ML systems are very strong at learning empirical associations in data but are less effective when the task requires long chains of reasoning or complex planning that rely on common sense or background knowledge unknown to the computer. Ng's "one-second rule" (4) suggests that ML will do well on video games that require quick reaction and provide instantaneous feedback but less well on games where choosing the optimal action depends on remembering previous events distant in time and on unknown background knowledge about the world (e.g., knowing where in the room a newly introduced item is likely to be found) (12). Exceptions to this are games such as Go and chess, because these nonphysical games can be rapidly simulated with perfect accuracy, so that millions of perfectly self-labeled training examples can be automatically collected. However, in most real-world domains, we lack such perfect simulations.

RIVERSIDE, CALIFORNIA – Authorities say a Southern California couple used a drone to deliver illegal drugs to their customers. Benjamin Baldassarre and Ashley Carroll, of Riverside, were charged Tuesday with possessing controlled substances for sale and child endangerment. Police arrested the couple last Thursday after neighbors suspected they were selling drugs. Authorities say a drone delivered drugs to customers at a nearby parking lot. The customers would then drive by the couple's home and throw their payments on the lawn.

Tensions between poachers and conservationists in the Gulf of California escalated over the weekend after a fisherman shot down a drone being used to monitor illegal activities. The drone belonged to the U.S. conservation group Sea Shepherd, which has two ships in the northern part of the Sea of Cortez as part of an effort to save the critically endangered vaquita porpoise. The vaquita have been inadvertently caught in nets that poachers use to catch the endangered totoaba fish. Fishermen can make huge sums on the black market for dried totoaba swim bladders, which are sold in China for their supposed medicinal properties. The environmental group has been searching for nets and pulling them from the water.

The child endangerment charges were filed because Baldassarre's 9-year-old girl lived in the home, where police said they found syringes and drugs believed to include methamphetamine, LSD-laced candy and powdered fentanyl.

Federal lawmakers want to have a say in defining artificial intelligence. Researchers are now using TV shows to feed the predictive capability of an AI system. Google said in recent days it's opening an AI-focused research facility in China. And on and on the headlines keep coming, all of which is to say that interest in AI remains acute -- and its presence pervasive -- as 2017 draws to a close. And, based on a few recent developments, 2018 should be another big year of AI-related leaps forward as machines expand their influence over the minutiae of our lives.

Scholars have long pointed out that the way languages are structured and used can say a lot about the worldview of their speakers: what they believe, what they hold sacred, and what their biases are. We know humans have their biases, but in contrast, many of us might have the impression that machines are somehow inherently objective. But does that assumption apply to a new generation of intelligent, algorithmically driven machines that are learning our languages and training from human-generated datasets? By virtue of being designed by humans, and by learning natural human languages, might these artificially intelligent machines also pick up on some of those same human biases too? It seems that machines can and do indeed assimilate human prejudices, whether they are based on race, gender, age or aesthetics.

We owe a lot to 9th century Persian scholar Muhammad ibn Musa al-Khwarizmi. Centuries after his death, al-Khwarizmi's works introduced Europe to decimals and algebra, laying some of the foundations for today's techno-centric age. The latinized version of his name has become a common word: algorithm. In 2017, it took on some sinister overtones. Take this exchange from the US House Intelligence Committee last month.

Finance has always been quite conservative but it hasn't missed out on technological progress. Now banks are separating from their customers due to technological progress. The number of local bank branches decreased by almost 50% between 1995 and 2015. Transactions are rapidly moving onto the web as more and more financial services are provided online. Digital channels are already dominating.

They say that "those who do not learn history are doomed to repeat it." However, those who machine-learn from history are also doomed to repeat it. Machine learning is in many ways copy-pasting history. A key challenge is thus to learn from history without copying its biases. For the first time in the history we have open data on past experiences in making public contracts.

We wouldn't trust a doctor employed by a tobacco company. We wouldn't let the automobile industry set vehicle-emissions limits. We wouldn't want an arms maker to write the rules of warfare. But right now, we are letting tech companies shape the ethical development of AI. In an attempt to help shape the future of AI, in October 2017, DeepMind, the world-leading AI company acquired by Google in 2014, launched a new ethics board "to help technologists put ethics into practice, and to help society anticipate and direct the impact of AI so that it works for the benefit of all."