Are we living in a golden age of stupidity? S tep into the Massachusetts Institute of Technology (MIT) Media Lab in Cambridge, US, and the future feels a little closer. Glass cabinets display prototypes of weird and wonderful creations, from tiny desktop robots to a surrealist sculpture created by an AI model prompted to design a tea set made from body parts. In the lobby, an AI waste-sorting assistant named Oscar can tell you where to put your used coffee cup. Five floors up, research scientist Nataliya Kosmyna has been working on wearable brain-computer interfaces she hopes will one day enable people who cannot speak, due to neurodegenerative diseases such as amyotrophic lateral sclerosis, to communicate using their minds. Kosmyna spends a lot of her time reading and analysing people's brain states.

With the continuous increase in the size and complexity of machine learning models, the need for specialized hardware to efficiently run such models is rapidly growing. To address such a need, silicon-photonic-based neural network (SP-NN) accelerators have recently emerged as a promising alternative to electronic accelerators due to their lower latency and higher energy efficiency. Not only can SP-NNs alleviate the fan-in and fan-out problem with linear algebra processors, their operational bandwidth can match that of the photodetection rate (typically 100 GHz), which is at least over an order of magnitude faster than electronic counterparts that are restricted to a clock rate of a few GHz. Unfortunately, the underlying silicon photonic devices in SP-NNs suffer from inherent optical losses and crosstalk noise originating from fabrication imperfections and undesired optical couplings, the impact of which accumulates as the network scales up. Consequently, the inferencing accuracy in an SP-NN can be affected by such inefficiencies -- e.g., can drop to below 10% -- the impact of which is yet to be fully studied. In this paper, we comprehensively model the optical loss and crosstalk noise using a bottom-up approach, from the device to the system level, in coherent SP-NNs built using Mach-Zehnder interferometer (MZI) devices. The proposed models can be applied to any SP-NN architecture with different configurations to analyze the effect of loss and crosstalk. Such an analysis is important where there are inferencing accuracy and scalability requirements to meet when designing an SP-NN. Using the proposed analytical framework, we show a high power penalty and a catastrophic inferencing accuracy drop of up to 84% for SP-NNs of different scales with three known MZI mesh configurations (i.e., Reck, Clements, and Diamond) due to accumulated optical loss and crosstalk noise.

In a car outside a convenience store in Flint, Michigan, in late 2016, Kemo Parks handed his cousin Dequavion Harris a gun. Things happened quickly after that: Witnesses saw Harris "with his arm up and extended" toward a red truck. The wounded driver sped off but crashed into a tree. EMTs rushed him to the hospital. He was dead on arrival.

As if defenders of software supply chains didn't have enough attack vectors to worry about, they now have a new one: machine learning models. ML models are at the heart of technologies such as facial recognition and chatbots. Like open-source software repositories, the models are often downloaded and shared by developers and data scientists, so a compromised model could have a crushing impact on many organizations simultaneously. Researchers at HiddenLayer, a machine language security company, revealed in a blog on Tuesday how an attacker could use a popular ML model to deploy ransomware. The method described by the researchers is similar to how hackers use steganography to hide malicious payloads in images.

In the last year, one topic of conversation has dominated the content writing sphere -- the rise of AI-generated articles. As someone who coaches international writers who spend years developing their skills, I think about it a lot. I don't want to see people lose their jobs, but is it really possible to stop the march of technology? Technology helps us, and complaining about change doesn't make it less real. I understand the needs of companies who want fast, rankable content, but I also hear those writers who say'Machines cannot feel.

You are free to share this article under the Attribution 4.0 International license. A new data-driven approach is offering insight into people with type 1 diabetes, who account for about 5-10% of all diabetes diagnoses. The researchers gathered information through health informatics and applied artificial intelligence (AI) to better understand the disease. In the study, they analyzed publicly available, real-world data from about 16,000 participants enrolled in the T1D Exchange Clinic Registry. By applying a contrast pattern mining algorithm, researchers were able to identify major differences in health outcomes among people living with type 1 diabetes who do or do not have an immediate family history of the disease.

An interdisciplinary team of researchers from the University of Missouri, Children's Mercy Kansas City, and Texas Children's Hospital has used a new data-driven approach to learn more about persons with Type 1 diabetes, who account for about 5-10% of all diabetes diagnoses. The team gathered its information through health informatics and applied artificial intelligence (AI) to better understand the disease. In the study, the team analyzed publicly available, real-world data from about 16,000 participants enrolled in the T1D Exchange Clinic Registry. By applying a contrast pattern mining algorithm developed at the MU College of Engineering, the team was able to identify major differences in health outcomes among people living with Type 1 diabetes who do or do not have an immediate family history of the disease. Chi-Ren Shyu, the director of the MU Institute for Data Science and Informatics (MUIDSI), led the AI approach used in the study and said the technique is exploratory.

Facebook CEO Mark Zuckerberg speaks about "News Tab" at the Paley Center, Friday, Oct. 25, 2019 in ... [ ] New York. The new feature in the Facebook mobile app will display headlines -- and nothing else -- from the Wall Street Journal, the Washington Post, BuzzFeed News, Business Insider, NBC, USA Today and the Los Angeles Times, among others.(AP Facebook has been ramping its acquisitions for AI (Artificial Intelligence) startups. While the deals appear to be relatively small, they still are likely to be critical for the company's future. The latest purchase was for Scape Technologies, which is focused on building computer vision applications that help with AR (Augmented Reality).

You take a picture of a person, upload it and get to see public photos of that person along with links to where those photos appeared. By Kashmir Hill Until recently, Hoan Ton-That's greatest hit was an app that let people put Donald Trump's distinctive yellow hair on their own photos. Then Ton-That did something momentous: He invented a tool that could end your ability to walk down the street anonymously and provided it to hundreds of law enforcement agencies. His tiny company, Clearview AI, devised a groundbreaking facial recognition app. You take a picture of a person, upload it and get to see public photos of that person along with links to where those photos appeared.

A self-driving shuttle got pulled over by police on its first day carrying passengers on a new Rhode Island route. Providence Police Chief Hugh Clements says an officer pulled over the odd-looking autonomous vehicle because he had never seen one before. The bus-like vehicle operated by Michigan-based May Mobility was dropping off passengers Wednesday morning when a police cruiser arrived with blinking lights and a siren. It was just hours after the public launch of a state-funded pilot shuttle service. The shuttle offers free rides on a 12-stop urban loop.