How does the Google Pixel XL stack up against the Apple iPhone 7 Plus on three main metrics? Note that is not a comprehensive review of all the features on both phones. I will do a comprehensive long-term review later. And for the record (iPhone fans please note), I think both phones are excellent and are two of the best smartphones on the planet. Intelligent Personal Assistant: Google Personal Assistant vs. Siri: The Google Pixel is the first Android phone to use Google Assistant.

Artificial intelligence (AI) is a sub-division of computer science. The main goal is to enable a smart device (e.g. First mentioned back in the 50s in the paper "Computing Machinery and Intelligence", written by mathematician Alan Turing, artificial intelligence is now a very popular field, and we have advanced technology to "blame" for that. This article is about learning Artificial Intelligence and we will give you a comprehensive guide that you can use as a starting point towards learning artificial intelligence. Today's AI-based computers can beat chess champions, so it's safe to say that little by little the world is taking a turn. Some people say that artificial intelligence will save humanity; others, claim it will destroy it.

Hi, I've always been into AI and Future technology and even slightly believe in the singularity. I think the best thing that will happen for the average person in the next 10 years due to AI will be the arrival of personal digital Assistants. Much like Siri or Cortana but with more advanced AI functions build in. The ability to learn their "clients?" I know we are a long way off from being able to have a program that can make new command libraries without manually going in and creating them yourself but I don't see why we can't have some sort of chat bot integrated with Cortana and a program similar to how Google decides what ads you see.

Ahead of CES 2017 this January, South Korea giant LG is teasing about the products and new technologies it would be showing off at the trade show. Based off of new information concerning the Soul-based electronics company, new AI robots could be unveiled at next month's event including one that would serve as a direct competitor to Amazon's Echo smart speaker. Amazon Echo proved to be a smash hit when it was released. Thus, it is not surprising that other companies have since tried to penetrate the market that Amazon established. Google introduced its voice-activated speaker, called Google Home, this year.

Taking a look back at seven days of news and headlines across the world of Android, this week's Android Circuit includes a new voice for the Galaxy S8, the return of the S-Pen, Pixel power problems, Android's battery win, the shutdown of Cyanogen, WileyFox's quick change to Nougat, a North Korean Android tablet's spyware, and Super Mario Run prepares for its Android arrival. Android Circuit is here to remind you of a few of the many things that have happened around Android in the last week (and you can find the weekly Apple news digest here). The Samsung Galaxy S8 could be picking up a new tool named Bixby, a voice-powered digital assistant along the lines of Siri and Google Assistant. Viv Labs is the company behind the technology, and Samsung recently acquired it, so it makes sense for the South Koreans to stake its claim in this space. But will that upset Google?

If the business IT market in 2016 was defined by an increased focus on cybersecurity vulnerabilities (including from the Internet of Things), cloud adoption and a shift to hyperconverged infrastructure, what does that augur for 2017? Often, predictions about the year ahead are untethered from the year that was, and do not have much of a connection to underlying trends. The world of enterprise technology likely will not be radically different next year than it was in 2016. However, trends that have been ongoing may accelerate or evolve, as technologies mature and businesses get more acclimated to them. For example, Hardware as a Service may start to take off.

In this paper, we introduce the public-private framework of data summarization motivated by privacy concerns in personalized recommender systems and online social services. Such systems have usually access to massive data generated by a large pool of users. A major fraction of the data is public and is visible to (and can be used for) all users. However, each user can also contribute some private data that should not be shared with other users to ensure her privacy. The goal is to provide a succinct summary of massive dataset, ideally as small as possible, from which customized summaries can be built for each user, i.e. it can contain elements from the public data (for diversity) and users' private data (for personalization). To formalize the above challenge, we assume that the scoring function according to which a user evaluates the utility of her summary satisfies submodularity, a widely used notion in data summarization applications. Thus, we model the data summarization targeted to each user as an instance of a submodular cover problem. However, when the data is massive it is infeasible to use the centralized greedy algorithm to find a customized summary even for a single user. Moreover, for a large pool of users, it is too time consuming to find such summaries separately. Instead, we develop a fast distributed algorithm for submodular cover, FASTCOVER, that provides a succinct summary in one shot and for all users. We show that the solution provided by FASTCOVER is competitive with that of the centralized algorithm with the number of rounds that is exponentially smaller than state of the art results. Moreover, we have implemented FASTCOVER with Spark to demonstrate its practical performance on a number of concrete applications, including personalized location recommendation, personalized movie recommendation, and dominating set on tens of millions of data points and varying number of users.

Matching users to the right items at the right time is a fundamental task in recommendation systems. As users interact with different items over time, users' and items' feature may evolve and co-evolve over time. Traditional models based on static latent features or discretizing time into epochs can become ineffective for capturing the fine-grained temporal dynamics in the user-item interactions. We propose a coevolutionary latent feature process model that accurately captures the coevolving nature of users' and items' feature. To learn parameters, we design an efficient convex optimization algorithm with a novel low rank space sharing constraints. Extensive experiments on diverse real-world datasets demonstrate significant improvements in user behavior prediction compared to state-of-the-arts.

We propose a novel and efficient algorithm for the collaborative preference completion problem, which involves jointly estimating individualized rankings for a set of entities over a shared set of items, based on a limited number of observed affinity values. Our approach exploits the observation that while preferences are often recorded as numerical scores, the predictive quantity of interest is the underlying rankings. Thus, attempts to closely match the recorded scores may lead to overfitting and impair generalization performance. Instead, we propose an estimator that directly fits the underlying preference order, combined with nuclear norm constraints to encourage low--rank parameters. Besides (approximate) correctness of the ranking order, the proposed estimator makes no generative assumption on the numerical scores of the observations. One consequence is that the proposed estimator can fit any consistent partial ranking over a subset of the items represented as a directed acyclic graph (DAG), generalizing standard techniques that can only fit preference scores. Despite this generality, for supervision representing total or blockwise total orders, the computational complexity of our algorithm is within a $\log$ factor of the standard algorithms for nuclear norm regularization based estimates for matrix completion. We further show promising empirical results for a novel and challenging application of collaboratively ranking of the associations between brain--regions and cognitive neuroscience terms.

Recommendation and collaborative filtering systems are important in modern information and e-commerce applications. As these systems are becoming increasingly popular in industry, their outputs could affect business decision making, introducing incentives for an adversarial party to compromise the availability or integrity of such systems. We introduce a data poisoning attack on collaborative filtering systems. We demonstrate how a powerful attacker with full knowledge of the learner can generate malicious data so as to maximize his/her malicious objectives, while at the same time mimicking normal user behaviors to avoid being detected. While the complete knowledge assumption seems extreme, it enables a robust assessment of the vulnerability of collaborative filtering schemes to highly motivated attacks. We present efficient solutions for two popular factorization-based collaborative filtering algorithms: the alternative minimization formulation and the nuclear norm minimization method. Finally, we test the effectiveness of our proposed algorithms on real-world data and discuss potential defensive strategies.