Lifeboat Foundation

Probabilistic computing with stochastic devices.

In recent decades, artificial intelligence has been successively employed in the fields of finance, commerce, and other industries. However, imitating high-level brain functions, such as imagination and inference, pose several challenges as they are relevant to a particular type of noise in a biological neuron network. Probabilistic computing algorithms based on restricted Boltzmann machine and Bayesian inference that use silicon electronics have progressed significantly in terms of mimicking probabilistic inference. However, the quasi-random noise generated from additional circuits or algorithms presents a major challenge for silicon electronics to realize the true stochasticity of biological neuron systems. Artificial neurons based on emerging devices, such as memristors and ferroelectric field-effect transistors with inherent stochasticity can produce uncertain non-linear output spikes, which may be the key to make machine learning closer to the human brain. In this article, we present a comprehensive review of the recent advances in the emerging stochastic artificial neurons (SANs) in terms of probabilistic computing. We briefly introduce the biological neurons, neuron models, and silicon neurons before presenting the detailed working mechanisms of various SANs. Finally, the merits and demerits of silicon-based and emerging neurons are discussed, and the outlook for SANs is presented.

Keywords: brain-inspired computing, artificial neurons, stochastic neurons, memristive devices, stochastic electronics.

Continue reading “Emerging Artificial Neuron Devices for Probabilistic Computing” »

A hot potato: ChatGPT, the chatbot that turned machine learning algorithms into a new gold rush for Wall Street speculators and Big Tech companies, is merely a “storefront” for large language models within the Generative Pre-trained Transformer (GPT) series. Developer OpenAI is now readying yet another upgrade for the technology.

OpenAI is busily working on GPT-5, the next generation of the company’s multimodal large language model that will replace the currently available GPT-4 model. Anonymous sources familiar with the matter told Business Insider that GPT-5 will launch by mid-2024, likely during summer.

OpenAI is developing GPT-5 with third-party organizations and recently showed a live demo of the technology geared to use cases and data sets specific to a particular company. The CEO of the unnamed firm was impressed by the demonstration, stating that GPT-5 is exceptionally good, even “materially better” than previous chatbot tech.

Can you imagine going back in time to visit a lost loved one? This heartwrenching desire is what propelled astrophysicist Professor Ron Mallett on a lifelong quest to build a time machine. After years of research, Professor Mallett claims to have finally developed the revolutionary equation for time travel.

The idea of bending time to our will – revisiting the past, altering history, or glimpsing into the future – has been a staple of science fiction for over a century. But could it move from fantasy to reality?

Professor Mallett’s obsession with time travel and its equation has its roots in a shattering childhood experience. When he was just ten years old, his father, a television repairman who fostered his son’s love of science, tragically passed away from a heart attack.

The term “artificial general intelligence” (AGI) has become ubiquitous in current discourse around AI. OpenAI states that its mission is “to ensure that artificial general intelligence benefits all of humanity.” DeepMind’s company vision statement notes that “artificial general intelligence…has the potential to drive one of the greatest transformations in history.” AGI is mentioned prominently in the UK government’s National AI Strategy and in US government AI documents. Microsoft researchers recently claimed evidence of “sparks of AGI” in the large language model GPT-4, and current and former Google executives proclaimed that “AGI is already here.” The question of whether GPT-4 is an “AGI algorithm” is at the center of a lawsuit filed by Elon Musk against OpenAI.

Given the pervasiveness of AGI talk in business, government, and the media, one could not be blamed for assuming that the meaning of the term is established and agreed upon. However, the opposite is true: What AGI means, or whether it means anything coherent at all, is hotly debated in the AI community. And the meaning and likely consequences of AGI have become more than just an academic dispute over an arcane term. The world’s biggest tech companies and entire governments are making important decisions on the basis of what they think AGI will entail. But a deep dive into speculations about AGI reveals that many AI practitioners have starkly different views on the nature of intelligence than do those who study human and animal cognition—differences that matter for understanding the present and predicting the likely future of machine intelligence.

Continue reading “Debates on the nature of artificial general intelligence” »

Researchers have revolutionized quantum sensing with an algorithm that simplifies the assessment of Quantum Fisher Information, thereby enhancing the precision and utility of quantum sensors in capturing minute phenomena.

Quantum sensors help physicists understand the world better by measuring time passage, gravity fluctuations, and other effects at the tiniest scales. For example, one quantum sensor, the LIGO gravitational wave detector, uses quantum entanglement (or the interdependence of quantum states between particles) within a laser beam to detect distance changes in gravitational waves up to one thousand times smaller than the width of a proton!

LIGO isn’t the only quantum sensor harnessing the power of quantum entanglement. This is because entangled particles are generally more sensitive to specific parameters, giving more accurate measurements.

This robot is equipped with AI-backed deep learning algorithms to autonomously manage assisting users with underlying physiological conditions.

The robot illustrated seamless functioning that supports users in walking, standing, and climbing stairs or ramps. Scientists call it, a “unified control framework.”

Particles colliding in accelerators produce numerous cascades of secondary particles. The electronics processing the signals avalanching in from the detectors then have a fraction of a second in which to assess whether an event is of sufficient interest to save it for later analysis. In the near future, this demanding task may be carried out using algorithms based on AI, the development of which involves scientists from the Institute of Nuclear Physics of the PAS.

Significant advancements have been made in quantum computing, with major international companies like Google and IBM now providing quantum computing services via the cloud. Nevertheless, quantum computers are not yet capable of addressing issues that arise when conventional computers hit their performance ceilings. This limitation is primarily 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.

MIT CSAIL researchers introduce FeatUp, a model-agnostic framework designed to significantly enhance the spatial resolution of deep learning features for improved performance in computer vision tasks such as semantic segmentation, depth prediction, and object detection.