To simplify, this can be revealed with the Implicit Association Test, where subjects look at pictures of humans or trolls, coupled with words with positive or negative connotations. Recent work, adapting the Implicit Association Test to another species, suggests that even other primates have implicit negative associations with Others. And monkeys would look longer at pairings discordant with their biases (e.g., pictures of members of their own group with pictures of spiders). Thus, the strength of Us/Them-ing is shown by the: speed and minimal sensory stimuli required for the brain to process group differences; tendency to group according to arbitrary differences, and then imbue those differences with supposedly rational power; unconscious automaticity of such processes; and rudiments of it in other primates.
For example, one core property of human languages is known as duality of patterning: meaningful linguistic units (such as words) break down into smaller meaningless units (sounds), so that the words sap, pass, and asp involve different combinations of the same sounds, even though their meanings are completely unrelated. ABSL contrasts sharply with other sign languages like American Sign Language (ASL), which creates words by re-combining a small collection of gestural elements such as hand shapes, movements, and hand positions. Instead, they argue, languages share certain properties because they all have to solve similar problems of communication under similar pressures, pressures that reflect the limits of human abilities to learn, remember, produce, and perceive information. The signs of ABSL, though, may be easier to learn because many of them are concretely related to the things they symbolize--for example, the sign for "lemon" resembles the motion of squeezing a lemon.
Penetration testing is a crucial defense against common web application security threats such as SQL injection and cross-site scripting attacks. A proposed web vulnerability scanner automatically generates test data with combinative evasion techniques, significantly expanding test coverage and revealing more vulnerabilities.
Context-aware inference apps have become pervasive as a result of the Internet of Things (IoT). However, most of these apps run continuously on a single device, resulting in limited sensor coverage and high energy consumption. Recent advances in IoT devices, specifically hardware heterogeneity, can be leveraged to improve the accuracy and energy efficiency of context inferences.
In the report, researchers at the Facebook Artificial Intelligence Research lab describe using machine learning to train their "dialog agents" to negotiate. At one point, the researchers write, they had to tweak one of their models because otherwise the bot-to-bot conversation "led to divergence from human language as the agents developed their own language for negotiating." In other words, the model that allowed two bots to have a conversation--and use machine learning to constantly iterate strategies for that conversation along the way--led to those bots communicating in their own non-human language. Already, there's a good deal of guesswork involved in machine learning research, which often involves feeding a neural net a huge pile of data then examining the output to try to understand how the machine thinks.
The world we experience is not the real world. Which raises the question: How would our world change if we had new and different senses? More recently, researchers in the emerging field of "sensory enhancement" have begun developing tools to give people additional senses--ones that imitate those of other animals, or that add capabilities nature never imagined. Researchers are working on other technologies that could restore sight or touch to those who lack it.
Despite the recent emergence of browser-based transcription aids, transcription's an area of drudgery in the modern Western economy where machines can't quite squeeze human beings out of the equation. That is until last year, when Microsoft built one that could. Automatic speech recognition, or ASR, is an area that has gripped the firm's chief speech scientist, Xuedong Huang, since he entered a doctoral program at Scotland's Edinburgh University. Huang and his colleagues used their software to transcribe the NIST 2000 CTS test set, a bundle of recorded conversations that's served as the benchmark for speech recognition work for more than 20 years.
The patient's mother had died in a state hospital of Huntington's disease--a genetic degenerative brain disease. Though I didn't know it at the time, I had run headlong into the "hard problem of consciousness," the enigma of how physical brain mechanisms create purely subjective mental states. My first hint of the interaction between religious feelings and theories of consciousness came from Montreal Neurological Institute neurosurgeon Wilder Penfield's 1975 book, Mystery of the Mind: A Critical Study of Consciousness and the Human Brain. To see how this might work, take a page from Penfield's brain stimulation studies where he demonstrates that the mental sensations of consciousness can occur independently from any thought that they seem to qualify.
Both local plasma membrane bending, resulting from the physical contact with collagen fibers, and local integrin engagement triggered CCS accumulation on fibers. Electron microscopy analyses revealed that the CCSs engaged with collagen fibers adopted a distinct, tubular morphology to wrap around and pinch the fiber. Cell adhesion and the cell's capacity to grab collagen fibers were inhibited by disruption of TCALs or by inhibition of integrin accumulation at TCALs by using AP-2 and Dab2 siRNAs, respectively. FAs were mostly found at both extremities of elongated cells migrating in the 3D environment, whereas CCSs were distributed all over the plasma membrane of cellular protrusions.
It now provides data for 60 million homes across the United States that it has already assessed with its algorithms. For the past two years, Project Sunroof has walked people through all the information-gathering steps of installing solar panels: After you tell it where you live, its algorithms estimate how much solar energy falls on your roof, calculate how much solar panels would reduce your electricity bill, and deliver estimates from local installation firms like Solar City. Google created the data for this feature in-house, training a machine-learning algorithm on the common appearance of rooftop solar panels and then letting it loose on the cities and towns that Project Sunroof already covers. Right now, the company has analyzed installations on about 60 million buildings in the United States; it hopes to get to the remaining 40 million buildings in the next few years.