Quora Questions are part of a partnership between Newsweek and Quora, through which we'll be posting relevant and interesting answers from Quora contributors throughout the week. Who is leading in AI research among big players like IBM, Google, Facebook, Apple and Microsoft? First, my response contains some bias, because I work at Google Brain, and I really like it there. My opinions are my own, and I do not speak for the rest of my colleagues or Alphabet as a whole. I rank "leaders in AI research" among IBM, Google, Facebook, Apple, Baidu, Microsoft as follows: I would say Deepmind is probably #1 right now, in terms of AI research.

When most people think about artificial intelligence (AI), they envision high-tech robots and automated processes, or, in a more romantic sense, the promise of technology finally being realized. Hardly anyone thinks about the actual people behind the technology. However, for every game-changing technology and innovative product, there is a team of humans conceiving new ideas, powering them from behind the scenes, and collaborating alongside it. AI technologies have existed in some form for a few decades, but until recently, the possibilities were more science fiction than reality. It's only been in the past few years that the potential of AI has started to come to life, and it is growing at an incredibly fast rate.

Google researchers say they are using deep learning algorithms to enable computers to examine slide images of tissue samples and diagnose cancer relatively reliably.

Artificial intelligence has made some great developments toward speeding up cancer diagnosis so far in 2017. Last month it was announced that AI from Sophia Genetics was helping to accelerate patient diagnosis across Latin America. Earlier this year researchers at Stanford University developed a deep learning algorithm that can analyse skin cancer as accurately as a human doctor. Now, Israel-based company, Medical EarlySign has announced the ability of its AI tool to identify the top 1% at highest risk of undiagnosed colorectal cancer (CRC). The machine learning developer announced the first-year results of its implementation with Maccabi Healthcare Services (MHS), for ColonFlag, a tool developed in collaboration with MHS to identify individuals with a high probability of having CRC.

Dr. Stephen Parker, VP of professional graphics, took the stage to give an overview of the use of artificial intelligence in graphics applications. While the first working algorithm using deep feedforward perceptrons was published around 52 years ago in 1965 by Alexey Ivakhnenko and Valentin Lapa, deep learning in graphics applications has reached a combinatorial explosion thanks to some great work that has been recently accomplished by a group of researchers at the University of Toronto. In 2012, professor Geoffrey E Hinton and two students, Alex Krizhevsky and Ilya Sutskever, entered an image recognition content to build computer vision algorithms that learned to identify millions of objects in millions of pictures. Using the most efficient algorithms at the time, the team was able to take the error rate of an average human and cut it in half. They later created a company called DNNresearch, which Google bought the following year.

Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C API. For this course, we will be using Python.

Computer vision engineers have used machine learning techniques for decades to detect objects of interest in images and to classify or identify categories of objects. They extract features representing points, regions, or objects of interest and then use those features to train a model to classify or learn patterns in the image data. In traditional machine learning, feature selection is a time-consuming manual process. Feature extraction usually involves processing each image with one or more image processing operations, such as calculating gradient to extract the discriminative information from each image. Deep learning algorithms can learn features, representations, and tasks directly from images, text, and sound, eliminating the need for manual feature selection.

And after all that, you reach a customer service agent who can't give you the help you need. No wonder we hate customer service. A San Francisco startup founded by particle physics researchers is working to ease that agony, with help from AI and GPUs. The company, Deepgram, created technology that businesses can use to quickly assess customer calls to improve service. "You really don't want to be calling customer service, and you don't want your time wasted," said Scott Stephenson, Deepgram co-founder and CEO.

The Washington fight over the future of Obamacare will have enormous repercussions for our health care system, which now accounts for nearly 18 percent of the U.S. economy. First there was "open washing," the marketing strategy for dressing up proprietary software as open source. Next came "cloud washing," whereby … Advances in artificial intelligence, machine learning, and deep learning are impacting businesses. But, the terms are often used interchangeably. As consumer banking becomes increasingly virtual, banks are setting the bar high for their non-human ambassadors.

That's what keeps me going.