This paper proposes a smart way to manage municipal solid waste by using the Internet of Things (IoT) and computer vision (CV) to monitor illegal waste dumping at garbage vulnerable points (GVPs) in urban areas. The system can quickly detect and monitor dumped waste using a street-level camera and object detection algorithm. Data was collected from the Sangareddy district in Telangana, India. A series of comprehensive experiments was carried out using the proposed dataset to assess the accuracy and overall performance of various object detection models. Specifically, we performed an in-depth evaluation of YOLOv8, YOLOv10, YOLO11m, and RT-DETR on our dataset. Among these models, YOLO11m achieved the highest accuracy of 92.39\% in waste detection, demonstrating its effectiveness in detecting waste. Additionally, it attains an mAP@50 of 0.91, highlighting its high precision. These findings confirm that the object detection model is well-suited for monitoring and tracking waste dumping events at GVP locations. Furthermore, the system effectively captures waste disposal patterns, including hourly, daily, and weekly dumping trends, ensuring comprehensive daily and nightly monitoring.

International Institute of Information Technology, Hyderabad (IIITH) and Agastya International Foundation are collaborating to create solutions that will bring innovative, high-quality and high-relevance learning and training to economically disadvantaged school children. The partners will jointly identify problems in the grassroots that can be addressed by solutions based on emerging technologies such as artificial intelligence (AI), machine learning (ML), language technologies and computer vision. The objective is to enable those on the wrong side of the digital divide by leveraging cutting-edge research. The partnership is set up under the aegis of the Raj Reddy Centre on Technology in Service of Society at IIITH. This centre is an initiative of IIITH to enable research and emerging technology-led solutions for grassroot education and public health, with specific emphasis on the rural population.

This Winter 2020 issue of Foresight--number 56 since the journal began in 2005--formally introduces a new section: Integrated Business Planning (IBP), the meaning of which is evolving from a term virtually synonymous with Sales and Operations Planning (S&OP) to something far more encompassing, especially for large enterprises. For more than a generation, S&OP has provided the infrastructure through which forecasts are shared across functional areas of the company, then debated and hopefully reconciled. The emphasis has been short term, generally a planning horizon of several months, and tactical. Now, Foresight's new Editor for IBP Dean Sorensen is proposing an expansive consideration of strategic as well as tactical elements. Dean's article in the Forecasting and Planning Perspectives section of this issue sets the stage for a dialog on how fully integrated the planning function should be if the organization is to optimize its resources.

Technologies of advanced data processing Industry 4.0, including above all Learning machines and Artificial Intelligence is also used in the attempt to build machines equipped with the ability to self-improve the performed tasks and programmed activities. Perhaps in the future there will be an attempt to build artificial awareness in which supercomputers will be equipped. In my opinion, consciousness can only be mathematically modeled in theory. Even if a mathematical model of artificial consciousness were built using ICT and Industry 4.0 and in the future Industry 5.0 and based on this model artificial intelligence would be created in quantum computers installed e.g. in autonomous robots, androids, it will still be only artificial intelligence without emotions and the essence of human consciousness. An analysis of the nature of human thoughts is necessary to distinguish between human intelligence and various artificial intelligence technologies being developed.

Technologies of advanced data processing Industry 4.0, including above all Learning machines and Artificial Intelligence is also used in the attempt to build machines equipped with the ability to self-improve the performed tasks and programmed activities. Perhaps in the future there will be an attempt to build artificial awareness in which supercomputers will be equipped. In my opinion, consciousness can only be mathematically modeled in theory. Even if a mathematical model of artificial consciousness were built using ICT and Industry 4.0 and in the future Industry 5.0 and based on this model artificial intelligence would be created in quantum computers installed e.g. in autonomous robots, androids, it will still be only artificial intelligence without emotions and the essence of human consciousness. An analysis of the nature of human thoughts is necessary to distinguish between human intelligence and various artificial intelligence technologies being developed.

We are pleased to introduce the India Region special section to Communications' readers. The Indian subcontinent has a population close to 1.8 billion, and is unique due to its diversity of people, cultures, spoken languages, and wide disparities in socioeconomic conditions. The region plays an important role in the global computing landscape with its highly trained manpower, software companies, and top universities that produce students that not only serve local needs, but move around the world and have global impact. We developed this special section to mirror all these facets. Last year, we publicized the plans for the special section and made an open call for contributions through ACM member channels and the ACM India website.

One of the definitional debates that bedevils the artificial intelligence (AI) field is whether machine learning is an AI-based method or technology. Or is it just an analytics-based activity? After all, it is statistical in nature, and attempts--as virtually analytical methods do--to fit a line or curve to a set of data points. And what difference does it make? Basic machine learning is practically indistinguishable from predictive analytics.

One of my typical activities in May is to teach the Analytics Academy, a short program offered to Harvard Ph.D. students and graduates from the School of Arts and Sciences. The session is offered through the Office of Career Services and explores how Ph.Ds from various fields can secure jobs and prosper outside of academia in the field of analytics, big data, and artificial intelligence. Since that same office provided me with some very useful business orientation when I was seeking a (partially, as it turns out) nonacademic career, I am always happy to return the favor. I've been doing this program for almost a decade, and the students have always been very enthusiastic and positive about analytics. But this year was different.

The rise of cloud computing brings with it the promise of infinite computing power. The rise of Big Data brings with it the possibility of ingesting all the world's log files. The combination of the two has sparked widespread interest in data science as truly the "one ring to rule them all." When we speculate about such a future, we tend to use two phrases to describe this new kind of analytics--artificial intelligence (AI) and machine learning. Most people use them interchangeably.

Many people and companies seem to think of "cognitive computing" as a separate area from analytics. Most large organizations today have significant analytical initiatives underway, but they think of the cognitive space as being an exotic science project. One executive told me, "We have no desire to win Jeopardy," an allusion of course to the IBM Watson project from 2011. But cognitive computing is not just about Watson, and it's not an exotic science project.