In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years. Since early 2013, IBM's Watson has been used in the medical field, and after winning an astounding series of games against with world's best living Go player, Google DeepMind's team decided to throw their weight behind the medical opportunities of their technologies as well. Many of the machine learning (ML) industry's hottest young startups are knuckling down significant portions of their efforts to healthcare, including Nervanasys(recently acquired by Intel), Ayasdi (raised $94MM as of 02/16), Sentient.ai With all the excitement in the investor and research communities, we at TechEmergence have found most machine learning executives have a hard time putting a finger on where machine learning is making its mark on healthcare today. We've written this article, not to be a complete catalogue of possible applications, but to highlight a number of current and future uses of machine learning in the medical field, with relevant links to external sources and related TechEmergence interviews. The list below is by no means complete, but provides a useful lay-of-the-land of some of ML's impact in the healthcare industry.

Artificial intelligence (AI) is playing an increasingly influential role in the modern world, powering more of the technology that impacts people's daily lives. For digital marketers, it allows for more sophisticated online advertising, content creation, translations, email campaigns, web design and conversion optimization. Outside the marketing industry, AI underpins some of the tools and sites that people use every day. It is behind the personal virtual assistants in the latest iPhone, Google Home, and Amazon Echo. It is used to recommend what films you watch on Netflix or what songs you listen to on Spotify, steers conversations you have with your favorite retailers, and powers self-driving cars and trucks that are set to become commonplace on roads around the world.

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Mapping the structure of biological networks in the nervous system -- a field of study known as connectomics -- is computationally intensive. The human brain contains around 86 billion neurons networked through 100 trillion synapses, and imaging a single cubic millimeter of tissue can generate more than 1,000 terabytes of data. Luckily, artificial intelligence can help. In a paper (High-Precision Automated Reconstruction of Neurons with Flood-Filling Networks) published in the journal Nature Methods, scientists at Google and the Max Planck Institute of Neurobiology demonstrated a recurrent neural network -- a type of machine learning algorithm that's often used in handwriting and speech recognition -- tailored made for connectomics analysis. Google researchers aren't the first to apply machine learning to connectomics -- in March, Intel partnered with the Massachusetts Institute of Technology's Computer Science and AI Laboratory to develop a "next-gen" brain image processing pipeline.

Video AI can help neurologists automatically map the connections between different neurons in brain scans, a tedious task that can take hundreds and thousands of hours. In a paper published in Nature Methods, AI researchers from Google collaborated with scientists from the Max Planck Institute of Neurobiology to inspect the brain of a Zebra Finch, a small Australian bird renowned for its singing. Although the contents of their craniums are small, Zebra Finches aren't birdbrains, their connectome* is densely packed with neurons. To study the connections, scientists study a slice of the brain using an electron microscope. It requires high resolution to make out all the different neurites, the nerve cells extending from neurons.

Video AI can help neurologists automatically map the connections between different neurons in brain scans, a tedious task that can take hundreds and thousands of hours. In a paper published in Nature Methods, AI researchers from Google collaborated with scientists from the Max Planck Institute of Neurobiology to inspect the brain of a Zebra Finch, a small Australian bird renowned for its singing. Although the contents of their craniums are small, Zebra Finches aren't birdbrains, their connectome* is densely packed with neurons. To study the connections, scientists study a slice of the brain using an electron microscope. It requires high resolution to make out all the different neurites, the nerve cells extending from neurons.

Thousands of scientists who specialise in artificial intelligence (AI) have declared that they will not participate in the development or manufacture of robots that can identify and attack people without human oversight. Demis Hassabis at Google DeepMind and Elon Musk at the US rocket company SpaceX are among more than 2,400 signatories to the pledge which intends to deter military firms and nations from building lethal autonomous weapon systems, also known as Laws. The move is the latest from concerned scientists and organisations to highlight the dangers of handing over life and death decisions to AI-enhanced machines. It follows calls for a preemptive ban on technology that campaigners believe could usher in a new generation of weapons of mass destruction. Orchestrated by the Boston-based organisation, The Future of Life Institute, the pledge calls on governments to agree norms, laws and regulations that stigmatise and effectively outlaw the development of killer robots.

Today during the Joint Conference on Artificial Intelligence, the Future of Life Institute announced that more than 2,400 individuals and 160 companies and organizations have signed a pledge, declaring that they will "neither participate in nor support the development, manufacture, trade or use of lethal autonomous weapons." The signatories, representing 90 countries, also call on governments to pass laws against such weapons. Google DeepMind and the Xprize Foundation are among the groups who've signed on while Elon Musk and DeepMind co-founders Demis Hassabis, Shane Legg and Mustafa Suleyman have made the pledge as well. The pledge comes as a handful of companies are facing backlash over their technologies and how they're providing them to government agencies and law enforcement groups. Google has come under fire for its Project Maven Pentagon contract, which is providing AI technology to the military in order to help them flag drone images that require additional human review. Similarly, Amazon is facing criticism for sharing its facial recognition technology with law enforcement agencies while Microsoft has been called out for providing services to Immigration and Customs Enforcement (ICE).

One popular test, called Raven's Progressive Matrices, features several rows of images with the final row missing its final image. It's up to the test taker to choose the image that should come next based on the pattern of the completed rows. The test doesn't outright tell the test taker what to look for in the images -- maybe the progression has to do with the number of objects within each image, their color, or their placement. It's up to them to figure that out for themselves using their ability to reason abstractly. To apply this test to AIs, the DeepMind researchers created a program that could generate unique matrix problems.

One popular test, called Raven's Progressive Matrices, features several rows of images with the final row missing its final image. It's up to the test taker to choose the image that should come next based on the pattern of the completed rows. The test doesn't outright tell the test taker what to look for in the images -- maybe the progression has to do with the number of objects within each image, their color, or their placement. It's up to them to figure that out for themselves using their ability to reason abstractly. To apply this test to AIs, the DeepMind researchers created a program that could generate unique matrix problems.