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.

AI is the technology behind many of today's most popular apps, such as Apple's Siri and Amazon's Alexa. But now AI researchers have set their sights on a new challenge: using computers not just for information processing but also for creative work like music composition and even poetry.

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It is considered one of the best examples of AI in everyday life with improvements that increase the quality of the platform as well as the customer experience. Notice how you can type in "Red Bags" and get a list of red-colored bags instantly? It is made possible by the underlying AI algorithms, regularly categorizing product searches for efficient indexing.

Rama Ramakrishnan is a professor of the practice at MIT Sloan. He specializes in data science and machine learning. He was a data science entrepreneur and tech executive for more than 20 years, most recently as senior vice president at Salesforce and chief data scientist for Salesforce Commerce Cloud. An increasing number of organizations are bringing data scientists on board as executives and managers recognize the potential of data science and artificial intelligence to boost performance. But hiring talented data scientists is one thing; harnessing their capabilities for the benefit of the organization is another.

In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However, such attempts have focused mostly on utilizing only the information of positive user-item interactions with high ratings. Thus, there is a challenge on how to make use of low rating scores for representing users' preferences since low ratings can be still informative in designing NE-based recommender systems. In this study, we present SiReN, a new sign-aware recommender system based on GNN models. Specifically, SiReN has three key components: 1) constructing a signed bipartite graph for more precisely representing users' preferences, which is split into two edge-disjoint graphs with positive and negative edges each, 2) generating two embeddings for the partitioned graphs with positive and negative edges via a GNN model and a multi-layer perceptron (MLP), respectively, and then using an attention model to obtain the final embeddings, and 3) establishing a sign-aware Bayesian personalized ranking (BPR) loss function in the process of optimization. Through comprehensive experiments, we empirically demonstrate that SiReN consistently outperforms state-of-the-art NE-aided recommendation methods.

In the last few years, artificial intelligence (AI) adoption has become increasingly widespread. With almost no industry remaining untouched, AI is changing the way we do our work, interact with others, shop online, watch content, and make decisions. In business world, AI has enabled companies to save costs, make better informed decision in comparatively less time while relying on more information. We see robo advisors, chat bots, personalised recommendation systems, search engines, AI assisted hiring systems and several such use cases that is changing the way businesses functions. AI has further enabled the emerging technologies such as Robotics, Internet of Things (IoT), Augmented Reality (AR).