"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
The co-founder of MoviePass has developed a new idea to get people to the theater. Called PreShow, users would be able to earn free movie tickets if they agree to watch advertisements for blocks of time between 15 and 20 minutes. There's also another, creepier, twist to the proposed app: It will only unlock with facial recognition and it also tracks your gaze using facial recognition technology to make sure you're actually watching the ads, according to CNET. A MoviePass co-founder has developed a new idea to get people to the theater. PreShow is being developed by MoviePass co-founder Stacy Spikes, who stepped down as CEO of the beleaguered ticketing company in 2016.
Food waste could become a thing of the past thanks to an AI powered smart bin that let's you know the type of items you throw away most regularly. The system uses a camera, a set of smart scales and the same type of machine learning technology found in self-driving cars. It comes pre-programmed with common items and learns to recognise different foods being thrown away regularly. It uses this information to calculate the financial and environmental cost of this wasted food, so that you can tailor your next food order accordingly. The smart bin is currently aimed at commercial kitchens but could one day be a common feature in people's homes, the firm hopes.
Nvidia has been more than a hardware company for a long time. As its GPUs are broadly used to run machine learning workloads, machine learning has become a key priority for Nvidia. In its GTC event this week, Nvidia made a number of related points, aiming to build on machine learning and extend to data science and analytics. Nvidia wants to "couple software and hardware to deliver the advances in computing power needed to transform data into insights and intelligence." Jensen Huang, Nvidia CEO, emphasized the collaborative aspect between chip architecture, systems, algorithms and applications.
A new area in artificial intelligence involves using algorithms to automatically design machine-learning systems known as neural networks, which are more accurate and efficient than those developed by human engineers. But this so-called neural architecture search (NAS) technique is computationally expensive. A state-of-the-art NAS algorithm recently developed by Google to run on a squad of graphical processing units (GPUs) took 48,000 GPU hours to produce a single convolutional neural network, which is used for image classification and detection tasks. Google has the wherewithal to run hundreds of GPUs and other specialized hardware in parallel, but that's out of reach for many others. In a paper being presented at the International Conference on Learning Representations in May, MIT researchers describe an NAS algorithm that can directly learn specialized convolutional neural networks (CNNs) for target hardware platforms -- when run on a massive image dataset -- in only 200 GPU hours, which could enable far broader use of these types of algorithms.
Our brains are incredibly good at processing faces, and even have specific regions specialized for this function. But what face dimensions are we observing? Do we observe general properties first, then look at the details? Or are dimensions such as gender or other identity details decoded interdependently? In a study published in Nature Communications, neuroscientists at the McGovern Institute for Brain Research measured the response of the brain to faces in real-time, and found that the brain first decodes properties such as gender and age before drilling down to the specific identity of the face itself.
A machine-learning model from MIT researchers computationally breaks down how segments of amino acid chains determine a protein's function, which could help researchers design and test new proteins for drug development or biological research. Proteins are linear chains of amino acids, connected by peptide bonds, that fold into exceedingly complex three-dimensional structures, depending on the sequence and physical interactions within the chain. That structure, in turn, determines the protein's biological function. Knowing a protein's 3-D structure, therefore, is valuable for, say, predicting how proteins may respond to certain drugs. However, despite decades of research and the development of multiple imaging techniques, we know only a very small fraction of possible protein structures -- tens of thousands out of millions.
Anyone who has ventured into online gaming knows text chat can approach nuclear-waste-levels of toxicity. But what happens when it all shifts to voice-based chat in the future? Intel says it can help. Or at least, it hopes it can. The company said on Wednesday night it's working with Spirit AI on ways to use machine learning and artificial intelligence to reduce the acidic speech gamers often fall back on during intense gaming sessions.
The 2020 Democratic candidates are out of the gate and the pollsters have the call! Bernie Sanders is leading by two lengths with Kamala Harris and Elizabeth Warren right behind, but Cory Booker and Beto O'Rourke are coming on fast! The political horse-race season is upon us and I bet I know what you are thinking: "Stop!" Every election we complain about horse-race coverage and every election we stay glued to it all the same. The problem with this kind of coverage is not that it's unimportant.
With public and academic attention increasingly focused on the new role of machine learning in the health information economy, an unusual and no-longer-esoteric category of vulnerabilities in machine-learning systems could prove important. These vulnerabilities allow a small, carefully designed change in how inputs are presented to a system to completely alter its output, causing it to confidently arrive at manifestly wrong conclusions. These advanced techniques to subvert otherwise-reliable machine-learning systems--so-called adversarial attacks--have, to date, been of interest primarily to computer science researchers (1). However, the landscape of often-competing interests within health care, and billions of dollars at stake in systems' outputs, implies considerable problems. We outline motivations that various players in the health care system may have to use adversarial attacks and begin a discussion of what to do about them.
Physical, chemical, and biological processes interact and have substantial influence on this complex geosystem, and humans interact with it in ways that are increasingly consequential to the future of both the natural world and civilization as the finiteness of Earth becomes increasingly apparent and limits on available energy, mineral resources, and fresh water increasingly affect the human condition. Earth is subject to a variety of geohazards that are poorly understood, yet increasingly impactful as our exposure grows through increasing urbanization, particularly in hazard-prone areas. We have a fundamental need to develop the best possible predictive understanding of how the geosystem works, and that understanding must be informed by both the present and the deep past. This understanding will come through the analysis of increasingly large geo-datasets and from computationally intensive simulations, often connected through inverse problems. Geoscientists are faced with the challenge of extracting as much useful information as possible and gaining new insights from these data, simulations, and the interplay between the two.