In 2013, James "Jimi" Crawford founded a company called Orbital Insight, barely noticed at the time amid the Silicon Valley froth. Crawford had worked at NASA for 15 years and wrote software for Mars rovers. He left NASA to run engineering for Google Books, and while there he noticed that Elon Musk's SpaceX and other new companies were driving down the cost of building and launching satellites. Crawford saw an opportunity to collect and analyze what he anticipated would be a deluge of images from a surfeit of new satellites that would circle the Earth, taking readings and pictures. Orbital Insight's first product looked at images of cornfields all over the world, analyzing the health of plants to predict yields for traders who bet on future price swings.

Artificial intelligence (AI) is the new technological frontier over which companies and countries are vying for control. According to a recent report from McKinsey, Alphabet invested roughly $30 billion in developing AI technologies. Baidu, which is the Chinese equivalent of Alphabet, invested $20 billion in AI last year. Companies aren't the only ones investing time, money and energy into advancing AI technology -- a recent article in The New Yorker reported that the Chinese government has been pursuing AI technology aggressively in an attempt to control a future cornerstone innovation. Considering that some of the largest entities in the world are focused on advancing AI tech, it is all but certain that 2018 will see significant advancements in the space.

Learning the structure of graphical models from data usually incurs a heavy curse of dimensionality that renders this problem intractable in many real-world situations. The rare cases where the curse becomes a blessing provide insight into the limits of the efficiently computable and augment the scarce options for treating very under-sampled, high-dimensional data. We study a special class of Gaussian latent factor models where each (non-iid) observed variable depends on at most one of a set of latent variables. We derive information-theoretic lower bounds on the sample complexity for structure recovery that suggest complexity actually decreases as the dimensionality increases. Contrary to this prediction, we observe that existing structure recovery methods deteriorate with increasing dimension. Therefore, we design a new approach to learning Gaussian latent factor models that benefits from dimensionality. Our approach relies on an unconstrained information-theoretic objective whose global optima correspond to structured latent factor generative models. In addition to improved structure recovery, we also show that we are able to outperform state-of-the-art approaches for covariance estimation on both synthetic and real data in the very under-sampled, high-dimensional regime.

Between 2009 and 2017, approximately 18,458 unique bills were introduced to the legislature of Massachusetts (out of 33,201 total). I know this because Dru Tarr, a legislative aide at the Massachusetts House of Representatives, very kindly compiled the publicly available list of bills into a neural network-digestible format. A neural network is a kind of computer program I train to imitate human data, usually to humorous effect. It looks at examples and then learns to generate more like them (at least, that's the idea). So, I gave the neural network this set of bills.

The debate over what to do about the approximately 700,000 Dreamers – immigrants who were brought to the U.S. illegally as children – has often been conducted at a playground level. Democrats are threatening to shut down the government at midnight Friday night unless the Dreamer issue is resolved on their terms. President Trump is reported to have used some kind of obscenity to describe his view of some of the countries Dreamers and other immigrants come from. Immigration activists have freely and openly accused the president of being a "racist" or worse. In this kind of a toxic environment, it will be amazing if both sides can bridge the enormous gap between them.

Amazon's hunt for a second headquarters, after several months of publicity stunts and dangled perks from cities and regions vying to lure the e-commerce giant, has been narrowed to 20 options from 238 bids. The company, which is based in Seattle, plans to invest $5 billion in development and create up to 50,000 jobs wherever it builds its newest hub. With the kind of enthusiasm normally reserved for bids to host the Olympics, governors, mayors, business leaders and others have pulled together proposals promoting the potential of their cities and regions, sometimes going to outlandish lengths. These are some of the places that caught Amazon's attention. Schools: The caliber of local schools is impressive, including Harvard University, Boston University, the Massachusetts Institute of Technology and Tufts University.

Developed democracies are settling an increased number of refugees, many of whom face challenges integrating into host societies. We developed a flexible data-driven algorithm that assigns refugees across resettlement locations to improve integration outcomes. The algorithm uses a combination of supervised machine learning and optimal matching to discover and leverage synergies between refugee characteristics and resettlement sites. The algorithm was tested on historical registry data from two countries with different assignment regimes and refugee populations, the United States and Switzerland. Our approach led to gains of roughly 40 to 70%, on average, in refugees' employment outcomes relative to current assignment practices.

That person would establish liaisons with the state departments of transportation, public safety, administrative services and insurance, the state workforce transformation office, the adjutant general and the heads of the Ohio Turnpike and Public Utilities commissions.

The "Industrial Machine Vision Market by Component (Hardware (Camera, Frame Grabber, Optics, Processor), and Software (Deep Learning, and Application Specific)), Product (PC-based, and Smart Camera-based), Application, End-User - Global Forecast to 2023" report has been added to ResearchAndMarkets.com's offering. The overall industrial machine vision market was valued at USD 7.91 Billion in 2017 and is expected to reach USD 12.29 Billion by 2023, at a CAGR of 7.61% between 2017 and 2023. This is because of the increasing need for quality inspection and automation, growing demand for AI and IoT integrated machine vision system, increasing adoption of Industrial 4.0, development of new connected technologies, and government initiatives to support smart factories, among others. Governments of different countries worldwide are encouraging investments in manufacturing, which is necessitating the use of various automation products for structural development. Software component is expected to grow at the highest rate between 2017 and 2023.

Malta is set to become the first country in the world to regulate services in the sector of artificial intelligence, blockchain and Internet of Things devices. Parliamentary secretary for financial services Silvio Schembri announced that a legal framework to regulate these sectors will be launched in the coming weeks. He said the law will satisfy anti money laundering and Know Your Client regulations, without stifling technological innovation. "This new regulator will help those who wish to invest in this sector to operate within a legal structure and gain the trust of millions of potential customers all over the world who want to make use of new services related to blockchain technology", Schembri told a blockchain conference in Dubai. During his visit to Dubai, Schembri also met up with representatives from Smart Dubai - an initiative by the Dubai government to promote smart technologies with the lofty ambition of turning the Arab emirate into the happiest city in the world.