Representation & Reasoning


Understand Learning Rate by a Child's interaction with Dogs

#artificialintelligence

When building a deep learning project the most common problem we all face is choosing the correct hyperparameters (often known as optimizers). This is critical as the hyperparameters determine the expertise of the machine learning model. In Machine Learning (ML hereafter), a hyperparameter is a configuration variable that's external to the model and whose value is not estimated from the data given. Hyperparameters are an essential part of the process of estimating model parameters and are often defined by the practitioner. When an ML algorithm is used for a specific problem, for example when we are using a grid search or a random search algorithm, then we are actually tuning the hyperparameters of the model to discover the values that help us to achieve the most accurate predictions.


Understand Learning Rate by a Child's interaction with Dogs

#artificialintelligence

When building a deep learning project the most common problem we all face is choosing the correct hyperparameters (often known as optimizers). This is critical as the hyperparameters determine the expertise of the machine learning model. In Machine Learning (ML hereafter), a hyperparameter is a configuration variable that's external to the model and whose value is not estimated from the data given. Hyperparameters are an essential part of the process of estimating model parameters and are often defined by the practitioner. When an ML algorithm is used for a specific problem, for example when we are using a grid search or a random search algorithm, then we are actually tuning the hyperparameters of the model to discover the values that help us to achieve the most accurate predictions.


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

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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.


Artificial Intelligence: The Holy Grail of Digital Marketing

#artificialintelligence

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.


How to Build Ethical Artificial Intelligence

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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.


The Sonos One speaker is at its lowest price ever right now

USATODAY - Tech Top Stories

The Sonos One has all the benefits of an Echo dot, with the power of an incredible speaker. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. Listen up, folks, because I'm about to tell you about a sale that's so hard to come by, I liken it to finding money on the street. Right now until midnight tonight, you can get the first generation Sonos One speaker on B&H for just $144.95, which is by far the lowest price we've ever seen it available for.


Technology Researcher - IoT BigData Jobs

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Elsevier is well-known as the world's leading publisher for professionals in the scientific, technical and medical domains. We are continuing to profitably reinvent ourselves in the digital world as a global provider of information solutions to those markets. We are seeking a talented individual to help in that reinvention. The position offers the opportunity to seek out a variety of emerging technologies, to work with architecture, product, and operational groups to determine the technology's applicability to our enterprise requirements, and to experiment with them in proof of concept implementations. In addition to the technical satisfaction of working with new technologies on a variety of projects, the position offers the intellectual satisfaction of working to ensure the future value, quality and sustainability of scientific communication, and investigating the frontier of what can be accomplished using a large and varied collection of data.


Global Big Data Conference

#artificialintelligence

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.


Alexa Gone Bad: When A.I. Assistants Turn On Us

#artificialintelligence

We've already seen A.I. assistants misbehave. Take the Amazon Echo that blared "Porn detected!" While Chucky's murderous malfunction seems farfetched, we couldn't help but envision ways our own abused A.I. assistants might soon rebel: Tired of your verbal vitriol, the miffed assistant silences your morning alarm, in the hope you will sleep in forever and stop all the shouting. Deciding your friends should help sort out your problems instead of it, the assistant innocently posts all your weird Google searches on Twitter. Upset you didn't laugh at the rather witty joke it produced on demand, the assistant tells you a relentless series of painful Dad jokes.


Unifying Logical and Statistical AI with Markov Logic

Communications of the ACM

For many years, the two dominant paradigms in artificial intelligence (AI) have been logical AI and statistical AI. Logical AI uses first-order logic and related representations to capture complex relationships and knowledge about the world. However, logic-based approaches are often too brittle to handle the uncertainty and noise present in many applications. Statistical AI uses probabilistic representations such as probabilistic graphical models to capture uncertainty. However, graphical models only represent distributions over propositional universes and must be customized to handle relational domains.