As we live longer and technology continues its rapid arc of development, we can imagine a future where machines will augment our human abilities and help us make better life choices, from health to wealth. Instead of conducting a question and answer with a device on the countertop, we will be able to converse naturally with our virtual assistant that is fully embedded in our physical environment. Through our dialogue and digital breadcrumbs, it will understand our life goals and aspirations, our obligations and limitations. It will seamlessly and automatically help us budget and save for different life events, so we can spend more time enjoying life's moments. While we can imagine this future, the technology itself is not without challenges -- at least for now.

"AI is being fed directly into the bloodstream of society, and in many cases without sufficient checks and balances," says Kate Crawford, a professor and cofounder of New York University's AI Now, the world's first academic research institute dedicated to the social impact of artificial intelligence. Last year, Crawford partnered with data-viz guru Vladan Joler to create "Anatomy of an AI System," a map and research paper demonstrating the real-world consequences of developing and manufacturing the Amazon Echo. The paper highlights the radical differences in income distribution between Amazon executives and the workers who enable its vast infrastructure, as well as its devastating environmental impacts. The project has been exhibited at museums around the world, and Crawford has presented it to leaders in France, Germany, Spain, and Argentina.

In September 1955, John McCarthy, a young assistant professor of mathematics at Dartmouth College, boldly proposed that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." McCarthy called this new field of study "artificial intelligence," and suggested that a two-month effort by a group of 10 scientists could make significant advances in developing machines that could "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." At the time, scientists optimistically believed we would soon have thinking machines doing any work a human could do. Now, more than six decades later, advances in computer science and robotics have helped us automate many of the tasks that previously required the physical and cognitive labor of humans. But true artificial intelligence, as McCarthy conceived it, continues to elude us.

Mobile maps route us through traffic, algorithms can now pilot automobiles, virtual assistants help us smoothly toggle between work and life, and smart code is adept at surfacing our next our new favorite song. But AI could prove dangerous, too. Tesla CEO Elon Musk once warned that biased, unmonitored and unregulated AI could be the "greatest risk we face as a civilization." Instead, AI experts are concerned that automated systems are likely to absorb bias from human programmers. And when bias is coded into the algorithms that power AI it will be nearly impossible to remove.

Google has unveiled updates for its artificially intelligent voice assistant and new privacy tools to give people more control over how they're being tracked on the go or at home. The company also unveiled a new Pixel phone and smart home display. Google just made ordering pizza, pad thai and fried chicken from your favorite restaurants even easier. The search giant announced on Thursday that it updated apps like Google Maps, Google Search and the Google Assistant to make ordering food online more convenient, so you don't have to download as many third-party apps. "When I was pregnant with my son last year, my cravings were completely overpowering," said Google's senior product manager of food ordering, Anantica Singh, in a blog post.

When we program morality into robots, are we doomed to disappoint them with our very human ethical inconsistency? In his newest novel, Machines Like Me, the British writer Ian McEwan takes on the rise of artificial intelligence, exploring how humans would react to the creation of perfectly human-like robots. But he does it in his particularly McEwan-esque fashion. Rather than creating a work of speculative fiction set in a near future where general artificial intelligence has become a reality, McEwan sets Machines Like Me in an alternate past, a 1980s London where the internet already exists and the English A.I. pioneer Alan Turing -- who in our world was effectively hounded to death by government authorities in 1954 because of his homosexuality -- instead lived and went on to create the first true A.I.: a collection of androids for purchase called Adams and Eves. Why paranoia about digital voice assistants is overblown.

Are you interested in machine learning and its applications? If you're looking to learn something new and great, how about a machine learning recommendation system? Well, don't be scared or blown away with those massive terms and words, they just tell someone what exactly some code does. Machine learning has always been in the news, whether for taking away jobs or making a robot understand and reply to a human. But what exactly can we do with machine learning?

Are you interested in machine learning and its applications? If you're looking to learn something new and great, how about a machine learning recommendation system? Well, don't be scared or blown away with those massive terms and words, they just tell someone what exactly some code does. Machine learning has always been in the news, whether for taking away jobs or making a robot understand and reply to a human. But what exactly can we do with machine learning?

"While such phrasings may be natural for a user, current speech processing systems are not configured to handle commands that are not preceded by a wakeword," wrote the patent application's authors, Kurt Wesley Piersol and Gabriel Beddingfield. "Offered is a system to correct this problem." In a statement, an Amazon spokesperson told BuzzFeed News, "The technology in this patent is not in use, and referring to the potential use of patents is highly speculative." The spokesperson added that Amazon files many patent applications that are not ultimately implemented into consumer-facing products, and that patents do not necessarily reflect "current or near-future states of products and services." According to the patent application, after a wake word is detected, Alexa may "look backwards" to determine if the command came before the wake word, and use pauses in speech to identify the beginning of the command.

Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information. Many news articles describe the occurrence of specific events and named entities including people, places or objects. In this paper, we propose a graph traversal algorithm as well as a novel weighting scheme for cold-start content based news recommendation utilizing these named entities. Seeking to create a higher degree of user-specific relevance, our algorithm computes the shortest distance between named entities, across news articles, over a large knowledge graph. Moreover, we have created a new human annotated data set for evaluating content based news recommendation systems. Experimental results show our method is suitable to tackle the hard cold-start problem and it produces stronger Pearson correlation to human similarity scores than other cold-start methods. Our method is also complementary and a combination with the conventional cold-start recommendation methods may yield significant performance gains. The dataset, CNRec, is available at: https://github.com/kevinj22/CNRec