If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Similarity search--for example, identifying similar images in a database or similar documents on the web--is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches. These strategies can be translated to improve the performance of computational similarity searches.
Originally published in France in 2016, Living with Robots combines the authors' expertise in philosophy--in particular, Paul Du mouchel's scholarship on the role of emo tion in shaping social life and Luisa Damiano's work on human and artificial cognition--to offer insight into problems raised by advances in robotics and artificial intelli gence that will be faced by future societies. Throughout the book, the authors provide a conceptual framework for thinking about possible scenarios of human-robot interac tions, most extensively with regard to our relationships with social robots.
Our brains are bigger, relative to body size, than other animals', but it's not just size that matters. Elephants and whales have bigger brains, so comparing anatomy or even genomes of humans and other animals reveals little about the genetic and developmental changes that sent our brains down such a different path. Geneticists have identified a few key differences in the genes of humans and apes. But specifically how human variants of such genes shape our brain in development--and how they drove its evolution--have remained largely mysterious. Now, researchers are deploying new tools to understand the molecular mechanisms behind the unique features of our brain.
Genetic elements compete for transmission through meiosis, when haploid gametes are created from a diploid parent. Selfish elements can enhance their transmission through a process known as meiotic drive. In female meiosis, selfish elements drive by preferentially attaching to the egg side of the spindle. This implies some asymmetry between the two sides of the spindle, but the molecular mechanisms underlying spindle asymmetry are unknown. Here we found that CDC42 signaling from the cell cortex regulated microtubule tyrosination to induce spindle asymmetry and that non-Mendelian segregation depended on this asymmetry.
The fundamental organization of excitatory and inhibitory neurons in the neocortex is still poorly understood. Subcerebral projection neurons, a major excitatory cell type in neocortical layer 5, form small cell clusters called microcolumns. Maruoka et al. examined large regions of mouse brain layer 5 and observed that thousands of these microcolumns make up a hexagonal lattice with a regular gridlike spacing. Microcolumns received common presynaptic inputs and showed synchronized activity in many cortical areas. These microcolumns developed from nonsister neurons coupled by cell type–specific gap junctions, suggesting that their development is lineage-independent but guided by local electrical transmission.
Dogs can detect human emotion from smell alone. It is often said that dogs can "smell fear," but the majority of research into communication between dogs and humans has focused on gestures, words, and facial expressions. Olfaction has tended to be overlooked, despite dogs' well-known olfactory capabilities. To explore smell discrimination in more detail, D'Aniello et al. tested pet dogs by exposing them to underarm secretions from humans who had experienced happy or fearful stimuli. Dogs exposed to fearful secretions were less likely to approach an unknown human and displayed elevated heart rates.
It is hard to obtain biological samples from whales. However, whales do shed lots of material as oily slicks behind them and in their massive exhalations, or blows, at the surface. Exhalations contain tissue debris and respiratory microorganisms. Apprill et al. used a small drone furnished with a Petri dish and a 96-well plate to capture exhaled material from 28 humpback whales off Vancouver Island, Canada, and Cape Cod, USA. Fortunately, in this study, no known cetacean respiratory pathogens were detected.
During evolution, the prefrontal region grew in size relative to the rest of the cortex. It reached its largest extent in the human brain, where it constitutes 30% of the total cortical area. This growth was accompanied by phylogenetic differentiation of the cortical areas. It has been argued that the human brain holds prefrontal regions that are both qualitatively and functionally unique. An important goal is to reveal how the prefrontal cortex enables complex behavior.
Nothing is more intuitive, yet more complex, than the concepts of space and time. In contrast to spacetime in physics, space and time in neuroscience remain separate coordinates to which we attach our observations. Investigators of navigation and memory relate neuronal activity to position, distance, time point, and duration and compare these parameters to units of measuring instruments. Although spatial-temporal sequences of brain activity often correlate with distance and duration measures, these correlations may not correspond to neuronal representations of space or time. Neither instruments nor brains sense space or time.
The neuroscience field is steaming ahead, fueled by a revolution in cutting-edge technologies. Concurrently, another revolution has been underway--the diversity of species utilized for neuroscience research is sharply declining, as the field converges on a few selected model organisms. Here, from the perspective of a young scientist, I naively ask: Is the great diversity of questions in neuroscience best studied in only a handful of animal models? I review some of the limitations the field is facing following this convergence and how these can be rectified by increasing the diversity of appropriate model species. I propose that at this exciting time of revolution in genetics and device technologies, neuroscience might be ready to diversify again, if provided the appropriate support.