Lifeboat Foundation

Here’s what you need to access OpenAI’s newest generative AI model.

Spur uses computer vision to help companies monitor and predict how a wind farm will impact a local bird population.

Oscar Wilde once said that sarcasm was the lowest form of wit, but the highest form of intelligence. Perhaps that is due to how difficult it is to use and understand. Sarcasm is notoriously tricky to convey through text—even in person, it can be easily misinterpreted. The subtle changes in tone that convey sarcasm often confuse computer algorithms as well, limiting virtual assistants and content analysis tools.

Computer science researchers at the University of Central Florida have developed a sarcasm detector.

Social media has become a dominant form of communication for individuals, and for companies looking to market and sell their products and services. Properly understanding and responding to customer feedback on Twitter, Facebook and other social media platforms is critical for success, but it is incredibly labor intensive.

That’s where sentiment analysis comes in. The term refers to the automated process of identifying the emotion—either positive, negative or neutral—associated with text. While artificial intelligence refers to logical data analysis and response, sentiment analysis is akin to correctly identifying emotional communication. A UCF team developed a technique that accurately detects sarcasm in social media text.

In an important step toward more effective gene therapies for brain diseases, researchers from the Broad Institute of MIT and Harvard have engineered a gene-delivery vehicle that uses a human protein to efficiently cross the blood-brain barrier and deliver a disease-relevant gene to the brain in mice expressing the human protein. Because the vehicle binds to a well-studied protein in the blood-brain barrier, the scientists say it has a good chance of working in patients.

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” »

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.