Since the Enlightenment, we've been making our own decisions. T his summer, I found myself battling through traffic in the sweltering streets of Marseille. At a crossing, my friend in the passenger seat told me to turn right toward a spot known for its fish soup. But the navigation app Waze instructed us to go straight. Tired, and with the Renault feeling like a sauna on wheels, I followed Waze's advice.

Breakthroughs, discoveries, and DIY tips sent every weekday. Tucked along the picturesque Zeravshan River in the rocky mountains of northwestern Tajikistan lies the ruins of a forgotten, ancient palace. The monumental royal complex once presided over a bustling city along the Silk Road, not far from modern Tajikistan's border with Uzbekistan. In its heyday, the palace's walls were covered with colorful murals and intricate wooden carvings, most of which have been lost to time--until now. A study recently published in the academic journal recreates and analyzes one of palace's most surprising murals .

A hymn dedicated to the ancient city of Babylon has been discovered after 2,100 years. Sung to the god Marduk, patron deity of the great city, the poem describes Babylon's flowing rivers, jewelled gates, and'bathed priests' in stunning detail. Although the song was lost to time after Alexander the Great captured the city, fragments of clay tablets survived in the ruins of Sippar, a city 40 miles to the North. In a process that would have taken'decades' to complete by hand, researchers used AI to piece together 30 different tablet pieces and recover the lost hymn. Originally 250 lines long, scientists have been able to translate a third of the original cuneiform text.

Navigation amongst densely packed crowds remains a challenge for mobile robots. The complexity increases further if the environment layout changes, making the prior computed global plan infeasible. In this paper, we show that it is possible to dramatically enhance crowd navigation by just improving the local planner. Our approach combines generative modelling with inference time optimization to generate sophisticated long-horizon local plans at interactive rates. More specifically, we train a Vector Quantized Variational AutoEncoder to learn a prior over the expert trajectory distribution conditioned on the perception input. At run-time, this is used as an initialization for a sampling-based optimizer for further refinement. Our approach does not require any sophisticated prediction of dynamic obstacles and yet provides state-of-the-art performance. In particular, we compare against the recent DRL-VO approach and show a 40% improvement in success rate and a 6% improvement in travel time.

Motion planning is a key aspect of robotics. A common approach to address motion planning problems is trajectory optimization. Trajectory optimization can represent the high-level behaviors of robots through mathematical formulations. However, current trajectory optimization approaches have two main challenges. Firstly, their solution heavily depends on the initial guess, and they are prone to get stuck in local minima. Secondly, they face scalability limitations by increasing the number of constraints. This thesis endeavors to tackle these challenges by introducing four innovative trajectory optimization algorithms to improve reliability, scalability, and computational efficiency. There are two novel aspects of the proposed algorithms. The first key innovation is remodeling the kinematic constraints and collision avoidance constraints. Another key innovation lies in the design of algorithms that effectively utilize parallel computation on GPU accelerators. By using reformulated constraints and leveraging the computational power of GPUs, the proposed algorithms of this thesis demonstrate significant improvements in efficiency and scalability compared to the existing methods. Parallelization enables faster computation times, allowing for real-time decision-making in dynamic environments. Moreover, the algorithms are designed to adapt to changes in the environment, ensuring robust performance. Extensive benchmarking for each proposed optimizer validates their efficacy. Overall, this thesis makes a significant contribution to the field of trajectory optimization algorithms. It introduces innovative solutions that specifically address the challenges faced by existing methods. The proposed algorithms pave the way for more efficient and robust motion planning solutions in robotics by leveraging parallel computation and specific mathematical structures.

Recently, researchers have made considerable improvements in dialogue systems with the progress of large language models (LLMs) such as ChatGPT and GPT-4. These LLM-based chatbots encode the potential biases while retaining disparities that can harm humans during interactions. The traditional biases investigation methods often rely on human-written test cases. However, these test cases are usually expensive and limited. In this work, we propose a first-of-its-kind method that automatically generates test cases to detect LLMs' potential gender bias. We apply our method to three well-known LLMs and find that the generated test cases effectively identify the presence of biases. To address the biases identified, we propose a mitigation strategy that uses the generated test cases as demonstrations for in-context learning to circumvent the need for parameter fine-tuning. The experimental results show that LLMs generate fairer responses with the proposed approach.

Efficient navigation in unknown and dynamic environments is crucial for expanding the application domain of mobile robots. The core challenge stems from the nonavailability of a feasible global path for guiding optimization-based local planners. As a result, existing local planners often get trapped in poor local minima. In this paper, we present a novel optimizer that can explore multiple homotopies to plan high-quality trajectories over long horizons while still being fast enough for real-time applications. We build on the gradient-free paradigm by augmenting the trajectory sampling strategy with a projection optimization that guides the samples toward a feasible region. As a result, our approach can recover from the frequently encountered pathological cases wherein all the sampled trajectories lie in the high-cost region. Furthermore, we also show that our projection optimization has a highly parallelizable structure that can be easily accelerated over GPUs. We push the state-of-the-art in the following respects. Over the navigation stack of the Robot Operating System (ROS), we show an improvement of 7-13% in success rate and up to two times in total travel time metric. On the same benchmarks and metrics, our approach achieves up to 44% improvement over MPPI and its recent variants. On simple point-to-point navigation tasks, our optimizer is up to two times more reliable than SOTA gradient-based solvers, as well as sampling-based approaches such as the Cross-Entropy Method (CEM) and VPSTO. Codes: https://github.com/fatemeh-rastgar/PRIEST

The Catholic Church in Asia recently turned to artificial intelligence to create a document for use by the wider Church in order that its members might more closely "walk together." The Vatican's official media outlet, Vatican News, published a report revealing that synod organizers in Asia had used artificial intelligence to help draft a final document, as Catholic news outlet The Pillar reported. A synod is a gathering, "traditionally of bishops," that helps the Church "to walk forward together in the same direction," notes the Salt and Light Catholic Media Foundation. The word "synod" comes from the Greek syn-hodos, meaning "the same way" or "the same path," it also notes. The Asian synodal continental assembly in Bangkok, Thailand, was held on Feb. 24-26 as part of the global synodal process.

On the face of it, it's difficult to see why the idea of hyperautomation is especially relevant to industrial automation as it exists in the real world. But Neil Ballinger, general manager EMEA of replacement, reconditioned and obsolete automation parts EU Automation, believes there's an opportunity for early adopter businesses to outpace the rest. There are two worlds of automation. One is the conservative, day-to-day, pick-and-place, realm, where countless businesses still play. Here, the idea of automating a process with a robot or collecting some data from a machine to perform advanced analytics is still quite radical.

Recently, Blake Lemoine, a Google AI engineer, caught the attention of the tech world by claiming that an AI is sentient. The AI in question is called LaMDA (short for Language Model for Dialogue Applications). It's a system based on large language models. "I know a person when I talk to it," Lemoine told the Washington Post. "It doesn't matter whether they have a brain made of meat in their head. Or if they have a billion lines of code. And I hear what they have to say, and that is how I decide what is and isn't a person."