralised AI


As AI algorithms--and the computing power that drives them--improve year-on-year, their ability to positively transform the world in which we live is unquestionable. In fact, PwC predicts that AI could contribute up to $15.7 trillion to the global economy by 2030. Indeed, as many as one-in-five (20 percent) of the 1,000 US organisations recently surveyed by PwC had plans to implement AI enterprise-wide in 2019. The PwC research also reveals how companies are increasingly initiating AI models at the very core of their production processes, in a bid to enhance operational decision-making and provide forward-looking intelligence to people in every function throughout the business. To many, this move to AI is no surprise.

Artificial Intelligence: The Holy Grail of Digital Marketing


AI offers exceptional opportunities particularly in digital marketing while irrefutably revolutionizing and propelling the industry. AI is the ability of a computer or computer-enabled robotic systems to process massive amounts of in-depth data and produce outcomes similar to the thought processes of humans in learning, analysing, decision making, and problem-solving. Hence, AI has enabled marketers to comprehend vast data to gain valuable consumer insights, and in turn, improve digital marketing strategies. The applications of AI are essentially limitless, and the field of computer science is on a stark ascendance. The global AI market was worth $7.35 billion in 2018, where the largest portion of revenue was stirred from enterprise applications.

Can The AI Economy Really Be Worth $150 Trillion By 2025?


Artificial intelligence is set to transform global productivity, working patterns and lifestyles and create enormous wealth. Research firm Gartner expects the global AI economy to increase from about $1.2 trillion last year to about $3.9 Trillion by 2022, while McKinsey sees it delivering global economic activity of around $13 trillion by 2030. By the same year, PricewaterhouseCoopers reckons on $15.7 trillion - more than the current combined output of China and India. Tech investor Tej Kohli, however, believes the impact will be much faster and exponentially larger, however, potentially worth $150 trillion by 2025. That's nearly double the IMF's forecast of $88 trillion for global gross domestic product this year but Kohli is undaunted.

How to Build Ethical Artificial Intelligence


The field of artificial intelligence is exploding with projects such as IBM Watson, DeepMind's AlphaZero, and voice recognition used in virtual assistants including Amazon's Alexa, Apple's Siri, and Google's Home Assistant. Because of the increasing impact of AI on people's lives, concern is growing about how to take a sound ethical approach to future developments. Building ethical artificial intelligence requires both a moral approach to building AI systems and a plan for making AI systems themselves ethical. For example, developers of self-driving cars should be considering their social consequences including ensuring that the cars themselves are capable of making ethical decisions. Here are some major issues that need to be considered.

How Data Center Infrastructure Management (DCIM) Response to AI and Machine Learning


We have an almost mystical faith in the ability of artificial intelligence (AI) to understand and solve problems. It's being applied across many areas of our daily lives and, as a result, the hardware to enable this is starting to populate our data centers. Data centers in themselves present an array of complex problems, including optimization and prediction. So, how about using this miracle technology to improve our facilities? Machine learning, and especially deep learning, can examine a large set of data, and find patterns within it that do not depend on the model that humans would use to understand and predict that data.

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USATODAY - Tech Top Stories

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