Artificial Intelligence is a brand-new term. The majority of us are unsure what it is. However, we all employ Artificial Intelligence in some manner in our daily lives. We begin our day with AI when we wake up to the alarm set by Google Assistant. When you use the Google search engine to find a local restaurant, you use AI once again.

If you know Siri, Cortana from Microsoft, Denise from Nextos, Alexa from Amazon or those handy voice GPS directions on smartphones, then congrats! This course will just help you bridge the gap through an ocean of knowledge with the power of Artificial intelligence based TTS tools. Text to speech, abbreviated as TTS, is a synthesis of speech that transforms text into voice output. Text to speech systems was first developed to help the visually impaired by providing the user with a spoken voice created by a machine that would "read" text. Text to speech enables content owners to adapt in terms of how they communicate with the content to the specific needs and desires of each user.

By this summer, Martine had her dating ritual figured out. It was a series of small actions that changed the mood in her Queens bedroom, so that she could contemplate making flirtatious chit-chat from the same space that she'd been using to perform her sales job, take online classes, exercise, and sleep. "I'd turn off everything," she told me. She'd sit on her floor, cross-legged, and meditate for twenty minutes. Next, she'd pull out her essential oils, open a bottle of something nice, lavender or peppermint, and take a sniff.

Simply put across, AI is described as as any task performed by a program or a machine that requires application of human like intelligence to accomplish the task. It's technical simulation i.e., technology which uses complex algorithmic techniques to simulate the way neurons works in human brain. Neurons are the basic unit of our nervous system. AI is superset of Machine learning, Cognitive learning and deep learning, Reinforcement Learning. ML is algorithmic & statistical approach to approximate conclusions, predictions without direct human input.

Understanding and comprehending video content is crucial for many real-world applications such as search and recommendation systems. While recent progress of deep learning has boosted performance on various tasks using visual cues, deep cognition to reason intentions, motivation, or causality remains challenging. Existing datasets that aim to examine video reasoning capability focus on visual signals such as actions, objects, relations, or could be answered utilizing text bias. Observing this, we propose a novel task, along with a new dataset: Trope Understanding in Movies and Animations (TrUMAn), with 2423 videos associated with 132 tropes, intending to evaluate and develop learning systems beyond visual signals. Tropes are frequently used storytelling devices for creative works. By coping with the trope understanding task and enabling the deep cognition skills of machines, data mining applications and algorithms could be taken to the next level. To tackle the challenging TrUMAn dataset, we present a Trope Understanding and Storytelling (TrUSt) with a new Conceptual Storyteller module, which guides the video encoder by performing video storytelling on a latent space. Experimental results demonstrate that state-of-the-art learning systems on existing tasks reach only 12.01% of accuracy with raw input signals. Also, even in the oracle case with human-annotated descriptions, BERT contextual embedding achieves at most 28% of accuracy. Our proposed TrUSt boosts the model performance and reaches 13.94% performance. We also provide detailed analysis to pave the way for future research. TrUMAn is publicly available at:https://www.cmlab.csie.ntu.edu.tw/project/trope

From automating the creation of personalized videos to enabling new virtual customer experiences, these companies are deploying AI to help brands and retailers create engaging digital content. Brands and retailers are relying more and more on digital content -- which can range from product images for e-commerce sites to virtual try-on features to online videos -- to increase brand awareness, convert online shoppers, and boost loyalty. Video content is gaining particular momentum, with the number of times execs have mentioned the term during earnings calls shooting up in Q2'21. But given these ever-growing digital content needs, a trend accelerated by the Covid-19 pandemic, conventional production approaches may not be sufficient for brands and retailers to deliver personalized and engaging content at scale. Enter synthetic media -- images, videos, sounds, or any other form of content that has been generated, edited, or enabled by artificial intelligence.

While Apple may have released Siri before Google Assistant and Amazon Alexa, in many ways its voice-activated assistant is the least advanced of the three. A lot of that has to do with the amount of data and training digital assistants need to understand different languages, dialects and speech patterns. In an effort to improve its digital assistant, Apple recently launched a study to collect speech data and feedback with the help of an app called Siri Speech Study. "The Siri Speech Study app allows participants to send certain data to Apple for product improvement, as detailed in the informed consent form," the company says in a listing spotted by TechCrunch. The software is available in the US, Canada, Germany, France, Hong Kong, India, Ireland, Italy, Japan, Mexico, New Zealand and Taiwan.

As technology continues to revolutionise the way we live and work beyond the pandemic, here are some early-stage companies innovating in the IoT space. The World Economic Forum (WEF) Technology Pioneers of 2021 represent a collection of 100 early to growth-stage companies identified as trailblazers working with new technologies and innovations. This year's list includes start-ups shaking up data and cybersecurity and blazing a trail in blockchain and digital assets. Here, we take a look at the IoT and smart city start-ups on the list, covering innovators that are finding advanced tech solutions to a burgeoning list of complex challenges in an increasingly digitised post-pandemic world. Founded by Andrea Thomaz and Vivian Chu in 2017, Diligent Robotics is a female-led early-stage company that makes AI-powered robot assistants for healthcare workers.

Content platforms thrive on suggesting related content to their users. The more relevant items the platform can provide, the longer the user will stay on the site, which often translates to increased ad revenue for the company. If you've ever visited a news website, online publication, or blogging platform, you've likely been exposed to a recommendation engine. Each of these takes input based on your reading history and then suggests more content you might like. As a simple solution, a platform might implement a tag-based recommendation engine -- you read a "Business" article, so here are five more articles tagged "Business."

Recommender system plays a crucial role in modern E-commerce platform. Due to the lack of historical interactions between users and items, cold-start recommendation is a challenging problem. In order to alleviate the cold-start issue, most existing methods introduce content and contextual information as the auxiliary information. Nevertheless, these methods assume the recommended items behave steadily over time, while in a typical E-commerce scenario, items generally have very different performances throughout their life period. In such a situation, it would be beneficial to consider the long-term return from the item perspective, which is usually ignored in conventional methods. Reinforcement learning (RL) naturally fits such a long-term optimization problem, in which the recommender could identify high potential items, proactively allocate more user impressions to boost their growth, therefore improve the multi-period cumulative gains. Inspired by this idea, we model the process as a Partially Observable and Controllable Markov Decision Process (POC-MDP), and propose an actor-critic RL framework (RL-LTV) to incorporate the item lifetime values (LTV) into the recommendation. In RL-LTV, the critic studies historical trajectories of items and predict the future LTV of fresh item, while the actor suggests a score-based policy which maximizes the future LTV expectation. Scores suggested by the actor are then combined with classical ranking scores in a dual-rank framework, therefore the recommendation is balanced with the LTV consideration. Our method outperforms the strong live baseline with a relative improvement of 8.67% and 18.03% on IPV and GMV of cold-start items, on one of the largest E-commerce platform.