A Happy Meal and AI might not be the most obvious pairing but 2019 has seen McDonald's hit the headlines for its fast-paced investments in tech. In March, it acquired online personalisation startup Dynamic Yield for more than £232.8m This sparked a series of acquisitions, paving the way for what seems like the beginning of a journey for the iconic fast food chain to become the Amazon of food. A month later, it bought a 9.9% stake in mobile software company Plexure in a deal valued at about £3.8m, according to reports. The New Zealand-based company already helps with McDonald's mobile app but this surge of investment will see the chain have unprecedented access to Plexure's new tech for its app.

When Amazon demonstrated the new Echo Studio at a company event in September, I was blown away. This was some of the best audio I had ever heard from a little speaker, filling a room of hundreds of journalists and sounding like it was many speakers connected together. My initial thought was that rival Sonos, which specializes in great-sounding Wi-Fi speakers, was in trouble. After spending several hours with the $200 Studio, out Thursday, there will be no need to schedule a benefit for Sonos. The Studio does sounds great.

For the past several years, data has been the primary driver behind business growth. Understanding how consumers behave, why they make the decisions they do, and how to use that information to improve marketing is key to a business's success. To say that AI will be able to improve our ability to interpret and utilize data is a vast understatement. Currently, data analysis is done by a person reasoning through data, an automated program that provides us with concise information, or some combination of the two. The big leap with AI is the emergence of software that can perform both tasks as a simultaneous process: automated reasoning.

In the consumer realm, technologies based on artificial intelligence (AI) are slowly changing the way we manage everyday tasks. Take the driving app Waze, for example. Waze uses crowdsourced data, social networking conversations, and cognitive learning to help shave time off daily commutes by providing the most efficient route based on current conditions and individual driving preferences. Or consider products like Nest. Gone are the days of paying to heat or cool your house while no one's home.

In the spring of last year, cybersecurity researcher Takeshi Sugawara walked into the lab of Kevin Fu, a professor he was visiting at the University of Michigan. He wanted to show off a strange trick he'd discovered. Sugawara pointed a high-powered laser at the microphone of his iPad--all inside of a black metal box, to avoid burning or blinding anyone--and had Fu put on a pair of earbuds to listen to the sound the iPad's mic picked up. As Sugawara varied the laser's intensity over time in the shape of a sine wave, fluctuating at about 1,000 times a second, Fu picked up a distinct high-pitched tone. The iPad's microphone had inexplicably converted the laser's light into an electrical signal, just as it would with sound.

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Google's latest smart display is larger and can recognise your face for proactively showing you personalised information making it just that little bit smarter than competitors. The £219 Nest Hub Max is Google's second own-brand smart display and is essentially a super-sized version of the excellent original Home Hub (now renamed Nest Hub). But where the Nest Hub is a veritable bargain at £119 or frequently much less, the Nest Hub Max is a different proposition at a little under twice the price. A bigger screen is definitely better for viewing from across a room. The 10in 720p HD screen is bright, crisp enough at normal viewing distances and has Google's ambient EQ colour tone system so that photos on it look very much like printed photos, not displayed on an overly white and clinical LCD screen.

This past year I hired, fired and then rehired an artificial intelligence scheduler called Amy. This is my first experience managing a machine. Sure, there was a novelty factor that was fun but she's also had a few West World episodes that were less fun. With so much media focus on how artificial intelligence and machines are replacing human knowledge workers, there is both excitementand fearabout the future of work. As intelligent technology can now interpret text, understand speech and make sense of images it has become possible to automate tasks that were previously the preserve of humans.

Today, most large-scale conversational AI agents (e.g. Alexa, Siri, or Google Assistant) are built using manually annotated data to train the different components of the system. Typically, the accuracy of the ML models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and time consuming. In this paper, we propose a system that leverages user-system interaction feedback signals to automate learning without any manual annotation. Users here tend to modify a previous query in hopes of fixing an error in the previous turn to get the right results. These reformulations, which are often preceded by defective experiences caused by errors in ASR, NLU, ER or the application. In some cases, users may not properly formulate their requests (e.g. providing partial title of a song), but gleaning across a wider pool of users and sessions reveals the underlying recurrent patterns. Our proposed self-learning system automatically detects the errors, generate reformulations and deploys fixes to the runtime system to correct different types of errors occurring in different components of the system. In particular, we propose leveraging an absorbing Markov Chain model as a collaborative filtering mechanism in a novel attempt to mine these patterns. We show that our approach is highly scalable, and able to learn reformulations that reduce Alexa-user errors by pooling anonymized data across millions of customers. The proposed self-learning system achieves a win/loss ratio of 11.8 and effectively reduces the defect rate by more than 30% on utterance level reformulations in our production A/B tests. To the best of our knowledge, this is the first self-learning large-scale conversational AI system in production.

A typical after-work scene at my house goes something like this. She chimes, then lights up. My husband says the persistent disconnect between me and Alexa is my fault--I need to pause more, speak more clearly, and maybe throw in a "please" now and then. But not long after she moved in--a necessary sidekick, I was told, to the new sound system he had installed--I started getting the feeling she preferred Bob over me, no matter how polite I was (although often I wasn't). Once she started piping up every time someone in the house called my name ("Alyssa!"),