If you find it hard to focus after a wakeful night, it's because your brain is busy trying to catch up on crucial housekeeping. Nearly everyone has experienced it--after a night of poor sleep, your brain might seem foggy, and your mind drifts off when you should be paying attention. A new MIT study reveals what happens biologically as these momentary lapses occur: Your brain is performing essential maintenance that it usually takes care of while you sleep. During a normal night of sleep, the cerebrospinal fluid (CSF) that cushions the brain helps flush away metabolic waste that has built up during the day. In a 2019 study, MIT electrical engineering and computer science professor Laura Lewis, PhD '14, and colleagues showed that the CSF flows rhythmically in and out in a way that's linked to changes in brain waves. To explore what might happen to this CSF flow in a sleep-deprived brain, Lewis, who is also a member of MIT's Institute for Medical Engineering and Science, and her colleagues tested 26 volunteers on several cognitive tasks after they'd been kept awake in the lab and when they were well-rested.

Executives at artificial intelligence companies may like to tell us that AGI is almost here, but the latest models still need some additional tutoring to help them be as clever as they can. Scale AI, a company that's played a key role in helping frontier AI firms build advanced models, has developed a platform that can automatically test a model across thousands of benchmarks and tasks, pinpoint weaknesses, and flag additional training data that ought to help enhance their skills. Scale, of course, will supply the data required. Scale rose to prominence providing human labor for training and testing advanced AI models. Large language models (LLMs) are trained on oodles of text scraped from books, the web, and other sources.

Major depressive disorder (MDD) presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of learning strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task (PRT) within the EMBARC study, we propose a novel RL-HMM framework for analyzing reward-based decision-making. Our model accommodates learning strategy switching between two distinct approaches under a hidden Markov model (HMM): subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient EM algorithm for parameter estimation and employ a nonparametric bootstrap for inference. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.

The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents' attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, memory, problem solving, and consciousness. Human attention lapses are particularly problematic and potentially catastrophic in industrial workplace, from assembling electronics to operating machines. Attention is indeed complex and cannot be easily measured with single-modality sensors. Eye state, head pose, posture, and manifold environment stimulus could all play a part in attention lapses. To this end, we propose a pipeline to annotate multimodal dataset of human attention tracking, including eye tracking, fixation detection, third-person surveillance camera, and sound. We produce a pilot dataset containing two fully annotated phone assembly sequences in a realistic manufacturing environment. We evaluate existing fatigue and drowsiness prediction methods for attention lapse detection. Experimental results show that human attention lapses in production scenarios are more subtle and imperceptible than well-studied fatigue and drowsiness.

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The Pentagon did not adequately document work on its flagship artificial intelligence effort according to a government watchdog report, increasing the risks of lapses in the future. The Department of Defense's inspector general evaluated whether the government monitored contacts in accordance with federal laws and policy for Project Maven, which aimed to accelerate the integration of big data and machine learning. It is frequently held up as the poster child for how DoD is using AI. Army Contracting Command and the Army Research Laboratory partnered with the Pentagon's Algorithmic Warfare Cross-Functional Team to support AI development and award four contracts and a cooperative agreement for Project Maven. ECS Federal scored three of the contracts, with Morse Corporation securing the fourth and Carnegie Mellon University receiving a cooperative agreement.

Media multitasking, such as scrolling through social media while watching a movie, may be linked to more lapses in attention and difficulty remembering things. "Our data support the idea that we should be aware of how we engage with media," says Kevin Paul Madore at Stanford University in California. He and his team compared people's self-reported levels of media multitasking with their performances in a memory task, as part of a study including 80 participants aged 18 to 26. The researchers specifically tested episodic memory, which helps us recall events, by presenting the participants with images of objects on a computer and then later asking them to recall whether they had seen the objects earlier or not. At the same time, the team used EEG and eye tracking to monitor people's attentiveness.

To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks. Parameter servers ease the implementation of distributed parameter management---a key concern in distributed training---, but can induce severe communication overhead. To reduce communication overhead, distributed machine learning algorithms use techniques to increase parameter access locality (PAL), achieving up to linear speed-ups. We found that existing parameter servers provide only limited support for PAL techniques, however, and therefore prevent efficient training. In this paper, we explore whether and to what extent PAL techniques can be supported, and whether such support is beneficial. We propose to integrate dynamic parameter allocation into parameter servers, describe an efficient implementation of such a parameter server called Lapse, and experimentally compare its performance to existing parameter servers across a number of machine learning tasks. We found that Lapse provides near linear scaling and can be orders of magnitude faster than existing parameter servers.

Facebook is taking a swipe at Tinder with a new dating service that is being rolled out today. The newly released service, called Facebook Dating, will harness the power of the platform's user data, including what you like, what events you go to, and what groups you're a part of, to connect you to both Facebook and Instagram users who have opted in. Unlike other major dating apps like Tinder, however, Facebook won't require users to mutually'match' before being able to connect, and instead will let participants browse profiles via a familiar card-style cue and interact with the ones that interest them. Facebook says users can interact with profiles in two ways: by liking a profile to let someone know that they're interested or by commentating directly on a picture. Facebook is jumping into the dating game with its new Facebook Dating service that will allow users to connect with people in and outside of their friend network.

Artificial intelligence is putting new teeth on the old saw that cheaters never prosper. New companies and new research are applying the cutting edge technology in at least three different ways to combat cheating -- on homework, on the job hunt and even on one's diet. In California, a new company called Crosschq is using machine learning and data analytics to help employers with the job reference process. The technology is meant to help companies avoid bad hires and compare how job candidates present themselves with how their references see them. In Pennsylvania, Drexel University researchers are developing an app that can predict when dieters are likely to lapse on their eating regimen, based on the time of day, the user's emotions -- even the temperature of their skin and heart rate.