Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan. Taking advantage of this property, we propose Adaptive Subgoal Search (AdaSubS), a search method that adaptively adjusts the planning horizon. To this end, AdaSubS generates diverse sets of subgoals at different distances. A verification mechanism is employed to filter out unreachable subgoals swiftly, allowing to focus on feasible further subgoals. In this way, AdaSubS benefits from the efficiency of planning with longer subgoals and the fine control with the shorter ones, and thus scales well to difficult planning problems. We show that AdaSubS significantly surpasses hierarchical planning algorithms on three complex reasoning tasks: Sokoban, the Rubik's Cube, and inequality proving benchmark INT.

Generative models for graphs have been actively studied for decades, and they have a wide range of applications. Recently, learning-based graph generation that reproduces real-world graphs has been attracting the attention of many researchers. Although several generative models that utilize modern machine learning technologies have been proposed, conditional generation of general graphs has been less explored in the field. In this paper, we propose a generative model that allows us to tune the value of a global-level structural feature as a condition. Our model, called GraphTune, makes it possible to tune the value of any structural feature of generated graphs using Long Short Term Memory (LSTM) and a Conditional Variational AutoEncoder (CVAE). We performed comparative evaluations of GraphTune and conventional models on a real graph dataset. The evaluations show that GraphTune makes it possible to more clearly tune the value of a global-level structural feature better than conventional models.

Multimodal reasoning, an area of artificial intelligence that aims at make inferences from multimodal signals such as vision, language and speech, has drawn more and more attention in recent years. People with different personalities may respond differently to the same situation. However, such individual personalities were ignored in the previous studies. In this work, we introduce a new Personality-aware Human-centric Multimodal Reasoning (Personality-aware HMR) task, and accordingly construct a new dataset based on The Big Bang Theory television shows, to predict the behavior of a specific person at a specific moment, given the multimodal information of its past and future moments. The Myers-Briggs Type Indicator (MBTI) was annotated and utilized in the task to represent individuals' personalities. We benchmark the task by proposing three baseline methods, two were adapted from the related tasks and one was newly proposed for our task. The experimental results demonstrate that personality can effectively improve the performance of human-centric multimodal reasoning. To further solve the lack of personality annotation in real-life scenes, we introduce an extended task called Personality-predicted HMR, and propose the corresponding methods, to predict the MBTI personality at first, and then use the predicted personality to help multimodal reasoning. The experimental results show that our method can accurately predict personality and achieves satisfactory multimodal reasoning performance without relying on personality annotations.

Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools. Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs.

One of the areas where technology law is likely to see development in South Africa is the regulation of data privacy. The Protection of Personal Information Act (PoPIA) protects personal information and regulates the processing of personal data. However, with the rise of big data and the increasing use of technology in various industries, the legal framework surrounding data privacy will likely evolve in the coming years. This may include changes to PoPIA itself, as well as new legislation and case law that addresses emerging issues in data protection. Another area where tech law will likely see development is regulating artificial intelligence (AI) and machine learning.

But if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid." "Our technology, our machines, is part of our humanity. We created them to extend ourselves, and that is what is unique about human beings." What are the most powerful technologies of our society 5.0 times? How can we cope with them?. The concept of society 5.0 is a vision of a future human centred society, nature where technology is used for the best possible outcomes. Society 5.0 is not about one single country or city. It is about humans, the planet and a balanced sustainable ecosystem. As we speak Human society and intelligence is being vastly amplified, changed and augmented by AI. Even with our basic forms of narrow AI we are already seeing the biggest disruption of human society with manipulation of entire societies with social media, fake news and dark data. This to add to our financial and capital markets that as we speak are all run by algorithms and deep learning / machine learning SAAS and PAAS. We can define technology as the application of tools, scientific knowledge for concrete practical uses, this happens in social aspects of our society life and especially in business and industry. When we speak about technology we tend to talk about "advances in technology, computing technology" and many forms of tools, machinery and equipment developed from the application of research, invention and based on practical and scientific knowledge. The usage of technologies can and is normally used to solve a specific problem and reduce a society and its many sectors, the industry's ability to use a solution to develop a tool and use a budget on a new technology or set of innovation tech. Nowadays in Society 5.0 we tend to define technology as a set of tools and innovations we use in the branch of knowledge dealing with engineering or applied sciences put in practice for our society. Society 5.0 where we live has a large and complex global population. According to recent data from the United Nations (2019) the world's population is expected to increase by 2 billion persons in the next 30 years, from 7.7 billion currently to 9.7 or over 10 billion in 2050. The same research estimates the world population in 2100 to be 10.9 billion. But the predictions vary according to different projections. "Seventy thousand years ago, homo sapiens was still an insignificant animal minding its own business in a corner of Africa.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The French government on Tuesday approved a key budget bill presented as the country's biggest military spending spree in more than 50 years, underscoring the impact of Russia's war in Ukraine. The bill foresees $450 billion in military spending or the period covering 2024-2030 - up by more than a third relative to the previous timeframe. Defense Minister Sébastien Lecornu said bill's political, budgetary, military and technological drive is comparable to the huge push in the 1960s that saw France develop nuclear weapons, making the country one of the world's major military powers.

Last week, a who's who of technologists called for artificial intelligence (AI) labs to stop training the most powerful AI systems for at least six months, citing "profound risks to society and humanity." In an open letter that now has more than 3,100 signatories, including Apple co-founder Steve Wozniak, tech leaders called out San Francisco-based OpenAI Lab's recently announced GPT-4 algorithm in particular, saying the company should halt further development until oversight standards are in place. That goal has the backing of technologists, CEOs, CFOs, doctoral students, psychologists, medical doctors, software developers and engineers, professors, and public school teachers from all over the globe. On Friday, Italy became the first Western nation to ban further development of ChatGPT over privacy concerns; the natural language processing app experienced a data breach last month involving user conversations and payment information. ChatGPT is the popular GPT-based chatbot created by OpenAI and backed by billions of dollars from Microsoft.

Here's an experiment being run by undergraduate computer science students everywhere: Ask ChatGPT to generate phishing emails, and test whether these are better at persuading victims to respond or click on the link than the usual spam. It's an interesting experiment, and the results are likely to vary wildly based on the details of the experiment. Bruce Schneier is a lecturer and fellow at the Harvard Kennedy School and chief of security architecture at Inrupt. His latest book is A Hacker's Mind. Barath Raghavan is a professor of computer science at USC and cofounder of INVISV.

Background Deep learning, which is a part of a broader concept of artificial intelligence (AI) and/or machine learning has achieved remarkable success in vision tasks. While there is growing interest in the use of this technology in diagnostic support for skin-related neglected tropical diseases (skin NTDs), there have been limited studies in this area and fewer focused on dark skin. In this study, we aimed to develop deep learning based AI models with clinical images we collected for five skin NTDs, namely, Buruli ulcer, leprosy, mycetoma, scabies, and yaws, to understand how diagnostic accuracy can or cannot be improved using different models and training patterns. Methodology This study used photographs collected prospectively in Côte d'Ivoire and Ghana through our ongoing studies with use of digital health tools for clinical data documentation and for teledermatology. Our dataset included a total of 1,709 images from 506 patients.