“China claims to have developed an AI that can read the minds of Communist Party members to determine how receptive they are to ‘thought education’ in since-deleted article”.
AI developed by experts at Hefei Comprehensive National Science Centre is reportedly intended to check loyalty of Communist Party members.
On-chip training of machine learning algorithms is challenging for photonic devices. Here, the authors construct nonlinear mapping functions in silicon photonic circuits, and experimentally demonstrate on-chip bacterial foraging training for projection-based classification.
Neuromorphic photonics/electronics is the future of ultralow energy intelligent computing and artificial intelligence (AI). In recent years, inspired by the human brain, artificial neuromorphic devices have attracted extensive attention, especially in simulating visual perception and memory storage. Because of its advantages of high bandwidth, high interference immunity, ultrafast signal transmission and lower energy consumption, neuromorphic photonic devices are expected to realize real-time response to input data. In addition, photonic synapses can realize non-contact writing strategy, which contributes to the development of wireless communication.
The use of low-dimensional materials provides an opportunity to develop complex brain-like systems and low-power memory logic computers. For example, large-scale, uniform and reproducible transition metal dichalcogenides (TMDs) show great potential for miniaturization and low-power biomimetic device applications due to their excellent charge-trapping properties and compatibility with traditional CMOS processes. The von Neumann architecture with discrete memory and processor leads to high power consumption and low efficiency of traditional computing. Therefore, the sensor-memory fusion or sensor-memory-processor integration neuromorphic architecture system can meet the increasingly developing demands of big data and AI for low power consumption and high performance devices. Artificial synaptic devices are the most important components of neuromorphic systems. The performance evaluation of synaptic devices will help to further apply them to more complex artificial neural networks (ANN).
Chemical vapor deposition (CVD)-grown TMDs inevitably introduce defects or impurities, showed a persistent photoconductivity (PPC) effect. TMDs photonic synapses integrating synaptic properties and optical detection capabilities show great advantages in neuromorphic systems for low-power visual information perception and processing as well as brain memory.
Making pizza is not rocket science, but for this actual rocket scientist it is now. Benson Tsai is a former SpaceX employee who is now using his skills to launch a new venture: Stellar Pizza, a fully automated, mobile pizza delivery service. When a customer places an order on an app, an algorithm decides when to start making the pizza based on how long it will take to get to the delivery address. Inside Edition Digital’s Mara Montalbano has more.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
How groups of humans working together collaboratively should redistribute the wealth they create is a problem that has plagued philosophers, economists, and political scientists for years. A new study from DeepMind suggests AI may be able to make better decisions than humans.
AI is proving increasingly adept at solving complex challenges in everything from business to biomedicine, so the idea of using it to help design solutions to social problems is an attractive one. But doing so is tricky, because answering these kinds of questions requires relying on highly subjective ideas like fairness, justice, and responsibility.
For an AI solution to work it needs to align with the values of the society it is dealing with, but the diversity of political ideologies that exists today suggests that these are far from uniform. That makes it hard to work out what should be optimized for and introduces the danger of the developers’ values biasing the outcome of the process.
An artificially intelligent first author presents many ethical questions—and could upend the publishing process.
Recently, researchers made AI write an academic paper about itself with legit citations and references, and quite to their surprise, the technology successfully executed this task!
In the midst of the heated debate about AI sentience, conscious machines and artificial general intelligence, Yann LeCun, Chief AI Scientist at Meta, published a blueprint for creating “autonomous machine intelligence.”
LeCun has compiled his ideas in a paper that draws inspiration from progress in machine learning, robotics, neuroscience and cognitive science. He lays out a roadmap for creating AI that can model and understand the world, reason and plan to do tasks on different timescales.
While the paper is not a scholarly document, it provides a very interesting framework for thinking about the different pieces needed to replicate animal and human intelligence. It also shows how the mindset of LeCun, an award-winning pioneer of deep learning, has changed and why he thinks current approaches to AI will not get us to human-level AI.
