Lifeboat Foundation

Neural networks have a remarkable ability to learn specific tasks, such as identifying handwritten digits. However, these models often experience “catastrophic forgetting” when taught additional tasks: They can successfully learn the new assignments, but “forget” how to complete the original. For many artificial neural networks, like those that guide self-driving cars, learning additional tasks thus requires being fully reprogrammed.

Biological brains, on the other hand, are remarkably flexible. Humans and animals can easily learn how to play a new game, for instance, without having to re-learn how to walk and talk.

Inspired by the flexibility of human and animal brains, Caltech researchers have now developed a new type of algorithm that enables neural networks to be continuously updated with new data that they are able to learn from without having to start from scratch. The algorithm, called a functionally invariant path (FIP) algorithm, has wide-ranging applications from improving recommendations on online stores to fine-tuning self-driving cars.

Neural networks have a remarkable ability to learn specific tasks, such as identifying handwritten digits. However, these models often experience “catastrophic forgetting” when taught additional tasks: They can successfully learn the new assignments, but “forget” how to complete the original. For many artificial neural networks, like those that guide self-driving cars, learning additional tasks thus requires being fully reprogrammed.

Biological brains, on the other hand, are remarkably flexible. Humans and animals can easily learn how to play a new game, for instance, without having to re-learn how to walk and talk.

Inspired by the flexibility of human and animal brains, Caltech researchers have now developed a new type of algorithm that enables neural networks to be continuously updated with new data that they are able to learn from without having to start from scratch. The algorithm, called a functionally invariant path (FIP) algorithm, has wide-ranging applications from improving recommendations on online stores to fine-tuning self-driving cars.

Scientists have invented an artificial plant that can simultaneously clean indoor air while generating enough electricity to power a smartphone.

A team from Binghamton University in New York created an artificial leaf “for fun” using five biological solar cells and their photosynthetic bacteria, before realising that the device could be used for practical applications.

A proof-of-concept plant with five artificial leaves was capable of generating electricity and oxygen, while removing CO₂ at a far more efficient rate than natural plants.

The US government has launched a new supercomputer in Livermore, California.

The Department of Defense (DoD) and National Nuclear Security Administration (NNSA) this month inaugurated a new supercomputing system dedicated to biological defense at the Lawrence Livermore National Laboratory (LLNL).

Specs not shared, but same architecture as upcoming El Capitan system.

Continue reading “DoD launches new biological defense supercomputer at Lawrence Livermore Lab” »

Living organisms constantly navigate dynamic and noisy environments, where they must efficiently sense, interpret, and respond to a wide range of signals. The ability to accurately process information is vital for both executing interspecies survival strategies and for maintaining stable cellular functions, which operate across multiple temporal and spatial scales [1] (Fig. 1). However, these systems often have access to only limited information. They interact with their surroundings through a subset of observable variables, such as chemical gradients or spatial positions, all while operating within constrained energy budgets. In this context, Giorgio Nicoletti of the Swiss Federal Institute of Technology in Lausanne (EPFL) and Daniel Maria Busiello of the Max Planck Institute for the Physics of Complex Systems in Germany applied information theory and stochastic thermodynamics to provide a unified framework addressing this topic [2]. Their work has unraveled potential fundamental principles behind transduction mechanisms that extract information from a noisy environment.

Bacteria, cells, swarms, and other organisms have been observed acquiring information about the environment at extraordinarily high precision. Bacteria can read surrounding chemical gradients to reach regions of high nutrients consistently [3], and cells form patterns during development repetitively and stably by receiving information on the distribution and concentration of external substances, called morphogens [4]. In doing so, they must interact with a noisy environment where the information available is scrambled and needs to be retrieved without corrupting the relevant signal [5]. All this comes at a cost.

The idea that precision is not free is an old one in the field of stochastic thermodynamics, and the cost usually comes in the form of energy dissipation [6]. This trade-off is even more relevant for biological systems that have limited access to energy sources. Living systems are pushed to find optimal strategies to achieve maximum precision while minimizing energy consumption. Consequently, a complete quantitative description of how these strategies are implemented requires the simultaneous application of information theory and stochastic—that is, noisy—thermodynamics.

Discover how the Quantum Zeno Effect can freeze quantum systems in time. Learn its applications in quantum computing and biology. Explore with us!

The organisms are 1.9 billion years older than the previously known record holders.

We tackle the hard problem of consciousness taking the naturally-selected, self-organising, embodied organism as our starting point. We provide a mathematical formalism describing how biological systems self-organise to hierarchically interpret unlabelled sensory information according to valence and specific needs. Such interpretations imply behavioural policies which can only be differentiated from each other by the qualitative aspect of information processing. Selection pressures favour systems that can intervene in the world to achieve homeostatic and reproductive goals. Quality is a property arising in such systems to link cause to affect to motivate real world interventions. This produces a range of qualitative classifiers (interoceptive and exteroceptive) that motivate specific actions and determine priorities and preferences.

Traditional optical tweezers, which trap and manipulate particles using light, usually require bulky microscope setups, but chip-based optical tweezers could offer a more compact, mass manufacturable, broadly accessible, and high-throughput solution for optical manipulation in biological experiments.

However, other similar integrated optical tweezers can only capture and manipulate cells that are very close to or directly on the chip surface. This contaminates the chip and can stress the cells, limiting compatibility with standard biological experiments.

Using a system called an integrated optical phased array, the MIT researchers have developed a new modality for integrated optical tweezers that enables trapping and tweezing of cells more than a hundred times further away from the chip surface.