Lifeboat Foundation

Artificial neural networks (ANNs) show a remarkable pattern when trained on natural data irrespective of exact initialization, dataset, or training objective; models trained on the same data domain converge to similar learned patterns. For example, for different image models, the initial layer weights tend to converge to Gabor filters and color-contrast detectors. Many such features suggest global representation that goes beyond biological and artificial systems, and these features are observed in the visual cortex. These findings are practical and well-established in the field of machines that can interpret literature but lack theoretical explanations.

Localized versions of canonical 2D Fourier basis functions are the most observed universal features in image models, e.g. Gabor filters or wavelets. When vision models are trained on tasks like efficient coding, classification, temporal coherence, and next-step prediction goals, these Fourier features pop up in the model’s initial layers. Apart from this, Non-localized Fourier features have been observed in networks trained to solve tasks where cyclic wraparound is allowed, for example, modular arithmetic, more general group compositions, or invariance to the group of cyclic translations.

Researchers from KTH, Redwood Center for Theoretical Neuroscience, and UC Santa Barbara introduced a mathematical explanation for the rise of Fourier features in learning systems like neural networks. This rise is due to the downstream invariance of the learner that becomes insensitive to certain transformations, e.g., planar translation or rotation. The team has derived theoretical guarantees regarding Fourier features in invariant learners that can be used in different machine-learning models. This derivation is based on the concept that invariance is a fundamental bias that can be injected implicitly and sometimes explicitly into learning systems due to the symmetries in natural data.

Once a buzzword, neuromorphic engineering is gaining traction in the semiconductor industry.

Neuromorphic engineering is finally getting closer to market reality, propelled by the AI/ML-driven need for low-power, high-performance solutions.

Continue reading “Running More Efficient AI/ML Code With Neuromorphic Engines” »

Superfast magnetic memory devices are possible after scientists engineer way to use lasers to magnetize non-magnetic materials.

Prof Roberto Maiolino, an astrophysicist at the University of Cambridge, and a member of team behind the observations, said: “One problem that we have in cosmology is explaining how these black holes manage to grow so big. In the past we have always talked about gobbling matter very quickly or being born big. Another possibility is that they grow very fast by merging.”

Until now it was not clear whether the merging of galaxies – which is known to have happened – would also result in the black holes at the centres morphing into a single cosmic sinkhole. Recent models have suggested that one of them would be kicked out into space to become a “wandering black hole”

The latest observations use the Webb telescope’s ability to get to the far reaches of the cosmos and so have provided the first glimpse of galactic mergers in the distant past.

The paradigm shift of artificial superintelligence (ASI) is imminent, promising unprecedented possibilities and profound perils for society.

Ten predictions for 2035 to reshape society, from AI and AGI to breakthroughs in brain-computer interfaces, living movies, and climate tech.

The letter, which is being sent to tech companies around the world this week, marks an escalation of the music group’s attempts to stop the melodies, lyrics and images from copyrighted songs and artists being used by tech companies to produce new versions or to train systems to create their own music.

The letter says that Sony Music and its artists “recognise the significant potential and advancement of artificial intelligence” but adds that “unauthorised use… in the training, development or commercialisation of AI systems deprives [Sony] of control over and appropriate compensation”

It says: “This letter serves to put you on notice directly, and reiterate, that [Sony’s labels] expressly prohibit any use of [their] content.”

O.o!!! The universe sure interesting because it so complex like a Euclidean hall of mirrors. Much of the universe is still misunderstood because much of what is known is still being understood like the holographic universe which seems to explain most everything but still doesn’t explain what is outside the universe.

Most models for the overall shape and geometry of the Universe—including some exotic ones—are compatible with the latest cosmic observations.

Building a useful quantum computer in practice is incredibly challenging. Significant improvements are needed in the scale, fidelity, speed, reliability, and programmability of quantum computers to fully realize their benefits. Powerful tools are needed to help with the many complex physics and engineering challenges that stand in the way of useful quantum computing.

AI is fundamentally transforming the landscape of technology, reshaping industries, and altering how we interact with the digital world. The ability to take data and generate intelligence paves the way for groundbreaking solutions to some of the most challenging problems facing society today. From personalized medicine to autonomous vehicles, AI is at the forefront of a technological revolution that promises to redefine the future, including many challenging problems standing in the way of useful quantum computing.

Quantum computers will integrate with conventional supercomputers and accelerate key parts of challenging problems relevant to government, academia, and industry. This relationship is described in An Introduction to Quantum Accelerated Supercomputing. The advantages of integrating quantum computers with supercomputers are reciprocal, and this tight integration will also enable AI to help solve the most important challenges standing in the way of useful quantum computing.