Lifeboat Foundation

New research from the University of Massachusetts Amherst shows that programming robots to create their own teams and voluntarily wait for their teammates results in faster task completion, with the potential to improve manufacturing, agriculture and warehouse automation. The study is published in 2024 IEEE International Conference on Robotics and Automation (ICRA).

This research was recognized as a finalist for Best Paper Award on Multi-Robot Systems at the IEEE International Conference on Robotics and Automation 2024.

Continue reading “Researchers create new method for orchestrating successful collaboration among robots” »

$665 million in cash is nothing to sneeze at, and for AMD, the acquisition is the latest step in the company’s broader pivot that puts its main focus on AI and AI-related technologies. This is nothing new; we’ve seen the same shift happen in other companies like Google, Meta, Apple, and, of course, NVIDIA. However, NVIDIA’s AI focus started many years ago.

“AI is our number one strategic priority,” said Vamsi Boppana, AMD senior vice president, AIG. “We continue to invest in both the talent and software capabilities to support our growing customer deployments and roadmaps.”

“The Silo AI team has developed state-of-the-art language models that have been trained at scale on AMD Instinct accelerators, and they have broad experience developing and integrating AI models to solve critical problems for end customers,” Vamsi Boppana adds. “We expect their expertise and software capabilities will directly improve the experience for customers in delivering the best performing AI solutions on AMD platforms.”

To understand where artificial intelligence might be heading, we must first understand what consciousness, the self and free will mean in ourselves.

AI might be able to allow aliens to communicate in real-time with humans — or a language model representing us, at least.

A pseudo-religion dressed up as technoscience promises human transcendence at the cost of extinction.

Biological neural networks demonstrate complex memory and plasticity functions. This work proposes a single memristor based on SrTiO3 that emulates six synaptic functions for energy efficient operation. The bio-inspired deep neural network is trained to play Atari Pong, a complex reinforcement learning task in a dynamic environment.

Fully forward mode learning for optical neural networks.

The Driving Training Based Optimization (DTBO) algorithm, proposed by Mohammad Dehghani, is one of the novel metaheuristic algorithms which appeared in 2022⁸⁰. This algorithm is founded on the principle of learning to drive, which unfolds in three phases: selecting an instructor from the learners, receiving instructions from the instructor on driving techniques, and practicing newly learned techniques from the learner to enhance one’s driving abilities^81,82. In this work, DTBO algorithm is used, due to its effectiveness, which was confirmed by a comparative study⁸³ with other algorithms, including particle swarm optimization⁸⁴, Gravitational Search Algorithm (GSA)⁸⁵, teaching learning-based optimization, Gray Wolf Optimization (GWO)⁸⁶, Whale Optimization Algorithm (WOA)⁸⁷, and Reptile Search Algorithm (RSA)⁸⁸. The comparative study has been done using various kinds of benchmark functions, such as constrained, nonlinear and non-convex functions.

Lyapunov-based Model Predictive Control (LMPC) is a control approach integrating Lyapunov function as constraint in the optimization problem of MPC^89,90. This technique characterizes the region of the closed-loop stability, which makes it possible to define the operating conditions that maintain the system stability^91,92. Since its appearance, the LMPC method has been utilized extensively for controlling a various nonlinear systems, such as robotic systems⁹³, electrical systems⁹⁴, chemical processes⁹⁵, and wind power generation systems⁹⁰. In contrast to the LMPC, both the regular MPC and the NMPC lack explicit stability restrictions and can’t combine stability guarantees with interpretability, even with their increased flexibility.

The proposed method, named Lyapunov-based neural network model predictive control using metaheuristic optimization approach (LNNMPC-MOA), includes Lyapunov-based constraint in the optimization problem of the neural network model predictive control (NNMPC), which is solved by the DTBO algorithm. The suggested controller consists of two parts: the first is responsible for calculating predictions using a neural network model of the feedforward type, and the second is responsible to resolve the constrained nonlinear optimization problem using the DTBO algorithm. This technique is suggested to solve the nonlinear and non-convex optimization problem of the conventional NMPC, ensure on-line optimization in reasonable time thanks to their easy implementation and guaranty the stability using the Lyapunov function-based constraint. The efficiency of the proposed controller regarding to the accuracy, quickness and robustness is assessed by taking into account the speed control of a three-phase induction motor, and its stability is mathematically ensured using the Lyapunov function-based constraint. The acquired results are compared to those of NNMPC based on DTBO algorithm (NNMPC-DTBO), NNMPC using PSO algorithm (NNMPC-PSO), Fuzzy Logic controller optimized by TLBO (FLC-TLBO) and optimized PID controller using PSO algorithm (PID-PSO)⁹⁵.

Liquid amounting to a 1-2km-deep ocean may be frozen up to 20km below surface, calculations suggest.