From his perch at Google, Shodjai said that in some of those cases, children already understand that they shouldn't talk to, say, their parents in the same way as they would a product. Since launch, the company has also added features meant to reinforce good etiquette -- in late 2018, it updated the Assistant with a new "pretty please" mode, where requests that include a "please" or "thank you" would garner a grateful response.

In this series we'll build our own AI Assistant, like Siri, Alexa, or even Jarvis from Ironman. It will listen to spoken commands, perform actions from an expandable set of skill and talk back to us. I created a popular video series on YouTube, which goes over each of the steps below. The playlist is featured below. This series has 7 parts and 2 bonus video, 3 parts have been released so far, with more parts will be added soon, so check back!

With the increasing demand for predictable and accountable Artificial Intelligence, the ability to explain or justify recommender systems results by specifying how items are suggested, or why they are relevant, has become a primary goal. However, current models do not explicitly represent the services and actors that the user might encounter during the overall interaction with an item, from its selection to its usage. Thus, they cannot assess their impact on the user's experience. To address this issue, we propose a novel justification approach that uses service models to (i) extract experience data from reviews concerning all the stages of interaction with items, at different granularity levels, and (ii) organize the justification of recommendations around those stages. In a user study, we compared our approach with baselines reflecting the state of the art in the justification of recommender systems results. The participants evaluated the Perceived User Awareness Support provided by our service-based justification models higher than the one offered by the baselines. Moreover, our models received higher Interface Adequacy and Satisfaction evaluations by users having different levels of Curiosity or low Need for Cognition (NfC). Differently, high NfC participants preferred a direct inspection of item reviews. These findings encourage the adoption of service models to justify recommender systems results but suggest the investigation of personalization strategies to suit diverse interaction needs.

Machine learning is a fundamental technology in the modern world. Computers can learn to recognize images, create artwork, and even write their own code, all with minimal human intervention. But how does machine learning work and how can you use it yourself? Machine learning is a relatively simple concept. Computer systems can learn and adapt by analyzing existing data patterns from pools of information.

Apple is working to simplify how users interact with Siri, according to Bloomberg's Mark Gurman. The company has reportedly spent the past few months training the digital assistant to respond to "Siri" instead of "Hey Siri." On the surface, that's a simple change, but one that Gurman says involves a "significant amount of AI training and underlying engineering work." The reason for that is that a two-word trigger phrase like "Hey Siri" increases the likelihood of the software responding to a request. The change would make it easier to string together multiple commands one after another.

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can offer high-performing predictive functionalities, it lacks tangible quantitative evidence on its benefits to the farmers. Field experiments can derive such evidence, but are often costly and time consuming. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. This way, we can increase farmers' trust by enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to increase productivity and secure a sustainable and resilient agriculture against a changing climate. As a case study, we perform an empirical evaluation of a recommendation system for optimal cotton sowing, which was used by a farmers' cooperative during the growing season of 2021. We leverage agricultural knowledge to develop a causal graph of the farm system, we use the back-door criterion to identify the impact of recommendations on the yield and subsequently estimate it using several methods on observational data. The results show that a field sown according to our recommendations enjoyed a significant increase in yield (12% to 17%).

"Tell me and I forget, teach me and I may remember, involve me and I learn." The pandemic not only propelled us to rethink the means of how we learn – accelerating the adoption of online learning, but also brought to the forefront discourses around the quality of education, and more importantly, accessibility for all. Adoption of emerging technologies such as Artificial Intelligence, Augmented Reality and Virtual Reality have enabled us to provide students with immersive and personalised learning experiences and, while doing so, achieve superior learning outcomes. And to my mind, adoption of AI bots as virtual teachers is the next big innovation that has the potential to transform the face of education. AI bots, chatbots in particular, have become a rather common phenomenon in today's time.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. At Nvidia's Speech AI Summit today, the company discussed its new speech artificial intelligence (AI) ecosystem, which it developed through a partnership with Mozilla Common Voice. The ecosystem focuses on developing crowdsourced multilingual speech corpuses and open-source pretrained models. Nvidia and Mozilla Common Voice aim to accelerate the growth of automatic speech recognition models that work universally for every language speaker worldwide. Nvidia found that standard voice assistants, such as Amazon Alexa and Google Home, support fewer than 1% of the world's spoken languages.

Soon enough, AI competency will be an essential workforce skill. A group of computer scientists and learning science experts are considering what a foundational introduction to AI might look like for middle school and high school students. The rise of artificial intelligence (AI) and a branch of AI called machine learning, which focuses on the use of data and algorithms to imitate the way that humans learn, is rapidly changing the way data-intensive scientific discovery is being done. Data-intensive science is a modern, exploration-centered style of science that heavily relies on advanced computing capabilities and software tools to manipulate and explore massive data sets. The introduction of new and better machine learning techniques is now being used to assist and automate scientific discovery of increasingly complex problems.

Stuttering is a speech disorder where the natural flow of speech is interrupted by blocks, repetitions or prolongations of syllables, words and phrases. The majority of existing automatic speech recognition (ASR) interfaces perform poorly on utterances with stutter, mainly due to lack of matched training data. Synthesis of speech with stutter thus presents an opportunity to improve ASR for this type of speech. We describe Stutter-TTS, an end-to-end neural text-to-speech model capable of synthesizing diverse types of stuttering utterances. We develop a simple, yet effective prosody-control strategy whereby additional tokens are introduced into source text during training to represent specific stuttering characteristics. By choosing the position of the stutter tokens, Stutter-TTS allows word-level control of where stuttering occurs in the synthesized utterance. We are able to synthesize stutter events with high accuracy (F1-scores between 0.63 and 0.84, depending on stutter type). By fine-tuning an ASR model on synthetic stuttered speech we are able to reduce word error by 5.7% relative on stuttered utterances, with only minor (< 0.2% relative) degradation for fluent utterances.