We earn a commission if you buy something through an affiliate link. Deepfakes are wrecking influencers' credibility, one fake ad at a time E arlier this year, Emily Schuman, the creator of the lifestyle blog Cupcakes and Cashmere, saw a photo of herself she didn't recognize on Instagram . In a sponsored ad, Schuman, who has more than 500,000 Instagram followers, is holding up a vial of GLP-1 drugs, advertising the telehealth company Gala. There was just one problem: Schuman had never heard of Gala and had nothing to do with the ad. The ad was based on a digitally altered version of a selfie Schuman had taken in her car and posted on Instagram years earlier.

Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless hours brainstorming robot configurations and policies to ``organically" create behaviors which do not need expensive sensors and perception. Existing research explores the possible emergent behaviors in swarms of robots with only a binary sensor and a simple but hand-picked controller structure. Even agents in this highly limited sensing, actuation, and computational capability class can exhibit relatively complex global behaviors such as aggregation, milling, and dispersal, but finding the local interaction rules that enable more collective behaviors remains a significant challenge. This paper investigates the feasibility of training spiking neural networks to find those local interaction rules that result in particular emergent behaviors. In this paper, we focus on simulating a specific milling behavior already known to be producible using very simple binary sensing and acting agents. To do this, we use evolutionary algorithms to evolve not only the parameters (the weights, biases, and delays) of a spiking neural network, but also its structure. To create a baseline, we also show an evolutionary search strategy over the parameters for the incumbent hand-picked binary controller structure. Our simulations show that spiking neural networks can be evolved in binary sensing agents to form a mill.

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.

These days it seems that nearly every product and startup boasts some kind of A.I. capability, but when it comes to advancing this domain beyond simplistic machine learning technologists at MIT Technology Review's Future Compute conference say these A.I. will need to be more human than not. When discussing A.I. during the conference's first day on December 2nd, speakers focused on two distinct paths for this technology: more human-like A.I.'s as well as more computer-like humans. This dual approach was presented as a potential future for human-machine symbiosis. But what exactly does that all mean, and is it even a good thing? A research Scientist from Oak Ridge National Laboratory, Catherine Schuman began the conversation by presenting her work on neuromorphic computing.

The human brain weighs three pounds and is made up of more than 100 billion nerve cells that allow us to remember birthdays, recognize and evade danger, compose symphonies, build bridges, and design super-smart machines to take over the tasks we find too difficult, too dirty, or too boring. But even though scientists admit that there's still a lot they don't know about how the brain works, Catherine Schuman of Oak Ridge National Laboratory (ORNL) thinks that machine brains might work better if they were designed to be more like human brains. Rather than looking to the seventy year history of neural networks constructed on Von Neumann architecture, researchers like Schuman instead take inspiration from current discoveries in neuroscience to build a very different kind of artificial intelligence. These neuromorphic computers are designed to be massively parallel, constructed from many simple computational elements connected to mimic the neurons and synapses in the human brain. Most recently, Schuman has been intrigued by the astrocytic glial cells that insulate neural pathways and allow signals to travel fasters along certain routes.

Chatbots were all the rage a year ago. USA TODAY's Jefferson Graham wonders where they went on #TalkingTech. LOS ANGELES -- At its annual conference for software developers in 2016, Facebook trumpeted chat bots as the next big thing in tech. A year later, users of Facebook Messenger are still waiting. Chat bots, those automated robots that respond to human queries, had an underwhelming debut.