USA Today's Jefferson Graham suggests tips on the best way to shop through Amazon's home assistant, Alexa. LOS ANGELES -- With seven different choices in its Amazon Echo family, consumers are naturally confused about which smart speaker, if any, to buy. The entry-level Dot is the most affordable and Amazon's best seller, but has the worst sound. The video versions of Echo haven't caught on, but are now costing about the same as the regular, full-featured Echo. USA TODAY is here to help, with a guide to all seven speakers, pros and cons on each, and a summation at the end. Amazon's Monday/Tuesday Prime Day sale is expected to have the best deals on Amazon's own products, with especially good discounts on the Echo speakers.

This post was done in partnership with Wirecutter. When readers choose to buy Wirecutter's independently chosen editorial picks, Wirecutter and Engadget may earn affiliate commissions. Read Wirecutter's continuously updated list of Prime Day deals here. This is just slightly above the lowest price we've seen on this TV, though only by a couple of dollars. The TCL Roku TV (32-inch) is the top pick in our guide to the best 32-Inch TV.

With Google Home and Apple HomePod gaining ground in the smart speaker space, it looks like Amazon wants to cement its popularity in the U.S. with some major price drops for the Echo LookRemove non-product link, Echo Show, and Echo Dot. Echo savings range from 40% to 50%, and two Fire TV devices are on sale as well. Of course, you'll need Amazon Prime to realize these deals (go here to sign up).Remove non-product link You may also consider our suggestions for getting a short-term Prime membership for free. We've done extensive testing of all Amazon Echo devices and have given Editors' Choice awards to the Show and Dot for vastly different reasons. So read our reviews to make sure you purchase the best Alexa device for you.

Earlier this week we learned that worldwide smart speaker sales are expected to increase sixfold within the next couple of years. This mirrors multiple studies that say the majority of U.S. households will have a smart speaker by 2022, powered by current leading intelligent assistants Google Assistant and Alexa. At the same time, tech giants making intelligent assistants seem to want to have it both ways, selling products to both consumers and governments. For example, Microsoft, maker of Cortana, may be supplying facial recognition software to ICE, the government agency tasked with capturing and detaining immigrants who are in the United States illegally. As Amazon rolls out deep learning camera Lens and fashion assistant Echo Look, the company has drawn pleas from employees, the ACLU, and a number of other organizations to stop sharing its facial recognition software with law enforcement agencies.

Tunstall-Pedoe:I think there's still huge further potential in conversational interaction with computers and devices. Alexa has shown we have crossed a magical point where the computers we have seen in sci-fi for decades are now useful enough to have mass adoption and usage but there is huge amounts of further improvement possible. Apart from that there is no particular sector where AI is limited. Many start-ups are of the form old-established-business AI new business with a competitive advantage - and these old established businesses come from all sectors.

You've just noticed a great price on an Amazon Echo Dot or Google Home Mini smart speaker. The prices are intended to make them impulse purchases, because both Amazon and Google are desperate to get their foot in the door of your home. Voice assistants and smart home automation are expected to be the next big thing in tech, and both companies know that once you experience the convenience of a smart speaker in one room, you're likely to want it in others. Before making a purchase, you really should do your homework. It all comes down to the hardware, the capabilities of their digital assistants, and the way they can do things such as play music and control other smart devices in your home.

On the one-year anniversary of the launch of Smith & Williamson's £100m Artificial Intelligence fund, Chris Ford, the strategy's lead manager, explains how AI is making its way into every part of our lives. The backdrop is that AI is transforming the way we drive, the way we communicate with each other and the way we learn, creating a host of opportunities for investors. But, said Ford, while the likes of Tesla and Google are obvious targets for investors seeking exposure to this rapidly growing field, many exciting AI opportunities also exist in finance, healthcare, media and many other sectors. Ford said: "When most of us think of artificial intelligence, we might think of self-driving cars made by Tesla, or Siri, Apple's virtual assistant. "But artificial intelligence is now being used by a whole host of companies and industries, many of which may surprise investors." With opportunities in most major industries, the Smith & Williamson Artificial Intelligence Fund invests across a wide spectrum of sectors around the globe in order to gain exposure to the trend. "From healthcare to commodities, companies are all starting to benefit from the implementation of different forms of artificial intelligence," Ford said. Below, Ford highlights three companies operating in unexpected sectors where AI is coming to the fore. Intuitive Surgical uses AI technology to conduct operations that are more effective, less invasive and easier on both surgeons and patients. Its da Vinci surgical system allows surgeons to carry out open surgery through just a few small incisions. Ford said: "There has been growth in the use of robot systems in hospital settings, which has led to a rise in the number of procedures that use its technology.

Surveillance companies are showing an increasing interest in hacking into IoT devices like the Amazon Echo. With an impressive seed raise of $12.5 million and ex-Israeli Prime Minister Ehud Barak as co-founder, alongside an "all-star" leadership team, Tel Aviv-based Toka Cyber can certainly claim to have nailed the definition of an auspicious beginning. But, as it comes out of stealth Monday, Toka is revealing itself as an atypical force in the digital security sphere, acting as a one-stop hacking shop for intelligence and law enforcement agencies. Whatever spy tool they need, Toka will try to craft it for them. Privacy activists are hoping the company follows through on its promise to operate ethically.

Collaborative Filtering (CF) is one of the most commonly used recommendation methods. CF consists in predicting whether, or how much, a user will like (or dislike) an item by leveraging the knowledge of the user's preferences as well as that of other users. In practice, users interact and express their opinion on only a small subset of items, which makes the corresponding user-item rating matrix very sparse. Such data sparsity yields two main problems for recommender systems: (1) the lack of data to effectively model users' preferences, and (2) the lack of data to effectively model item characteristics. However, there are often many other data sources that are available to a recommender system provider, which can describe user interests and item characteristics (e.g., users' social network, tags associated to items, etc.). These valuable data sources may supply useful information to enhance a recommendation system in modeling users' preferences and item characteristics more accurately and thus, hopefully, to make recommenders more precise. For various reasons, these data sources may be managed by clusters of different data centers, thus requiring the development of distributed solutions. In this paper, we propose a new distributed collaborative filtering algorithm, which exploits and combines multiple and diverse data sources to improve recommendation quality. Our experimental evaluation using real datasets shows the effectiveness of our algorithm compared to state-of-the-art recommendation algorithms.

Recommendation systems are an integral part of Artificial Intelligence (AI) and have become increasingly important in the growing age of commercialization in AI. Deep learning (DL) techniques for recommendation systems (RS) provide powerful latent-feature models for effective recommendation but suffer from the major drawback of being non-interpretable. In this paper we describe a framework for explainable temporal recommendations in a DL model. We consider an LSTM based Recurrent Neural Network (RNN) architecture for recommendation and a neighbourhood-based scheme for generating explanations in the model. We demonstrate the effectiveness of our approach through experiments on the Netflix dataset by jointly optimizing for both prediction accuracy and explainability.