Lifeboat Foundation

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.

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 reservoir 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.

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.

Continue reading “IBM and IonQ Researchers Design Classical Algorithm to Tackle Recent Harvard-Led Study’s Computational Task” »

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.

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.

Research conducted by a team of scientists from Kaunas universities, Lithuania, revealed that low-frequency ultrasound influences blood parameters. The findings suggest that ultrasound’s effect on haemoglobin can improve oxygen’s transfer from the lungs to bodily tissues.

The research was undertaken on 300 blood samples collected from 42 pulmonary patients. The samples were exposed to six different low-frequency ultrasound modes at the Institute of Mechatronics of Kaunas University of Technology (KTU).

The changes in 20 blood parameters were registered using the blood analysing equipment at the Lithuanian University of Health Sciences (LSMU) laboratories. For the prediction of ultrasound exposure, artificial intelligence, i.e. analysis of variance (ANOVA), non-parametric Kruskal-Wallis method and machine learning algorithms were applied. The calculations were made at the KTU Artificial Intelligence Centre.

Mastercard has announced that it has developed an in-house generative AI to help combat fraud on its payment processing network.

Instead of relying on textual inputs, Mastercard’s algorithm uses a cardholder’s merchant visit history as a prompt to determine whether a transaction involves a business that the customer would likely visit. The algorithm generates pathways through Mastercard’s network, akin to heat-sensing radar, to provide a score as an answer.

Continue reading “Mastercard’s new anti-fraud AI has a success rate of up to 300%” »

An AI algorithm outperformed other screening methods in identifying cervical precancer. The approach could be especially valuable in low-resource settings.

There has been significant progress in the field of quantum computing. Big global players, such as Google and IBM, are already offering cloud-based quantum computing services. However, quantum computers cannot yet help with problems that occur when standard computers reach the limits of their capacities because the availability of qubits or quantum bits, i.e., the basic units of quantum information, is still insufficient.

One of the reasons for this is that bare qubits are not of immediate use for running a quantum algorithm. While the binary bits of customary computers store information in the form of fixed values of either 0 or 1, qubits can represent 0 and 1 at one and the same time, bringing probability as to their value into play. This is known as quantum superposition.

This makes them very susceptible to external influences, which means that the information they store can readily be lost. In order to ensure that quantum computers supply reliable results, it is necessary to generate a genuine entanglement to join together several physical qubits to form a logical qubit. Should one of these physical qubits fail, the other qubits will retain the information. However, one of the main difficulties preventing the development of functional quantum computers is the large number of physical qubits required.