Lifeboat Foundation

The recent success of machine learning (ML) methods in answering similar questions in human languages (Natural Language Processing or NLP) is related to the availability of large-scale datasets. The effort of creating a biological dataset in a format, level of detail, scale, and time span amenable to ML-based analysis is capital intensive and necessitates a multidisciplinary expertise to develop, deploy, and maintain specialized hardware to collect acoustic and behavioral signals, as well as software to process and analyze them, develop linguistic models that reveal the structure of animal communication and ground it in behavior, and finally perform playback experiments to attempt bidirectional communication for validation ( Figure 1 ). Yet, the deployment of graphics processing unit’s (GPU) is following a trajectory akin to Moore’s Law ( https://openai.com/blog/ai-and-compute) and, at the same time, the success of such an endeavor could potentially yield cross-applications and advancements in broader communities investigating non-human communication and animal behavioral research. One of the main drivers of progress making deep learning successful has been the availability of large (both labeled and unlabeled) datasets (and of architectures capable of taking advantage of such large data). To build a more complete picture and capture the full range of a species’ behavior, collecting datasets containing measurements across a broad set of factors is essential. In turn, setting up infrastructure that allows for the collection of broad and sizable datasets would facilitate studies that allow the autonomous discovery of the meaning-carrying units of communication.

A dedicated interdisciplinary initiative toward a detailed understanding of animal communication could arguably be made with a number of species as its focus. Birds, primates, and marine mammals have all given insight into the capacity of animal communication. In some ways, the collective understanding of the capacity for and faculty of communication in non-humans has been built through experimentation and observation across a wide number of taxa ( Fitch, 2005 ; Hauser et al., 2002). The findings on both the underlying neurobiological systems underpinning communicative capacity, and the complexity and diversity of the communication system itself often mirror our ability with which to work with a given species, or the existence of prominent long-term field research programs.

Animal communication researchers have conducted extensive studies of various species, including spiders (e.g. Elias et al., 2012 ; Hebets et al., 2013), pollinators (e.g Kulahci et al., 2008), rodents (e.g Ackers and Slobodchikoff, 1999 ; Slobodchikoff et al., 2009), birds (e.g Baker, 2001 ; Griesser et al., 2018), primates (e.g. Clarke et al., 2006 ; Jones and Van Cantfort, 2007 ; Leavens, 2007 ; Ouattara et al., 2009 ; Schlenker et al., 2016 ; Seyfarth et al., 1980), and cetaceans (e.g Janik, 2014 ; Janik and Sayigh, 2013), showing that animal communication involves diverse strategies, functions, and hierarchical components, and encompasses multiple modalities. Previous research efforts often focused on the mechanistic, computational, and structural aspects of animal communication systems. In human care, there have been several successful attempts of establishing a dialogue with birds (e.g.

Illuminating mathematics, physics, biology and computer science research through public service journalism.

More biologists are returning to the use of design-based assumptions, tools, models, and language to study the natural world. Dr. Brian Miller explains why in Part 1 of a conversation with Casey Luskin.

Why do AI ethics conferences fail? They fail because they don’t have a metatheory to explain how it is possible for ethical disagreements to emerge from phenomenologically different worlds, how those are revealed to us, and how shifts between them have shaped the development of Western civilization for the last several thousand years from the Greeks and Romans, through the Renaissance and Enlightenment.

So perhaps we’ve given up on the ethics hand-wringing a bit too early. Or more precisely, a third nonzero sum approach that combines ethics and reciprocal accountability is available that actually does explain this. But first, let’s consider the flaw in simple reciprocal accountability. Yes, right now we can use chatGPT to catch Chat-GPT cheats, and provide many other balancing feedbacks. But as has been noted above with reference to the colonization of Indigenous nations, once the technological/ developmental gap is sufficiently large those dynamics which operate largely under our control and in our favor can quickly change, and the former allies become the new masters.

Continue reading “The only way out of the AI dilemma” »

A new robot just 10 microns across is able to navigate in a physiological environment and perform a variety of tasks, both autonomously or through external control by a human operator.

Researchers from Tel Aviv University (TAU) have developed a new “hybrid micro-robot” the size of a single biological cell. This can be controlled and moved using two different mechanisms – electric and magnetic.

Researchers from Tel Aviv University, Israel, have created a micro-robot the size of a single biological cell that navigates using both electricity and magnetic fields and can identify and capture a single cell, opening the door to a vast array of applications.

Inspired by biological “swimmers” such as bacteria and sperm, the researchers developed a micro-robot (about 10 microns across) with the ability to move around the body autonomously or controlled by an operator.

Continue reading “Tiny hybrid robot can identify, capture a single cell” »

Life comes in all shapes in sizes, but some sizes are more popular than others, new research from the University of British Columbia (UBC) has found.

In the first study of its kind published today (March 29) in PLOS ONE, Dr. Eden Tekwa, who conducted the study as a postdoctoral fellow at UBC’s department of zoology, surveyed the body sizes of all Earth’s living organisms, and uncovered an unexpected pattern. Contrary to what current theories can explain, our planet’s biomass—the material that makes up all living organisms—is concentrated in organisms at either end of the size spectrum.

“The smallest and largest organisms significantly outweigh all other organisms,” said Dr. Tekwa, lead author of “The size of life,” and now a research associate with McGill University’s department of biology. “This seems like a new and emerging pattern that needs to be explained, and we don’t have theories for how to explain it right now. Current theories predict that biomass would be spread evenly across all body sizes.”

This year’s NVIDIA GPU Technology Conference (GTC) could not have come at a more auspicious time for the company. The hottest topic in technology today is the Artificial Intelligence (AI) behind ChatGPT, other related Large Language Models (LLMs), and their applications for generative AI applications. Underlying all this new AI technology are NVIDIA GPUs. NVIDIA’s CEO Jensen Huang doubled down on support for LLMs and the future of generative AI based on it. He’s calling it “the iPhone moment for AI.” Using LLMs, AI computers can learn the languages of people, programs, images, or chemistry. Using the large knowledge base and based on a query, they can create new, unique works: this is generative AI.

Jumbo sized LLM’s are taking this capability to new levels, specifically the latest GPT 4.0, which was introduced just prior to GTC. Training these complex models takes thousands of GPUs, and then applying these models to specific problems require more GPUs as well for inference. Nvidia’s latest Hopper GPU, the H100, is known for training, but the GPU can also be divided into multiple instances (up to 7), which Nvidia calls MIG (Multi-Instance GPU), to allow multiple inference models to be run on the GPU. It’s in this inference mode that the GPU transforms queries into new outputs, using trained LLMs.

Nvidia is using its leadership position to build new business opportunities by being a full-stack supplier of AI, including chips, software, accelerator cards, systems, and even services. The company is opening up its services business in areas such as biology, for example. The company’s pricing might be based on use time, or it could be based on the value of the end product built with its services.

Squids and octopuses are masters of camouflage, blending into their environment to evade predators or surprise prey. Some aspects of how these cephalopods become reversibly transparent are still “unclear,” largely because researchers can’t culture cephalopod skin cells in the lab.

Today, however, researchers report that they have replicated the tunable transparency of some squid skin cells in mammalian cells, which can be cultured. The work could not only shed light on basic squid biology, but also lead to better ways to image many cell types.

The researchers will present their results at the spring meeting of the American Chemical Society (ACS). ACS Spring 2023 is a hybrid meeting being held virtually and in-person March 26–30, and features more than 10,000 presentations on a wide range of science topics.