Artificial Intelligence
Human-Computer Interaction (HCI)Strategic PlanningManagementComputersArtificial IntelligenceBusiness
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131 бумажная страница
- Дата публикации оригинала
- 2019
- Год выхода издания
- 2019
Впечатления
- Chiefделится впечатлением3 года назад
👍Worth reading🔮Hidden Depths💡Learnt A Lot🎯Worthwhile🚀Unputdownable
Цитаты
- Chiefцитирует3 года назадThough all of these advances are relatively recent, they hark back to the very beginnings of AI in the 1950s, when a number of researchers began to pursue top-down models for mimicking human intelligence. But when progress proved elusive and the rich potential for bottom-up machine learning methods became apparent, the top-down approach was largely abandoned. Today, however, through powerful new research and computational techniques, top-down AI has been reborn. As its great promise begins to be fulfilled, smart companies will put their money where the mind is.
TAKEAWAYS
As AI develops, it will rely less on bottom-up big data and more on top-down reasoning that resembles the way humans approach problems and tasks. This will enable us to apply AI more broadly - Chiefцитирует3 года назадHumans routinely, and often effortlessly, sort through probabilities and act on the likeliest, even with relatively little prior experience. Machines are now being taught to mimic such reasoning through the application of Gaussian processes—probabilistic models that can deal with extensive uncertainty, act on sparse data, and learn from experience. Alphabet, Google’s parent company,
- Chiefцитирует3 года назадbroadly applied than ever, creating opportunities for early adopters even in businesses and activities to which it previously seemed unsuited.
In the recent past, AI advanced through deep learning and machine learning, building up systems from the bottom by training them on mountains of data. For instance, driverless vehicles are trained on as many traffic situations as possible. But these data-hungry neural networks, as they are called, have serious limitations. They especially have trouble handling “edge” cases—situations where little data exists. A driverless car that can handle crosswalks, pedestrians, and traffic has trouble processing anomalies like children dressed in unusual Halloween costumes, weaving across the street after dusk.
Many systems are also easily stumped. The iPhone X’s facial recognition system doesn’t recognize “morning faces”—a user’s
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