If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
As Artificial Intelligence is becoming a mainstream and easily available commercial technology, both organizations and criminals are trying to take full advantage of it. In particular, there are predictions by cyber security experts that going forward, the world will witness many AI-powered cyber attacks1. This mandates the development of more sophisticated cyber defense systems using autonomous agents which are capable of generating and executing effective policies against such attacks, without human feedback in the loop. In this series of blog posts, we plan to write about such next generation cyber defense systems. One effective approach of detecting many types of cyber threats is to treat it as an anomaly detection problem and use machine learning or signature-based approaches to build detection systems.
We often hear in the news about this thing called "machine learning" and how computers are "learning" to perform certain tasks. From the examples we see, it almost seems like magic when a computer creates perfect landscapes from thin air or makes a painting talk. But what is often overlooked, and what we want to cover in this tutorial, is that machine learning can be used in video game creation as well. In other words, we can use machine learning to make better and more interesting video games by training our AIs to perform certain tasks automatically with machine learning algorithms. This tutorial will show you how we can use Unity ML agents to make an AI target and find a game object. More specifically, we'll be looking at how to customize the training process to create an AI with a very specific proficiency in this task. Through this, you will get to see just how much potential machine learning has when it comes to making AI for video games. So, without further ado, let's get started and learn how to code powerful AIs with the power of Unity and machine learning combined!
An AI-controlled fighter jet will battle a US Air Force pilot in a simulated dogfight next week -- and you can watch the action online. The clash is the culmination of DARPA's AlphaDogfight competition, which the Pentagon's "mad science" wing launched to increase trust in AI-assisted combat. DARPA hopes this will raise support for using algorithms in simpler aerial operations, so pilots can focus on more challenging tasks, such as organizing teams of unmanned aircraft across the battlespace. The three-day event was scheduled to take place in-person in Las Vegas from August 18-20, but the COVID-19 pandemic led DARPA to move the event online. Before the teams take on the Air Force on August 20, the eight finalists will test their algorithms against five enemy AIs developed by Johns Hopkins Applied Physics Laboratory.
DisCSP (Distributed Constraint Satisfaction Problem) is a general framework for solving distributed problems arising in Distributed Artificial Intelligence. A wide variety of problems in artificial intelligence are solved using the constraint satisfaction problem paradigm. However, there are several applications in multi-agent coordination that are of a distributed nature. In this type of application, the knowledge about the problem, that is, variables and constraints, may be logically or geographically distributed among physical distributed agents. This distribution is mainly due to privacy and/or security requirements.
"GPT-3 is not a mind, but it is also not entirely a machine. It's something else: a statistically abstracted representation of the contents of millions of minds, as expressed in their writing." In recent years, the AI circus really has come to town and we've been treated to a veritable parade of technical aberrations seeking to dazzle us with their human-like intelligence. Many of these sideshows have been "embodied" AI, where the physical form usually functions as a cunning disguise for a clunky, pre-programmed bot. Like the world's first "AI anchor", launched by a Chinese TV network and -- how could we ever forget -- Sophia, Saudi Arabia's first robotic citizen.
Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer upon further review. The AI accurately detected cancer in this tricky case. A study published today (July 27, 2020) in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program. "Humans are good at recognizing anomalies, but they have their own biases or past experience," said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer -- significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing, and invasion of the surrounding nerves. These all are clinically important features required as part of the pathology report. AI also flagged six slides that were not noted by the expert pathologists. But Dhir explained that this doesn't necessarily mean that the machine is superior to humans. For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A nonspecialized person may not be able to make the correct assessment.
Every feature of intelligence or learning aspects in principle can be so precisely described that a machine can seamlessly simulate it. John McCarthy, who is the Father of Artificial Intelligence, was a pioneer in the fields of AI. He not only is credited to be the founder of AI, but also one who coined the term Artificial Intelligence. In 1955, John McCarthy coined the term Artificial Intelligence, which he proposed in the famous Dartmouth conference in 1956. This conference attended by 10-computer scientists, saw McCarthy explore ways in which machines can learn and reason like humans.
August 8 is International Female Orgasm Day, and we're celebrating with an entire week dedicated to exploring the business and pleasure of porn. Porn video games come with a lot of stigma, and understandably so. But if you push through all that, you'll find there actually are some really good, very NSFW adult games out there. It won't surprise you that many of the most popular adult games share similar issues to regular mainstream Tube porn - aggressive heternormativity and a near-exclusive catering to a cis, male gaze. Still, the world of adult sex games is far more expansive, diverse, and varied than people think. And, finding the right erotic game for you can help open you up to new aspects of your sexuality you'd never thought about before.