An application such as Reuter's News Tracer can track down breaking news, so that journalists are not tied down to grunt work and can be used in parallel with Connexun's news api.

Something tells us that, very soon, the Camera app on our phones will take care of the annoying shadows of the photos using artificial intelligence …

We are living through one of the most challenging and devastating health crises in living memory. This year has brought untold loss of life and livelihoods, the true worldwide repercussions of which are still to be seen. The COVID-19 pandemic has also altered the global business landscape, accelerating the pace and volume of data created through increased remote working and digital transacting, and fast shifting the economic realities for all business leaders. Against this backdrop, technology – artificial intelligence (AI), in particular – now has an even bigger role to play in helping organizations and countries to adapt, keep us safe and improve how we live and work. But the thirst and drive to innovate with these new technologies at speed must be balanced with the need to carefully build consumer trust in those same innovations.

Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that current state-of-the-art, end-to-end reinforcement learning approaches still require thousands or millions of data samples to converge to a satisfactory policy and are subject to catastrophic failures during training. Conversely, in real world scenarios and after just a few data samples, humans are able to either provide demonstrations of the task, intervene to prevent catastrophic actions, or simply evaluate if the policy is performing correctly. This research investigates how to integrate these human interaction modalities to the reinforcement learning loop, increasing sample efficiency and enabling real-time reinforcement learning in robotics and real world scenarios. This novel theoretical foundation is called Cycle-of-Learning, a reference to how different human interaction modalities, namely, task demonstration, intervention, and evaluation, are cycled and combined to reinforcement learning algorithms. Results presented in this work show that the reward signal that is learned based upon human interaction accelerates the rate of learning of reinforcement learning algorithms and that learning from a combination of human demonstrations and interventions is faster and more sample efficient when compared to traditional supervised learning algorithms. Finally, Cycle-of-Learning develops an effective transition between policies learned using human demonstrations and interventions to reinforcement learning. The theoretical foundation developed by this research opens new research paths to human-agent teaming scenarios where autonomous agents are able to learn from human teammates and adapt to mission performance metrics in real-time and in real world scenarios.

In this paper, we propose a two-stage ranking approach for recommending linear TV programs. The proposed approach first leverages user viewing patterns regarding time and TV channels to identify potential candidates for recommendation and then further leverages user preferences to rank these candidates given textual information about programs. To evaluate the method, we conduct empirical studies on a real-world TV dataset, the results of which demonstrate the superior performance of our model in terms of both recommendation accuracy and time efficiency.

Before this pandemic, many Artificial Intelligence (AI) researches had predicted that we would have a flying car by 2020.

Global Artificial Intelligence in Radiotech Market statistical report provides a wide-ranging research on the key players and in-depth insights which …

A chatbot is a designed tool powered by the rules of Artificial Intelligence (AI), acts as a simulator of human conversation for the purpose of automating a business process. Chatbots facilitate user interactions when placed within the messenger apps that act as a platform for supporting the bots. Chatbot acts as an intermediary, imitates human conversations by initiating live chats instantaneously and responding tirelessly to the user queries at any point in time. Chatbot optimises customer support operations and strengthens the relationship with the customers, supports digital marketing by interacting with brands in the easiest and fastest manner; as a result improves business responsiveness and customer satisfaction. One major advantage of bots is, they execute tasks in a human-like fashion through a set of automated personalised messages constantly learning and improving from interactions. Bots let you use the channel preferred by your customers, employees and stakeholders to communicate with you.

Time travel becomes a family affair in Bill and Ted Face the Music, the long-awaited third film in the popular Bill and Ted comedy franchise. Fans won't be disappointed: the film is most excellent, capturing that same breezy, chaotic, let's-just-have-fun-with-this madcap magic of its predecessors. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. "We were trying to pay some homage to the original two [films] while making it feel like it was contemporary," Parisot told Ars about how he approached bringing the Bill and Ted franchise into the 21st century.

Labor Day hasn't arrived just yet, but various retailers are already kick-starting their sales. No matter how you'll be spending the last few weeks of summer, this weekend's best deals might be able to make the experience even better. Don't miss more great deals, picked by our editors. If you buy something using links in our stories, we may earn a commission. This helps support our journalism.