We introduce StockBabble, a conversational agent designed to support understanding and engagement with the stock market. StockBabble's value and novelty is in its ability to empower retail investors -- many of which may be new to investing -- and supplement their informational needs using a user-friendly agent. Users have the ability to query information on companies to retrieve a general and financial overview of a stock, including accessing the latest news and trading recommendations. They can also request charts which contain live prices and technical investment indicators, and add shares to a personal portfolio to allow performance monitoring over time. To evaluate our agent's potential, we conducted a user study with 15 participants. In total, 73% (11/15) of respondents said that they felt more confident in investing after using StockBabble, and all 15 would consider recommending it to others. These results are encouraging and suggest a wider appeal for such agents. Moreover, we believe this research can help to inform the design and development of future intelligent, financial personal assistants.

You won't find it written anywhere in the press materials, but the Beats Studio Buds may as well be called the AirPods 3. From their funky new design and minimalist theme to Apple-ready features like voice-activated Siri, these are immediately the obvious choice right now for AirPods fanatics who don't want to spend up for the AirPods Pro. Wisely, though, Apple has crafted these buds to be nearly as useful for either side of the mobile aisle, including native one-touch pairing for Android or iPhone, a simple but useful Android app, and quick access to alternative voice assistants. The result is a pair of buds that easily outdo the aging AirPods, no matter which phone you choose. The beats have a new pill-like design at their exterior for controls as well as easy insertion. The last Beats earbuds, the Powerbeats, were simply a step-down version of the Powerbeats Pro, chaining the clip-ons together for a more affordable workout option.

Doubling up on Amazon's Echo speakers has its perks. You can pair them together to get stereo sound or use them to create a Home Theatre setup for your Fire TV. To get you to multiply, Amazon is offering two Echo Dots for $50 -- which is how much you'd normally pay for one -- as part of an early Prime Day sale. If you're signed up to Prime, you can grab the discount by using the code PDDOT2PK at checkout. Those seeking even bigger sound, should check out the $120 deal on two Echo speakers, activated with the ECHOPRIME code and also exclusive to Prime members.

The latest version of Amazon Echo Buds only arrived a month ago, but the company is already offering them at a discount just ahead of Prime Day. It's the first time that the second-gen Echo Buds have been on sale. A bundle with the wireless charging case is also $40 off at $100. In our second-gen Echo Buds review, we found that the true wireless earbuds were an upgrade from the original model on almost all fronts. Along with a smaller design, they offer true active noise cancellation (ANC) and better audio quality than the first-gen Echo Buds.

Amazon's Echo Show 5 just got an upgrade, but if you don't mind missing out on relatively minor additions like a higher-resolution camera, you can pick up the first-gen model at its lowest price to date. Just ahead of Prime Day, the 2019 Echo Show 5 is available for $45. Amazon has been selling the device for $50 recently, but we saw it drop to $45 during Prime Day and Black Friday last year. As the name suggests, Echo Show 5 has a smaller screen than other devices in the lineup at 5.5 inches. It's primarily designed for desks and nightstands.

Implementation of Machine learning algorithms to assist consumers in discovering new products and services is new form of digital marketing on e-commerce website. When you shop online, a recommendation system directs you to the product that is most likely to be purchased.Users are often frustrated by options and need assistance discovering what they're looking for, so recommender systems are an important element of our digital environment. Customers will be happier, and profits will increase as a result. Recommender systems are similar to salespeople who know what you want based on your history and interests. In this blog, I am going to show different approaches for Recommendation system that can built on Brazilian e-commerce public dataset.

Collaborative filtering (CF), as a fundamental approach for recommender systems, is usually built on the latent factor model with learnable parameters to predict users' preferences towards items. However, designing a proper CF model for a given data is not easy, since the properties of datasets are highly diverse. In this paper, motivated by the recent advances in automated machine learning (AutoML), we propose to design a data-specific CF model by AutoML techniques. The key here is a new framework that unifies state-of-the-art (SOTA) CF methods and splits them into disjoint stages of input encoding, embedding function, interaction function, and prediction function. We further develop an easy-to-use, robust, and efficient search strategy, which utilizes random search and a performance predictor for efficient searching within the above framework. In this way, we can combinatorially generalize data-specific CF models, which have not been visited in the literature, from SOTA ones. Extensive experiments on five real-world datasets demonstrate that our method can consistently outperform SOTA ones for various CF tasks. Further experiments verify the rationality of the proposed framework and the efficiency of the search strategy. The searched CF models can also provide insights for exploring more effective methods in the future

Abstract: Conversational agents are a recent trend in human-computer interaction, deployed in multidisciplinary applications to assist the users. In this paper, we introduce "Atreya", an interactive bot for chemistry enthusiasts, researchers, and students to study the ChEMBL database. Atreya is hosted by Telegram, a popular cloud-based instant messaging application. This user-friendly bot queries the ChEMBL database, retrieves the drug details for a particular disease, targets associated with that drug, etc. This paper explores the potential of using a conversational agent to assist chemistry students and chemical scientist in complex information seeking process.

We consider the problem of minimizing regret in an $N$ agent heterogeneous stochastic linear bandits framework, where the agents (users) are similar but not all identical. We model user heterogeneity using two popularly used ideas in practice; (i) A clustering framework where users are partitioned into groups with users in the same group being identical to each other, but different across groups, and (ii) a personalization framework where no two users are necessarily identical, but a user's parameters are close to that of the population average. In the clustered users' setup, we propose a novel algorithm, based on successive refinement of cluster identities and regret minimization. We show that, for any agent, the regret scales as $\mathcal{O}(\sqrt{T/N})$, if the agent is in a `well separated' cluster, or scales as $\mathcal{O}(T^{\frac{1}{2} + \varepsilon}/(N)^{\frac{1}{2} -\varepsilon})$ if its cluster is not well separated, where $\varepsilon$ is positive and arbitrarily close to $0$. Our algorithm is adaptive to the cluster separation, and is parameter free -- it does not need to know the number of clusters, separation and cluster size, yet the regret guarantee adapts to the inherent complexity. In the personalization framework, we introduce a natural algorithm where, the personal bandit instances are initialized with the estimates of the global average model. We show that, an agent $i$ whose parameter deviates from the population average by $\epsilon_i$, attains a regret scaling of $\widetilde{O}(\epsilon_i\sqrt{T})$. This demonstrates that if the user representations are close (small $\epsilon_i)$, the resulting regret is low, and vice-versa. The results are empirically validated and we observe superior performance of our adaptive algorithms over non-adaptive baselines.

Machine Learning, What is machine learning?, How machine learning works, Machine learning methods, Deep learning & more You may also have heard machine learning and AI used interchangeably. AI includes machine learning, but machine learning doesn't fully define AI. Machine learning and AI both have strong engineering components. You find AI and machine learning used in a great many applications today. Artificial Intelligence (AI) is a huge topic today, and it's getting bigger all the time thanks to the success of technologies such as Siri.