In this trail camera photo, provided by Oregon State University undergraduate student Owen S Okuley, bighorn sheep are seen at the Kerr Guzzler in the Mojave Desert. Oregon State University scientists have improved artificial intelligence's ability to identify wildlife species in photos taken by motion-activated cameras. Their study, which introduces a less-is-more approach to the data on which an AI model is trained, opens the door to wildlife image analysis that's more accurate and also more cost effective. Motion-activated cameras are an important wildlife monitoring tool, but reviewing thousands of images manually can be prohibitively time consuming, and current AI models are at times too inaccurate to be useful for scientists and wildlife managers. "One of the biggest problems in using AI in wildlife research is limited accuracy when we use the model to classify images at a novel location – one the model has never'seen' before," said study co-author Christina Aiello, a research associate in the Oregon State University College of Agricultural Sciences.

Google search queries and social media posts provide a means of peering into the ideas, concerns, and expectations of millions of people around the world. Using the right web-scraping bots and big data analytics, everyone from marketers to social scientists can analyze this information and use it to draw conclusions about what's on the mind of massive populations of users. Could A.I. analysis of our dreams help do the same thing? That's a bold, albeit intriguing, concept -- and it's one that researchers from Nokia Bell Labs in Cambridge, U.K., have been busy exploring. They've created a tool called "Dreamcatcher" that can, so they claim, use the latest Natural Language Processing (NLP) algorithms to identify themes from thousands of written dream reports.

Dreams and nightmares are a natural occurrence to humans. Some say that dreams reflect the mentality and thoughts of a person, while some others think it is a desire that dreams project. They portray the deep fear thorough a vision. As everyone stays curious about what dreams represent, Artificial Intelligence (AI) took its way to find an answer. Earlier, technology has accelerated and made artificial intelligence dream.

Heron Systems fielded an artificial intelligence system that managed Thursday to not only outgun a top current U.S. Air Force fighter pilot and weapons school graduate, but to score a flawless victory against its human opponent, winning all five dogfighting engagements in the culmination of a two-year Defense Advanced Research Projects Agency competition. "It's a giant leap," said Lt. Col. Justin "Glock" Mock, another weapons school graduate who also co-hosted the livestream. The DARPA program, known as the AlphaDogfight Trials, was designed to "demonstrate the feasibility of developing effective, intelligent autonomous agents capable of defeating adversary aircraft in a dogfight." In other words, if this were the movie Top Gun, Maverick and Goose would have just been smoked by an unmanned drone in head-to-head combat, five times in a row. Perhaps most impressive is the timeline under which teams rapidly developed their systems.

Continental has gained a minority stake in Cartica AI, which develops artificial intelligence (AI) software that helps to speed up machine learning in the field of object recognition in automobiles. Continental has been participating in the field of AI for object recognition in ADAS. The sensor data and images are employed to detect objects in the road with the help of the software in the vehicle control units. Continental believes Cartica AI software has the potential to help new vehicle systems from various companies and manufacturers work on the fast development of object recognition for the entire industry. "Cartica AI offers the opportunity for faster production of AI projects in the automotive sector. It could be an alternative to lengthy and complex human safeguards in the area of data quality in machine learning," said Continental's head of Artificial Intelligence and Robotics, Demetrio Aiello.

Researchers and engineers from BMW are working at IBM's Munich headquarters for its Watson Internet of Things (IoT) technology. They'll help develop IBM's Watson cognitive system, which can learn from and pass back intelligence into the physical world, for integration into automotive products. BMW is providing the IBM site with four i8s to be used as prototypes for these new artificial intelligence systems. The systems will run on IBM's Bluemix cloud platform, which IBM said will enable Watson technology to heighten a driver's connection with their car. The technology uses machine learning to enable a car to improve its understanding of its driver, so it can cater its systems and cabin experience to suit their preferences.

BMW is working with American technology giant IBM to co-develop artificial intelligent technology that will one day be used in road cars. Researchers and engineers from BMW are working at IBM's Munich headquarters for its Watson Internet of Things (IoT) technology. They'll help develop IBM's Watson cognitive system for integration into automotive products, which will be able to learn from the physical world and pass intelligence back to it. BMW is providing the IBM site with four i8s to be used as prototypes for the new artificial intelligence systems. The systems will run on IBM's Bluemix cloud platform, which IBM says will enable Watson technology to heighten a driver's connection with their car.

The Advanced Architectures Project at Stanford University's Knowledge Systems Laboratory seeks to gain higher performance for expert system applications through the design of new, innovative software and hardware architectures. This research concentrates particularly on the use of parallel machines to gain speedup and the design of the software to exploit emergent paral-lel hardware architectures. This article describes the project and details its goals and the work performed in the pursuance of these goals. A brief description is given of each of the project components, and a complete bibliography appears of the publications produced for the project.