Complete Machine Learning and Data Science: Zero to Mastery

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Council Post: Four Ways To Use AI For Marketing

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When you hear the phrase "artificial intelligence," what's the first thing that comes to mind? Depending on how you've seen AI, you might see it as a beautiful or scary thing. As the CEO of a startup that's built an AI-based marketing software tool, I believe it's the former. In fact, according to a 2019 study, "40% of marketing and sales teams say data science encompassing AI and machine learning is critical to their success as a department." It's easy to see why.


The Coming Era of Decision Machines

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These concerns have been present whenever we make important decisions. What's new is the much, much larger scale at which we now rely on algorithms to help us decide. Human errors that may have once been idiosyncratic may now become systematic. "Artificial intelligence is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals," wrote Harvard University Professor David Parkes in "A Responsibility to Judge Carefully in the Era of Decision Machines," an essay recently published as part of Harvard's Digital Initiative. "Machines need to be able to predict to decide, but decision making requires much more," he wrote.


Top 14 AI Use Cases: Artificial Intelligence in Smart Cities 7wData

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Thanks to the advent of the latest innovations in Artificial Intelligence (AI) and machine learning (ML), smart cities -- with a specific focus on the utilities sector -- are undergoing unprecedented changes. The Capgemini Research Institute estimated that, together with the energy sector, the utility vertical can save between $237 billion to $813 billion USD from intelligent automation at scale. Utility companies have been experimenting with AI use cases such as predictive maintenance, yield optimization, and demand/load forecasting. In 2019, more than half of energy and utilities organizations have deployed at least one practical implementation of AI technology, reaping its consistent benefits. Even the public seems eager to enjoy the positive innovations brought forward by the AI transformation.


Genius triumphs: Japanese mathematician's solution to number theory riddle validated

The Japan Times

KYOTO – A proof by mathematician Shinichi Mochizuki of a major conundrum in number theory that went unresolved for over 30 years has finally been validated, Kyoto University said Friday following a controversy over his method, which was often labeled too novel or complicated to understand. Accepted for publication by the university's Research Institute for Mathematical Sciences was Mochizuki's 600-page proof of the abc conjecture, which provides immediate proofs for many other famous mathematical problems, including Fermat's last theorem, which took almost 350 years to be demonstrated. The abc conjecture, proposed by European mathematicians in 1985, is an equation of three integers a, b, and c composed of different prime numbers, where a b c, and describing the relationship between the product of the prime numbers and c. "There are a number of new notions and it was hard to understand them," Masaki Kashiwara, head of the team that examined the professor's theory, said at a news conference. He proved the abc conjecture with a "totally new, innovative theory," said fellow professor Akio Tamagawa. "His achievement creates a huge impact in the field of number theory."


Kyocera plans health-analysis device based on odor of feces

The Japan Times

Kyocera Corp. has started developing a device to check human health and immunity from the odor of one's stool, aiming to put it into practical use in three years. In collaboration with AuB Inc., a Tokyo-based startup, Kyocera will analyze data from the device, which will be installed in toilet seats. The Kyoto-based electronics giant will create a system that infers the intestinal environment of the user with the aid of artificial intelligence technology and data collected by AuB, according to Kyocera officials. Kyocera will deliver the results to clients through a smartphone application and propose measures to improve diet and other elements of their lives to improve health, the officials said. As part of the development process, AuB will gather stool samples from 29 players of a youth team belonging to Kyoto Sanga F.C., a professional soccer team.


LoCoQuad: An arachnoid-inspired robot for research and education purposes

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Machine learning techniques, such as reinforcement learning models, are now playing a crucial role in the development of smart and efficient robots.


6 Issues Marketers Need to Consider for Successful AI Implementations

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Despite the many unanswered questions that remain about the use of artificial intelligence (AI) in the workplace and in customer-facing and servicing departments, the growth of AI appears unstoppable. Even as early as two years ago, research from the UK-based digital marketing agency Big Rock found after interviewing 100 senior marketers globally, that AI applications, even at that stage had become one of the marketing departments mainstays. The interviews showed -- again at that stage -- that 55% of companies were either currently implementing or actively investigating some form of AI initiative within their marketing practices. Meaning, AI was already shaking things up in the industry. Unsurprisingly, the research read, this inevitable rise of AI technologies in marketing is causing a major shift in the way companies work.


5 More Things Business Leaders Need to Know About Machine Learning

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In a previous blog post, we explored the importance of machine learning (ML) and delved into the five most important things that business leaders need to know about ML. First, recall that supervised learning is concerned with the prediction and classification of data. Now it's time to dive deeper. We saw that accuracy (the percentage of your data that your model predicts/classifies correctly) is not always the best metric to measure the success of your model, such as when your classes are imbalanced (for example, when 99% of emails are spam and 1% non-spam). Another space where metrics such as accuracy may not be enough is when you need your model to be interpretable.


Edge computing environments: what you need to know

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The saying goes: "If you're not on the edge, you're taking up too much space". And compute itself is now moving to the edge, forcing datacentre operators to wring the last drops of productivity from their infrastructure, ahead of a future supporting multi-sensor internet of things (IoT) devices over 5G for machine learning, and even artificial intelligence (AI). Jennifer Cooke, research director of cloud-to-edge datacentre trends at IDC, says datacentre operators need to start thinking about how many systems they will need to roll out, and the people they will need to support them. "Cost becomes the prohibitive factor," she says. Edge will take different forms.