Turns out, the first animals to walk on land weren't amphibians. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. An illustration shows prehistoric baby crocodile-like animal known as an embolomere swimming with their mother in the background. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Some similar research tactics were reported earlier this year by Mickey Pardo, a postdoctoral researcher, now at Cornell University, who spent 14 months in Kenya recording elephant calls. Elephants sound alarms by trumpeting, but in reality most of their vocalizations are deep rumbles that are only partly audible to humans. Pardo also found evidence that elephants use vocal labels, and he says he can definitely get an elephant's attention by playing the sound of another elephant addressing it. But does this mean researchers are now "speaking animal"? Real language, he thinks, would mean the ability to discuss things that happened in the past or string together more complex ideas.

Scientists have observed another human-like behavior among elephants - they call each other by name. Researchers from Colorado State University (CSU) recorded 470 unique noises from elephants in Kenya, capturing different rumbles and pitches. Using machine learning, the team found the calls contained a unique tune depending on which elephant they were communicating with. To test their theory that these noises corresponded with different names, the team played them to the herds - and the elephant being named responded by returning a noise or approaching the speaker. The findings suggest elephants may be capable of abstract thinking, making them much more socially complex mammals than previously thought.

UC Berkeley Assistant Professor Zachary Pardos and his team have developed a machine learning approach that promises to help more community college students position themselves to transfer and succeed at four-year colleges and universities. Along the way, they've discovered that considering course enrollment patterns -- or the classes that students take before, along with, and after a particular course -- can help provide a more complete picture of what courses should "count" when students transfer. Roughly 80% of community college students aim to continue their education at four-year institutions, but the vast majority never make the transfer. Contributing to the problem are the complexities of "articulation," or determining which course at one institution will count for credit at another. This entails assessing the similarity of thousands, or potentially even millions, of pairs of courses, an endeavor that's impossible to comprehensively achieve and keep current across all institutions manually.

Recommending media objects to users typically requires users to rate existing media objects so as to understand their preferences. The number of ratings required to produce good suggestions can be reduced through collaborative filtering. Collaborative filtering is more difficult when prior users have not rated the same set of media objects as the current user or each other. In this work, we describe an approach to applying prior user data in a way that does not require users to rate the same media objects and that does not require imputation (estimation) of prior user ratings of objects they have not rated. This approach is applied to the problem of finding good equalizer settings for music audio and is shown to greatly reduce the number of ratings the current user must make to find a good equalization setting.

The current paradigm in student modeling has continued to show the power of its simplifying assumption of knowledge as a binary and monotonically increasing construct, the value of which directly causes the outcome of student answers to questions. Recent efforts have focused on optimizing the prediction accuracy of responses to questions using student models. Incorporating individual student parameter interactions has been an interpretable and principled approach which has improved the performance of this task, as demonstrated by its application in the 2010 KDD Cup challenge on Educational Data. Performance prediction, however, can have limited practical utility. The greatest utility of such student models can be their ability to model the tutor and the attributes of the tutor which are causing learning. Harnessing the same simplifying assumption of learning used in student modeling, we can turn this model on its head to effectively tease out the tutor attributes causing learning and begin to optimize the tutor model to benefit the student model.