In the 1970s a young gorilla known as Koko drew worldwide attention with her ability to use human sign language. But skeptics maintain that Koko and other animals that "learned" to speak (including chimpanzees and dolphins) could not truly understand what they were "saying"--and that trying to make other species use human language, in which symbols represent things that may not be physically present, is futile. "There's one set of researchers that's keen on finding out whether animals can engage in symbolic communication and another set that says, 'That is anthropomorphizing. We need to ... understand nonhuman communication on its own terms,'" says Karen Bakker, a professor at the University of British Columbia and a fellow at the Harvard Radcliffe Institute for Advanced Study. Now scientists are using advanced sensors and artificial intelligence technology to observe and decode how a broad range of species, including plants, already share information with their own communication methods.

"What is it like to be you?" This question may often be asked of your robot, but rarely is it asked of you. As humans, we generally do not ask what it is like to be a human because, by default, we assume that we know what is going on in their minds. Frequently, however, this assumption can cause us to misread and misunderstand each other by placing our unique experiences and interpreting the world through our black-and-white lens without seeing the rich spectrum of color others may experience. So what would change if, instead of only imagining the abstractions of those around us with their bodies and brains as they see with their eyes and ears, somebody decided to actually try experiencing those things firsthand? What if instead of asking what it would be like to be them, we told those people what it is like to be us?

If there's one thing that AI can't do, it's be human. But that's not necessarily a bad thing because, even the least sophisticated bots today can analyze massive amounts of data in seconds, spit out mathematical results and continue those tasks nigh on forever. So, while AI tools can't technically be human (they are still robots, after all), they can do a lot of things only humans could once do. And bots' sophistication is hardly limited to self-driving cars or board-game world championships: Human or not, this technologycan be used to build healthier, longer-lasting relationships with customers. One very human experience is giving and receiving feedback, quality feedback.

By now, you are well-acquainted with the four main processes that come with having employees in your company. Depending on the size of your company, the processes of hiring, training, compensating and firing can range from fairly simple to highly complex. But those processes can be simplified. Thanks to Natural Language Processing (NLP), the future is here. Will your next employee come in a box?

To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e.g., social networks vs. web graphs) are distinguishable from one another (using both categories of real-world networks and synthetic graphs). A classification accuracy of $94.2\%$ was achieved using a random forest classifier with both real and synthetic networks. This work makes two important findings. First, real-world networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of an arbitrary network. Second, classifying synthetic networks is trivial as our models can easily distinguish between synthetic graphs and the real-world networks they are supposed to model.

In the workplace of the future, people will need to be comfortable working alongside digital intelligence, and businesses will need to find the optimal pairing of humans and machines.

Before we create a new intelligence in our image, we have to reconsider the fundamental lie that enables human civilization: that nature and culture are separate and distinct, rather than neighbors on the same continuum. At 1:17:00 in the video I refer to a Bruno Latour quote which I think is fundamental to this debate. The full quote is this: "Instead of two powers, one hidden and indisputable (nature), and the other disputable and despised (politics), we will have two different tasks in the same collective. The first task will be to answer the question: How many humans and nonhumans are to be taken into account? The second will be to answer the most difficult of all questions: Are you ready, and at the price of what sacrifice, to live the good life together? That this highest of political and moral questions could have been raised, for so many centuries, by so many bright minds, for humans only without the nonhumans that make them up, will soon appear, I have no doubt, as extravagant as when the Founding Fathers denied slaves and women the vote...There is a future, and it does differ from the past. But where once it was a matter of hundreds and thousands, now millions and billions have to be accommodated--billions of people, of course, but also billions of animals, stars, prions, cows, robots, chips, and bytes... That there was a decade when people could believe that history had drawn to a close simply because an ethnocentric--or better yet, epistemocentric--conception of progress had drawn a closing parenthesis will appear as the greatest and let us hope last outburst of an exotic cult of modernity that has never been short on arrogance."

People regularly face tasks that can be understood as navigation in information networks, where the goal is to find a path between two given nodes. In many such situations, the navigator only gets local access to the node currently under inspection and its immediate neighbors. This lack of global information about the network notwithstanding, humans tend to be good at finding short paths, despite the fact that real-world networks are typically very large. One potential reason for this could be that humans possess vast amounts of background knowledge about the world, which they leverage to make good guesses about possible solutions. In this paper we ask the question: Are human-like high-level reasoning skills really necessary for finding short paths? To answer this question, we design a number of navigation agents without such skills, which use only simple numerical features. We evaluate the agents on the task of navigating Wikipedia, a domain for which we also possess large-scale human navigation data. We observe that the agents find shorter paths than humans on average and therefore conclude that, perhaps surprisingly, no sophisticated background knowledge or high-level reasoning is required for navigating the complex Wikipedia network.