Consequently, the United Nations has set a target of halving this number by 2020. A new technology being readied for its debut could be a step forward in achieving that ambitious goal: greatly improved automotive video cameras meant to replace mirrors on vehicles. In its annual R&D Open House on 14 February, Mitsubishi Electric described the development of what it believes is the industry's highest-performance rendition of mirrorless car technology. According to the company, today's conventional camera-based systems featuring motion detection technology can detect objects up to about 30 meters away and identify them with a low accuracy of 14 percent. By comparison, Mitsubishi's new mirrorless technology extends the recognition distance to 100 meters with an 81 percent accuracy.

Although deep learning has shown some significant achievements in image analysis and classification, their application to medical images has only recently started gaining momentum. This is because medical images are intrinsically noisier and prone to artifacts. Despite these challenges, these techniques have been shown to provide more accurate diagnoses than human doctors in certain scenarios. A major hurdle that has to be overcome in the effective classification of medical images in diagnosis is preprocessing and cleaning. This task is a major bottleneck as it requires a significant amount of time to prepare images for training, is computationally very demanding, and requires expertise about the domain.

I enjoy a nice steak. How about you? [Miss Dawes:] Father always used to say that if we ever had the money you have . . . Jenson:] I eat all the steak and chicken too, even bacon. Jenson:] Yeah you said that already. Miss Dawes is a bot, and her response is typical of even the world's best chatbots when they try to make chitchat.

More and more companies are building and applying deep learning models in their business. Several practical issues should be taken into consideration before these models are put into production. Consider this scenario: you may build a model that works perfectly with training and validation data, but it doesn't perform well after deploying the model in real scenarios. Or, you may struggle with getting better performance compared to traditional machine learning models. While the latter case will make you rethink whether to invest more resourcing on this, the former situation is more risky and you may not realize it until you put your models into production.

This repository contains the projects completed as a part of Udacity's Artificial Intelligence Nanodegree. In this project, an extension of a Sudoku solving agent is developed. The project is capable of solving any Classic or Diagonal Sudoku puzzle using three ideas: Constraint Propagation, Search (DFS) and Naked-Twins Strategy. This game-playing agent uses techniques such as Iterative Deepening, Minimax, and Alpha-Beta Pruning to compete in the game of Isolation (a two-player discrete competitive game with perfect information). The different heuristics used are then compared to find the best heuristic.

From a marketing standpoint, here are three ways AI can serve as a creative partner, courtesy of Chris Neff, Executive Producer, Director of Digital and Experiential, with Tool of North America. Look no further than tools like Wordsmith. Machine learning is an obvious choice for simple, time-sensitive copy needs, freeing up creative minds to concentrate on strategic ideation. You'll save both time and money along the way. Accenture reports that 90 percent of marketers expect their content needs to grow over the next two years.

Gartner reports that 85 percent of information in a company is unstructured, and that a company's information doubles every 18 months. Moreover, according to a Dimensional Research survey commissioned by my company, M-Files, nearly 50 percent of professionals struggle with documents and content scattered across disparate applications and storage locations. In order to keep up with the exponential growth in the volume of information, the paradigm of knowledge and information management in today's digital workplace must shift to include contextual understanding. Artificial intelligence (AI) plays a major role in this, and the industry is taking three steps toward the day when fully automated digital assistants will support an intelligent workplace. The journey to an intelligent workplace begins with the content itself.

Today on the Datanauts podcast, we talk with Vivian Zhang, a Machine Learning (ML) expert. If you've been hearing about ML from IT marketing folks and it all sounds like magic unicorn dust, this is your show. We're cutting through the cruft to get to what's real. Vivian Zhang is CTO and Chief Data Scientist at the NYC Data Science Academy. We establish a baseline of what machine learning is, how it fits into the broader category of artificial intelligence, and how ML might move the needle in IT infrastructure.

In 2018, AI (Artificial Intelligence) will be at the forefront of major developments in marketing. As this emerging technology becomes more available to marketing professionals, here are a few capabilities adopters can expect over the months to come. The digital footprint we all leave as we go through our daily lives is opening new doors for behavior-based marketing. Simple personalization is maturing into the ability to read real-time buying signals, witness trends emerge and die, and detect even temporary shifts in preferences. Knowing an individual's buying preferences and needs at a point in time, then matching that up with where an individual is in their buying journey is powerful and possible.

From analyzing millions of satellite images to finding healthy strains of plant microbiome, these startups have raised over $500M to bring AI and robotics to agriculture. Agricultural tech startups have raised over $800M in the last 5 years. Deals to startups using robotics and machine learning to solve problems in agriculture started gaining momentum in 2014, in line with the rising interest in artificial intelligence across multiple industries like healthcare, finance, and commerce. Smart money VCs like Bessemer Venture Partners, Accel Partners, Khosla Ventures, Lux Capital, and Data Collective have invested in general-purpose drone and computer vision companies with a focus on agricultural applications, like DJI and Orbital Insight, as well as ag tech startups like Blue River Technology. Big corporations like Monsanto and Syngenta, which are active ag tech investors, have also backed companies like Resson and previously mentioned Blue River Technology.