Market hype and growing interest in artificial intelligence (AI) are forcing established software vendors to introduce AI into their product strategy, creating considerable confusion in the process, according to Gartner. While there is a widely held fear that AI will replace humans, the reality is that today's AI and machine learning technologies can and do greatly augment human capabilities. Similar to greenwashing, in which companies exaggerate the environmental-friendliness of their products or practices for business benefit, many technology vendors are now "AI washing" by applying the AI label a little too indiscriminately, according to Gartner. To build trust with end-user organisations vendors should focus on building a collection of case studies with quantifiable results achieved using AI.
The company just launched a blog dubbed the Apple Machine Learning Journal, with the intent to show off its research into the technology. Welcome to the Apple Machine Learning Journal. Here, you can read posts written by Apple engineers about their work using machine learning technologies to help build innovative products for millions of people around the world. The first research blog post details about how Apple is working to create better synthetic images to train neural networks – compete with creepy simulated eyes.
Google's People AI Research (PAIR) project aims to develop and release tools designed to help make the inner workings of artificial intelligence (AI) systems more transparent. In addition, Google is launching several research initiatives aimed at finding new ways for humans and AI systems to collaborate effectively. Google researchers Fernanda Viegas and Martin Wattenberg, who specialize in developing visualizations that make complex information more comprehensible, are leading PAIR. The project recently released two tools for visualizing the types of large datasets used to train a machine learning model to make useful predictions.
Apple has launched the Apple Machine Learning Journal, an outlet that focuses on the work Apple engineers are doing with machine learning. Machine learning refers to a growing subfield within artificial intelligence research that focuses on machines developing the independent ability to dynamically learn and understand data without direct human control. The journal launch also reflects the burgeoning interest companies have developed in artificial intelligence. Earlier this month, Google launched Gradient Ventures, a venture firm that solely focuses on investment in companies doing work with artificial intelligence.
Chances are, you've already encountered artificial intelligence today. Did your email spam filter keep junk out of your inbox? We constantly hear that we're on the verge of an AI revolution, but the technology is already everywhere. And Coursera co-founder Andrew Ng predicts that smart technology will help humans do even more.
Scientists trained a neural network to'quantify' the beauty of outdoor spaces and found that natural scenes such as coasts and mountains ranked highly alongside man-made spectacles such as castles and towers. The deep learning model processed all 200,000 images, rated by 1.5 million people in total, and labelled them with information on what was in the picture, such as'valley', 'grass', 'no horizon' or'open space'. The deep learning model processed all 200,000 images, rated by 1.5 million people in total, and labelled them with information on what was in the picture, such as'valley', 'grass', 'no horizon' or'open space'. Scientists trained a neural network to'quantify' the beauty of outdoor spaces and found that natural scenes such as coasts and mountains ranked highly alongside man-made spectacles such as castles and towers, including this image of the Tower of London, are the most beautiful The scientists then trained a new deep learning model to look at pictures and rate them itself.
Monterrey itself has a strong incentive to take part in this study, since it loses an estimated 40 percent of its water supply to leaks every year, costing the city about $80 million in lost revenue. That's why that desert nation's King Fahd University of Petroleum and Minerals has sponsored and collaborated on much of the MIT team's work, including successful field tests there earlier this year that resulted in some further design improvements to the system, Youcef-Toumi says. Currently there is not an effective tool to locate leaks in those plastic pipes, and MIT PipeGuard's robot is the disruptive change we have been looking for." The MIT system was actually first developed to detect gas leaks, and later adapted for water pipes.
Apple hasn't always been very open about its technology or its research, but the company surprised everyone last year when AI director Russ Salakhutdinov announced that Apple would begin publishing its machine learning research. Shortly thereafter, it published its first AI paper in an academic journal and today Apple takes its transparency another step with the debut of its Machine Learning Journal. In a short welcome post on the new blog, the company said, "Here, you can read posts written by Apple engineers about their work using machine learning technologies to help build innovative products for millions of people around the world." The first and only research post so far describes the findings and methods from the academic paper Apple published last year.
Good teachers meet their students where they are, and they adapt their methods accordingly. Tutoring systems, language learning apps, and educational games are all designed to change our mental abilities. It's when we consider what it takes to change mental abilities or behaviors that things start to get interesting. It isn't just that people adapt to technology, and that technology adapts to people.
This is a shift from analytics that automatically collect and analyze data from past interactions (buying behavior, customer service engagements, etc.) Now, instead of simply using chatbots and other AI tools as communication devices to interact with customers at various phases of their shopping and decision making process, my clients are now exploring how bots can collect and analyze customer needs and business data simultaneously in order to fuel product/solution development. In the near-future, these AI technologies will be collecting and analyzing sophisticated business data as they engage with customers. One of the greatest ironies today in the advance toward leveraging more robotics and AI is that businesses need highly skilled people to make it work.