Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science – Page 65

In the adrenaline-fueled arena of sports gambling, a revolution is unfolding — one powered by artificial intelligence (AI). This technological marvel is transforming the art of sports betting from a game of chance into a symphony of data-driven precision. Let us explore the burgeoning world where AI intersects with sports gambling, turning bettors from mere spectators into strategic players in a game where data, algorithms, and probabilities redefine the odds.

Sports gambling, a realm where intuition, experience, and sometimes sheer luck have traditionally dictated the rules, is undergoing a transformative shift. AI, with its unparalleled ability to analyze vast datasets and discern patterns beyond human capability, is emerging as the new MVP in this field. This transition from gut-driven bets to AI-powered predictions is not just about increasing the odds of winning; it’s about elevating sports gambling to an art of calculated strategies.

At the heart of AI’s influence in sports gambling lies predictive analytics. Companies like Stratagem and Stats Perform are harnessing the power of AI to analyze historical data, player statistics, and even weather conditions to predict game outcomes with astonishing accuracy. For instance, Stratagem uses advanced machine learning algorithms to turn data from thousands of past games into insightful betting strategies, offering gamblers an edge that was unimaginable a few years ago.

Read more | >

Quantum computing has been hailed as a technology that can outperform classical computing in both speed and memory usage, potentially opening the way to making predictions of physical phenomena not previously possible.

Many see quantum computing’s advent as marking a paradigm shift from classical, or conventional, computing. Conventional computers process information in the form of digital bits (0s and 1s), while quantum computers deploy quantum bits (qubits) to store in values between 0 and 1.

Under certain conditions, this ability to process and store information in qubits can be used to design that drastically outperform their classical counterparts. Notably, quantum’s ability to store information in values between 0 and 1 makes it difficult for to perfectly emulate quantum ones.

Read more | >

SingularityNET’s community leaders reflect back on last year’s progress, ecosystem updates, as well as the massive push towards building beneficial AGI in 2024 and beyond.

Register for our BGI Summit today by visiting: https://bgi24.ai.

#agi #decentralizedai #singularitynet.
–

SingularityNET is a decentralized marketplace for artificial intelligence. We aim to create the world’s global brain with a full-stack AI solution powered by a decentralized protocol.

We gathered the leading minds in machine learning and blockchain to democratize access to AI technology. Now anyone can take advantage of a global network of AI algorithms, services, and agents.

Website: https://singularitynet.io.

Continue reading “SingularityNET’s Big AGI Plans for 2024” | >

Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve desired target emissivity spectra, as in typical emissivity engineering, for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc.

However, previous designs required prior knowledge of materials or structures for different applications, and the designed WS-TEs usually vary from application to application in terms of materials and structures, thus there is no general design for emissivity engineering across different applications. Moreover, previous designs fail to tackle the simultaneous design of both materials and structures, as they either fix materials to design structures or fix structures to select suitable materials.

In a new paper published in Light: Science & Applications, a team of scientists, led by Professor Run Hu from School of Energy and Power Engineering, Huazhong University of Science and Technology, China, and coworkers have proposed a general deep learning framework based on the deep Q-learning network algorithm (DQN) for efficient optimal design of WS-TEs across different applications.

Read more | >

In the dynamic and fast-paced world of private equity, AI integration is not just a passing trend; it’s a transformative force reshaping the landscape of the industry. As firms navigate the complexities of investments, market analysis, and financial predictions, AI emerges as a beacon of efficiency, insight, and innovation.

Currently, AI’s integration in private equity is impressive but not expansive. Most firms primarily focused on data analysis, deal sourcing, and risk assessment. Firms like KKR & Co. and Blackstone pioneered this industry revolution, leveraging AI to analyze market trends, evaluate potential investments, and enhance decision-making processes. For instance, consider how AI algorithms process vast amounts of data to identify promising investment opportunities. By sifting through global financial reports, news, and company data, AI provides a deeper understanding of risks and rewards, at level of volume and understanding that most human analysts would find overwhelming.

Additionally, private equity firms find AI-driven risk assessment models indispensable. These models predict market fluctuations, assess potential investment hazards, and offer a more nuanced understanding of various sectors. This predictive power allows firms to make more informed decisions, balancing risks with potential returns more effectively.

Read more | >

In the future, modern machines should not only follow algorithms quickly and precisely, but also function intelligently—in other words, in a way that resembles the human brain. Scientists from Dortmund, Loughborough, Kiev and Nottingham have now developed a concept inspired by eyesight that could make future artificial intelligence much more compact and efficient.

They built an on-chip phonon-magnon for neuromorphic computing which has recently been featured as Editor’s Highlight by Nature Communications.

The human sensory organs convert information such as light or scent into a signal that the brain processes through myriads of neurons connected by even more synapses. The ability of the brain to train, namely transform synapses, combined with the neurons’ huge number, allows humans to process very complex external signals and quickly form a response to them.

Read more | >

Despite the Harvard 48 logical #qubits paper is perhaps the biggest leap in #quantum technologies, still the final circuit is classically simulable.

Politics makes strange bedfellows, apparently so does quantum benchmarking.

In a surprising development, IBM Quantum and IonQ researchers teamed up to reveal an alternative classical simulation algorithm for an impressive error correction study conducted by a Harvard and QuEra team and published recently in Nature. IBM is a leader in superconducting quantum computers, while IonQ is noted as a pioneer in trapped ion devices.

The IBM-IonQ team reports in ArXiv that their classical algorithm accomplished the same computational task that was performed by the 48-qubit quantum setup in that Nature study, in a mere 0.00257947 seconds.

Read more | >

Google has launched Gemini, a new artificial intelligence system that can seemingly understand and speak intelligently about almost any kind of prompt—pictures, text, speech, music, computer code, and much more.

This type of AI system is known as a multimodal model. It’s a step beyond just being able to handle text or images like previous algorithms. And it provides a strong hint of where AI may be going next: being able to analyze and respond to real-time information from the outside world.

Although Gemini’s capabilities might not be quite as advanced as they seemed in a viral video, which was edited from carefully curated text and still-image prompts, it is clear that AI systems are rapidly advancing. They are heading towards the ability to handle more and more complex inputs and outputs.

Read more | >

Tokamak fusion plasmas benefit from high pressures but are then susceptible to modes of instability. These magnetohydrodynamic (MHD) modes are macroscopic distortions of the plasma, but certain collective motions of individual particles can provide stabilizing effects opposing them. The presence of a resistive wall slows the mode growth, converting a kink to a resistive wall mode (RWM). A kinetic MHD model includes Maxwell’s equations, ideal MHD constraints, and kinetic effects included through the pressure tensor, calculated with the perturbed drift-kinetic distribution function of the particles. The kinetic stabilizing effects on the RWM arise through resonances between the plasma rotation and particle drift motions: precession, bounce, and transit. A match between particle motions and the mode allows efficient transfer of energy that would otherwise drive the growth of the mode, thus damping the growth. The first approach to calculating RWM stability is to write a set of equations for the complex mode frequency in terms of known quantities and then to solve the system. The “energy principle” approach, which has the advantage of clarity in distinguishing the various stabilizing and destabilizing effects, is to change the force balance equation into an equation in terms of changes of kinetic and potential energies, and then to write a dispersion relation for the mode frequency in terms of those quantities. These methods have been used in various benchmarked codes to calculate kinetic effects on RWM stability. The theory has illuminated the important roles of plasma rotation, energetic particles, and collisions in RWM stability.

Read more | >