The problem of finding the shortest path in a graph G(V, E) has been widely studied. However, in many applications it is necessary to compute an arbitrary number of them, k. Even though the problem has raised a lot of interest from different research communities and many applications of it are known, it has not been addressed to the same extent as the single shortest path problem. The best algorithm known for efficiently solving this task has a time complexity of O (|E| + |V|log{|V|}+k|V|)$ when computing paths in explicit form, and is based on best-first search. This paper introduces a new search algorithm with the same time complexity, which results from a natural evolution of A* thus, it preserves all its interesting properties, making it widely applicable to many different domains. Experiments in various testbeds show a significant improvement in performance over the state of the art, often by one or two orders of magnitude.

Mathematical formulas serve as the means of communication between humans and nature, encapsulating the operational laws governing natural phenomena. The concise formulation of these laws is a crucial objective in scientific research and an important challenge for artificial intelligence (AI). While traditional artificial neural networks (MLP) excel at data fitting, they often yield uninterpretable black box results that hinder our understanding of the relationship between variables x and predicted values y. Moreover, the fixed network architecture in MLP often gives rise to redundancy in both network structure and parameters. To address these issues, we propose MetaSymNet, a novel neural network that dynamically adjusts its structure in real-time, allowing for both expansion and contraction. This adaptive network employs the PANGU meta function as its activation function, which is a unique type capable of evolving into various basic functions during training to compose mathematical formulas tailored to specific needs. We then evolve the neural network into a concise, interpretable mathematical expression. To evaluate MetaSymNet's performance, we compare it with four state-of-the-art symbolic regression algorithms across more than 10 public datasets comprising 222 formulas. Our experimental results demonstrate that our algorithm outperforms others consistently regardless of noise presence or absence. Furthermore, we assess MetaSymNet against MLP and SVM regarding their fitting ability and extrapolation capability, these are two essential aspects of machine learning algorithms. The findings reveal that our algorithm excels in both areas. Finally, we compared MetaSymNet with MLP using iterative pruning in network structure complexity. The results show that MetaSymNet's network structure complexity is obviously less than MLP under the same goodness of fit.

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World is Evolving with Artificial Intelligence, Artificial Intelligence (AI) is a buzzword in the present times. Fundamentally, AI means machines mimicking human intelligence and executing some functions in the manner of a human brain, such as problem-solving, understanding language, learning from experience, etc. Momentous developments transforming manufacturing, healthcare, marketing, financing, and so on are occurring in AI. Organizations and people are increasingly banking on AI, robotics, data science, and Machine Learning (ML), to enhance customer experience and to make their processes and products more efficient. Whether it is the phones we use, the television we watch, or the cars we drive, AI changes the way we communicate, travel, and are entertained. There are endless applications of AI.

In todays' modern scenario, we see more and more companies embracing and truly unlocking the power of Data Science. Most businesses would benefit from structuring their data, and the application of Data Science can be used in various fields. In the future, we will going to have even more powerful applications and will have streamlined the process, which enables smaller businesses and individuals to take advantage of data and processes like Deep Learning and Machine Learning which are all a part of Data Science. Data Science is simply nothing but a quantitative approach to a problem. Previously, due to lack of data or processing power, we relied on other things like an authoritarian whim, consensus, and expert intuition.

Benefits of chatbots 2021: To define it in simple terms, chatbots are artificial intelligence-driven (AI) robots that are integrated into smart devices so that they can talk with humans without letting them know that they are conversing with something artificial. Chatbots use natural language processing (NLP) and sentiment analysis to stimulate conversations with users. Chatbots are known by different names today. Some of these names are interactive agents, smart bots, digital assistants. Chatbots are the most innovative and significant developments in the domain of AI.

AI has now made it onto CEOs' agendas. While the topic certainly isn't new, CEOs have learned that the idea of AI is far simpler than its effective application. To get there, companies need to start with their business objectives and then use AI in ways that advance those objectives rather than just implementing AI for AI's sake and hoping it can add value later. Meanwhile, CEO attitudes about AI and machine learning or ML (a subset of AI techniques) have been changing as it relates to digital disruption. In the beginning, it was about understanding what digital disrupters do and how they do it.

The pandemic that has raged across the globe over the past year has shone a cold, hard light on many things--the varied levels of preparedness to respond; collective attitudes toward health, technology, and science; and vast financial and social inequities. As the world continues to navigate the covid-19 health crisis, and some places even begin a gradual return to work, school, travel, and recreation, it's critical to resolve the competing priorities of protecting the public's health equitably while ensuring privacy. The extended crisis has led to rapid change in work and social behavior, as well as an increased reliance on technology. The expanded and rapid adoption of artificial intelligence (AI) demonstrates how adaptive technologies are prone to intersect with humans and social institutions in potentially risky or inequitable ways. "Our relationship with technology as a whole will have shifted dramatically post-pandemic," says Yoav Schlesinger, principal of the ethical AI practice at Salesforce.

When designing your cybersecurity defenses for the new normal, it's important to look beyond the technology. You'll need a true architecture-led approach, one that's driven by your business needs. The global pandemic has jolted many organizations into a new reality where virtual and remote become more important than physical and local. Security architectures deployed over decades have suddenly become irrelevant. Concurrent to this, organizations are looking inward and challenging themselves by asking, "How do I justify new investment in tight economic times? In fact, how do I justify an entirely new security architecture for this remote work reality?"

This week we are reprinting our top stories of 2020. This article first appeared online in our Facts So Romantic blog in May, 2020. In May, the cover of New Scientist ran the headline, "Is the Universe Conscious?" Mathematician and physicist Johannes Kleiner, at the Munich Center for Mathematical Philosophy in Germany, told author Michael Brooks that a mathematically precise definition of consciousness could mean that the cosmos is suffused with subjective experience. "This could be the beginning of a scientific revolution," Kleiner said, referring to research he and others have been conducting.