AI: How we arrived at the 4th industrial revolution


You could be forgiven for wondering why AI is so big all of a sudden. Hasn't humankind been dreaming about human-like robots for a long time? The first Star Wars film (with crowd-pleasing'droids' R2D2, C-3PO) was released in 1977; Terminator (starring Arnold Schwarzenegger as a cyborg assassin) was a massive success in the mid -1980s, a few years after Blade Runner (starring synthetic – or not? The idea of an intelligent machine is not exactly a new one, yet our ability to create something with Artificial Intelligence has increased dramatically in the last decade or so. There is now scope to use AI to make legal assessments, create games, predict purchases, navigate through traffic, translate words into different languages and diagnose diseases.

Hearst Newspapers Case Study Google Cloud Platform


Instead of hiring a larger team, Hearst Newspapers is solving the problem with Google Cloud AI. Using Google Cloud Natural Language API to enable content classification with powerful machine learning models in an easy-to-use REST API, Hearst Newspapers can understand what its content is about, regardless of how it is structured and presented on the company's many websites. Although Hearst Newspapers previously used a legacy system that attempted to automate the classification process, it was not as fast or as accurate. "Google Cloud Natural Language API is unmatched in its accuracy for content classification," says Naveed Ahmad, Senior Director of Data at Hearst Newspapers, who is responsible for data centralization and business intelligence using Google Cloud Platform. At Hearst Newspapers, we publish several thousand articles a day across more than 30 properties.

ELIZA - Wikipedia


ELIZA is an early natural language processing computer program created from 1964 to 1966[1] at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum.[2] Created to demonstrate the superficiality of communication between humans and machines, Eliza simulated conversation by using a'pattern matching' and substitution methodology that gave users an illusion of understanding on the part of the program, but had no built in framework for contextualizing events.[3] Directives on how to interact were provided by'scripts', written originally in MAD-Slip, which allowed ELIZA to process user inputs and engage in discourse following the rules and directions of the script. The most famous script, DOCTOR, simulated a Rogerian psychotherapist and used rules, dictated in the script, to respond with non-directional questions to user inputs. As such, ELIZA was one of the first chatterbots, but was also regarded as one of the first programs capable of passing the Turing Test.[clarification needed] ELIZA's creator, Weizenbaum regarded the program as a method to show the superficiality of communication between man and machine, but was surprised by the number of individuals who attributed human-like feelings to the computer program, including Weizenbaum's secretary.[2] Many academics believed that the program would be able to positively influence the lives of many people, particularly those suffering from psychological issues and that it could aid doctors working on such patients' treatment.[2][4]

Why the 'Last Jedi' Rotten Tomatoes audience score is way below its critics score


If you ask the critics, The Last Jedi is an unequivocal win for the Star Wars franchise. It's stunning, it's heartbreaking, it's cute af and clever to boot. If you ask the fans who aren't critics? Well ... that's where things get complicated. SEE ALSO: Critics swoon over'Star Wars: The Last Jedi' As of writing, The Last Jedi has a 93% fresh rating from critics on Rotten Tomatoes – not quite as high as we predicted, but solid nonetheless, and on a par with its the last chapter in this story, The Force Awakens.

AI and machine learning: Looking beyond the hype


In every federal agency, critical insights are hidden within the massive data sets collected over the years. But because of a shortage of data scientists in the federal government, extracting value from this data is time consuming, if it happens at all.

Optimizing Government The Regulatory Review


The Optimizing Government Project brings together scholars and researchers to discuss the use of machine learning by government. In recent years, the private sector has succeeded in finding many ways to leverage machine learning--a type of artificial intelligence that enables computers to "learn and adapt through experience." Well-known private sector applications of machine learning include Google's self-driving car project, online recommendations personalized for customers on websites like Amazon and Netflix, and fraud detection by credit card companies. But as the private sector embraces machine learning in new ways, the application of machine learning by government agencies has only started to take root. The use of artificial intelligence by government, though, raises important questions for a democratic society--about fairness, equality, transparency, and accountability.

These are the 10 best tech deals on Amazon for Free Shipping Day


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. The holidays are underway, and Christmas is inching closer and closer. If you've still got shopping to do, Free Shipping Day is the perfect time to get a lot done. But there's not a lot that's actually on sale, at least not a lot of gift-worthy stuff.

AI in 2017 can't nearly match the smarts of 'Star Wars' droids — it barely understands us


At the Star Wars: The Last Jedi Hollywood premiere this week, Radiohead frontman Thom Yorke sat down cross-legged on the red carpet to speak with BB-8, an intelligent robotic character from the Star Wars galaxy. He looked intently at the robot and pointed his finger as he spoke, as if in animated conversation with the famous space droid. This was, of course, a light mockery of modern robots. Machines -- while capable of extremely impressive automated feats -- lack true intellectual and emotional development. Still, in the past year, we were inundated with reports of artificial intelligence seeping into our homes and cars.

Accenture Uses Artificial Intelligence to Help the Elderly Better Navigate Their Care and Improve Their Well-Being


Accenture Uses Artificial Intelligence to Help the Elderly Better Navigate Their Care and Improve Their Well-Being LAS VEGAS and LONDON; Nov. 28, 2017 – Accenture (NYSE: ACN) has completed a pilot program that uses artificial intelligence (AI) and the ease of voice to help older people manage the daunting challenges of navigating their care delivery and well-being. The Accenture Liquid Studio in London developed an AI-powered platform (the Accenture Platform) that can learn user behaviors and preferences and suggest activities to support the overall physical and mental health of individuals ages 70 and older. The Accenture platform, which runs on the Amazon Web Services (AWS) cloud, includes a'Family and Carer' portal that lets family and caregivers check on the individual's daily activities, such as whether they have taken their medication or made new requests for caregivers. The Accenture platform can also spot abnormalities in behavior and alert family or friends, based on user defined permissions. Elder Care Pilot: Accenture Uses AI to Help Navigate Elder Care Other services provided by the Accenture platform helped participants find local events as well as potential new friends, encouraging them to become more active and social.

Using Word2vec for Music Recommendations – Towards Data Science


Streaming services have changed the way in which we experience content. While recommendation systems previously focused on presenting you with content you might want to purchase for later consumption, modern streaming platforms have to focus instead on recommending content you can, and will want to, enjoy in the moment. Since any piece of content is immediately accessible, the streaming model enables new methods of discovery in the form of personalized radios or recommendation playlists, in which the focus is now more on generating sequences of similar songs that go well together. With now over 700 million songs streamed every month, Anghami is the leading music streaming platform in the MENA region. What this also means, is that the amount of data generated by all those streams proves to be an invaluable training set that we can use to teach machine learning models to better understand user tastes, and improve our music recommendations.