Diego Oppenheimer is worried that the Googles and the Facebooks will dominate the world of artificial intelligence. Elon Musk and Sam Altman are worried about the same thing. That's why they created a startup called OpenAI. In recent years, Google and Facebook have snapped up so many researchers at the heart of the deep learning movement, an AI movement that's rapidly reinventing everything from speech recognition to security. So, Musk and Altman grabbed several top AI researchers from Google and Facebook and vowed to share their work with the world at large. Now, Oppenheimer and his startup, Algorithmia, are doing their part in the battle against AI hegemony.

Green and Hogan had hypothesized that pauses in children's speech, as they struggled to either find a word or string together the motor controls required to produce it, were a source of useful diagnostic data. So that's what Gong and Guttag concentrated on. They identified a set of 13 acoustic features of children's speech that their machine-learning system could search, seeking patterns that correlated with particular diagnoses. These were things like the number of short and long pauses, the average length of the pauses, the variability of their length, and similar statistics on uninterrupted utterances.

Modern technology has brought about an explosion of data. We now produce about 2.5 quintillion bytes of it every day, with 90pc of all the data in the world estimated to have been created in the last two years. The internet, of course, has been the major catalyst for this development, enabling us to create, share and store information on a massive scale. And, as mobile phone ownership continues to increase (70pc of the world's population is forecast to have one by next year – up from 61pc in 2013) and the internet of things evolves as everything from fitness trackers to fridges come online, there's only going to be more of it. But having this wealth of data is only part of the equation.

Are you good at hiding your feelings? No issues, your Wi-Fi router may soon be able to tell how you feel, even if you have a good poker face. A team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a device that can measure human inner emotional states using wireless signals. Dubbed EQ-Radio, the new device measures heartbeat, and breath to determine whether a person is happy, excited, sad, or angry. Using EQ-Radio, which emits and captures reflected radio frequency (RF) waves, the team bounced waves off a person's body to measure subtle changes in breathing patterns and heart rates.

A few weeks ago, Moz CEO Rand Fishkin approached MonkeyLearn team with a question which later turned into a project. The goal was to build an online tool that provides great value to the SEO industry. Also, we wanted to showcase what can be developed with machine learning technologies by using MonkeyLearn. Basically, SEOs can use this tool to compare their website's keywords to those on the Google search results for a related term. Randy presented this Keyword Comparison Extractor on his keynote at Mozcon 2015, the largest SEO conference out there, with more than 1,500 attendees and speakers from companies like Google, Buffer, Optimizely, Unbounce, Basecamp and others.

Government agencies are tasked with the challenge of providing citizens with more efficient, effective, and transparent services with strict and often decreasing budgets. Government agencies can use machine learning to increase operational efficiencies by analyzing datasets, finding patterns and anomalies, and making predictions about future events. Skytree's state-of-the art machine learning software can analyze both structured and unstructured data sets in real-time to produce fast, accurate and scalable results that are up to 10,000 times faster than previous approaches. Skytree comes with a breadth of advanced machine learning methods that utilize the research available to you to make predictions with the highest accuracy available, far surpassing what's possible with basic analytics. Detect and prevent fraudulent transactions, accounts and vendors.

In developing algorithms to differentiate between suspicious nodules in the thyroid gland, researchers in China have found that their machine-learning computations separate malignant from benign properties more accurately than an inexperienced radiologist--but not as accurately as the experienced radiologist whose know-how was used to create the algorithms. Their research is running in the October edition of the American Journal of Roentgenology. Dr. Hongxun Wu of Jiangyuan Hospital in the province of Jiangsu and colleagues worked with 970 histopathologically proven thyroid nodules in 970 patients. They had two radiologists retrospectively review ultrasound images of the nodules, grading them according to a five-tier scoring system. One of the rads--the one whose clinical interpretations would feed the computations--had 17 years of experience.

We already know that artificial intelligence systems can work in law firms and beat the world champion at a game of Go. Now it turns out that AI can write some pretty good pop songs, too.

Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code.

However, Oren Etzioni, professor of Computer Science at the University of Washington and CEO of the Allen Institute for Artificial Intelligence, argues that such headlines are in fact strongly influenced by the work of one man: professor Nick Bostrom of the Faculty of Philosophy at Oxford University, author of the bestselling treatise Superintelligence: Paths, Dangers, and Strategies. Essentially, Bostrom claims that if machine brains surpass human brains in general intelligence, the resultant new'superintelligence' could replace humans as the dominant lifeform on Earth. Furthermore, according to his findings, there's a 10-percent probability that human-level AI will be attained by 2022, a 50-percent probability that this feat will be achieved by 2040, and 90-percent probability that such an entity will be created by 2075. However, in his article published in the MIT Technology Review magazine Etzioni points out that Bostrom's main source of data is an aggregate of four different surveys of groups, including participants of the Philosophy and Theory of AI conference that was held in 2011 in Thessaloniki, and members of the Greek Association for Artificial Intelligence. Furthermore, it appears that Bostrom didn't provide the response rates or the phrasing of questions used during those surveys, and neither did he account for the reliance on data collected in Greece.