Rainfall prediction is one of the challenging and uncertain tasks which has a significant impact on human society. Timely and accurate predictions can help to proactively reduce human and financial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities of Australia. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.

A coalition of agents, or a single agent, has an ethical dilemma between several statements if each joint action of the coalition forces at least one specific statement among them to be true. For example, any action in the trolley dilemma forces one specific group of people to die. In many cases, agents face ethical dilemmas because they are restricted in the amount of the resources they are ready to sacrifice to overcome the dilemma. The paper presents a sound and complete modal logical system that describes properties of dilemmas for a given limit on a sacrifice.

We show that consistency of Statistical EL knowledge bases, as defined by Penaloza and Potyka in SUM 2017 [4] is ExpTime-hard. Together with the existing ExpTime upper bound by Baader in FroCos 2017 [1], the result leads to the ExpTime-completeness of the mentioned logic. Our proof goes via a reduction from consistency of EL extended with an atomic negation, which is known to be equivalent to the well-known ExpTime-complete description logic ALC.

Robotics and Artificial Intelligence (AI) are suffering the same fate as Gemini's hoping to foresee their future by watching Carl Sagan's "Cosmos." While Robotics and AI are often associated with one another, these two fields originate from entirely different disciplines. Read on to learn how companies like Tomahawk Robotics are leveraging these two different, yet complementary, technologies to solve important problems. Often used interchangeably, it is important for industry professionals to properly differentiate between these two technologies. The world of Computer Science claims AI as it relates to the computation, reasoning, and instruction within a computer operation.

Australia's Woodside Energy had decades of knowledge instilled in its retiring workforce of oil pipeline engineers and wanted to scale that wealth to a much more expansive workforce. When Shelley Kalms, Woodside's Chief Digital Officer, describes her mission to transform her company into a "true learning organization," she's speaking about a "team effort in unlocking the collective intelligence of our organization -- both past and present." Through using IBM Watson, Woodside created the ability to have expansive knowledge at their fingertips. Today's incoming employees in the field have access to as much know-how as a person who has worked on the oil rigs for decades. Today, Woodside has 18 successful Watson-enabled A.I. projects in place, ranging from health to safety -- all of which presents compelling evidence of the potential of A.I. to optimize jobs and increase productivity.

Although once it was just a far-fetched idea in Silicon Valley, the use of artificial intelligence in our everyday lives is becoming more common by the day. The adoption of AI in areas that need massive amounts of data analysis has grown dramatically, particularly in human resource departments. By using AI, companies can reduce the time spent taking care of mundane tasks and speed up data gathering, which leads to improved business performance. Let's take a look at a few of the crucial areas where AI is in use and how it will continue to help HR departments improve employee motivation and well being. Artificial intelligence is the use of computer algorithms to analyze and perform cognitive functions that mimic human behavior.

Fox News Flash top entertainment and celebrity headlines for Nov. 1 are here. Check out what's clicking today in entertainment. A reality TV star in Australia is accused of trying to extort her Tinder date -- and then assaulting him with another man when he refused to pay, according to a new report. Suzi Taylor, who starred on the hit home renovation show "The Block," allegedly lured the 33-year-old victim, whom she met through the dating app, to a home in Brisbane just after midnight Wednesday, News.com.au She then demanded money from him -- but when he refused to comply, Taylor's alleged accomplice, Ali Ebrahimi, entered the room and assaulted the man, police said in a statement.

Up to 20 million manufacturing jobs could be lost to robots by 2030, according to a new report by Oxford Economics. The study found that robots will lead to twice as many manufacturing job losses in low-skill areas, thereby aggravating income inequality. The report, "How Robots Change the World", estimates that each new industrial robot eliminates 1.6 manufacturing jobs on average, and calls on governments to prepare with policies including better training and welfare programs, and a universal basic income. It suggests that in Australia, South Australia is most vulnerable to the future robot rollout. The state is Australia's most manufacturing intensive but has the slowest-growing economy and low levels of manufacturing productivity, the report argued.

Machines are getting better at writing. They can finish our sentences. They can reply to our emails. They can write news reports and even novels. But just because they can doesn't mean they should.

Should we believe headlines claiming nearly half of all jobs will be lost to robots and artificial intelligence? We think not, and in a newly released study we explain why. Headlines trumpeting massive job losses have been in abundance for five or so years. Even The Conversation has had its had its share. Most come from a common source.