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.

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.

Cognitive technologies are increasingly being used to solve business problems; indeed, many executives believe that AI will substantially transform their companies within three years. But many of the most ambitious AI projects encounter setbacks or fail. A survey of 250 executives familiar with their companies' use of cognitive technology and a study of 152 projects show that companies do better by taking an incremental rather than a transformative approach to developing and implementing AI, and by focusing on augmenting rather than replacing human capabilities. Broadly speaking, AI can support three important business needs: automating business processes (typically back-office administrative and financial activities), gaining insight through data analysis, and engaging with customers and employees. To get the most out of AI, firms must understand which technologies perform what types of tasks, create a prioritized portfolio of projects based on business needs, and develop plans to scale up across the company.