AI is taking the world by storm. While it was once a thing of sci-fi movies, it's no longer fiction. In fact, AI--tech that can think, learn, and make autonomous decisions--is now seeping its way into our lives. Think of self-driving cars (Tesla), navigation (Google Maps), or even virtual assistants (Siri): all of them rely heavily on AI. You might have also heard about the AI-powered robot from Boston Dynamics that can do a summersault, a handstand, or even a split leap that's now making a lot of noise online.

Meet Brooke, Automotive's First Intelligent Digital Voice Assistant to Answer, Handle, & Appoint Inbound Service Calls for dealers, Reducing Costs while Improving CX.

Hit play on the player above to hear the podcast and follow along with the transcript below.This transcript was automatically generated, and then edited for clarity in its current form. There may be some differences between the audio and the text. Welcome back to Talking Tech. If you used Amazon's Alexa, I will only be saying this name once by the way, because I don't want to trigger anyone who has an Echo speaker, but if you've used Amazon's digital voice assistant, obviously it's got many different ways it can help you play a song, set a reminder, set an alarm. Now Amazon's digital assistant has a new role, helping seniors and caregivers.

It has been over a year since international travel as we knew it ground to a halt. When the COVID-19 pandemic hit, air travel in the US dropped by 95% – from around two million travellers per day to fewer than 100,000. Until recently, flights in and out of Australia have been limited to those trying to get home, reuniting with their loved ones, or fleeing places that were no longer safe. Slowly, vaccination is making the possibility of taking to the skies again seem within reach. But what might have changed?

The term Artificial Intelligence (AI) may conjure up images of futuristic robots and scenes from movies like The Matrix. While some highly sophisticated applications of AI still feel as though they belong to a distant future, Artificial Intelligence is in fact already all around us in daily life. Through AI technology, machines are trained to evaluate stimuli in an intentional and intelligent manner, adapt to it, and make decisions. And according to McKinsey & Company's 2020 global survey on the State of AI, over half of organizations are already using AI to facilitate at least one business function. In truth, the vast majority of us are already interacting with Artificial Intelligence every day.

Deloitte Global predicts that the industry for smart speakers--internet-connected speakers with integrated digital voice assistants--will be worth US$7 billion in 2019, selling 164 million units at an average selling price of US$43.1 We expect 2018 sales of 98 million units at an average of US$44 each, for a total industry revenue of US$4.3 billion. This 63 percent growth rate would make smart speakers the fastest-growing connected device category worldwide in 2019, and lead to an installed base of more than 250 million units by year-end.2 Robust sales performance in 2019, although high, will represent a deceleration from the prior year: In Q2 of 2018, smart speaker sales were up 187 percent year over year.3 Smart speakers have, literally, a world of opportunity for growth. Much of that opportunity comes from expansion into non-English-speaking countries.

The Guardian reports the plan to launch an Alexa rival, which has been given the working title "Beeb," and will apparently be light on features, given the Corp's relatively slender developer resources versus major global tech giants. The BBC's own news site says the digital voice assistant will launch next year without any proprietary hardware to house it. Instead the corporation is designing the software to work on "all smart speakers, TVs and mobiles." Why is a publicly funded broadcaster ploughing money into developing an AI when the market is replete with commercial offerings -- from Amazon's Alexa to Google's Assistant, Apple's Siri and Samsung's Bixby to name a few? The intent is to "experiment with new programmes, features and experiences without someone else's permission to build it in a certain way," a BBC spokesperson told BBC news.

The Guardian reports the plan to launch an Alexa rival, which has been given the working title'Beeb', and will apparently be light on features given the Corp's relatively slender developer resources vs major global tech giants. The BBC's own news site says the digital voice assistant will launch next year without any proprietary hardware to house it. Instead the corporation is designing the software to work on "all smart speakers, TVs and mobiles". Why is a publicly funded broadcaster ploughing money into developing an AI when the market is replete with commercial offerings -- from Amazon's Alexa to Google's Assistant, Apple's Siri and Samsung's Bixby to name a few? The intent is to "experiment with new programmes, features and experiences without someone else's permission to build it in a certain way", a BBC spokesperson told BBC news.

For a quality conversation between a human and a machine, responses have to be quick, intelligent and natural-sounding. But up to now, developers of language-processing neural networks that power real-time speech applications have faced an unfortunate trade-off: Be quick and you sacrifice the quality of the response; craft an intelligent response and you're too slow. That's because human conversation is incredibly complex. Every statement builds on shared context and previous interactions. From inside jokes to cultural references and wordplay, humans speak in highly nuanced ways without skipping a beat.

Deloitte found the financial services firms participating in their study's most common use cases for machine learning include the following. Predicting cash-flow events and proactively advising customers on spending and saving habits; expanding the data set for developing credit scores and applying machine learning to build advanced credit models for expanding reach and reducing defaults; providing machine-learning-based merchant analytics "as a service"; and detecting patterns in transactions and identifying fraudulent transactions as early as possible. Common NLP use cases include the following: reading documents and identifying errors for support activities such as information verification; user identification, and approvals; improving the underwriting process and capital efficiency; understanding customer queries via voice search on digital voice assistants or smartphones. Deloitte found the financial services firms participating in their study's most common use cases for machine learning include the following. Predicting cash-flow events and proactively advising customers on spending and saving habits; expanding the data set for developing credit scores and applying machine learning to build advanced credit models for expanding reach and reducing defaults; providing machine-learning-based merchant analytics "as a service"; and detecting patterns in transactions and identifying fraudulent transactions as early as possible.