Create and implement AI-based features in your Swift apps for iOS, macOS, tvOS, and watchOS. With this practical book, programmers and developers of all kinds will find a one-stop shop for AI and machine learning with Swift. Taking a task-based approach, you ll learn how to build features that use powerful AI features to identify images, make predictions, generate content, recommend things, and more.AI is increasingly essential for every developer and you don t need to be a data scientist or mathematician to take advantage of it in your apps. Learn where and how AI-driven features make sense. Inspect tools such as Apple s Python-powered Turi Create and Google s Swift for TensorFlow to train and build models.

Predicting the scale of AI-induced job losses has become a cottage industry for economists and consulting firms the world over. Depending on which model one uses, estimates range from terrifying to totally not a problem. Since 2013, the most cited predictions include a study from Oxford University that predicted 47% of US jobs could be automated within the next decade or two; an Organization for Economic Cooperation and Development (OECD) report that suggested just 9% of jobs in the United States were at high risk of automation; PwC research that found 38% of jobs in the United States were at high risk of automation by the early 2030s; and a McKinsey report that found around 50% of work tasks around the world are already automatable. A lot of this difference can be explained by the methodology of these studies. Looking at whether an entire occupation can be automated is a different question than whether tasks within that occupation can be automated.

In my previous blog on artificial intelligence (AI), I dealt with the general characteristics of AI and machine learning. Thanks to complex virtual learning techniques, machines are now able to perform a wide range of physical and cognitive tasks. And the efficiency and accuracy of their work is expected to increase as AI systems advance through machine learning, big data and increased computational power. The benefits are clear, but there are also concerns for the future of human work and employment. If indeed machines continue to improve their performance beyond human levels, a natural question to ask is whether machines will put humans' jobs at risk and reduce employment.

In my previous blog on artificial intelligence (AI), I dealt with the general characteristics of AI and machine learning. Thanks to complex virtual learning techniques, machines are now able to perform a wide range of physical and cognitive tasks. And the efficiency and accuracy of their work is expected to increase as AI systems advance through machine learning, big data and increased computational power. The benefits are clear, but there are also concerns for the future of human work and employment. If indeed machines continue to improve their performance beyond human levels, a natural question to ask is whether machines will put humans' jobs at risk and reduce employment.

In my previous blog on artificial intelligence (AI), I dealt with the general characteristics of AI and machine learning. Thanks to complex virtual learning techniques, machines are now able to perform a wide range of physical and cognitive tasks. And the efficiency and accuracy of their work is expected to increase as AI systems advance through machine learning, big data and increased computational power. The benefits are clear, but there are also concerns for the future of human work and employment. If indeed machines continue to improve their performance beyond human levels, a natural question to ask is whether machines will put humans' jobs at risk and reduce employment.