AI solves Rubik's Cube in fraction of a second - smashing human record

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The human record for solving a Rubik's Cube has been smashed by an artificial intelligence. The bot, called DeepCubeA, completed the popular puzzle in a fraction of a second - much faster than the quickest humans. While algorithms have previously been developed specifically to solve the Rubik's Cube, this is the first time it has done without any specific domain knowledge or in-game coaching from humans. It brings researchers a step closer to creating an advanced AI system that can think like a human. "The solution to the Rubik's Cube involves more symbolic, mathematical and abstract thinking," said senior author Professor Pierre Baldi, a computer scientist at the University of California, Irvine.


Machine Learning in Java - Programmer Books

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As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data.


Elon Musk's Neuralink looks to begin outfitting human brains with faster input and output starting next year – TechCrunch

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Neuralink, the Elon Musk-led startup that the multi-entrepreneur founded in 2017, is working on technology that's based around'threads' which it says can be implanted in human brains with much less potential impact to the surrounding brain tissue vs. what's currently used for today's brain-computer interfaces. "Most people don't realize, we can solve that with a chip," Musk said to kick off Neuralink's event, talking about some of the brain disorders and issues the company hopes to solve. Musk also said that long-term Neuralink really is about figuring out a way to "achieve a sort of symbiosis with artificial intelligence." "This is not a mandatory thing," he added. "This is something you can choose to have if you want."


Thought AIs could never replace human imagination? Think again

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This AI, meanwhile, simulates day from a night picture. This is valuable, as creating self-driving cars that work and can locate themselves precisely in all conditions - day, night, fog, rain, snow and so on - requires a lot of data that covers all scenarios. Collecting large amounts of data in all conditions is practically very difficult, as certain conditions (such as snow) occur very rarely in some areas. Instead of collecting more data, scientists have come up with this night-to-day workaround. This could also lead to better night vision for the military, airplane pilots and human drivers.


The CompassIntel 2019 A-List in AI Chipset Index Executive Brief

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The overall AI chipset market for 2019 is estimated at $10 billion USD. Nvidia leads the overall AI chipset segment with 38% market share by extending its GPU capabilities from focusing mostly on gaming to cloud datacenter infrastructure.


Neuralink: Elon Musk unveils brain microchip to let humans 'merge with computers'

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Elon Musk has unveiled plans to implant computer chips in people's brains that the US billionaire says will treat brain diseases and enable superhuman intelligence. Neuralink, a secretive company set up by Mr Musk two years ago, has said it plans to begin tests of its "brain-computer interface" technology on humans in the next year. Mr Musk, 48, the chief executive of Tesla and SpaceX, said the technology will help "solve brain disorders of all kinds" and allow humans to merge with artificial intelligence. It has so far been tested on monkeys and rats, he said. The tiny chip, which measures 4x4mm, is connected to a thousand microscopic threads that enter the brain through four holes drilled in the skull.


Text Analytics: the convergence of Big Data and Artificial Intelligence

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The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers-- comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain--s representation, and many more.


The Real World Potential and Limitations of Artificial Intelligence - By Khushi Kaur

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No longer does artificial intelligence only exist in sci-fi movies and books about dystopian futures. It's in the here and now, continuously transforming the way in which we live and work. Many of us interact with AI on a daily basis - we call on Siri to give us directions to nearby coffee shops or ask Alexa to order us goods on Amazon. AI is also seamlessly supplementing and enhancing operations across a variety of industries and increasingly disrupting internal company functions. However, at the same time, it's also becoming more and more apparent where AI still has limitations that prevent it from fully replicating human behavior.


Machine Learning: What it is and why it matters

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Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either "F" (failed) or "R" (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.


How AI Can Help Marketers Harness Big Data Opportunities in 2019 - insideBIGDATA

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In this special guest feature, Solomon Thimothy, CEO of DMA Digital Marketing Agency, believes that digital marketing advancements in 2018 have set a high bar for customer expectations. Customers now expect, deserve and demand that personalized, seamless transactions will only increase in 2019. By focusing on data-based AI solutions, organizations can ensure the customer journey will be more personalized and more profitable in the year to come. Solomon focuses his expertise and passion in helping businesses invest in long-term digital marketing for financial growth. His education from Northeastern Illinois University and North Park University provide him with the tools needed to lives up to its digital marketing commitments.