Transfer learning algorithms are used when one has sufficient training data for one supervised learning task (the source/training domain) but only very limited training data for a second task (the target/test domain) that is similar but not identical to the first. Previous work on transfer learning has focused on relatively restricted settings, where specific parts of the model are considered to be carried over between tasks. Recent work on covariate shift focuses on matching the marginal distributions on observations $X$ across domains. Similarly, work on target/conditional shift focuses on matching marginal distributions on labels $Y$ and adjusting conditional distributions $P(X Y)$, such that $P(X)$ can be matched across domains. However, covariate shift assumes that the support of test $P(X)$ is contained in the support of training $P(X)$, i.e., the training set is richer than the test set.
Keeping abreast of shopping trends online is straightforward enough -- whole categories of startups achieve this with predictive modeling. But what about when that shopping takes place in-store? Tracking the behaviors of mall, outlet, and department store shoppers is of critical importance to physical store brands, particularly considering that the percentage of brick-and-mortar sales increased by 2% from $2.99 trillion in 2016 to $3.04 trillion in 2017. To meet this need, Miron Mironiuk founded Cosmose AI, a Shanghai-based analytics software provider that anticipates how people shop offline. Brands like Subway, Samsung, Walmart, Airbnb, Tencent, Burberry, Omnicom, Mercedes-Benz, Anheuser-Busch InBev, LVMH, Kering, L'Oréal, Gucci, Cartier, P&G, Nestle, and Coca-Cola use its tool suite to granularly track offline visitors' purchasing habits and target them with online ads via WeChat, Weibo, Facebook, Google, and over 100 other internet platforms.
Do you know the way to San Jose? As they previewed earlier this year, Bosch and Mercedes-Benz have commenced trials for an automated ride-hailing service in the Silicon Valley city of San Jose. To start with, autonomous S-Class Mercedes-Benz vehicles (with safety drivers at the wheel) will shuttle "a select group of users" between North San Jose and downtown. The busy San Carlos/Stevens Creek corridor between west San Jose and downtown should be good test for the self-driving tech used by Mercedes and Bosch. Rather than just playing with prototypes, the companies want to create a production-ready SAE Level 4/5 self-driving system that can be built into different makes and models.
The upshot: Mindtech provides a capability for creating fully annotated synthetic training images to complement real images for improved AI training. We've spent a lot of time looking at AI training and AI inference and the architectures and processes used for each of those. Where the AI task involves images, we've blithely referred to the need for training sets; that's easy, right? After all, if you're trying to train your algorithm to recognize a dog, then just give it a bunch of pictures of dogs (OK, tag them with, "This one contains a dog") and then a bunch of pictures without dogs ("This one contains no dog"), and off you go! Right? And the behemoths like Google and Facebook have oodles of images and videos (videos being collections of frames, each of which is an image), thanks to the free stuff willingly served up by unsuspecting users (including images now and 10 years ago to help improve aging algorithms).
Topics like Artificial Intelligence (AI), the internet of things (IoT), and machine learning are getting lots of hype, but digital twin technology might just be the real game-changer. Digital twin software uses aspects of all the trending tech mentioned (AI, IoT, ML) in a unique way that's changing the way businesses optimize production and investment, and the big boys are already heavily invested. A digital twin is a highly advanced simulation that's used in computer-aided engineering (CAE). It's a digital duplicate that represents a physical object or process, but it is not intended to replace a physical object; it is merely to inform its optimization. Other terms used to refer to digital twin technology include virtual prototyping, hybrid twin technology, and digital asset management, but digital twin is quickly winning out as the most popular name.
The Information Commissioner's Office (ICO) has opened consultation on its first draft of artificial intelligence (AI) guidance, urging organisations deploying the technology to be transparent and accountable. In a blog post, the organisation, in partnership with the Alan Turing Institute, explains that the complex nature of AI means that it is often implemented without those affected properly understanding how it works, and says that "the decisions made using AI need to be properly understood by the people they impact" in order to avoid creating "doubt, uncertainty and mistrust". The non-departmental public body has set out four key principles for the development of AI decision making systems. Organisations should be transparent, ensuring that AI decision making is explained to the individuals affected; accountable by having "appropriate oversight" of AI systems; take context into account and consider the ethical impacts and purpose of AI projects. The ICO will now consult on this draft guidance until 24 January 2020, before the final version is published next year.
A weighted graph is used as an underlying structure of many algorithms like semi-supervised learning and spectral clustering. The edge weights are usually deter-mined by a single similarity measure, but it often hard if not impossible to capture all relevant aspects of similarity when using a single similarity measure. In par-ticular, in the case of visual object matching it is beneficial to integrate different similarity measures that focus on different visual representations. In this paper, a novel approach to integrate multiple similarity measures is pro-posed. First pairs of similarity measures are combined with a diffusion process on their tensor product graph (TPG).
AI-driven networking equipment provider Juniper Networks said it is ready to address the challenge and the inherent complexity that comes with networking in the multicloud era at a time when artificial intelligence is changing the IT game and fuelling the age of self-driving networks. Kicking off Juniper's fifth annual customer and partner summit - Nxtwork 2019 Emea - in London, Juniper Networks CEO Rami Rahim said the technology firm would take on the new challenges with products, solutions and services that transform the way people connect, work and live. He said Juniper is reorienting itself toward the enterprise and is relying heavily on the channel to help the former "box company" reach more of those customers with software and services. "What made us successful in the past is not necessarily going to make us successful in the future," said Rahim during his keynote, adding the company has been making "painful" changes that were ultimately needed to transform it. Rahim, one of the longest-serving Juniper team members, observed that AI is a "used and abused word today".
We offer PhD position in the university environment while being partially supervised by the top-tier researcher from Valeo.AI research group. Show your research to be meaningful by running your codes on the real self-driving car to improve human safety. In case of interest, please, send your application by email to the principal investigator Karel Zimmermann, email@example.com. The application should be a single PDF file including applicant CV and a short research statement.
Beyond trendy names like Tesla and Alphabet chasing self-driving cars, a host of auto brands and other tech heavyweights are also investing in autonomous R&D. Private companies working in auto tech are attracting record levels of deals and funding, with autonomous driving startups leading the charge. Along with early-stage startups, VCs, and other investors, large corporations are also angling to get a slice of the self-driving pie. From autonomy to telematics to ride sharing, the auto industry has never been at more risk. Get the free 67-page report PDF. Using CB Insights' investment, acquisition, and partnership data, we identified over 40 companies developing road-going self-driving vehicles. They are a diverse group of players, ranging from automotive industry stalwarts to leading technology brands and telecommunications companies. This list is organized alphabetically and focuses on larger corporate players in the space (as opposed to earlier-stage startups). Companies working on industrial autonomous vehicles were not included in this analysis. A few of the companies or brands listed below belong to the same parent organization but are detailed separately if they are operating distinct autonomous development programs. Some companies are grouped together by key partnerships or alliances. Given the complex web of relationships between these players, other collaborations are also noted in each profile. This is not intended to be an exhaustive list of corporations working on autonomous vehicle technology. This brief was originally published on 9/25/2015 and featured 25 select corporations. It was updated and expanded on 5/17/2017, 9/4/2018, and 8/28/2019. Over the last decade, Amazon has spent billions of dollars working on finding ever-better solutions to the last-mile problem in delivery. It's built its own fleet of cargo jets, explored delivery by drone in the form of "Prime Air," and more. More recently, an increasing percentage of that investment has been directed toward autonomous vehicle technology. In February 2019, Amazon invested in Aurora Innovation, an autonomous tech startup run by former executives from two other firms with strong ties to self-driving technology: Google and Tesla. "Autonomous technology has the potential to help make the jobs of our employees and partners safer and more productive, whether it's in a fulfillment center or on the road, and we're excited about the possibilities." The Aurora investment isn't the only autonomous technology play that Amazon is pursuing. In January 2019, the company introduced the Amazon Scout, a six-wheeled electric-powered delivery robot.