The researchers used a neural algorithm based on our brain's olfactory circuits to train the Loihi research chip to sniff out the scents of 10 hazardous chemicals, including ammonia and methane. In order to do so, the team fed Loihi a dataset consisting of the activity of 72 chemical sensors in response to these smells and configured the circuit diagram of biological olfaction on Loihi, according to a news release. The chip quickly learnt the neural representation of each smell and recognized each odour, even in the presence of significant background interference. The findings were published recently in the journal Nature Machine Intelligence. Such neuromorphic chips could be used to build intelligent "electronic nose systems" that could then be used in robots to detect hazardous materials or even for environmental monitoring.

A common pitfall a lot of machine learning (ML) companies run into is mistaking data as knowledge. Several enterprises think that having a lot of data makes them ripe for harvesting insights instantly through AI and ML techniques. It is not entirely true. Data is not equal to knowledge, or more precisely, not the knowledge you think it equals. Ernesto Miguel, 47 is a plant operator in a leading cement company.

Computer chips using AI might put sniffer dogs out of work -- at least in the area of smelling dangerous chemicals in drugs, explosives, and other substances -- according to a new study published in the journal Nature. Researchers for Cornell University and Intel produced a "neuromorphic" chip called Loihi that reportedly makes computers think like biological brains, according to Daily Mail. The researchers created the circuit on the chip, mirroring organic circuits found in the olfactory bulbs of a dog's brain, which is how they process their sense of smell. The Loihi chip can identify a specific odor on the first try and even differentiate other, background smells, said Intel, according to Daily Mail. The chip can even detect smells humans emit when sick with a disease -- which varies depending on the illness -- and smells linked to environmental gases and drugs.

Of all the senses, scent is a particularly difficult one to teach AI, but that doesn't stop researchers from trying. Most recently, researchers from Intel and Cornell University trained a neuromorphic chip to learn and recognize the scents of 10 hazardous chemicals. In the future, the tech might enable "electronic noses" and robots to detect weapons, explosives, narcotics and even diseases. Using Intel's Loihi, a neuromorphic chip, the team designed an algorithm based on the brain's olfactory circuit. When you take a whiff of something, molecules stimulate olfactory cells in your nose.

In the architecture and construction industries, structural design for large buildings has always been laborious, time-consuming, and difficult to optimize. It is an iterative process that involves two steps: analyzing the current structural design by a slow and computationally expensive simulation, and then manually revising the design based on professional experience and rules. In this work, we propose an end-to-end learning pipeline to solve the size design optimization problem, which is to design the optimal cross-sections for columns and beams, given the design objectives and building code as constraints. We pre-train a graph neural network as a surrogate model to not only replace the structural simulation for speed but also use its differentiable nature to provide gradient signals to the other graph neural network for size optimization. Our results show that the pre-trained surrogate model can predict simulation results accurately, and the trained optimization model demonstrates the capability of designing convincing cross-section designs for buildings under various scenarios.

In high energy physics experiments Particle Flow (PFlow) algorithms are designed to reach optimal calorimeter reconstruction and jet energy resolution. A computer vision approach to PFlow reconstruction using deep Neural Network techniques based on Convolutional layers (cPFlow) is proposed. The algorithm is trained to learn, from calorimeter and charged particle track images, to distinguish the calorimeter energy deposits from neutral and charged particles in a non-trivial context, where the energy originated by a $\pi^{+}$ and a $\pi^{0}$ is overlapping within calorimeter clusters. The performance of the cPFlow and a traditional parametrized PFlow (pPFlow) algorithm are compared. The cPFlow provides a precise reconstruction of the neutral and charged energy in the calorimeter and therefore outperform more traditional pPFlow algorithm both, in energy response and position resolution.

Search techniques, such as Monte Carlo Tree Search (MCTS) and Proof-Number Search (PNS), are effective in playing and solving games. However, the understanding of their performance in industrial applications is still limited. We investigate MCTS and Depth-First Proof-Number (DFPN) Search, a PNS variant, in the domain of Retrosynthetic Analysis (RA). We find that DFPN's strengths, that justify its success in games, have limited value in RA, and that an enhanced MCTS variant by Segler et al. significantly outperforms DFPN. We address this disadvantage of DFPN in RA with a novel approach to combine DFPN with Heuristic Edge Initialization.

An emerging form of AI known as neuromorphic computing has been used to recognize scents emitted by explosives, chemical weapons, and narcotics. Researchers from Intel and Cornell University made the breakthrough by equipping Intel's neuromorphic test chip Loihi with neural algorithms that mimic what happens in your brain when you smell something. This enabled the system to recognize the smell of each hazardous chemical from just a single sample. The study could pave the way to a vast range of applications of neuromorphic computing, which mimics the brain's basic mechanics to make machine learning more efficient. Intel believes the "electronic nose systems" could be used by airport security to detect weapons and explosives, by police and border control to find narcotics, by robots to monitor gases pimped out into the atmosphere, and by the makers of smoke detectors to improve their products.

While the world is gearing up to fight the pandemic in the form of coronavirus, Intel has made a significant breakthrough. Intel has designed a chip that can smell various chemicals present in the air. Based on Intel's Loihi platform, the chip unsurprisingly uses machine learning algorithms to smell scents in the air. And that includes hazardous chemicals as well. The neuromorphic chip is "based on the architecture of the mammalian olfactory bulb".

Computers can already boast superhuman sensory abilities in sight and hearing, but smell has been much more difficult. The human nose isn't a particularly good one compared to the rest of the animal kingdom, but it's still a complex piece of machinery, with around 450 different types of olfactory receptors. Each of those receptor types can be activated by a range of different airborne odor molecules, each of which ping multiple different receptors at different strengths. This allows humans to distinguish between more than a trillion different scents, on top of which we can overlay a bunch of taste information to generate the sensation of flavor. Of course, it's not just how our body senses these things that's amazing – the brain's got the job of taking that huge and constantly changing swarm of electrical sensor data and processing it in real time, cross-referencing each smell signature against an impossibly massive data bank of past experiences so we can recognize it and work out whether to get hungry, or sexually aroused, or simply to wait for the next elevator.