We have, up to this point, had decades of benefits accrued through computing -- but what innovations will push the envelope for our seniors? Artificial intelligence (AI) is the information of science. AI is misunderstood and glamorized by some, making it out to be something other than what it really is. It gives us the ability to use computers to perform tasks that usually require human intelligence. What was dubbed "artificial" is coming to be more appropriately thought to be cognitive intelligence, giving us tremendous benefits in all fields including aging, health, and safety.

The Human Choice and Computers conference is taking place in Manchester, UK and exploring themes related to technology and human intimacy. Experts from the fields of psychology, human behavior and technology are gathering to discuss the impact of tech innovations on wider society. When it comes to the emergence of AI (artificial intelligence) and "robot dolls" in particular, it is said that "robot love" could become addictive enough for it to even be able to overtake human-on-human intimacy. The fear of "The Robots Taking Over" has been well documented, and even world renowned scientist Stephen Hawking once said that "the development of full artificial intelligence could spell the end of the human race," and with any diminishing impact on real human contact, this could literally be the case. In a world where robot technology is able to achieve the same or even better results than a human can, then in the case of robot sex dolls, it could eventually lead to many also deeming amorous encounters with a robot as being better than that with another human being. Robot encounters will certainly require little to no emotional attachments, and no requirement to be rated in terms of ability.

The idea that we have brains hardwired with a mental template for learning grammar--famously espoused by Noam Chomsky of the Massachusetts Institute of Technology--has dominated linguistics for almost half a century. Recently, though, cognitive scientists and linguists have abandoned Chomsky's "universal grammar" theory in droves because of new research examining many different languages--and the way young children learn to understand and speak the tongues of their communities. That work fails to support Chomsky's assertions. The research suggests a radically different view, in which learning of a child's first language does not rely on an innate grammar module. Instead the new research shows that young children use various types of thinking that may not be specific to language at all--such as the ability to classify the world into categories (people or objects, for instance) and to understand the relations among things. These capabilities, coupled with a unique hu man ability to grasp what others intend to communicate, allow language to happen. The new findings indicate that if researchers truly want to understand how children, and others, learn languages, they need to look outside of Chomsky's theory for guidance. This conclusion is important because the study of language plays a central role in diverse disciplines--from poetry to artificial intelligence to linguistics itself; misguided methods lead to questionable results.

Researchers propose an emotion test for artificially intelligent systems. Furby and Clippy were early forms; driverless cars and Facebook's chatbots pick up the mantle today. But if AI is to continue its evolution, it'll have to get more convincingly human. Right now, its capacity for emotional depth is seriously lacking. At a cognitive architectures conference in New York last week, Alexei Samsonovich, a professor in the Cybernetics Department at the Moscow Engineering Physics Institute, proposed a multi-part test.

Darktrace, the U.K. cybersecurity startup whose backers include Autonomy founder Mike Lynch's Invoke Capital, has closed 65 million in fresh funding. The new round was led by global investment firm KKR, with participation from existing investor Summit Partners and new investors TenEleven Ventures and SoftBank. I understand the new investment gives the 2013-founded company a valuation of more than 400 million, while in a call Lynch told me the new backing was a typical growth round and will be used for further international expansion and for R&D. In particular, he said Darktrace is developing technology to help companies respond to not only human-written cyberattacks but also the pending threat of machine-learning-based attacks that, in classic sci-fi-comes-true-fashion, will increasingly see AI battling it out with AI on behalf of the good and bad folks, respectively. But let's step back and take a look at what Darktrace offers today.

DataRobot automates the data science process. The system lets you upload your data and tell it what you want to predict. It automatically and instantly creates hundreds of models. Earlier this year, the company raised 33 million in a round led by New Enterprises Associates that included Intel Capital, Accomplice, and IA Ventures.

If you're experiencing a mental health issue, one of the people you probably least want to speak to about it is your employer. Disclosing depression or anxiety has long been seen as the last workplace taboo, for fear of repercussions. This is despite the existence of the Equality Act 2010, which protects employees with physical and mental disabilities from discrimination. But just over a third of workers with a mental health condition discuss it with their employer, according to a survey of 1,388 employees carried out by Willis PMI Group, one of the UK's largest providers of employee healthcare and risk management services. The research found that 30% of respondents were concerned that they wouldn't receive adequate support, 28% believed their employer wouldn't understand, and 23% feared that disclosing it would lead to management thinking less of them.

When working with data, machine learning can be used to do incredible things, including predicting future events. Its ease of use combined with the power of scikit-learn is causing Python to become the preferred development language for many machine learning practitioners. In this course, Understanding Machine Learning with Python, you will learn how Python developers and data scientists use machine learning to predict the likelihood of events based on data. Throughout this course, you will use Python and the scikit-learn library. Specifically, you will learn how to format your problem to be solvable, how to prepare your data for use in a prediction, and finally how to combine that data with algorithms to create models that can predict the future, all performed in the Jupyter Notebook environment. By the end of this course, you will have a better understanding of how machine learning can help you put your data to good use in predicting future events, and you'll also know how to use Python to make it happen.

Zegami is a visual data exploration tool for viewing large collections of images and other visual data. By presenting vast quantities of images within a single field of view, Zegami allows users to quickly see correlations, outliers and relationships by leveraging the innate pattern recognition capabilities of the human brain. This combined with image processing and advanced machine learning techniques like Deep Learning presents a formidable combination of the best aspects of man and machine. In addition, by including associated metadata (structured and unstructured) users can search, sort and filter the images to find exactly what is required. In the above example researchers at the Weatherall Institute of Molecular Medicine, University of Oxford were able to quickly observe the effects of various concentrations of a particular drug against a control group.

Without question, Github is the biggest code sharing platform on the planet. With more than 14 millions users and 35 million repositories, the insights you can discover by analyzing the data available through its API are surprising and revealing. I've been very passionate about this data for a long time, as much as I am about Neo4j. Importing this data into a graph database can reveal interesting new information. Two years ago I created a Github Gist with some interesting queries on that dataset.