More than 83 million U.S. households have at least one smart device, and most of us have more than one. In fact, in the U.S., on average we have 11 devices connected to our home networks, according to one study. Take a quick tally of your own devices. The list goes on and is only going to grow. The bad news is that it's often difficult to understand how your network router interacts with the various devices in your home.

The technology field is constantly evolving and growing. Yesterday's tech disrupters have quickly become today's industry standards, just as today's innovations will become commonplace in the future. To stay ahead of the curve, we asked the members of Forbes Technology Council to name the biggest tech disrupter they see impacting business and industry in the 2020s. Here are the 14 technologies they believe consumers and businesses should watch as the decade unfolds and how these disrupters might shape tomorrow's standards. Digital voice assistants are at the beginning of their disruption curve.

Ecommerce is in developing trend right now. Your success in online business depends upon the type of marketing strategy you are using. To reach the exact crowd, a business should adopt intelligent digital marketing. You can enhance your business with the aid of artificial intelligence. This brief will discuss the e-commerce factors that can be optimized with artificial intelligence.

We build a platform, that totally automizes the wealth management process. Lars started out as a management consultant inside of Deutsche Bank, in their unit called in-house-consulting, focusing on their retail business. Our clients are wealthy, but not wealthy enough to qualify for traditional wealth management. You can now support us on Patreon https://www.patreon.com/bePatron?u 35246148 if you like what you see and hear consider to support us, so we can keep bringing you great content. There was no one at my company interested in what is today robo advisors, so I started my own and do not regret it until this day. They are wealthy enough to have money to invest, but in Germany, the normal threshold to enter the wealth management services of large banks is 2 million Euros, and they do not qualify yet. Ginmon wants to be the online financial advisor for this clientele. Therefore, they became fully licensed as a wealth manager in 2017, by German financial services oversight body BaFin. They now have an investment volume of more than 100 mn Euros for approx. According to Lars, they have approx.

The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing users by considering the dynamic nature of user preferences, and (2) not fully optimized for the ranking task when using implicit feedback. Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users.Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items. We illustrate our cross-network model using Twitter auxiliary information for recommendations on YouTube target network. Extensive comparisons against multiple time aware and cross-network base-lines show that the proposed solution is superior in terms of accuracy, novelty and diversity. Furthermore, experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques.

A major drawback of cross-network recommender solutions is that they can only be applied to users that are overlapped across networks. Thus, the non-overlapped users, which form the majority of users are ignored. As a solution, we propose CnGAN, a novel multi-task learning based, encoder-GAN-recommender architecture. The proposed model synthetically generates source network user preferences for non-overlapped users by learning the mapping from target to source network preference manifolds. The resultant user preferences are used in a Siamese network based neural recommender architecture. Furthermore, we propose a novel user based pairwise loss function for recommendations using implicit interactions to better guide the generation process in the multi-task learning environment.We illustrate our solution by generating user preferences on the Twitter source network for recommendations on the YouTube target network. Extensive experiments show that the generated preferences can be used to improve recommendations for non-overlapped users. The resultant recommendations achieve superior performance compared to the state-of-the-art cross-network recommender solutions in terms of accuracy, novelty and diversity.

The overwhelming volume and complexity of information in online applications make recommendation essential for users to find information of interest. However, two major limitations that coexist in real world applications (1) incomplete user profiles, and (2) the dynamic nature of user preferences continue to degrade recommender quality in aspects such as timeliness, accuracy, diversity and novelty. To address both the above limitations in a single solution, we propose a novel cross-network time aware recommender solution. The solution first learns historical user models in the target network by aggregating user preferences from multiple source networks. Second, user level time aware latent factors are learnt to develop current user models from the historical models and conduct timely recommendations. We illustrate our solution by using auxiliary information from the Twitter source network to improve recommendations for the YouTube target network. Experiments conducted using multiple time aware and cross-network baselines under different time granularities show that the proposed solution achieves superior performance in terms of accuracy, novelty and diversity.

Hands-free news and entertainment has never been easier on-the-go thanks to the arrival of the first Amazon Echo device designed for use in the car. The Echo Auto launched back in June but if you've yet to trial it yourself, now might be the best time to invest as the device has gone into the Amazon End of Summer Sale, reduced from £49.99 down to £39.99. The Echo Auto allows drivers to play music, check the news and make calls without taking their hands off the wheel or eyes off the road. Add Alexa to your car with the new Echo Auto available on Amazon now for £39.99 Just like the rest of the Amazon Echo smart speaker range, the Echo Auto allows you to connect to Alexa. The device works through your phone's Alexa app and plays through your car's speakers via Bluetooth or an auxiliary input jack.

It is always good to have a practical insight of any technology that you are working on. Though textbooks and other study materials will provide you all the knowledge that you need to know about any technology but you can't really master that technology until and unless you work on real-time projects. In this tutorial, you will find 21 machine learning projects ideas for beginners, intermediates, and experts to gain real-world experience of this growing technology. These machine learning project ideas will help you in learning all the practicalities that you need to succeed in your career and to make you employable in the industry. Learning through projects is the best investment that you are going to make.

Online food ordering marketplaces are multi-stakeholder systems where recommendations impact the experience and growth of each participant in the system. A recommender system in this setting has to encapsulate the objectives and constraints of different stakeholders in order to find utility of an item for recommendation. Constrained-optimization based approaches to this problem typically involve complex formulations and have high computational complexity in production settings involving millions of entities. Simplifications and relaxation techniques (for example, scalarization) help but introduce sub-optimality and can be time-consuming due to the amount of tuning needed. In this paper, we introduce a method involving multi-goal sampling followed by ranking for user-relevance (Sample-Rank), to nudge recommendations towards multi-objective (MO) goals of the marketplace. The proposed method's novelty is that it reduces the MO recommendation problem to sampling from a desired multi-goal distribution then using it to build a production-friendly learning-to-rank (LTR) model. In offline experiments we show that we are able to bias recommendations towards MO criteria with acceptable trade-offs in metrics like AUC and NDCG. We also show results from a large-scale online A/B experiment where this approach gave a statistically significant lift of 2.64% in average revenue per order (RPO) (objective #1) with no drop in conversion rate (CR) (objective #2) while holding the average last-mile traversed flat (objective #3), vs. the baseline ranking method. This method also significantly reduces time to model development and deployment in MO settings and allows for trivial extensions to more objectives and other types of LTR models.