Bayesian optimization is a powerful technique for optimizing expensive-to-evaluate black-box functions, consisting of two main components: a surrogate model and an acquisition function. In recent years, myopic acquisition functions have been widely adopted for their simplicity and effectiveness. However, their lack of look-ahead capability limits their performance. To address this limitation, we propose FigBO, a generalized acquisition function that incorporates the future impact of candidate points on global information gain. FigBO is a plug-and-play method that can integrate seamlessly with most existing myopic acquisition functions. Theoretically, we analyze the regret bound and convergence rate of FigBO when combined with the myopic base acquisition function expected improvement (EI), comparing them to those of standard EI. Empirically, extensive experimental results across diverse tasks demonstrate that FigBO achieves state-of-the-art performance and significantly faster convergence compared to existing methods.

Rapid and efficient assessment of the future impact of research articles is a significant concern for both authors and reviewers. The most common standard for measuring the impact of academic papers is the number of citations. In recent years, numerous efforts have been undertaken to predict citation counts within various citation windows. However, most of these studies focus solely on a specific academic field or require early citation counts for prediction, rendering them impractical for the early-stage evaluation of papers. In this work, we harness Scopus to curate a significantly comprehensive and large-scale dataset of information from 69707 scientific articles sourced from 99 journals spanning multiple disciplines. We propose a deep learning methodology for the impact-based classification tasks, which leverages semantic features extracted from the manuscripts and paper metadata. To summarize the semantic features, such as titles and abstracts, we employ a Transformer-based language model to encode semantic features and design a text fusion layer to capture shared information between titles and abstracts. We specifically focus on the following impact-based prediction tasks using information of scientific manuscripts in pre-publication stage: (1) The impact of journals in which the manuscripts will be published. (2) The future impact of manuscripts themselves. Extensive experiments on our datasets demonstrate the superiority of our proposed model for impact-based prediction tasks. We also demonstrate potentials in generating manuscript's feedback and improvement suggestions.

Google has developed an artificial intelligence system that can play video games like a human and take orders from players and could eventually even have real-world implications down the line. "This work isn't about achieving high game scores," the SIMA research team wrote in a Google DeepMind post earlier this month. "Learning to play even one video game is a technical feat for an AI system, but learning to follow instructions in a variety of game settings could unlock more helpful AI agents for any environment." SIMA, which stands for Scalable Instructable Multiworld Agent, isn't like a typical computer player that's built into a specific game. Rather, the AI agent plays alongside and learns like a human -- through image recognition and from native language commands -- and plays with keyboard and mouse outputs.

Why Will It Affect You? How Do You Survive and Thrive through the AI Revolution? How Do You Survive and Thrive through the AI Revolution? "A fresh, thought provoking, entertaining and accessible post pandemic account of the present and future impact of AI and how to live with it, packed full of useful facts and quotable analogies and anecdotes." "A fresh, thought provoking, entertaining and accessible post pandemic account of the present and future impact of AI and how to live with it, packed full of useful facts and quotable analogies and anecdotes."

Reinforcement Learning -- How can you become an expert? Reinforcement Learning -- How can you become an expert? You will never believe how Machine can learn like humans! You will never believe how Machine can learn like humans!

By taking a step back and applying a joined-up, strategic approach to implementing AI-enhanced technologies such as intelligent automation, leaders can derive clear business benefits, including – but not limited to – improved customer service, increased competitiveness, greater productivity, and a more satisfied workforce. Eric Tyree, head of research and AI at Blue Prism, explained: "Whether it's cutting customer wait times in financial services, enabling a more resilient and agile supply chain, or improving patient care by minimising manual admin work, intelligent automation can be the key driver in achieving strategic corporate initiatives. The emphasis becomes a shift of human capital towards revenue generating or customer-centric activity, which gives way to enhanced capacity, more fulfilling work for staff, and more agility and scalability of resource across the entire organisation. AI is now having a significant impact on businesses by replacing restrictive, error-prone networks and relieving overburdened IT teams tasked with'finding and fixing' problems instead of'empowering and enabling' people and connections. And for schools, a connected classroom can be created to help children overcome learning challenges through supportive software or monitor attendance to proactively keep at-risk students engaged in education."

This article will explore how artificial intelligence is set to impact organisations in the future, gauging the insights of experts in the space. Artificial intelligence (AI) is changing how businesses work and interact with their processes, products and people on both the employee and client side of operations. Gartner predicts the worldwide AI software market to reach $62 billion in 2022, an increase of over 20%. This digitisation is game-changing for companies in all sectors, as it underpins smarter, more streamlined and more cost-effective running of businesses, as well as driving more agile operations in today's disruptive climate. With this in mind, we take a look at the possible future impact of artificial intelligence, as the technology continues to develop and infiltrate more business use cases.

Artificial Intelligence (AI) scientists from Mila, the Quebec AI Institute, has developed a tool that lets people envision the potential ravages of climate change on their homes and other destinations. As part of the "This Climate Does Not Exist" project, users can enter their address, or the address of any other destination in the world, to see what it might look years into the future if certain measures are not enacted concerning the climate. Images of these destinations under flooding, layers of smog or other extreme weather events that appear through the project were achieved using generative adversarial networks (GANs) -- the technology responsible for deepfakes, or fake images that look real. The system was trained on images from both real-life flooding and smog events and scenes manufactured by video game makers. The project aims to raise awareness using the images and also offers consumers steps that can be taken -- engage with local representatives, change diets and consumption patterns, for instance -- to lessen the impact of climate change.

Owing to the benefits for customers (lower prices), drivers (higher revenues), aggregation companies (higher revenues) and the environment (fewer vehicles), on-demand ride pooling (e.g., Uber pool, Grab Share) has become quite popular. The significant computational complexity of matching vehicles to combinations of requests has meant that traditional ride pooling approaches are myopic in that they do not consider the impact of current matches on future value for vehicles/drivers. Recently, Neural Approximate Dynamic Programming (NeurADP) has employed value decomposition with Approximate Dynamic Programming (ADP) to outperform leading approaches by considering the impact of an individual agent's (vehicle) chosen actions on the future value of that agent. However, in order to ensure scalability and facilitate city-scale ride pooling, NeurADP completely ignores the impact of other agents actions on individual agent/vehicle value. As demonstrated in our experimental results, ignoring the impact of other agents actions on individual value can have a significant impact on the overall performance when there is increased competition among vehicles for demand. Our key contribution is a novel mechanism based on computing conditional expectations through joint conditional probabilities for capturing dependencies on other agents actions without increasing the complexity of training or decision making. We show that our new approach, Conditional Expectation based Value Decomposition (CEVD) outperforms NeurADP by up to 9.76% in terms of overall requests served, which is a significant improvement on a city wide benchmark taxi dataset.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Raia Hadsell, a research scientist at Google DeepMind, believes "responsible AI is a job for all." That was her thesis during a talk today at the virtual Lesbians Who Tech Pride Summit, where she dove into the issues currently plaguing the field and the actions she feels are required to ensure AI is ethically developed and deployed. "AI is going to change our world in the years to come. But because it is such a powerful technology, we have to be aware of the inherent risks that will come with those benefits, especially those that can lead to bias, harm, or widening social inequity," she said.