Apple is late to the consumer priced smart speaker market, but it finally joined Amazon and Google with the $99 HomePod Mini. Here's what you need to know: The Mini is way smaller in size than both the new Amazon Echo fourth generation speaker and Google Nest Audio. And while it doesn't sound as great for music as either of them, (it is way smaller, after all) in our unscientific home ears test, it probably doesn't matter. This is a really useful speaker for anyone living in the Apple ecosystem and it makes the Siri personal assistant way more competitive with Amazon's Alexa and the Google Assistant. The HomePod Mini sounds fantastic as a TV speaker.

We present a new approach to dialogue specification for Virtual Personal Assistants (VPAs) based on so-called dialogue workflow graphs, with several demonstrated advantages over current ontology-based methods. Our new dialogue specification language (DSL) enables customers to more easily participate in the VPA modeling process due to a user-friendly modeling framework. Resulting models are also significantly more compact. VPAs can be developed much more rapidly. The DSL is a new modeling layer on top of our ontology-based Dialogue Management (DM) framework OntoVPA. We explain the rationale and benefits behind the new language and support our claims with concrete reduced Level-of-Effort (LOE) numbers from two recent OntoVPA projects.

Conversational practice, while crucial for all language learners, can be challenging to get enough of and very expensive. Chatbots are computer programs developed to engage in conversations with humans. They are designed as software avatars with limited, but growing conversational capability. The most natural and potentially powerful application of chatbots is in line with their fundamental nature - language practice. However, their role and outcomes within (in)formal language learning are currently tangential at best. Existing research in the area has generally focused on chatbots' comprehensibility and the motivation they inspire in their users. In this paper, we provide an overview of the chatbots for learning languages, critically analyze existing approaches, and discuss the major challenges for future work.

Have you ever used Google Assistant, Apple's Siri or Amazon Alexa to make decisions for you? Perhaps you asked it what new movies have good reviews, or to recommend a cool restaurant in your neighbourhood. Artificial intelligence and virtual assistants are constantly being refined, and may soon be making appointments for you, offering medical advice, or trying to sell you a bottle of wine. Although AI technology has miles to go to develop social skills on par with ours, some AI has shown impressive language understanding and can complete relatively complex interactive tasks. In several 2018 demonstrations, Google's AI made haircut and restaurant reservations without receptionists realising they were talking with a non-human.

The very mention of Artificial Intelligence reminds most people of movies like the Terminator but in actuality, AI is already very present in our daily lives making things much easier for us in a multitude of fields. For example, according to a Harvard Business Review study, companies that were using AI for sales managed to bring in 50% more leads and reduce their costs by 40%-60%. AI applications are not necessarily actual robots walking about the office. In most cases, it means the introduction of software and tools that make conducting business easier, more affordable, and faster by automating as much as possible. Mathematician Alan Turing was the first to really ask the question'Can machines think?'.

Artificial Intelligence is a very trending topic in the current competitive world. AI is offering smart business solutions to small and large enterprises. The AI is also benefiting the eCommerce in a large way. AI technology has helped e-commerce to reach greater heights. In other words, AI is revolutionizing the mobile app world and the whole structure of businesses.

The dynamic software agents of Robotic Process Automation represent many things to the modern enterprise. They're simultaneously a digital workforce of tireless, ubiquitous employees, a cogent means of actuating statistical Artificial Intelligence, and a critical platform to effect low code workplace automation. Not surprisingly, Gartner projects RPA revenues to increase nearly 20 percent to total approximately 1.9 billion in 2021. But when the agency of these bots is amalgamated across easily accessible interfaces for any number of comprehensive use cases, they become something altogether more than digital agents: they become digital assistants, as easy to use as Siri, yet much more actionable for mission critical workloads. According to Automation Anywhere CTO Prince Kohli, synthesizing the prowess of bots into a digital assistant enables it "to orchestrate a process, which is more than starting and stopping bots.

Since the HAL9000 and Star Trek's M-5 Multitronic, the power and capabilities of AI have always been oversold by both Hollywood and Silicon Valley. Although we're still waiting on machines that can carry on an intelligent conversation, AI has been creeping into many objects in our everyday lives behind the scenes, making them more useful and proactive. People are most familiar with the intelligent assistants built into devices like the Amazon Echo, Google Nest Hub and Apple HomePod, but as I wrote more than three years ago, these rely on cloud backend services for most of their smarts, using local hardware primarily to recognize their wake word and listen for follow-up questions. The combination allows surprisingly sophisticated deep and machine learning models to run on embedded systems. Until recently, shoehorning AI software into a battery-powered device has required data scientists skilled in working with the constraints of an embedded SoC, but recent advances in AI development and automation frameworks, categorically termed TinyML, greatly expands the realm of smart devices.

In my last post, I introduced one of the biggest trends in AI-driven marketing: personalisation. But today's consumers expect personalised messaging with every brand interaction, and luckily, thanks to masses of data, big data techniques, and AI, marketers can deliver it. In this post, I'll show you just how the marketing playbook is being rewritten, using real-world examples from both tech giants and innovative startups*. All of these examples involve machine learning algorithms, which learn to predict a target variable based on patterns in some input data. If you're not so familiar with the workings of machine learning, you can roughly infer how each example works by asking: what hidden variable do I need to solve my problem, and what kind of information would help me predict it?

A new University of Saskatchewan (USask) artificial intelligence computer model holds promise for making "smart" apps such as Amazon, Apple, and Google's virtual assistants safer, faster and more energy efficient. "Smart" services such as facial recognition, weather forecasting, virtual assistants, and language translators rely on an artificial intelligence (AI) technology called "deep learning" to predict user patterns. But these AI processes often require too much storage to be run locally on mobiles, so the data is sent to external servers over the Internet, which requires lots of power, drains the phone battery, and may increase a user's privacy risk. "My method breaks down the AI computational processes in smaller'chunks' and this helps run the'smart' apps locally on the phone, rather than relying on external servers, while reducing power consumption," said Hao Zhang, a USask electrical and computer engineering post-doctoral fellow. "This research may lead to a different way to design apps and operating systems for our digital devices such as tablets, phones and computers."