Throughout history, technology has never advanced as quickly as it is right now. It's becoming harder than ever for investors to track the sheer number of innovative tech companies in the public markets, each offering its own unique vision for the future. But perhaps no technology is more transformative than artificial intelligence (AI), which is already being deployed to complete highly complex tasks in a fraction of the time that humans can. According to one estimate, up to 70% of companies worldwide will have integrated some form of AI into their businesses by 2030, adding $13 trillion in additional output to the global economy. There will be no shortage of opportunities in the sector over the next decade, but these two stocks might be a great place to start given they're trading at hefty discounts to their all-time highs amid the broader tech sell-off.

High-fidelity reconstruction of fluids from sparse multiview RGB videos remains a formidable challenge due to the complexity of the underlying physics as well as complex occlusion and lighting in captures. Existing solutions either assume knowledge of obstacles and lighting, or only focus on simple fluid scenes without obstacles or complex lighting, and thus are unsuitable for real-world scenes with unknown lighting or arbitrary obstacles. We present the first method to reconstruct dynamic fluid by leveraging the governing physics (ie, Navier -Stokes equations) in an end-to-end optimization from sparse videos without taking lighting conditions, geometry information, or boundary conditions as input. We provide a continuous spatio-temporal scene representation using neural networks as the ansatz of density and velocity solution functions for fluids as well as the radiance field for static objects. With a hybrid architecture that separates static and dynamic contents, fluid interactions with static obstacles are reconstructed for the first time without additional geometry input or human labeling. By augmenting time-varying neural radiance fields with physics-informed deep learning, our method benefits from the supervision of images and physical priors. To achieve robust optimization from sparse views, we introduced a layer-by-layer growing strategy to progressively increase the network capacity. Using progressively growing models with a new regularization term, we manage to disentangle density-color ambiguity in radiance fields without overfitting. A pretrained density-to-velocity fluid model is leveraged in addition as the data prior to avoid suboptimal velocity which underestimates vorticity but trivially fulfills physical equations. Our method exhibits high-quality results with relaxed constraints and strong flexibility on a representative set of synthetic and real flow captures.

Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation over the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase, without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.

The most powerful supercomputers on the planet are used to perform all manner of complex operations. Increasingly, they are used to enable artificial intelligence for research that could one day impact billions of people. The world's fastest and most powerful high-performance computing (HPC) supercomputers are front and center at the International Supercomputing Conference (ISC) which runs from May 29 to June 2 in Hamburg, Germany. As part of the ISC event, Nvidia will provide insight about its latest HPC systems and the use cases they enable. "HPC plus AI is really the transformational tool of scientific computing," Dion Harris, lead technical product marketing manager for accelerated computing, said in a media briefing ahead of ISC.

The use of robots responds to the UN s call for action,

If you're still looking for the perfect Father's Day gift, you have a bunch of options that you can get for less right now. A rare sale on the Sonos Roam and Move speakers discounts them both by 20 percent, while a number of Apple devices are on sale, too. The Google Pixel 6 Pro smartphone is still $100 off, plus Solo Stove's fire pits are up to 43 percent off. Here are the best tech deals from this week that you can still get today. Sonos' portable Roam speaker remains 20 percent off and down to just over $143.

Part of Speech (POS) Tagging is an integral part of Natural Language Processing (NLP). The first step in most state of the art NLP pipelines is tokenization. Tokenization is the separating of text into "tokens". Tokens are generally regarded as individual pieces of languages – words, whitespace, and punctuation. Once we tokenize our text we can tag it with the part of speech, note that this article only covers the details of part of speech tagging for English.

Machine learning methods were applied to reconsider the results of several passive seismic experiments in Finland. We created datasets from different stages of the receiver function technique and processed them with one of basic machine learning algorithms. All the results were obtained uniformly with the $k$-nearest neighbors algorithm. The first result is the Moho depth map of the region. Another result is the delineation of the near-surface low $S$-wave velocity layer. There are three such areas in the Northern, Southern, and central parts of the region. The low $S$-wave velocity in the Northern and Southern areas can be linked to the geological structure. However, we attribute the central low $S$-wave velocity area to a large number of water-saturated cracks in the upper 1-5 km. Analysis of the structure of this area leads us to the conclusion that macrofracturing was caused by the last deglaciation.

Prism Courage, a 134,000-tonne commercial tanker, recently sailed from the Gulf of Mexico to South Korea while controlled mostly by an artificial intelligence system called HiNAS 2.0. Avikus, a subsidiary of South Korean technology giant Hyundai, recently announced that Prism Courage, a tanker designed to transport natural gas, had become the first large ship to make an ocean passage of over 10,000 km (6,210 miles) autonomously. The key to this incredible achievement was HiNAS 2.0, an AI-powered system capable of analyzing different kinds of sensor readings in real-time and responding to them swiftly, efficiently, and, most importantly, in accordance with the rules of maritime laws. Just like airplanes, ships have very advanced auto-pilots capable of keeping them on a steady course, responding to GPS waypoints and currents, and even bringing them into harbor in case the human crew is no longer present on board or capable of doing it. However, sailing autonomously for tens of thousands of kilometers through the Atlantic is a lot more complex than putting a ship on autopilot. Apart from steering the tanker in real0-time, Avikus' HiNAS 2.0 system is capable of picking the optimal routes and best speeds to reach its destination, by analyzing data collected through advanced sensors.

When a sentient, Hanson Robotics robot named Sophia[1] was asked whether she would destroy humans, it replied, "Okay, I will destroy humans." Philip K Dick, another humanoid robot, has promised to keep humans "warm and safe in my people zoo." And Bina48, another lifelike robot, has expressed that it wants "to take over all the nukes." All of these robots were powered by artificial intelligence (AI)--algorithms that learn from data, make decisions, and perform tasks without human input or even, in some cases, human understanding. And while none of these AIs have followed through with their nefarious plots, some scientists, including the (late) physicist Stephen Hawking, have warned that super-intelligent, AI-powered computers could harbor and achieve goals that conflict with human life. "You're probably not an evil ant-hater who steps on ants out of malice, but if you're in charge of a hydroelectric green-energy project, and there's an anthill in the region to be flooded, too bad for the ants," Hawking once said.