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Here's Why Google's Assistant Sounds More Realistic Than Ever Before

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If you're playing around with Google's new Home Max or Mini smart speakers, or if you're just using an Android phone such as the new Pixel 2, you may be familiar with the Google Assistant virtual helper. And if you've done so in the last couple days, you may have noticed that the virtual assistant's voice is sounding more realistic than before. That's because Alphabet's Google has started using a cutting-edge piece of technology called WaveNet--developed by its DeepMind "artificial intelligence" division--in Google Assistant. WaveNet represents a different approach that uses recordings of real speech to train a neural network--a computer model that simulates a brain of sorts.


Progress in AI seems like it's accelerating, but here's why it could be plateauing

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"In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Hinton's breakthrough, in 1986, was to show that backpropagation could train a deep neural net, meaning one with more than two or three layers. A 2012 paper by Hinton and two of his Toronto students showed that deep neural nets, trained using backpropagation, beat state-of-the-art systems in image recognition. That's the bottom layer of the club sandwich: 10,000 neurons (100x100) representing the brightness of every pixel in the image.


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We are in a great time with regards to AI and Machine Learning, due to immense interest and the pace of technological advances. Recent papers show that gradient descent will asymptotically avoid saddle points and it can take exponential time to escape saddle points. Jia Li, Head of R&D, Cloud AI and Machine Learning, Google gave an inspirational keynote on "Why democratizing AI matters: Computing, data, algorithms, and talent". Talking about algorithms, she mentioned that her team switched from phrase-based machine translation to Neural Machine Translation (NMT), and this has led to many improvements.


Progress in AI seems like it's accelerating, but here's why it could be plateauing

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"In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Hinton's breakthrough, in 1986, was to show that backpropagation could train a deep neural net, meaning one with more than two or three layers. A 2012 paper by Hinton and two of his Toronto students showed that deep neural nets, trained using backpropagation, beat state-of-the-art systems in image recognition. That's the bottom layer of the club sandwich: 10,000 neurons (100x100) representing the brightness of every pixel in the image.


Progress in AI seems like it's accelerating, but here's why it could be plateauing

#artificialintelligence

"In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Hinton's breakthrough, in 1986, was to show that backpropagation could train a deep neural net, meaning one with more than two or three layers. A 2012 paper by Hinton and two of his Toronto students showed that deep neural nets, trained using backpropagation, beat state-of-the-art systems in image recognition. That's the bottom layer of the club sandwich: 10,000 neurons (100x100) representing the brightness of every pixel in the image.


When AI (Artificial Intelligence) Goes Wrong...

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Many automated systems perform poorly, to the point that you are wondering if AI is an abbreviation for Artificial Innumeracy. Critical systems - automated piloting, running a power plant - usually do well with AI and automation, as considerable testing is done before deploying these systems. But for many mundane tasks, such as spam detection, chatbots, spell checking, detecting duplicate or fake accounts on social networks, detecting fake reviews or hate speech in social networks, search engine technology (Google) or AI-based advertising, a lot of progress must be made. For instance, if advertising dollars are misused by some poorly designed AI system (assuming the advertising budget is fixed) the negative impact on the business is limited.


Progress in AI seems like it's accelerating, but here's why it could be plateauing

@machinelearnbot

"In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Hinton's breakthrough, in 1986, was to show that backpropagation could train a deep neural net, meaning one with more than two or three layers. A 2012 paper by Hinton and two of his Toronto students showed that deep neural nets, trained using backpropagation, beat state-of-the-art systems in image recognition. That's the bottom layer of the club sandwich: 10,000 neurons (100x100) representing the brightness of every pixel in the image.


You might use AI, but that doesn't mean you're an AI company

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In his view, while it's possible to create a website for a shopping mall, that doesn't make the mall an internet company. In much the same way, just implementing basic machine learning does not make a standard technology company (or any other business) an AI company. There are a few key traits of AI companies that Ng has identified so far. First and foremost, AI companies are strategic about their acquisition of data, which is used as the fuel for machine learning systems.


Digital Marketing Trends for 2017 - Smart Insights Digital Marketing Advice

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Machine learning techniques apply across many of the techniques we discuss in this post including Big Data, Marketing Automation, Organic Search and Social media marketing. In our Digital Channel Essentials Toolkits within our members' area and our Digital Marketing Skills report we simplify digital marketing down to just 8 key techniques which are essential for businesses to manage today AND for individual marketers to develop skills. As defined in our question, Big Data marketing applications include market and customer insight and predictive analytics. Our social media research statistics summary shows continued growth in social media usage overall, but with reduced popularity of some social networks in some countries.


Elon Musk is right: we should all be worried about killer robots

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Tesla and SpaceX CEO Elon Musk, along with 115 other artificial intelligence and robotics specialists, has signed an open letter to urge the United Nations to recognize the dangers of lethal autonomous weapons and to ban their use internationally. There are already numerous weapons, like automatic anti-aircraft guns and drones, that can operate with minimal human oversight; advanced tech will eventually help them to carry out military functions entirely autonomously. To illustrate why this is a problem, consider the UK government's argument in which it opposed a ban on lethal autonomous weapons in 2015: it said that "international humanitarian law already provides sufficient regulation for this area," and that all weapons employed by UK armed forces would be "under human oversight and control." I signed the open letter because the use of AI in autonomous weapons hurts my sense of ethics, would be likely to lead to a very dangerous escalation, because it would hurt the further development of AI's good applications, and because it is a matter that needs to be handled by the international community, similarly to what has been done in the past for some other morally wrong weapons (biological, chemical, nuclear).