Long short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data. However, it requires significant computational power for learning and implementing from both software and hardware aspects. This paper proposes a novel LiteLSTM architecture based on reducing the computation components of the LSTM using the weights sharing concept to reduce the overall architecture cost and maintain the architecture performance. The proposed LiteLSTM can be significant for learning big data where time-consumption is crucial such as the security of IoT devices and medical data. Moreover, it helps to reduce the CO2 footprint. The proposed model was evaluated and tested empirically on two different datasets from computer vision and cybersecurity domains.

In this thesis a probabilistic framework is developed and proposed for Dynamic Object Recognition in 3D Environments. A software package is developed using C++ and Python in ROS that performs the detection and tracking task. Furthermore, a novel Gaussian Process Regression (GPR) based method is developed to detect ground points in different urban scenarios of regular, sloped and rough. The ground surface behavior is assumed to only demonstrate local input-dependent smoothness. kernel's length-scales are obtained. Bayesian inference is implemented sing \textit{Maximum a Posteriori} criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame because adjacent segments may not have similar ground structure in an uneven scene while having shared hyper-parameter values. Simulation results shows the effectiveness of the proposed method in uneven and rough scenes which outperforms similar Gaussian process based ground segmentation methods.

Monitoring machine learning models once they are deployed is challenging. It is even more challenging to decide when to retrain models in real-case scenarios when labeled data is beyond reach, and monitoring performance metrics becomes unfeasible. In this work, we use non-parametric bootstrapped uncertainty estimates and SHAP values to provide explainable uncertainty estimation as a technique that aims to monitor the deterioration of machine learning models in deployment environments, as well as determine the source of model deterioration when target labels are not available. Classical methods are purely aimed at detecting distribution shift, which can lead to false positives in the sense that the model has not deteriorated despite a shift in the data distribution. To estimate model uncertainty we construct prediction intervals using a novel bootstrap method, which improves upon the work of Kumar & Srivastava (2012). We show that both our model deterioration detection system as well as our uncertainty estimation method achieve better performance than the current state-of-the-art. Finally, we use explainable AI techniques to gain an understanding of the drivers of model deterioration. We release an open source Python package, doubt, which implements our proposed methods, as well as the code used to reproduce our experiments.

Over the past several years, the United States and several of its leading allies have expressed a commitment to the responsible development and use of artificial intelligence (AI) for national security. Most notably, the U.S. Department of Defense adopted five principles for the safe and ethical application of AI in 2020, and the U.S. Defense Innovation Unit published a set of Responsible AI Guidelines in fall 2021. Meanwhile, NATO recently released its strategy for the responsible development and use of AI, and the United Kingdom's Ministry of Defense is actively developing ethical principles of its own. On January 31, Brookings will host a virtual event to compare and discuss how the United States and its allies are addressing ethical considerations in their pursuit and integration of military applications of AI-enabled technologies. What are the processes used by countries and international organizations to define applicable AI principles? What lessons have been learned from implementing those principles into practice?

A new series of patents by Facebook's parent company Meta suggest possible plans from the company to capture users' behavioral biometrics data. Specifically, the patents mention pupil movements, body poses, and crumpled noses, which the company would use to make digital avatars realistically animated. The patents were reviewed by The Financial Times, according to which Meta also intends to use the biometric data to provide hyper-targeted advertising and sponsored content. In fact, one of the patents analyzed by the publication was granted to Meta by the United States Patent and Trademark Office (USPTO) earlier this month and refers to the tracking of users' facial expressions through a virtual reality headset to "adapt media content" based on those responses. A separate patent describes an avatar personalization engine capable of creating a 3D avatar of a user based on biometrics collected from a submitted photo.

The CEO of ID.me, a service used by dozens of states to verify unemployment benefits claimants as well as several federal agencies, has walked back previous claims that the company does not use a more powerful method of facial recognition. Founder and CEO @Blake_Hall issues an important statement around "1 to Many" check on selfies to combat identity theft. To learn more about the example of Eric Jaklitsch of New Jersey referenced in the statement below, visit: https://t.co/OLQX1gAhYL "ID.me uses a specific '1 to Many' check on selfies tied to government programs targeted by organized crime to prevent prolific identity thieves and members of organized crime from stealing the identities of innocent victims en masse," Blake Hall said in a statement. "This step is internal to ID.me and does not involve any external or government database."

When Jahanzaib Ansari was looking for work in 2016, his resume was not the problem. Despite a CV boasting experience as a programmer and attending the University of Toronto, Ansari's job search soon hit a dead end. At the suggestion of a friend, he changed his first name on his resume and saw almost immediate results. "I wouldn't hear back from employers until my [colleague] said, 'Why don't you just Anglicize it?' I went with variations of Jason, Jordan, Jacob, and literally in four to six weeks, I got a job," says the CEO of Knockri, a technology firm that created an artificial intelligence tool that aims to reduce bias in the hiring process.

On Monday, ID.me released a statement from CEO and founder Blake Hall about what the vendor said is its commitment to federal guidelines for facial recognition technology. Hall said the vendor uses one-to-one face match technology and not one-to-many facial recognition. One-to-one face match is a simple application of the technology that is comparable to using one's face to unlock a smartphone or be verified at an airport, Hall said in an interview with TechTarget. "It's something that Americans do broadly all across the country when they're proving their identity in person," Hall said. "What it specifically is not is like taking one person's photo and then taking like a city's worth of images and trying to like match that person's face."

Don't miss our upcoming virtual workshop with John Snow Labs, Improve Drug Safety with NLP, to learn more about our joint NLP solution accelerator for adverse drug event detection. The World Health Organization defines pharmacovigilance as "the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine/vaccine-related problem." While all medicines and vaccines undergo rigorous testing for safety and efficacy in clinical trials, certain side effects may only emerge once these products are used by a larger and more diverse patient population, including people with other concurrent diseases. To support ongoing drug safety, biopharmaceutical manufacturers must report adverse drug events (ADEs) to regulatory agencies, such as the US Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in the EU. Adverse drug reactions or events are medical problems that occur during treatment with a drug or therapy.

The basic challenge of cybersecurity is seeing enough of what is going on to determine when things are happening that shouldn't. This means detecting anomalies in the logs of system and network events that stream from every piece of an infrastructure, as well as all the major application and cloud services and environments. AI tools, without feeling bored or exhausted, can pay unwavering attention to event data streams, correlate and learn from events or observations in other environments via threat feeds. This makes AI central to the most important kind of security analysis: behavioral threat analytics (BTA). BTA, sometimes called user and entity behavioral analytics or UEBA, looks at the event streams with a focus on individual actors.