About a decade ago, deep-learning models started achieving superhuman results on all sorts of tasks, from beating world-champion board game players to outperforming doctors at diagnosing breast cancer. These powerful deep-learning models are usually based on artificial neural networks, which were first proposed in the 1940s and have become a popular type of machine learning. A computer learns to process data using layers of interconnected nodes, or neurons, that mimic the human brain. As the field of machine learning has grown, artificial neural networks have grown along with it. Deep-learning models are now often composed of millions or billions of interconnected nodes in many layers that are trained to perform detection or classification tasks using vast amounts of data.

About a decade ago, deep-learning models started achieving superhuman results on all sorts of tasks, from beating world-champion board game players to outperforming doctors at diagnosing breast cancer. These powerful deep-learning models are usually based on artificial neural networks, which were first proposed in the 1940s and have become a popular type of machine learning. A computer learns to process data using layers of interconnected nodes, or neurons, that mimic the human brain. As the field of machine learning has grown, artificial neural networks have grown along with it. Deep-learning models are now often composed of millions or billions of interconnected nodes in many layers that are trained to perform detection or classification tasks using vast amounts of data.

About a decade ago, deep-learning models started achieving superhuman results on all sorts of tasks, from beating world-champion board game players to outperforming doctors at diagnosing breast cancer. These powerful deep-learning models are usually based on artificial neural networks, which were first proposed in the 1940s and have become a popular type of machine learning. A computer learns to process data using layers of interconnected nodes, or neurons, that mimic the human brain. As the field of machine learning has grown, artificial neural networks have grown along with it. Deep-learning models are now often composed of millions or billions of interconnected nodes in many layers that are trained to perform detection or classification tasks using vast amounts of data.

About a decade ago, deep-learning models started achieving superhuman results on all sorts of tasks, from beating world-champion board game players to outperforming doctors at diagnosing breast cancer. These powerful deep-learning models are usually based on artificial neural networks, which were first proposed in the 1940s and have become a popular type of machine learning. A computer learns to process data using layers of interconnected nodes, or neurons, that mimic the human brain. As the field of machine learning has grown, artificial neural networks have grown along with it. Deep-learning models are now often composed of millions or billions of interconnected nodes in many layers that are trained to perform detection or classification tasks using vast amounts of data.

How does artificial intelligence learn? Machine learning is the process of using computers to detect patterns in a large number of data sets, and then make predictions based on what the computer learns from these patterns. This makes machine learning a specific and narrow artificial intelligence. Fully artificial intelligence involves machines that can perform the thinking capabilities of humans and intelligent animals, such as perception, learning, and problem-solving. All machine learning is based on algorithms.

Colorectal cancer is the second most common type of cancer worldwide with about 90% of cases occurring in people 50 or older. Arising from the inner surface, or muscosal layer, of the colon, cancerous cells can penetrate through the deeper layers of the colon and spread to other organs. Left untreated, the disease is fatal. Current colon cancer screening is performed by flexible colonoscopy. The procedure involves visual inspection of the mucosal lining of the colon and rectum with a camera mounted on an endoscope.

Even Elon Musk and Mark Zuckerberg sparked a heated, and ongoing, debate about whether or not AI will take control of humanity. Indeed, AI continues to demonstrate impressive capabilities. Earlier this year, the AI built into Google's DeepMind AlphaGo defeated the world's top Go player, Chinese professional, Ke Jie. Go is a popular Asian board game reputed to be much more complex than chess. AI is not just for gaming, it also demonstrates a high reliability for detecting cancerous tissue in medical images.

The MasSpec Pen, which researchers at the University of Texas at Austin said could detect cancerous tissue in roughly 10 seconds. It might look like a stylus, but it's capable of much more than just writing. Researchers at the University of Texas at Austin created a medical "pen" that can detect cancer in roughly 10 seconds. The MasSpec Pen is a handheld device resembling a stylus, and can identify cancerous tissue during surgery. Tests performed by researchers on tissues removed from 253 human cancer patients found the device was accurate more than 96% of the time.

An affordable and effective device for detecting skin cancer has picked up an award of £30,000 ($40,000) from Britain's best-known inventor. This year's James Dyson prize for engineering was given to a group of four Canadian graduates, for their sKan system. The gadget picks up on subtle changes in the skin's ability to retain heat, which can indicate the presences of cancerous tissue. The device costs £760 ($1,000), compared with the £20,000 ($26,000) for high-resolution thermal imaging cameras. An affordable and effective device for detecting skin cancer has picked up an award of £30,000 ($40,000) from Britain's best-known inventor.

Investigators from Brigham and Women's Hospital and Dana-Farber Cancer Institute are leveraging the power of artificial intelligence to develop a new technique to detect ovarian cancer early and accurately. The team has identified a network of circulating microRNAs - small, non-coding pieces of genetic material - that are associated with risk of ovarian cancer and can be detected from a blood sample. Their findings are published online in eLife. Most women are diagnosed with ovarian cancer when the disease is at an advanced stage, at which point only about a quarter of patients will survive for at least five years. But for women whose cancer is serendipitously picked up at an early stage, survival rates are much higher.