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 Personal Assistant Systems


Flamingo raises $5 million in 12 minutes

#artificialintelligence

Two artificial virtual assistants called Rosie and Maggie are set to become more ubiquitous after artificial intelligence start-up Flamingo raised $5.1 million last month to fund their rollout. Chief executive Catriona Wallace, who founded Flamingo in 2014, says she is "super pleased" with the raise after it was opened and closed within 12 minutes with $10 million bid on the book. "We were well oversubscribed," she says. "It was one of the fastest capital raises that [lead investment partner] Bell Potter had ever conducted, which I believe is testament to Australia's growing interest in artificial intelligence and machine learning." Flamingo is headquartered in Sydney but also has offices in the US with 30 staff across the two countries. The start-up's flagship technology is its artificial virtual assistants Rosie and Maggie.


8 things you didn't know you could do with Google Assistant

#artificialintelligence

Google Assistant keeps on growing. New features and functionality are constantly appearing and that's made keeping track of all the service's little quirks and features tougher than ever. You can do much more than just searches these days. What is Google Assistant, and what devices use it? You don't need a Google Home to be the ear in your living room, Google Assistant on your smartphone can also control the various smart doodads dotted around your home.


Flipboard on Flipboard

#artificialintelligence

The robots are coming, and that's fine with most people--as long as they don't try to run their lives. Over the summer we surveyed 1,600 Quartz readers for their opinions on artificial intelligence. Among respondents, 79% said they were very or somewhat familiar with AI and most were aware of where they use it, with search, travel (maps) and assistants (Siri, Alexa, Google Assistant) rated as the top places where they interact with AI. Respondents expect their use of AI to increase a lot over the next five years. Currently, 30% of respondents use it "all the time" but 65% expect to use AI "all the time" in five years.


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@machinelearnbot

Samsung's revamped Bixby takes on Amazon Alexa - Samsung announced it is upgrading its Bixby digital assistant and making it available for a range of connected devices, setting up a clash with Amazon's Alexa and others competing for leadership in artificial intelligence. The South Korean electronics giant, which is the world's biggest smartphone maker, launched Bixby last year but only for its own flagship Galaxy handsets....


Business News: Tourism marketers warned to get on board the Voice Search revolution

#artificialintelligence

Operators in the travel industry in the Asia-Pacific region should prepare themselves to maximise business from voice searches online, one of Australia's leading tourism strategists has cautioned. Simply optimise your digital online presence by building reputation, trust, authority and relevance and the voice recognition algorithms of Google, Apple, Amazon and others will do the rest for you โ€“ mostly. That was the recommendation by Bronwyn White, co-founder of MyTravelResearch.com, when she was addressing an audience of tourism marketers at the Tourism Marketing Rockstar Convention in Sydney last month. Ms White explained that voice search and chatbots were the latest digital marketing outcomes of the artificial intelligence revolution currently transforming the travel industry by stealth. Apple Siri, Google Now, Windows Cortana and Amazon Alexa are the leading voice activated chatbots helping consumers search without typing," she said. "They all use artificial intelligence algorithms to drive voice search.


Sequential Matrix Completion

arXiv.org Machine Learning

We propose a novel algorithm for sequential matrix completion in a recommender system setting, where the $(i,j)$th entry of the matrix corresponds to a user $i$'s rating of product $j$. The objective of the algorithm is to provide a sequential policy for user-product pair recommendation which will yield the highest possible ratings after a finite time horizon. The algorithm uses a Gamma process factor model with two posterior-focused bandit policies, Thompson Sampling and Information-Directed Sampling. While Thompson Sampling shows competitive performance in simulations, state-of-the-art performance is obtained from Information-Directed Sampling, which makes its recommendations based off a ratio between the expected reward and a measure of information gain. To our knowledge, this is the first implementation of Information Directed Sampling on large real datasets. This approach contributes to a recent line of research on bandit approaches to collaborative filtering including Kawale et al. (2015), Li et al. (2010), Bresler et al. (2014), Li et al. (2016), Deshpande & Montanari (2012), and Zhao et al. (2013). The setting of this paper, as has been noted in Kawale et al. (2015) and Zhao et al. (2013), presents significant challenges to bounding regret after finite horizons. We discuss these challenges in relation to simpler models for bandits with side information, such as linear or gaussian process bandits, and hope the experiments presented here motivate further research toward theoretical guarantees.


The best devices and apps to up your selfie game

Engadget

The first time a stranger on the train told me I had a nice smile, I didn't believe her. Back then, I hadn't yet had my crooked teeth fixed, and my self-esteem wasn't anywhere as high as it is today. I was an ugly kid, and it took a shocking number of selfies to convince myself that I'm not an ugly adult. It may seem like a superficial pastime, but selfie-taking has real benefits. I'm not alone in believing there are psychological advantages here.


The Morning After: Weekend Edition

Engadget

We'll recap this week's news highlights, plus big stories from Friday like Project Loon-distributed internet going live in Puerto Rico. Former Google X Lab (and now Alphabet X innovation lab) resident Project Loon is getting its first use in the US, as it's partnering with AT&T to provide service in Puerto Rico. As part of the restoration efforts, the high-flying balloons are launching from Nevada and floating over the island, all in hopes of beaming LTE to areas still without service a month after Hurricane Maria. The first Cortana speaker sounds amazing.Harman Kardon Invoke review The good news about this $199 smart speaker is that it sounds great, and Microsoft's Cortana voice assistant is a natural addition. The bad news is that as a latecomer to the game, it has fewer music service integrations, and right now, Cortana isn't as capable as competitors like Amazon's Alexa. You say replicant, we say repli-can.Bad Password: Apps and gadgets for the'Blade Runner' future we didn't ask for This week, Violet Blue explains how technology can help make the best of our dystopian present -- at least until Harrison Ford and Ryan Gosling show up to fix things.


Tech Q&A: Recovering from Equifax, improving passwords, throwing Google off your scent and more

FOX News

Isabelle Olsson, Google's Head of Industrial Design for Home, speaks about the Google Home Mini during a launch event in San Francisco, California, U.S. October 4, 2017. Q: With the Equifax breach, I am worried that hackers can steal money from my bank account. A: The sheer number of victims is massive, and it keeps climbing with every new report. Meanwhile, Equifax has done a miserable job of comforting its customers, and the fallout has left far more questions than answers. The best thing you can do for your security is to establish two-factor authentication on your bank account.


Adaptive Matching for Expert Systems with Uncertain Task Types

arXiv.org Artificial Intelligence

Upwork) critically rely on the ability to propose adequate matches based on imperfect knowledge of the two parties to be matched. This prompts the following question: Which matching recommendation algorithms can, in the presence of such uncertainty, lead to efficient platform operation? To answer this question, we develop a model of a task / server matching system. For this model, we give a necessary and sufficient condition for an incoming stream of tasks to be manageable by the system. We further identify a so-called back-pressure policy under which the throughput that the system can handle is optimized. We show that this policy achieves strictly larger throughput than a natural greedy policy. Finally, we validate our model and confirm our theoretical findings with experiments based on logs of Math.StackExchange, a StackOverflow forum dedicated to mathematics.