Today, we're very happy to announce that Amazon Personalize now supports batch recommendations/ Launched at AWS re:Invent 2018, Personalize is a fully-managed service that allows you to create private, customized recommendations for your applications, with little to no machine learning experience required. With Personalize, you provide the unique signals in your activity data (page views, sign-ups, purchases, and so forth) along with optional customer demographic information (age, location, etc.). You then provide the inventory of the items you want to recommend, such as articles, products, videos, or music: as explained in previous blog posts, you can use both historical data stored in Amazon Simple Storage Service (S3) and streaming data sent in real-time from a JavaScript tracker or server-side. Then, entirely under the covers, Personalize will process and examine the data, identify what is meaningful, select the right algorithms, train and optimize a personalization model that is customized for your data, and is accessible via an API that can be easily invoked by your business application. However, some customers have told us that batch recommendations would be a better fit for their use cases.

We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor's risk preference, but learns it over time by observing her portfolio choices in different market environments. We develop an exploration-exploitation algorithm which trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor's risk aversion. We show that the algorithm's value function converges to the optimal value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor's mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor's opportunity cost for making portfolio decisions.

I've lived in New England for six years now, which means two things: I've learned to never call Tom Brady overrated, and I've learned the importance of having a thermostat that actually works. If you live in an old home or apartment like me, you likely have a bulky thermostat with supposedly programmable settings that mostly work, but don't do much for actually regulating temperature reliably. Or you've got a clunky dial that allows you to manually change the temperature, but only when physically standing in front of it. Naturally, there's been a surge in the market for people hunting for better ways to control their home environment. If you're looking for a sleek and easy way to upgrade your home's temperature controls, you've likely already started researching smart thermostats.

Contestants at the Pwn2Own Tokyo 2019 took down an impressive number of high-profile products during the competition's first two days, including a Sony smartTV, Netgear router and an Amazon Echo Show 5. The two-day event paid contestants a total of $315,000 with Team Fluoroacetate, Amat Cama and Richard Zhu, being named Masters of PWN. Day One, November 6, saw more than $195,000 awarded for 12 bugs that were found. Overall, those participants had nine successful attempts against seven targets in five categories, several of which were new for 2019. The first day was dominated by the eventual event winners Team Fluoroacetate.

Such is computing's future--to each of our voices. Voice assistants hang on every word we say, when prompted. Their genesis has created an entire family named Siri, Alexa, Cortana, Pepper, Watson, and, most recently, Duplex. One wonders when smart speakers will replace what you're looking at now--this screen. The promise of voice interfaces was demonstrated again in May when Google rolled out Duplex, an AI-driven voice assistant so lifelike and sophisticated that some found it astounding but others deemed it unsettling and wondered if a new era of robocall abuse just dawned.

What is AI? Everything you need to know about Artificial Intelligence Google Cloud on Thursday announced the general availability of Contact Center AI. The contact center software enables businesses to deploy virtual agents for basic customer interactions, and it offers an "agent assist" feature to transcribe calls, recommend workflows and provide other kinds of AI-driven assistance. Google also announced updates to Dialogflow, the development suite for building conversational interfaces such as chat bots and interactive voice responses (IVR). With a new agent validation feature, designers can get feedback on the quality and performance of their virtual agents. Dialogflow also now supports compliance with the Health Insurance Portability and Accountability Act (HIPAA) and Payment Card Industry (PCI) data security standards.

Many businesses and consumers are extending the capabilities of voice-based services such as Amazon Alexa, Google Home, Microsoft Cortana, and Apple Siri to create custom voice experiences (also known as skills). As the number of these experiences increases, a key problem is the discovery of skills that can be used to address a user's request. In this paper, we focus on conversational skill discovery and present a conversational agent which engages in a dialog with users to help them find the skills that fulfill their needs. To this end, we start with a rule-based agent and improve it by using reinforcement learning. In this way, we enable the agent to adapt to different user attributes and conversational styles as it interacts with users. We evaluate our approach in a real production setting by deploying the agent to interact with real users, and show the effectiveness of the conversational agent in helping users find the skills that serve their request.

You already know about the original Siri, but in addition to that game-changing personal assistant, GM Voices has powered numerous other industry-leading virtual assistants, AI, and TTS devices and applications in more than a dozen languages. Watch our latest video to see how we handle these audio-intensive projects, and why your app needs a natural, brand-consistent sound!

This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms? Some modern algorithms such as collaborative filtering, recommendation engine, segmentation, or attribution modeling, are missing from the lists below. Algorithms from graph theory (to find the shortest path in a graph, or to detect connected components), from operations research (the simplex, to optimize the supply chain), or from time series, are not listed either. And I could not find MCM (Markov Chain Monte Carlo) and related algorithms used to process hierarchical, spatio-temporal and other Bayesian models. My point of view is of course biased, but I would like to also add some algorithms developed or re-developed at the Data Science Central's research lab: These algorithms are described in the article What you wont learn in statistics classes.

Artificial Intelligence (AI) is playing a bigger role in patient care, in mental health diagnoses, and in analyzing big data to deliver more targeted treatments. And now, with tech that could pick up on patterns in our speech, eye movements and facial expressions, could machines be the future in global health? If you've ever asked Siri a question, been motivated to run by a nagging watch, or suddenly realised you were flirting with a charming chatbot, you know our daily interactions, our access to information, our choices, are increasingly mediated by machines. And, if you're anything like me, (Co-Director of Common Thread, global health communications), it feels less like: "the Robots are coming!!!" and more like, "damn, when did they get here, and who invited them?" Algorithms, pattern recognition, neural networks and deep learning -- all part of the broader family of machine learning and artificial intelligence -- will only continue to expand their influence on our lives.