Lifeboat Foundation

The first step is to understand qubits and superposition. The next one is to get a handle on the business advantage that this technology represents.

If “figure out quantum computing” is still in your future file, it’s time to update your timeline. The industry is nearing the end of the early adopter phase, according to one expert, and the time is now to get up to speed.

Denise Ruffner, the vice president of business development at IonQ, said that quantum computing is evolving much faster than many people realize.

Neuroscientists find that interpreting code activates a general-purpose brain network, but not language-processing centers.

In some ways, learning to program a computer is similar to learning a new language. It requires learning new symbols and terms, which must be organized correctly to instruct the computer what to do. The computer code must also be clear enough that other programmers can read and understand it.

In spite of those similarities, MIT neuroscientists have found that reading computer code does not activate the regions of the brain that are involved in language processing. Instead, it activates a distributed network called the multiple demand network, which is also recruited for complex cognitive tasks such as solving math problems or crossword puzzles.

“People want to know what makes someone a good programmer,” Liu said. “If we know what kind of neuro mechanisms are activated when someone is programming, we might be able to find a better training program for programmers.” By mapping the brain activity of expert computer programmers while they puzzled over code, Johns Hopkins University scientists have found the neural mechanics behind this increasingly vital skill.

Though researchers have long suspected the brain mechanism for computer programming would be similar to that for math or even language, this study revealed that when seasoned coders work, most brain activity happens in the network responsible for logical reasoning, though in the left brain region, which is favored by language.

“Because there are so many ways people learn programming, everything from do-it-yourself tutorials to formal courses, it’s surprising that we find such a consistent brain activation pattern across people who code,” said lead author Yun-Fei Liu, a Ph.D. student in the university’s Neuroplasticity and Development Lab. “It’s especially surprising because we know there seems to be a crucial period that usually terminates in early adolescence for language acquisition, but many people learn to code as adults.”

Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used functional magnetic resonance imaging to investigate two candidate brain systems: the multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.

Second, we chose 2 major Appraisals with well-established roles in emotion elicitation, but interactive game paradigms could also investigate the neural basis of other appraisals (e.g., novelty, social norms). Furthermore, our study did not elucidate the precise cognitive mechanisms of particular appraisals or their neuroanatomical substrates but rather sought to dissect distinct brain networks underlying appraisals and other emotion components in order to assess any transient synchronization among them during emotion-eliciting situations. Importantly, even though different appraisals would obviously engage different brain networks, a critical assumption of the CPM is that synchronization between these networks and other components would arise through similar mechanisms as found here.

Third, our task design and event durations were chosen for fMRI settings, with blocked conditions and sufficient repetitions of similar trials. The limited temporal resolution of fMRI did not allow the investigation of faster, within-level dynamics which may be relevant to emotions. Additionally, this slow temporal resolution and our brain-based synchronization approach are insufficient to uncover fast and recurrent interactions among component networks during synchronization, as hypothesized by the CPM. Nonetheless, our computational model for the peripheral synchronization index did include recurrence as one of its parameters, allowing us refine our model-based analysis of network synchronization in ways explicitly taking recurrent effects into account (see S1 Text and Table J in S1 Table). In any case, neither the correlation of a model-based peripheral index nor an instantaneous phase synchronization approach could fully verify this hypothesis at the neuronal level using fMRI. To address these limitations, future studies might employ other paradigms with different game events or other imaging analyses and methodologies with higher temporal resolution. Higher temporal resolution may also help shed light on causality factors hypothesized by the CPM, which could not be addressed here. Finally, our study focused on the 4 nonexperiential components of emotion, with feelings measured purely retrospectively for manipulation-check purposes. This approach was motivated conceptually by the point of view that an emotion can be characterized comprehensively by the combination of its nonexperiential parts [10] and methodologically by the choice to avoid self-report biases and dual task conditions in our experimental setting. However, future work will be needed to link precise moments of component synchronization more directly to concurrent measures along relevant emotion dimensions, without task biases, as previously examined in purely behavioral research [20].

Nevertheless, by investigating emotions from a dynamic multi-componential perspective with interactive situations and model-based parameters, our study demonstrates the feasibility of a new approach to emotion research. We provide important new insights into the neural underpinnings of emotions in the human brain that support theoretical accounts of emotions as transient states emerging from embodied and action-oriented processes which govern adaptive responses to the environment. By linking transient synchronization between emotion components to specific brain hubs in basal ganglia, insula, and midline cortical areas that integrate sensorimotor, interoceptive, and self-relevant representations, respectively, our results provide a new cornerstone to bridge neuroscience with psychological and developmental frameworks in which affective functions emerge from a multilevel integration of both physical/bodily and psychological/cognitive processes [62].

Creative technology studio playtronica has found a way of making music with pretty much anything including vegetables. their electronic devices transform touch into midi notes making anything into a midi controller including one that turns the human body into a keyboard. how it works is by effectively creating a circuit between the device and human body or the fruit. it’s then connected to a computer so when you touch the instrument the circuit is closed, and a specified sound is played. the tools are designed to work with organic materials and mostly anything that has water inside.

CSL’s Systems and Networking Research Group (SyNRG) is defining a new sub-area of mobile technology that they call “earable computing.” The team believes that earphones will be the next significant milestone in wearable devices, and that new hardware, software, and apps will all run on this platform.

“The leap from today’s earphones to ‘earables’ would mimic the transformation that we had seen from basic phones to smartphones,” said Romit Roy Choudhury, professor in electrical and computer engineering (ECE). “Today’s smartphones are hardly a calling device anymore, much like how tomorrow’s earables will hardly be a smartphone accessory.”

Instead, the group believes tomorrow’s earphones will continuously sense human behavior, run acoustic augmented reality, have Alexa and Siri whisper just-in-time information, track user motion and health, and offer seamless security, among many other capabilities.

Light-emitting diodes—LEDs—can do way more than illuminate your living room. These light sources are useful microelectronics too.

Smartphones, for example, can use an LED proximity sensor to determine if you’re holding the phone next to your face (in which case the screen turns off). The LED sends a pulse of light toward your face, and a timer in the phone measures how long it takes that light to reflect back to the phone, a proxy for how close the phone is to your face. LEDs are also handy for distance measurement in autofocus cameras and gesture recognition.

One problem with LEDs: It’s tough to make them from silicon. That means LED sensors must be manufactured separately from their device’s silicon-based processing chip, often at a hefty price. But that could one day change, thanks to new research from MIT’s Research Laboratory of Electronics (RLE).

Virgin Galactic will fly again when VSS Unity is ready.

A bad computer connection foiled Virgin Galactic’s attempt to reach space over the weekend, company officials said.

VSS Unity, Virgin Galactic’s newest SpaceShipTwo vehicle, lifted off Saturday morning (Dec. 12) from New Mexico’s Spaceport America beneath the wings of its carrier airplane, VMS Eve.