Weather conditions can drastically alter the state of crops and rangelands, and in turn, impact the incomes and food security of individuals worldwide. Satellite-based remote sensing offers an effective way to monitor vegetation and climate variables on regional and global scales. The annual peak Normalized Difference Vegetation Index (NDVI), derived from satellite observations, is closely associated with crop development, rangeland biomass, and vegetation growth. Although various machine learning methods have been developed to forecast NDVI over short time ranges, such as one-month-ahead predictions, long-term forecasting approaches, such as one-year-ahead predictions of vegetation conditions, are not yet available. To fill this gap, we develop a two-phase machine learning model to forecast the one-year-ahead peak NDVI over high-resolution grids, using the Four Corners region of the Southwestern United States as a testbed. In phase one, we identify informative climate attributes, including precipitation and maximum vapor pressure deficit, and develop the generalized parallel Gaussian process that captures the relationship between climate attributes and NDVI. In phase two, we forecast these climate attributes using historical data at least one year before the NDVI prediction month, which then serve as inputs to forecast the peak NDVI at each spatial grid. We developed open-source tools that outperform alternative methods for both gross NDVI and grid-based NDVI one-year forecasts, providing information that can help farmers and ranchers make actionable plans a year in advance.

Land cover classification involves the production of land cover maps, which determine the type of land through remote sensing imagery. Over recent years, such classification is being performed by machine learning classification models, which can give highly accurate predictions on land cover per pixel using large quantities of input training data. However, such models do not currently take account of input measurement uncertainty, which is vital for traceability in metrology. In this work we propose a Bayesian classification framework using generative modelling to take account of input measurement uncertainty. We take the specific case of Bayesian quadratic discriminant analysis, and apply it to land cover datasets from Copernicus Sentinel-2 in 2020 and 2021. We benchmark the performance of the model against more popular classification models used in land cover maps such as random forests and neural networks. We find that such Bayesian models are more trustworthy, in the sense that they are more interpretable, explicitly model the input measurement uncertainty, and maintain predictive performance of class probability outputs across datasets of different years and sizes, whilst also being computationally efficient.

In this paper, we propose a series of fuzzy temporal protoforms in the framework of the automatic generation of quantitative and qualitative natural language descriptions of processes. The model includes temporal and causal information from processes and attributes, quantifies attributes in time during the process life-span and recalls causal relations and temporal distances between events, among other features. Through integrating process mining techniques and fuzzy sets within the usual Data-to-Text architecture, our framework is able to extract relevant quantitative temporal as well as structural information from a process and describe it in natural language involving uncertain terms. A real use-case in the cardiology domain is presented, showing the potential of our model for providing natural language explanations addressed to domain experts.

Abstract: Global Artificial Intelligence (AI) Robots Market to Reach $21. 4 Billion by 2026. AI or artificial intelligence in robotics is the integration of AI technology with robots enabling them to more efficiently perform repetitive tasks without human intervention.New York, Oct. 08, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence (AI) Robots Industry" - https://www.reportlinker.com/p06030753/?utm_source=GNW AI also enables robots

If I were to choose what was hottest trend in 2020, it would not be a single item in this top 10 list. The hottest trend would be a hybrid (convergence) of several of these items. That hybrid would include: Observability, coupled with Edge and the ever-rising ubiquitous IoT (sensors on everything), boosted by 5G and cloud technologies, fueling ever-improving ML and DL algorithms, all of which are enabling "just-in-time" intelligence and intelligent automation (for data-driven decisions and action, at the point of data collection), deployed with a data-literate workforce, in a sustainable and trusted MLOps environment, where algorithms, data, and applications work harmoniously and are governed and secured by AIOps. If we learned anything from the year 2020, it should be that trendy technologies do not comprise a menu of digital transformation solutions to choose from, but there really is only one combined solution, which is the hybrid convergence of data innovation technologies. From my perspective, that was the single most significant data innovation trend of the year 2020.

The year 2020 saw a lot of new developments in the AI and data science field -- from Neuralink to GPT-3, along with some significant announcements from events such as Nvidia GTC 2020 and RAISE 2020. These exciting developments were accompanied by quotes and remarks by tech leaders. As the year 2020 comes to an end, we round up a few of these quotes that defined the year 2020. "AI is a tribute to human intelligence power. At every step of history, India has led the world in knowledge and learning."

The inventions in Artificial Intelligence are thriving the pace of invention despite the existing pandemic. The year 2020 has surprised humans in many ways. From encountering a pandemic, addressing a global recession, and witnessing the global geopolitical changes, humanity is standing in ambiguous times. However, not everything is uncertain. Throughout the year, emerging technologies such as artificial intelligence, robotics, Internet of Things, and augmented/virtual reality, amongst others have spearheaded innovation with a promising future.

The year 2020 took us on a roller coaster ride. A lot of technological changes happened in every sector. Starting from healthcare to manufacturing, business, automobile and education, everything took a U-turn in a single year. No one has ever imagined that people will stay at home on lockdowns and work and learn through remote modes. As we are almost at the end of the year, the train to'somewhere' in automation is still on the track.

As we near the end of this year, it's time to watch out for emerging HR trends in 2020. According to Forrester, more than 47% of interviewed executives believe that by 2020, technology will have an impact on more than half of their sales as well as the future workplace trends. Organizations will focus on embracing HR technology to their advantage, strengthening workforce capabilities, ensuring data security, improving candidate and employee experiences, and more. In the year 2020, AI-driven solutions will provide immense innovation in industries including Banking, Finance, Manufacturing, Retail, Healthcare, Transportation, Social Media, etc. Organizations will adopt AI and use it in recruitment and hiring processes. AI-powered solutions will rule in the year ahead with the following advantages.

The year 2020 has inarguably been an unprecedented year for humanity. With a global pandemic upending people's lives, the cyber world has been no less affected. On the upside, the virus-enforced digital transition in nearly all aspects of our lives has created massive momentum and scale for the uptake of cyber technologies. However, the downside is the increased opportunities this creates for unethical hackers and cyber criminals. In this backdrop, how is the cyber security landscape going to unfold this year?