Breakthroughs, discoveries, and DIY tips sent every weekday. Our planet runs on pollinators. Without bees, moths, weevils, and more zooming around and spreading plants' reproductive cells, plants and important crops would not grow. Without plants we would not breathe or eat. When these crucial pollinating species visit flowers and other plants, they produce a number of characteristic sounds, such as wing flapping when hovering, landing, and taking off.

Anyone who has tried to keep porch plants or a home garden alive through seasonal changes knows it's a task easier said than done. Abrupt temperature changes--like cold snaps--and prolonged periods of drought can stress plants, disrupting their normal biochemistry. If not addressed quickly enough, those stresses can eventually kill the plant. Disappointed growers often only see the tell-tale signs (like shriveling or browning leaves) after it's too late. But a new plant-wearable device developed by researchers at the American Chemical Society could offer an early warning system. The wearable, detailed this week in the journal ACS Sensors, comes in the form of an electromagnetic sensor attached directly to plant leaves.

Planning an optimal route in a complex environment requires efficient reasoning about the surrounding scene. While human drivers prioritize important objects and ignore details not relevant to the decision, learning-based planners typically extract features from dense, high-dimensional grid representations containing all vehicle and road context information. In this paper, we propose PlanT, a novel approach for planning in the context of self-driving that uses a standard transformer architecture. PlanT is based on imitation learning with a compact object-level input representation. On the Longest6 benchmark for CARLA, PlanT outperforms all prior methods (matching the driving score of the expert) while being 5.3x faster than equivalent pixel-based planning baselines during inference. Combining PlanT with an off-the-shelf perception module provides a sensor-based driving system that is more than 10 points better in terms of driving score than the existing state of the art. Furthermore, we propose an evaluation protocol to quantify the ability of planners to identify relevant objects, providing insights regarding their decision-making. Our results indicate that PlanT can focus on the most relevant object in the scene, even when this object is geometrically distant.

From analyzing millions of satellite images to finding healthy strains of plant microbiome, these startups have raised over $500M to bring AI and robotics to agriculture. Agricultural tech startups have raised over $800M in the last 5 years. Deals to startups using robotics and machine learning to solve problems in agriculture started gaining momentum in 2014, in line with the rising interest in artificial intelligence across multiple industries like healthcare, finance, and commerce. Smart money VCs like Bessemer Venture Partners, Accel Partners, Khosla Ventures, Lux Capital, and Data Collective have invested in general-purpose drone and computer vision companies with a focus on agricultural applications, like DJI and Orbital Insight, as well as ag tech startups like Blue River Technology. Big corporations like Monsanto and Syngenta, which are active ag tech investors, have also backed companies like Resson and previously mentioned Blue River Technology.

SINCE WE ARE FIRMLY CONVINCED that ideas like people have histories and can only be fully understood in the context of those histories, we will begin by discussing the underlying ideas that motivated us to initiate the Steamer effort. Without richer and more detailed understandings of the nature of these models, instructional applications will be severely limited. Graphical Interfaces for Interactave Inspectable Simulatzons - We believe that graphical interfaces to simulations of physical systems deserve extensive exploration. They make possible new types of instructional interactions by allowing one to control, manipulate, and monitor simulations of dynamic systems at many different hierarchical levels The key idea in Steamer is the conception of an znteractive inspectable simulation. We have consistently sought to make the system inspectable.

The need for online diagnostics in the electric powergeneration industry is driven by a number of significant factors . Due to the low number of new power plants being built by electric utilities, the average age of existing power plant equipment in the United States and its susceptibility to failure is increasing rapidly. Figure 1 shows the percentage of power-generation equipment over 20 years old as a function of year. Note the rapid increase of average age after 1980 and the fact that by the year 2000 fully 50 percent of all generation equipment in the United States will be over 20, the oldest average age of power plant equipment ever experienced by U.S. utilities. Thus, there is a need to know what the actual operating condition of the equipment is at all times, so that outages can be avoided by taking corrective actions at the earliest possible time and by preplanning for outages if they become necessary in order to to minimize their length.

The long-term future of space exploration at the National Aeronautics and Space Administration (NASA) is dependent on the full exploitation of autonomous and adaptive systems, but mission managers are worried about the reliability of these more intelligent systems. The main focus of the workshop was to address these worries; hence, we invited NASA engineers working on autonomous and adaptive systems and researchers interested in the verification and validation of software systems. The dual purpose of the meeting was to (1) make NASA engineers aware of the verification and validation techniques they could be using and (2) make the verification and validation community aware of the complexity of the systems NASA is developing. The workshop was held 5 to 7 December 2000 at the Asilomar Conference Center in Pacific Grove, California. Mission managers are, however, worried about the reliability of these more intelligent systems. The main focus of the workshop was to address these worries; ...

A wide range of sensor-rich, networked embedded systems are being created that must operate robustly for years in the face of novel failures by managing complex autonomic processes. These systems are being composed, for example, into vast networks of space, air, ground, and underwater vehicles. Our objective is to revolutionize the way in which we control these new artifacts by creating reactive model-based programming languages that enable everyday systems to reason intelligently and enable machines to explore other worlds. A model-based program is state and fault aware; it elevates the programming task to specifying intended state evolutions of a system. The program's executive automatically coordinates system interactions to achieve these states, entertaining known and potential failures, using models of its constituents and environment.

Workshops The Workshop on Advances in Real-Time Expert System Technologies was held on 3 August 1992 in conjunction with the Tenth European Conference on AI. Participation was limited to invited researchers only. The workshop focused on practical problems occurring during the implementation of real-time expert systems. In this respect, different industrial applications were discussed. The debate covered a wide range of applications, such as qualitative simulation and anytime algorithms for real-time process control.

The robot exhibition had a very successful 1998. At the conference, we had 11 robot demonstrations (including three multirobot demos), 5 oral presentations, and an additional 5 video or poster submissions. The exhibition also included a published video proceedings for the first time. One of the most interesting features of the exhibition was the variety of capabilities shown. From a mechanical point of view, indoor wheeled robots were, as usual, the most common form of robot, but the exhibit also featured several outdoor wheeled robots, several legged robots, two humanoids, a snake, and a plant.