After a decade of stop-and-go development, Artificial Intelligence has now begun to provide real, tangible value to the business world. McKinsey published an 80-page report titled "Artificial Intelligence: The Next Digital Frontier?" which provides a comprehensive analysis of the value that Artificial Intelligence (AI) creates for businesses.

The report points out that "wide application of Artificial Intelligence technology will bring great returns to businesses." This means that the disruptive nature of AI will continue to become more apparent in the future. Governments, enterprises, and developers should all be clear on this point. Moreover, the report raises some interesting points (all of which we will discuss later in this article):


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For digital marketing enthusiasts, the theory that chatbots will probably overtake mobile apps soon is no shock. It is expected that chatbots will replace mobile apps as a medium of client engagement by 2020. According to a Gartner’s report, 85% of the interactions that happen between companies and their customers will soon be happening without any human intervention.

A chatbot, as we know, is a software program that conducts a conversation through a voice or text medium. Chatbots are a reasonably new phenomenon. Since 2016, chatbots started surfacing as a method of handling customer interactions in an automated manner. As different domains within any organization see the implementation of various automation tools, client servicing, and support has not been left behind either.


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Posting of projects sources by Microsoft is a good reason to perform their analysis. This time is no exception, and today, we will look at suspicious places found in Infer.NET code.

Briefly About the Project and the Analyzer

Infer.NET is a Machine Learning system developed by Microsoft specialists. Project source code has become recently available on GitHub, which gave rise to its check. More details about the project can be found here.


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Deep Learning has spurred interest in novel floating point formats. Algorithms often don’t need as much precision as standard IEEE-754 doubles or even single precision floats. Lower precision makes it possible to hold more numbers in memory, reducing the time spent swapping numbers in and out of memory. Since this where a lot of time goes, low precision formats can speed things up quite a bit.

Here I want to look at bfloat16, or BF16 for short, and compare it to 16-bit number formats I’ve written about previously, IEEE and posit.


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Introduction

The goal of this article is to explain how you can detect a drowsy person using facial landmarks as an input of a neural network, a 3D convolutional neural network, in this case, to sound an alarm to awake the user and to prevent some kind of accident.

The idea is to extract a group of frames from a webcam and then extract from them the facial landmarks, specifically the position of both eyes, then pass these coordinates to the neural model to get a final classification which will tell us if the user is awake, or falling sleep.


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It was great speaking with Michael Berthold, Founder and CEO at KNIME during their fall summit. Michael created KNIME after seeing all of the great data pharmaceutical companies were generating but also seeing the difficulty they had garnering insights due to the challenges of massaging and analyzing the data.

KNIME is an open platform that enables organizations to put their data to good use. Open data science platforms enable:


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Despite objections from employees, law enforcement officers, and the ACLU, Amazon Web Services announced last Thursday that it would continue to sell facial recognition software, the AWS Rekognition system.

In an all-hands meeting on Thursday, AWS CEO, Andrew Jassy, explained their reasons for continuing to sell Rekognition to law enforcement, saying, "Rekognition is actively been used to help stop human trafficking, to reunite missing kids with parents for educational applications, for security and multi-factor authentication to prevent theft."


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It’s well known that user reviews are a fundamental part of the web economy, with consumers tending to trust the word of their fellow customer above any other form of marketing. Making sure those reviews are truthful and authentic therefore is key, especially as unscrupulous vendors are happy to produce fake reviews to puff up their service. A recent study from Aalto University highlighted how it’s increasingly possible to create fake reviews autonomously.

The authors state that around 40% of us make a decision based upon the feedback received from other people, whilst a good review can boost sales by around 30%. In other words, they’re important to the success of a product, which creates an incentive to create fake reviews to boost your ratings. Are AI-driven technologies increasingly capable of producing accurate reviews?


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Data Science, Machine Learning, Deep Learning, and Artificial Intelligence are really hot at this moment and offering a lucrative career to programmers with high pay and exciting work. It’s a great opportunity for programmers who are willing to learn these new skills and upgrade themselves. It’s also important from the job perspective because Robots and Bots are getting smarter day by day, thanks to these technologies and most likely will take over some of the jobs which many programmers do today. Hence, it’s important for software engineers and developers to upgrade themselves with these skills. Programmers with these skills are also commanding significantly higher salaries as data science is revolutionizing the world around us. Machine Learning specialist is one of the top paid technical jobs in the world. However, most developers and IT professionals are yet to learn these valuable set of skills.

For those, who don’t know what is a Data Science, Machine Learning, or Deep Learning, they are very related terms with all pointing towards machine doing jobs which is only possible for humans till date and analyzing the huge set of data collected by modern day application.


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Technology can be a blessing in disguise. It is especially true in the modern classroom. While the Internet and a host of multimedia tech do make it easier to perform various tasks and deliver educational content, there are always concerns about its overall effectiveness in learning.

In a world where most interactions either begin or, at a minimum, include the use of screens and online content, teachers and students have two choices: avoid the tech and leave it out of the process or embrace it and develop ways to use it to the student’s advantage.


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