Greedy algorithms are known to provide, in polynomial time, near optimal approximation guarantees for Combinatorial Auctions (CAs) with multidimensional bidders. It is known that truthful greedy-like mechanisms for CAs with multi-minded bidders do not achieve good approximation guarantees. In this work, we seek a deeper understanding of greedy mechanism design and investigate under which general assumptions, we can have efficient and truthful greedy mechanisms for CAs. Towards this goal, we use the framework of priority algorithms and weak and strong verification, where the bidders are not allowed to overbid on their winning set or on any subset of this set, respectively. We provide a complete characterization of the power of weak verification showing that it is sufficient and necessary for any greedy fixed priority algorithm to become truthful with the use of money or not, depending on the ordering of the bids. Moreover, we show that strong verification is sufficient and necessary to obtain a 2-approximate truthful mechanism with money, based on a known greedy algorithm, for the problem of submodular CAs in finite bidding domains. Our proof is based on an interesting structural analysis of the strongly connected components of the declaration graph.

We propose a formulation of visual localization that does not require construction of explicit maps in the form of point clouds or voxels. The goal is to learn an implicit representation of the environment at a higher, more abstract level, for instance that of objects. To study this approach we consider procedurally generated Minecraft worlds, for which we can generate visually rich images along with camera pose coordinates. We first show that Generative Query Networks (GQNs) enhanced with a novel attention mechanism can capture the visual structure of 3D scenes in Minecraft, as evidenced by their samples. We then apply the models to the localization problem, investigating both generative and discriminative approaches, and compare the different ways in which they each capture task uncertainty. Our results show that models with implicit mapping are able to capture the underlying 3D structure of visually complex scenes, and use this to accurately localize new observations, paving the way towards future applications in sequential localization. Supplementary video available at https://youtu.be/iHEXX5wXbCI.

Autoencoders are popular among neural-network-based matrix completion models due to their ability to retrieve potential latent factors from the partially observed matrices. Nevertheless, when training data is scarce their performance is significantly degraded due to overfitting. In this paper, we mit- igate overfitting with a data-dependent regularization technique that relies on the principles of multi-task learning. Specifically, we propose an autoencoder-based matrix completion model that performs prediction of the unknown matrix values as a main task, and manifold learning as an auxiliary task. The latter acts as an inductive bias, leading to solutions that generalize better. The proposed model outperforms the existing autoencoder-based models designed for matrix completion, achieving high reconstruction accuracy in well-known datasets.

Recently, computer scientists have been working on teaching machines to do a wider range of tasks around the house. In a new paper spearheaded by MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the University of Toronto, researchers demonstrate "VirtualHome," a system that can simulate detailed household tasks and then have artificial "agents" execute them, opening up the possibility of one day teaching robots to do such tasks. The team trained the system using nearly 3,000 programs of various activities, which are further broken down into subtasks for the computer to understand. A simple task like "making coffee," for example, would also include the step "grabbing a cup." The researchers demonstrated VirtualHome in a 3-D world inspired by the Sims video game.

I can't run a starship with twenty crew. KIRK: And what am I supposed to do? WESLEY: You've got a great job, Jim. All you have to do is sit back and let the machine do the work. One clear message from the presidential election is that the dream of good factory jobs still resonates in America's rust belt. Despite the push for students to pursue STEM careers or move into the service sector, Americans still want to make stuff.

A never-ending debate over artificial intelligence is whether this exciting, deeply-complicated software can really boost a company's bottom line. One way to find out: build a quick-and-easy neural network and plug it into your legacy system. In Sweden, engineers who once built the "fruit-accounting" mechanics to support hundreds of millions of Candy Crush users, have joined a startup that's claims it's cracked the problem. Senior engineers from a raft of other Swedish tech unicorns - Spotify, Klarna and Truecaller - have joined too, says their CEO, Luka Crnkovic-Friis. When it comes to attracting talent in the Nordics, "no one comes close," he says.

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What if every time you walked down any street in any city, automated cameras - attached to street lights, business facades, mailboxes or homes, or in the form of "body cams" worn by police officers and parking attendants - automatically scanned your face and uploaded a biometric fingerprint to a central server? Or if every time you took a photograph, your smartphone sent a copy to a server for biometric analysis? And what if these servers were monitored by a third-party provider that shared the fingerprints with marketing firms and law enforcement agencies, including border control agencies? The biometric facial recognition technology required to underpin such an undertaking continues to be refined and made available by the likes of Affectiva, Amazon, Google, IBM, Kairos, Microsoft, NEC and OpenCV, among others. Amazon Web Services, for example, in 2016 began to offer biometric capabilities via Amazon Rekognition, and it's ready to highlight positive use cases.

A chatbot is an artificial intelligence or a computer program that conducts a conversation through textual or auditory methods. This kind of program is frequently designed to persuasively pretend how a person would behave like a conversational partner, thus passing the Turing test. They are normally utilized in dialog systems for different practical objectives like information acquisition or customer service. Present circumstances show that it is mandatory for you to invest in technology with a vision and with a purpose. It encompasses augmenting your website or app with a chatbot tool or creating a separate chatbot to serve your customers.

Powered by a Xilinx automotive platform consisting of system-on-a-chip (SoC) devices and AI acceleration software, the scalable solution will deliver high performance, low latency and best power efficiency for embedded AI in automotive applications today. There are not many additional details about what exactly it will be working on, it is apparently too early for these details. Daimler AG recognized the Xilinx's advancements and the fact that the company managed to ship 40 million cumulative automotive units to automakers and Tier 1 suppliers in the last twelve years. Some of their solutions like FPGA or Over the Air FPGA based silicon where hardware and software can be updated over the air are definitely unique compared to its competitors. You can simply add features to the hardware, something you cannot do in ASIC /SoC approach.