Lifeboat Foundation

For decades, scientists and pathologists have tried, without much success, to come up with a way to determine which individual lung cancer patients are at greatest risk of having their illness spread, or metastasize, to other parts of the body.

Now a team of scientists from Caltech and the Washington University School of Medicine in St. Louis has fed that problem to artificial intelligence (AI) algorithms, asking computers to predict which cancer cases are likely to metastasize. In a novel pilot study of non-small cell lung cancer (NSCLC) patients, AI outperformed expert pathologists in making such predictions.

These predictions about the progression of lung cancer have important implications in terms of an individual patient’s life. Physicians treating early-stage NSCLC patients face the extremely difficult decision of whether to intervene with expensive, toxic treatments, such as chemotherapy or radiation, after a patient undergoes lung surgery. In some ways, this is the more cautious path because more than half of stage I–III NSCLC patients eventually experience metastasis to the brain. But that means many others do not. For those patients, such difficult treatments are wholly unnecessary.

The idea of objects seamlessly disappearing, not just in controlled laboratory environments but also in real-world scenarios, has long captured the popular imagination. This concept epitomizes the trajectory of human civilization, from primitive camouflage techniques to the sophisticated metamaterial-based cloaks of today.

Recently, this goal was further highlighted in Science, as one of the “125 questions: exploration and discovery.” Researchers from Zhejiang University have made strides in this direction by demonstrating an intelligent aero amphibious invisibility cloak. This cloak can maintain invisibility amidst dynamic environments, neutralizing external stimuli.

Despite decades of research and the emergence of numerous invisibility cloak prototypes, achieving an aero amphibious cloak capable of manipulating electromagnetic scattering in real-time against ever-changing landscapes remains a formidable challenge. The hurdles are multifaceted, ranging from the need for complex-amplitude tunable metasurfaces to the absence of intelligent algorithms capable of addressing inherent issues such as non-uniqueness and incomplete inputs.

A novel architecture for optical neural networks utilizes wavefront shaping to precisely manipulate the travel of ultrashort pulses through multimode fibers, enabling nonlinear optical computation.

Present-day artificial intelligence systems rely on billions of adjustable parameters to accomplish complex objectives. Yet, the vast quantity of these parameters incurs significant expenses. The training and implementation of such extensive models demand considerable memory and processing power, available only in enormous data center facilities, consuming energy on par with the electrical demands of medium-sized cities. In response, researchers are currently reevaluating both the computing infrastructure and the machine learning algorithms to ensure the sustainable advancement of artificial intelligence continues at its current rate.

Optical implementation of neural network architectures is a promising avenue because of the low-power implementation of the connections between the units. New research reported in Advanced Photonics combines light propagation inside multimode fibers with a small number of digitally programmable parameters and achieves the same performance on image classification tasks with fully digital systems with more than 100 times more programmable parameters.

Without a more comprehensive set of big data, AI algorithms are more likely to generate an inaccurate or incomplete data model. Insufficient data leads to a model that is not capable of predicting outcomes with the level of accuracy that’s needed in the real world.

Anyone with experience in the art market also knows that markets can fluctuate without any indication as to why. AI will not have the answer. Tech entrepreneur Boris Pevzner, founder of AI-powered data platform Live Art, asserts that while AI is a tool that can be used as an indicator, it is not something that can predict any real-world auction prices.

Although AI is becoming increasingly prevalent in the art business, it does not have to be seen as a threat. Many people view AI as a dangerous tool, but AI does not need to be perceived in this way. Instead of a replacement for human expertise, we should see it as a tool of advancement to be used alongside humans to improve the quality of their work.

RoseTTAFold extended to predict structures of proteins bound to small molecules.

The rapid advancement of deep learning algorithms and generative models has enabled the automated production of increasingly striking AI-generated artistic content. Most of this AI-generated art, however, is created by algorithms and computational models, rather than by physical robots.

Researchers at Universidad Complutense de Madrid (UCM) and Universidad Carlos III de Madrid (UC3M) recently developed a deep learning-based model that allows a humanoid robot to sketch pictures, similarly to how a human artist would. Their paper, published in Cognitive Systems Research, offers a remarkable demonstration of how robots could actively engage in creative processes.

“Our idea was to propose a robot application that could attract the scientific community and the general public,” Raúl Fernandez-Fernandez, co-author of the paper, told Tech Xplore. “We thought about a task that could be shocking to see a robot performing, and that was how the concept of doing art with a humanoid robot came to us.”

Elon Musk is suing OpenAI and Sam Altman for allegedly abandoning OpenAI’s original mission to develop artificial intelligence to benefit humanity.

“OpenAI, Inc. has been transformed into a closed-source de facto subsidiary of the largest technology company in the world: Microsoft,” Musk’s lawyers wrote in the lawsuit, which was filed late on Thursday in San Francisco.

“Under its new board, it is not just developing but is refining an AGI [Artificial General Intelligence] to maximize profits for Microsoft, rather than for the benefit of humanity,” claims the filing. “On information and belief, GPT-4 is an AGI algorithm.”

The most popular words of 2023 were recently released, with AI Large Language Model (LLM) unquestionably topping the list. As a front-runner, ChatGPT also emerged as one of the international buzzwords of the year. These disruptive innovations in AI owe much to big data, which has played a pivotal role. Yet, AI has simultaneously presented new opportunities and challenges to the development of big data.

High-capacity data storage is indispensable in today’s digital economy. However, major storage devices like hard disk drives and semiconductor flash devices face limitations in terms of cost-effectiveness, durability, and longevity.

Optical data storage offers a promising green solution for cost-effective and long-term data storage. Nonetheless, optical data storage encounters a fundamental limitation in the spacing of adjacent recorded features, owing to the optical diffraction limit. This physical constraint not only impedes the further development of direct laser writing machines but also affects optical microscopy and storage technology.

The purpose of this work is to investigate how several inflationary and bouncing scenarios can be realized by imperfect fluids. We shall use two different theoretical frameworks, namely classical cosmology and Loop Quantum Cosmology (LQC) (see where the derivation of the Hamiltonian in LQC was firstly derived to yield the modified Friedman equation, and also see for a recent derivation of the effective Hamiltonian in LQC, which was derived by demanding repulsive gravity, as in Loop Quantum Gravity). In both cases we shall investigate which imperfect fluid can realize various inflationary and bouncing cosmology scenarios. The inflationary cosmology and bouncing cosmology are two alternative scenarios for our Universe evolution. In the case of inflation, the Universe starts from an initial singularity and accelerates at early times, while in the case of the bouncing cosmology, the Universe initially contracts until it reaches a minimum radius, and then it expands again. With regards to inflation, we shall be interested in four different inflationary scenarios, namely the intermediate inflation, the Starobinsky inflation, and two constant-roll inflation scenarios. With regards to bouncing cosmologies, we shall be interested in realizing several well studied bouncing cosmologies, and particularly the matter bounce scenario, the superbounce scenario and the singular bounce.

As we already mentioned we shall use two theoretical frameworks, that of classical cosmology and that of LQC. After presenting the reconstruction methods for realizing the various cosmologies with imperfect fluids, we proceed to the realization of the cosmologies by using the reconstruction methods. In the case of classical cosmology, we will calculate the power spectrum of primordial curvature perturbations, the scalar-to-tensor ratio and the running of the spectral index for all the aforementioned cosmologies, and we compare the results to the recent Planck data. The main outcome of our work is that, although the cosmological scenarios we study in this paper are viable in other modified gravity frameworks, these are not necessarily viable in all the alternative modified gravity descriptions. As we will demonstrate, in some cases the resulting imperfect fluid cosmologies are not compatible at all with the observational data, and in some other cases, there is partial compatibility.

We need to note that the perturbation aspects in LQC are not transparent enough and assume that there are no non-trivial quantum gravitational modifications arising due to presence of inhomogeneities. As it was shown in, a consistent Hamiltonian framework does not allow this assumption to be true. The perturbations issues that may arise in the context of the present work, are possibly more related to some early works in LQC, so any calculation of the primordial power spectrum should be addressed as we commented above.