They were created to imitate neural networks within the human brain.
Artificial neural networks (ANNs) mimic biological neural networks in the human brain. ANN consists of an input layer, a hidden layer, and an output layer.
Also called neural nets, ANNs are used daily in healthcare, social media when suggesting people you might know, and in marketing when recommending products to consumers.
Artificial neural networks (ANNs), also known as neural nets, are computing systems that are inspired by the way biological neural networks work in the human, or other animals, brain.
With the help of an AI, researchers at Chalmers University of Technology, Sweden, have succeeded in designing synthetic DNA that controls the cells’ protein production. The technology can contribute to the development and production of vaccines, drugs for severe diseases, as well as alternative food proteins much faster and at significantly lower costs than today.
How genes are expressed is a process that is fundamental to the functionality of cells in all living organisms. Simply put, the genetic code in DNA is transcribed to the molecule messenger RNA (mRNA), which tells the cell’s factory which protein to produce and in which quantities.
Researchers have put a lot of effort into trying to control gene expression because, among other things, it can contribute to the development of protein-based drugs. A recent example is the mRNA vaccine against COVID-19, which instructed the body’s cells to produce the same protein found on the surface of the coronavirus.
The San Francisco Police Department is proposing a new policy that would give robots the license to kill, as reported earlier by Mission Local (via Engadget). The draft policy, which outlines how the SFPD can use military-style weapons, states robots can be “used as a deadly force option when risk of loss of life to members of the public or officers is imminent and outweighs any other force option.”
As reported by Mission Local, members of the city’s Board of Supervisors Rules Committee have been reviewing the new equipment policy for several weeks. The original version of the draft didn’t include any language surrounding robots’ use of deadly force until Aaron Peskin, the Dean of the city’s Board of Supervisors, initially added that “robots shall not be used as a Use of Force against any person.”
However, the SFPD returned the draft with a red line crossing out Peskin’s addition, replacing it with the line that gives robots the authority to kill suspects. According to Mission Local, Peskin eventually decided to accept the change because “there could be scenarios where deployment of lethal force was the only option.” San Francisco’s rules committee unanimously approved a version of the draft last week, which will face the Board of Supervisors on November 29th.
DNA can be utilized to reliably store massive amounts of digital data. However, it has hitherto proven challenging to retrieve or manipulate the specific data embedded in these molecules. Now, scientists from the CNRS and the University of Tokyo have developed the use of a novel enzyme-based technique, providing the initial clues as to how these technical obstacles may be overcome. Their research was recently published in the journal Nature.
Nature has unquestionably developed the best method for massive data storage: DNA. Based on this knowledge, DNA has been used to store digital data by translating binary (0 or 1) values into one of the four different DNA “letters” (A, T, C, or G).
But how can one search through the database of data encoded in DNA to discover a certain datum? And how is it possible to execute computations using DNA-encoded data without first transforming it into electronic form? These are the questions that research teams from the LIMMS (CNRS / University of Tokyo) and Gulliver (CNRS / ESPCI) laboratories have attempted to answer. They are experimenting with a new approach using enzymes and artificial neurons and neural networks for direct operations on DNA data.
Learning-based computer-generated holography (CGH) has shown remarkable promise to enable real-time holographic displays. Supervised CGH requires creating a large-scale dataset with target images and corresponding holograms. We propose a diffraction model-informed neural network framework (self-holo) for 3D phase-only hologram generation. Due to the angular spectrum propagation being incorporated into the neural network, the self-holo can be trained in an unsupervised manner without the need of a labeled dataset. Utilizing the various representations of a 3D object and randomly reconstructing the hologram to one layer of a 3D object keeps the complexity of the self-holo independent of the number of depth layers. The self-holo takes amplitude and depth map images as input and synthesizes a 3D hologram or a 2D hologram. We demonstrate 3D reconstructions with a good 3D effect and the generalizability of self-holo in numerical and optical experiments.
Adolescent (or juvenile) delinquency is defined as the habitual engagement in unlawful behavior of a minor under the age of majority. According to studies, the likelihood of acquiring a deviant personality increases significantly during adolescence. As a result, identifying deviant youth early and providing proper medical counseling makes perfect sense. Due to the scarcity of qualified clinicians, human appraisal of individual adolescent behavior is subjective and time-consuming. As a result, a machine learning-based intelligent automated system for assessing and grading delinquency levels in teenagers at an early stage must be devised.
An artificial intelligence (AI) agent named CICERO has mastered the online board game of Diplomacy. This is according to a new study by the Meta Fundamental AI Research Diplomacy Team (FAIR) that will be published today (November 22) in the journal Science.
AI has already been successful at playing competitive games like chess and Go which can be learned using only self-play training. However, games like Diplomacy, which require natural language negotiation, cooperation, and competition between multiple players, have been challenging.
The new agent developed by FAIR is not only capable of imitating natural language, but more importantly, it also analyzes some of the goals, beliefs, and intentions of its human partners in the game. It uses that information to figure out a plan of action that accounts for aligned and competing interests, and to communicate that plan in natural language, the researchers say.
Dr. Peterson’s extensive catalog is available now on DailyWire+: https://utm.io/ueSXh.
Dr. Jordan B. Peterson, Jonathan Pageau, and Jim Keller dive into the world of artificial intelligence, debating the pros and cons of technological achievement, and ascertaining whether smarter tech is something to fear or encourage.
Jim Keller is a microprocessor engineer known for his work at Apple and AMD. He has served in the role of architect for numerous game changing processors, has co-authored multiple instruction sets for highly complicated designs, and is credited for being the key player behind AMD’s renewed ability to compete with Intel in the high-end CPU market. In 2016, Keller joined Tesla, becoming Vice President of Autopilot Hardware Engineering. In 2018, he became a Senior Vice President for Intel. In 2020, he resigned due to disagreements over outsourcing production, but quickly found a new position at Tenstorrent, as Chief Technical Officer.
Jonathan Pageau is a French-Canadian liturgical artist and icon carver, known for his work featured in museums across the world. He carves Eastern Orthodox and other traditional images, and teaches an online carving class. He also runs a YouTube channel dedicated to the exploration of symbolism across history and religion.
The new work, from MIT’s Center for Bits and Atoms (CBA), builds on years of research, including recent studies demonstrating that objects such as a deformable airplane wing and a functional racing car could be assembled from tiny identical lightweight pieces — and that robotic devices could be built to carry out some of this assembly work. Now, the team has shown that both the assembler bots and the components of the structure being built can all be made of the same subunits, and the robots can move independently in large numbers to accomplish large-scale assemblies quickly.
The new work is reported in the journal Nature Communications Engineering, in a paper by CBA doctoral student Amira Abdel-Rahman, Professor and CBA Director Neil Gershenfeld, and three others.
