The Times hosts an annual crossword-solving competition and it remains, until such time as the Guardian has its own version, the gold standard. This year's competitors included a dog. Rather, an AI represented as a jolly coffee-drinking dog named Ross (a name hidden in "crossword"), and who is embedded on the Crossword Genius smartphone app. The human competitors at the event – which took place at Times' parent company News UK's London headquarters, in the shadow of the Shard – were, as usual, bafflingly fast: pondering the next clue while scribbling the letters of the previous. An AI can conceivably "think" about multiple puzzles at once: so did it outwit us mortals?

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ARTIFICIAL intelligence systems have mastered some of mankind's best creations and natural intuitions. These AI systems notched some of the first wins for the machines. Artificial intelligence and table games make a good pair because humans have been trying to develop perfect tactics for strategy games for decades or even centuries. Chess is "known as a game that requires strategy, foresight, logic--all sorts of qualities that make up human intelligence," IBM researcher Murray Campbell told Scientific American. Campbell and a team developed Deep Blue, a six-foot supercomputer that defeated chess grandmaster Garry Kasparov in a six-game series in 1997.

One of IBM's ethical principles of AI is that this technology should be used to augment human intelligence, not replace it, affording employees more time to focus on higher-value work. In a business setting, that means that AI needs a certain level of reasoning ability which, today, it frankly doesn't have. But a newer, more hybrid approach to AI is beginning to bridge that gap. Neuro-Symbolic Learning (NSL) combines the strengths of neural networks and classical symbolic AI to accomplish complex tasks that neither of these methods can perform individually. At IBM, we are working with NSL to demonstrate AI's ability to solve much harder problems, learn with dramatically less data, and provide inherently understandable and controllable decisions and actions.

If you've been using smartphones for the past quinquennial or even more, you'll understand exactly where this is coming from. I'm sorry, but if you're on your first or second smartphone, you were already born into this to begin with. You see, going back in time all the way to the Nokia 808 PureView, it was the phone that revolutionized smartphone photography. It was the first mobile device on the market with a whopping 40MP camera sensor. The Nokia Lumia 1020 followed, with the same size sensor and Zeiss lens.

Not long ago, artificial intelligence (AI) seemed like part of some distant, cyborgian future. Now, everywhere you turn, AI is shaping the way we live. From algorithms that design our social media feeds to voice-activated technology ready to answer the most mundane of questions ("Hey Siri, what's the forecast for today?"), AI has crossed over from otherworldly to the here and now. AI can be a huge boon for marketers who have spent decades in the dark, wondering what tactics work and what doesn't when it comes to their marketing and customer service practices.

Amber Wolff, campaign specialist at McAfee, also contributed to this article. The 20th century was uniquely fascinated with the idea of artificial intelligence (AI). From friendly and helpful humanoid machines -- think Rosie the Robot maid or C-3PO -- to monolithic and menacing machines like HAL 9000 and the infrastructure of the Matrix, AI was a standard fixture in science fiction. Today, as we've entered the AI era in earnest, it's become clear that our visions of AI were far more fantasy than prophecy. But what we did get right was AI's potential to revolutionize the world around us -- in the service of both good actors and bad.

Artificial intelligence, specifically machine learning (ML), enables a new world of complex decision-making using novel relationships between data. This paradigm of a system "learning" from data instead of tedious rules-based programming on an outcome, while exciting in its possibilities, opens up a series of new challenges. Distrust, unfairness, bias and ethical ramifications of automated ML decisions are now increasingly common. The recent story about the inadvertent bias in Amazon's recruiting or face recognition software are examples of unforeseen effects of these applications of AI. They occur because, by and large, the relationships absorbed are opaque, thereby dissuading model developers in fixing it.

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Artificial intelligence (AI) is increasingly mocked as being used as a marketing term. But AI is also being used to create some legitimately useful tools. FarmLogs is an example of complex data analysis that tracks weather, soil conditions, historical satellite imagery and helps farmers determine what kind of plant growth to expect and how to maximize crop yields. Watson made this use of AI famous, and while you can debate its effectiveness, others like Intel are working on things like precision medicine. Machine learning can compare molecular tests with previous cases to customize treatments.