The Effects of Machine Learning and Artificial Intelligence on the Analysis of Environmental Big Data and the Prediction of the Future of the Environment
A workshop to encourage creative ideas how we can fight climate change, on various of different factors and aspects, using machine learning, technology and artificial intelligence.
4 Ways Artificial Intelligence Can Help Save the PlanetTyrone Systems
As the scale and urgency of the economic and human health impacts from our deteriorating natural environment grows, we have an opportunity to look at how AI can help transform traditional sectors and systems to address climate change, deliver food and water security, build sustainable cities, and protect biodiversity and human wellbeing.
The following presentation was delivered by Robert Morrison, Principal Consultant at Esri Ireland, at the 2019 NICS ICT Conference in October 2019.
The presentation focuses on taking a geographic approach to machine learning to help you "see what other's can't".
Imagery and remotely sensed data is a valuable resource for many organisations who have made substantial investment obtaining the data. The field of Machine Learning is both broad and deep and is constantly evolving. Using ArcGIS and Machine Learning allows organisations to derive valuable new content.
ArcGIS is an open, interoperable platform that allows for the integration of complementary methods and techniques that empower ArcGIS users to solve complex, real-world problems in a fundamentally spatial way.
Learn how by combining powerful built-in Image analysis tools with any machine learning package users can benefit from the spatial validation, geo-enrichment and visualisation. See how this Machine Learning is being applied in real world use-cases from marine farming and crime analysis to agriculture and sustainability.
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
The Effects of Machine Learning and Artificial Intelligence on the Analysis of Environmental Big Data and the Prediction of the Future of the Environment
A workshop to encourage creative ideas how we can fight climate change, on various of different factors and aspects, using machine learning, technology and artificial intelligence.
4 Ways Artificial Intelligence Can Help Save the PlanetTyrone Systems
As the scale and urgency of the economic and human health impacts from our deteriorating natural environment grows, we have an opportunity to look at how AI can help transform traditional sectors and systems to address climate change, deliver food and water security, build sustainable cities, and protect biodiversity and human wellbeing.
The following presentation was delivered by Robert Morrison, Principal Consultant at Esri Ireland, at the 2019 NICS ICT Conference in October 2019.
The presentation focuses on taking a geographic approach to machine learning to help you "see what other's can't".
Imagery and remotely sensed data is a valuable resource for many organisations who have made substantial investment obtaining the data. The field of Machine Learning is both broad and deep and is constantly evolving. Using ArcGIS and Machine Learning allows organisations to derive valuable new content.
ArcGIS is an open, interoperable platform that allows for the integration of complementary methods and techniques that empower ArcGIS users to solve complex, real-world problems in a fundamentally spatial way.
Learn how by combining powerful built-in Image analysis tools with any machine learning package users can benefit from the spatial validation, geo-enrichment and visualisation. See how this Machine Learning is being applied in real world use-cases from marine farming and crime analysis to agriculture and sustainability.
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
VISION / AMBITION
-Australia the first drone-sensed nation (cm-scale)
-Pre-competitive data release for industry, environmental management, education & research
-Conventional survey & remote sensing techniques at ultra-high resolution and flexibility (time-series, rapid response etc)
-Next gen “UNDERCOVER” techniques (minerals and water resources)
Relationship Between Big Data & AI
Human intelligence builds up on what we read, observe, learn, sense and experience. It's our ability to store large amount of data, accumulated over years and co-relating a few data points to answer a certain question, that makes us intelligent.
Similarly for machines to replicate human intelligence, they'll need to absorb large amount of data to make an intelligent decision............... (read more)
I was honored to be able to give a short presentation in Markus Sunela's dissertation party about artificial intelligence in water sector. Such an interesting topic that we yet know so little. How would you utilize an AI in water sector?
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
In this deck from GTC 2019, Seongchan Kim, Ph.D. presents: How Deep Learning Could Predict Weather Events.
"How do meteorologists predict weather or weather events such as hurricanes, typhoons, and heavy rain? Predicting weather events were done based on supercomputer (HPC) simulations using numerical models such as WRF, UM, and MPAS. But recently, many deep learning-based researches have been showing various kinds of outstanding results. We'll introduce several case studies related to meteorological researches. We'll also describe how the meteorological tasks are different from general deep learning tasks, their detailed approaches, and their input data such as weather radar images and satellite images. We'll also cover typhoon detection and tracking, rainfall amount prediction, forecasting future cloud figure, and more."
Watch the video: https://wp.me/p3RLHQ-k2T
Learn more: http://en.kisti.re.kr/
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
It’s the age of getting smart or smarter. Technology has been seeping into every sphere of our lives in the past few years. After our phones and televisions have gotten smarter, it’s time to envisage our cities to become smarter. Big Data and the Internet of Things (IoT) have a significant role to play in making our lives simpler by inter-connecting our scattered digital footprints to create an efficient and cohesive habitable unit for us. While the idea of a smart city has been floating around for some time now, its successful implementation needs to counter and conquer many roadblocks.
Read the full blog here: http://suyati.com/the-role-of-big-data-in-smart-cities/
Reach us at: achoudhury@suyati.com
Machine Learning in Agriculture Module 1Prasenjit Dey
Discuss the opportunities of incorporation of machine learning in agriculture. Briefly discuss different machine learning strategies. Briefly discuss the ways of machine learning can be used
Training #ArtificialIntelligence is an energy-intensive process. Some estimate that creating an #AI can be five times worse for the planet than a car. Learn more about #redAI versus #greenAI and latest AI use-cases to benefit the environment and what can we do to use tech and AI in a more sustainable way.
Session presented in the "AI-powered innovation in the digital era" session of the #ClujInnovationDays 2021.
This is about survey the crop yield prediction using some data mining classification methods namely prdiction with classification,residue climate control, feature selection extraction, crop classification models,evaluation metrics, accuracy level,classification decision, result analysis,rain fall pH, principal component analysis, information gain
We show how deep learning can be effectively applied to remote sensing. Many problems we faced, solutions we have had discovered were highlighted too. Remotely sensed data, unlike other vision tasks are very challenging and posses extra difficulties. Objects are very small compared to the image size, and even small pixel sizes of 8*10 pixel can contain huge amount of informations.
To the best of our knowledge there is no automated or simi-automated tool that uses deep learning to detect features from satellite imagery.
AI for SDGs and International Development - Basics of AIAtsushi Koshio
This siled was prepared for the training seminar on Artificial Intelligence for International Organizations. Introducing AI technologies into International Development fields for achieving SDGs would be great opportunities to accelerate development. . This material is just explaining basic of AI and some examples of AI application in this field.
Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Grow...Bayes Ahmed
This is my final Mater thesis presentation. The thesis defense was held on March' 07, 2011 at 15:30 in the seminar room of Universitat Jaume I (UJI), Castellón, Spain.
Artificial Intelligence in Education focusing on the Skills3.0 projectInge de Waard
This presentation was given during the Elearning Fusion conference in Warsaw, Poland - April 2019. The presentation begins with a bit of algorithm, AI, machine learning history and background, provides some examples of AI in learning and finalizes with the Skills 3.0 project where InnoEnergy is working on.
VISION / AMBITION
-Australia the first drone-sensed nation (cm-scale)
-Pre-competitive data release for industry, environmental management, education & research
-Conventional survey & remote sensing techniques at ultra-high resolution and flexibility (time-series, rapid response etc)
-Next gen “UNDERCOVER” techniques (minerals and water resources)
Relationship Between Big Data & AI
Human intelligence builds up on what we read, observe, learn, sense and experience. It's our ability to store large amount of data, accumulated over years and co-relating a few data points to answer a certain question, that makes us intelligent.
Similarly for machines to replicate human intelligence, they'll need to absorb large amount of data to make an intelligent decision............... (read more)
I was honored to be able to give a short presentation in Markus Sunela's dissertation party about artificial intelligence in water sector. Such an interesting topic that we yet know so little. How would you utilize an AI in water sector?
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
In this deck from GTC 2019, Seongchan Kim, Ph.D. presents: How Deep Learning Could Predict Weather Events.
"How do meteorologists predict weather or weather events such as hurricanes, typhoons, and heavy rain? Predicting weather events were done based on supercomputer (HPC) simulations using numerical models such as WRF, UM, and MPAS. But recently, many deep learning-based researches have been showing various kinds of outstanding results. We'll introduce several case studies related to meteorological researches. We'll also describe how the meteorological tasks are different from general deep learning tasks, their detailed approaches, and their input data such as weather radar images and satellite images. We'll also cover typhoon detection and tracking, rainfall amount prediction, forecasting future cloud figure, and more."
Watch the video: https://wp.me/p3RLHQ-k2T
Learn more: http://en.kisti.re.kr/
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
It’s the age of getting smart or smarter. Technology has been seeping into every sphere of our lives in the past few years. After our phones and televisions have gotten smarter, it’s time to envisage our cities to become smarter. Big Data and the Internet of Things (IoT) have a significant role to play in making our lives simpler by inter-connecting our scattered digital footprints to create an efficient and cohesive habitable unit for us. While the idea of a smart city has been floating around for some time now, its successful implementation needs to counter and conquer many roadblocks.
Read the full blog here: http://suyati.com/the-role-of-big-data-in-smart-cities/
Reach us at: achoudhury@suyati.com
Machine Learning in Agriculture Module 1Prasenjit Dey
Discuss the opportunities of incorporation of machine learning in agriculture. Briefly discuss different machine learning strategies. Briefly discuss the ways of machine learning can be used
Training #ArtificialIntelligence is an energy-intensive process. Some estimate that creating an #AI can be five times worse for the planet than a car. Learn more about #redAI versus #greenAI and latest AI use-cases to benefit the environment and what can we do to use tech and AI in a more sustainable way.
Session presented in the "AI-powered innovation in the digital era" session of the #ClujInnovationDays 2021.
This is about survey the crop yield prediction using some data mining classification methods namely prdiction with classification,residue climate control, feature selection extraction, crop classification models,evaluation metrics, accuracy level,classification decision, result analysis,rain fall pH, principal component analysis, information gain
We show how deep learning can be effectively applied to remote sensing. Many problems we faced, solutions we have had discovered were highlighted too. Remotely sensed data, unlike other vision tasks are very challenging and posses extra difficulties. Objects are very small compared to the image size, and even small pixel sizes of 8*10 pixel can contain huge amount of informations.
To the best of our knowledge there is no automated or simi-automated tool that uses deep learning to detect features from satellite imagery.
AI for SDGs and International Development - Basics of AIAtsushi Koshio
This siled was prepared for the training seminar on Artificial Intelligence for International Organizations. Introducing AI technologies into International Development fields for achieving SDGs would be great opportunities to accelerate development. . This material is just explaining basic of AI and some examples of AI application in this field.
Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Grow...Bayes Ahmed
This is my final Mater thesis presentation. The thesis defense was held on March' 07, 2011 at 15:30 in the seminar room of Universitat Jaume I (UJI), Castellón, Spain.
Artificial Intelligence in Education focusing on the Skills3.0 projectInge de Waard
This presentation was given during the Elearning Fusion conference in Warsaw, Poland - April 2019. The presentation begins with a bit of algorithm, AI, machine learning history and background, provides some examples of AI in learning and finalizes with the Skills 3.0 project where InnoEnergy is working on.
CCAPS and AidData built an interactive app with the ArcGIS API for Microsoft Silverlight/WPF to map effects of aid, climate change, and conflict in Africa.
Sustainability GIS and Planning
Geography drives energy consumption.
Geography drives energy alternatives.
Geography drives resilient adaptation to effects of climate change.
GIS is a toolset for managing all aspects of sustainability.
Examples
Lessons learned from a Microsoft AI for Earth-WRI Collaboration
Learn more: https://www.wri.org/events/2020/06/webinar-ai-global-environmental-challenges
In this video from the HPC User Forum at Argonne, Dr. Brett Bode from NCSA presents: Research on Blue Waters.
"Blue Waters is one of the most powerful supercomputers in the world and is one of the fastest supercomputers on a university campus. Scientists and engineers across the country use the computing and data power of Blue Waters to tackle a wide range of challenging problems, from predicting the behavior of complex biological systems to simulating the evolution of the cosmos."
Watch the video: https://wp.me/p3RLHQ-kYx
Learn more: http://www.ncsa.illinois.edu/enabling/bluewaters
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This is the English translation, with some relevant corrections, of the talk I gave at University of Calabria, about the contemporary and post-contemporary flood forecasting.
Civic Engagement via En-ROADS Simulation/Game and EOfactory PlatformFarhan Helmy
My thoughts and the ongoing activities in using En-ROADS simulation and EOfactory platform as a tools ciivic engagement, particularly on natural resources, environment and climate change,
Panel: AI and Climate Change: Where is the funding and is it sufficient to meet the 2030 and 2050 goals?
https://www.climatechange.ai/events/aaaifss2022#scheduleThursday, November 17, 2016, 5:10 pm – 6:00 pm
On March 22, 2019, ICLR hosted a Friday Forum webinar title 'Hail: Challenges Solutions and Future Prospects', led by Dr. Julian Brimelow of Environment and Climate Change Canada (ECCC).
The growth of hail, and especially large hail, requires the alignment of several key ingredients and processes spanning a wide range of scales. Consequently, modelling hail growth and forecasting hail size is challenging. Given that hailstorms inflict billions of dollars in damages, it is important to improve the lead time of warnings. However, predicting the occurrence and size of hail remains problematic. Central to this problem is the lack of skillful short-term forecast guidance. This is in turn partly attributable to the scarcity of reliable surface hail reports for verification.
The first half of the talk focuses on the challenges associated with observing hail, and identifying methods for obtaining accurate estimates of hail size by pairing information from social media and weather radar. It also highlights the key challenges and limitations facing users of social media data and provides some potential solutions. The number of days when the environmental conditions favour severe thunderstorms over N. America has been predicted to increase under anthropogenic climate change (ACC). However, how hail might be affected by ACC is unclear. The second half of the talk will speak to the first study to investigate the spatiotemporal response of hail frequency and size to over N. America.
Dr. Julian Brimelow is a scientist at Environment and Climate Change Canada and an expert in hazardous convective weather. Julian graduated from the University of Pretoria with a BSc in meteorology in 1993, completed his MSc at the University of Alberta in 1999, and in 2011 finished his PhD at the University of Manitoba. Julian is currently working to improve the detection and prediction of hail using radar products and data from Canada’s weather models. Julian has a broad publication record on deep convection, thunderstorms, hail, flooding and drought. Julian has worked as a meteorologist for the South African Weather Service and the British Antarctic Survey and as a research assistant at the University of Alberta.
Artificial intelligence in the post-deep learning eraDeakin University
Deep learning has recently reached the heights that pioneers in the field had aspired to, serving as the driving force behind recent breakthroughs in AI, which have arguably surpassed the Turing test. At present, the spotlight is on scaling Transformers and diffusion models on Internet-scale data. In this talk, I will provide an overview of the fundamental principles of deep learning, its powers, and limitations, and explore the new era of post-deep learning. This new era encompasses novel objectives, dynamic architectures, abstract reasoning, neurosymbolic hybrid systems, and LLM-based agent systems.
Deep learning has recently reached the height the pioneers wished for, serving as the driving force behind recent breakthroughs in AI, which have arguably surpassed the Turing test. In this tutorial, we will provide an overview of the fundamental principles of deep learning and explore the latest advances in the field, including Foundation Models. We will also examine the powers and limitations of deep learning, exploring how reasoning may emerge from carefully crafted neural networks and massively pre-trained models.
AI for automated materials discovery via learning to represent, predict, gene...Deakin University
A brief overview of how our AI can help automate the materials discovery process, covering a wide range of problems, from drug design to crystal plasticity.
Deep learning, enabled by powerful compute, and fuelled by massive data, has delivered unprecedented data analytics capabilities. However, major limitations remain. Chiefly among those is that deep neural networks tend to exploit the surface statistics in the data, creating short-cuts from the input to the output, without really deeply understanding of the data. As a result, these networks fail miserably to generalize to novel combinations. This is because the networks perform shallow pattern matching but not deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. Second, machine learning is often trained to do just one task at a time, making it impossible to re-define tasks on the fly as needed in a complex operating environment. This talk presents our recent developments to extend the capacity of neural networks to remove these limitations. Our main focus is on learning to reason from data, that is, learning to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.
Generative AI represents a pivotal moment in computing history, opening up new opportunities for scientific discoveries. By harnessing extensive and diverse datasets, we can construct new general-purpose Foundation Models that can be fine-tuned for specific prediction and exploration tasks. This talk introduces our research program, which focuses on leveraging the power of Generative AI for materials discovery. Generative AI facilitates rapid exploration of vast materials design spaces, enabling the identification of new compounds and combinations. However, this field also presents significant challenges, such as effectively representing crystals in a compact manner and striking the right balance between utilizing known structural regions and venturing into unexplored territories. Our research delves into the development of a new kind of generative models specifically designed to search for diverse molecular/crystal regions that yield high returns, as defined by domain experts. In addition, our toolset includes Large Language Models that have been fine-tuned using materials literature and scientific knowledge. These models possess the ability to comprehend extensive volumes of materials literature, encompassing molecular string representations, mathematical equations in LaTeX, and codebases. We explore the open challenges, including effectively representing deep domain knowledge and implementing efficient querying techniques to address materials discovery problems.
AI as a general-purpose technology akin to steam engines and electricity, holds the potential for profound global socio-economic change. In this talk, we delve into a new form of disruptive AI known as Generative AI (GenAI) and its revolutionary impact on how we live, work, and interact with our environment. This discussion will cover GenAI’s arrival, capability and its impact. We will also discuss the challenges and opportunities that GenAI presents to industry leaders and practitioners including the defence sector. We'll explore its potential to reshape industries, push creative boundaries, and expand consolidated knowledge -- GenAI has become the cornerstone upon which new platforms, companies, and industries are built.
TL;DR: This tutorial was delivered at KDD 2021. Here we review recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion.
The rise of big data and big compute has brought modern neural networks to many walks of digital life, thanks to the relative ease of construction of large models that scale to the real world. Current successes of Transformers and self-supervised pretraining on massive data have led some to believe that deep neural networks will be able to do almost everything whenever we have data and computational resources. However, this might not be the case. While neural networks are fast to exploit surface statistics, they fail miserably to generalize to novel combinations. Current neural networks do not perform deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. This tutorial reviews recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.
Tutorial delivered at ECML-PKDD 2021.
TL;DR: This tutorial reviews recent developments on drug discovery using machine learning methods.
Powered by neural networks, modern machine learning has enjoyed great successes in data-intensive domains such as computer vision and languages where human can naturally perform well. Machine learning equipped with reasoning is now accelerating fields that traditionally require deep expertise such as physics, chemistry and biomedicine. This tutorial provides an overview of how machine learning and reasoning are speeding up and lowering the cost of drug discovery. This includes how machine learning can help in wide range of areas such as novel molecule identification, protein representation, drug-target binding, drug re-purposing, generative drug design, chemical reaction, retrosynthesis planning, drug-drug interaction, and safety assessment. We will also discuss relevant machine learning models for graph classification, molecular graph transformation, drug generation using deep generative models and reinforcement learning, and chemical reasoning.
The current deep learning revolution has brought unprecedented changes to how we live, learn, interact with the digital and physical worlds, run business and conduct sciences. These are made possible thanks to the relative ease of construction of massive neural networks that are flexible to train and scale up to the real world. But the flexibility is hitting the limits due to excessive demand of labelled data, the narrowness of the tasks, the failure to generalize beyond surface statistics to novel combinations, and the lack of the key mental faculty of deliberate reasoning. In this talk, I will present a multi-year research program to push deep learning to overcome these limitations. We aim to build dynamic neural networks that can train themselves with little labelled data, compress on-the-fly in response to resource constraints, and respond to arbitrary query about a context. The networks are equipped with capability to make use of external knowledge, and operate that the high-level of objects and relations. The long-term goal is to build persistent digital companions that co-live with us and other AI entities, understand our need and intention, and share our human values and norms. They will be capable of having natural conversations, remembering lifelong events, and learning in an open-ended fashion.
Deep Learning has taken the digital world by storm. As a general purpose technology, it is now present in all walks of life. Although the fundamental developments in methodology have been slowing down in the past few years, applications are flourishing with major breakthroughs in Computer Vision, NLP and Biomedical Sciences. The primary successes can be attributed to the availability of large labelled data, powerful GPU servers and programming frameworks, and advances in neural architecture engineering. This combination enables rapid construction of large, efficient neural networks that scale to the real world. But the fundamental questions of unsupervised learning, deep reasoning, and rapid contextual adaptation remain unsolved. We shall call what we currently have Deep Learning 1.0, and the next possible breakthroughs as Deep Learning 2.0.
This is part 2 of the Tutorial delivered at IEEE SSCI 2020, Canberra, December 1st (Virtual).
Deep Learning has taken the digital world by storm. As a general purpose technology, it is now present in all walks of life. Although the fundamental developments in methodology have been slowing down in the past few years, applications are flourishing with major breakthroughs in Computer Vision, NLP and Biomedical Sciences. The primary successes can be attributed to the availability of large labelled data, powerful GPU servers and programming frameworks, and advances in neural architecture engineering. This combination enables rapid construction of large, efficient neural networks that scale to the real world. But the fundamental questions of unsupervised learning, deep reasoning, and rapid contextual adaptation remain unsolved. We shall call what we currently have Deep Learning 1.0, and the next possible breakthroughs as Deep Learning 2.0.
This is part 1 of the Tutorial delivered at IEEE SSCI 2020, Canberra, December 1st (Virtual).
This is the talk given at the Faculty of Information Technology, Monash University on 19/08/2020. It covers our recent research on the topics of learning to reason, including dual-process theory, visual reasoning and neural memories.
A discussion of the nature of AI/ML as an empirical science. Covering concepts in the field, how to position ourselves, how to plan for research, what are empirical methods in AI/ML, and how to build up a theory of AI.
Introducing research works in the area of machine reasoning at our Applied AI Institute, Deakin University, Australia. Covering visual & social reasoning, neural Turing machine and System 2.
Describing latest research in visual reasoning, in particular visual question answering. Covering both images and videos. Dual-process theories approach. Relational memory.
Micro RNA genes and their likely influence in rice (Oryza sativa L.) dynamic ...Open Access Research Paper
Micro RNAs (miRNAs) are small non-coding RNAs molecules having approximately 18-25 nucleotides, they are present in both plants and animals genomes. MiRNAs have diverse spatial expression patterns and regulate various developmental metabolisms, stress responses and other physiological processes. The dynamic gene expression playing major roles in phenotypic differences in organisms are believed to be controlled by miRNAs. Mutations in regions of regulatory factors, such as miRNA genes or transcription factors (TF) necessitated by dynamic environmental factors or pathogen infections, have tremendous effects on structure and expression of genes. The resultant novel gene products presents potential explanations for constant evolving desirable traits that have long been bred using conventional means, biotechnology or genetic engineering. Rice grain quality, yield, disease tolerance, climate-resilience and palatability properties are not exceptional to miRN Asmutations effects. There are new insights courtesy of high-throughput sequencing and improved proteomic techniques that organisms’ complexity and adaptations are highly contributed by miRNAs containing regulatory networks. This article aims to expound on how rice miRNAs could be driving evolution of traits and highlight the latest miRNA research progress. Moreover, the review accentuates miRNAs grey areas to be addressed and gives recommendations for further studies.
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
Diabetes is a rapidly and serious health problem in Pakistan. This chronic condition is associated with serious long-term complications, including higher risk of heart disease and stroke. Aggressive treatment of hypertension and hyperlipideamia can result in a substantial reduction in cardiovascular events in patients with diabetes 1. Consequently pharmacist-led diabetes cardiovascular risk (DCVR) clinics have been established in both primary and secondary care sites in NHS Lothian during the past five years. An audit of the pharmaceutical care delivery at the clinics was conducted in order to evaluate practice and to standardize the pharmacists’ documentation of outcomes. Pharmaceutical care issues (PCI) and patient details were collected both prospectively and retrospectively from three DCVR clinics. The PCI`s were categorized according to a triangularised system consisting of multiple categories. These were ‘checks’, ‘changes’ (‘change in drug therapy process’ and ‘change in drug therapy’), ‘drug therapy problems’ and ‘quality assurance descriptors’ (‘timer perspective’ and ‘degree of change’). A verified medication assessment tool (MAT) for patients with chronic cardiovascular disease was applied to the patients from one of the clinics. The tool was used to quantify PCI`s and pharmacist actions that were centered on implementing or enforcing clinical guideline standards. A database was developed to be used as an assessment tool and to standardize the documentation of achievement of outcomes. Feedback on the audit of the pharmaceutical care delivery and the database was received from the DCVR clinic pharmacist at a focus group meeting.
UNDERSTANDING WHAT GREEN WASHING IS!.pdfJulietMogola
Many companies today use green washing to lure the public into thinking they are conserving the environment but in real sense they are doing more harm. There have been such several cases from very big companies here in Kenya and also globally. This ranges from various sectors from manufacturing and goes to consumer products. Educating people on greenwashing will enable people to make better choices based on their analysis and not on what they see on marketing sites.
Characterization and the Kinetics of drying at the drying oven and with micro...Open Access Research Paper
The objective of this work is to contribute to valorization de Nephelium lappaceum by the characterization of kinetics of drying of seeds of Nephelium lappaceum. The seeds were dehydrated until a constant mass respectively in a drying oven and a microwawe oven. The temperatures and the powers of drying are respectively: 50, 60 and 70°C and 140, 280 and 420 W. The results show that the curves of drying of seeds of Nephelium lappaceum do not present a phase of constant kinetics. The coefficients of diffusion vary between 2.09.10-8 to 2.98. 10-8m-2/s in the interval of 50°C at 70°C and between 4.83×10-07 at 9.04×10-07 m-8/s for the powers going of 140 W with 420 W the relation between Arrhenius and a value of energy of activation of 16.49 kJ. mol-1 expressed the effect of the temperature on effective diffusivity.
Willie Nelson Net Worth: A Journey Through Music, Movies, and Business Venturesgreendigital
Willie Nelson is a name that resonates within the world of music and entertainment. Known for his unique voice, and masterful guitar skills. and an extraordinary career spanning several decades. Nelson has become a legend in the country music scene. But, his influence extends far beyond the realm of music. with ventures in acting, writing, activism, and business. This comprehensive article delves into Willie Nelson net worth. exploring the various facets of his career that have contributed to his large fortune.
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Introduction
Willie Nelson net worth is a testament to his enduring influence and success in many fields. Born on April 29, 1933, in Abbott, Texas. Nelson's journey from a humble beginning to becoming one of the most iconic figures in American music is nothing short of inspirational. His net worth, which estimated to be around $25 million as of 2024. reflects a career that is as diverse as it is prolific.
Early Life and Musical Beginnings
Humble Origins
Willie Hugh Nelson was born during the Great Depression. a time of significant economic hardship in the United States. Raised by his grandparents. Nelson found solace and inspiration in music from an early age. His grandmother taught him to play the guitar. setting the stage for what would become an illustrious career.
First Steps in Music
Nelson's initial foray into the music industry was fraught with challenges. He moved to Nashville, Tennessee, to pursue his dreams, but success did not come . Working as a songwriter, Nelson penned hits for other artists. which helped him gain a foothold in the competitive music scene. His songwriting skills contributed to his early earnings. laying the foundation for his net worth.
Rise to Stardom
Breakthrough Albums
The 1970s marked a turning point in Willie Nelson's career. His albums "Shotgun Willie" (1973), "Red Headed Stranger" (1975). and "Stardust" (1978) received critical acclaim and commercial success. These albums not only solidified his position in the country music genre. but also introduced his music to a broader audience. The success of these albums played a crucial role in boosting Willie Nelson net worth.
Iconic Songs
Willie Nelson net worth is also attributed to his extensive catalog of hit songs. Tracks like "Blue Eyes Crying in the Rain," "On the Road Again," and "Always on My Mind" have become timeless classics. These songs have not only earned Nelson large royalties but have also ensured his continued relevance in the music industry.
Acting and Film Career
Hollywood Ventures
In addition to his music career, Willie Nelson has also made a mark in Hollywood. His distinctive personality and on-screen presence have landed him roles in several films and television shows. Notable appearances include roles in "The Electric Horseman" (1979), "Honeysuckle Rose" (1980), and "Barbarosa" (1982). These acting gigs have added a significant amount to Willie Nelson net worth.
Television Appearances
Nelson's char
Natural farming @ Dr. Siddhartha S. Jena.pptxsidjena70
A brief about organic farming/ Natural farming/ Zero budget natural farming/ Subash Palekar Natural farming which keeps us and environment safe and healthy. Next gen Agricultural practices of chemical free farming.
Alert-driven Community-based Forest monitoring: A case of the Peruvian Amazon
AI for tackling climate change
1. Truyen Tran
A/Professor
Photo credit: Tienphong.vn, 09/07/2019
“Climate change is a driver of
global wildfire trends” (WWF)
CLIMATE CHANGE:
CHALLENGES &
-DRIVEN SOLUTIONS
AI
10. What are the effect?
• large-scale singular events (such as further sea level rise as major ice
sheets melt over Greenland and Antarctica)
• threatening the survival of certain ecosystems
• exacerbating extreme weather events (e.g. heat waves, drought, extreme
rainfall, and coastal flooding)
• altering sea ice concentrations, river flow and coastal erosion
• pushing plant and animal species towards the poles and to higher
elevations
• slowing productivity gains for some crops such as wheat and maize
• severe impacts on the world’s poorest and most vulnerable populations
Source: The Committee on Climate Change, UK
15. What is AI?
11/12/2019
15
Among the most challenging scientific questions of our
time are the corresponding analytic and synthetic
problems:
• How does the brain function?
• Can we design a machine which will simulate a brain?
-- Automata Studies, 1956.
18. Machine learning
(system that improves its performance with more experience)
Supervised learning
(mostly machine)
A B
Unsupervised learning
(mostly human)
Will be quickly solved for “easy”
problems (Andrew Ng)
11/12/2019
18
Anywhere in between: semi-supervised learning,
reinforcement learning, lifelong learning, meta-learning, few-
shot learning, knowledge-based ML
19. ML starts with feature engineering learning
• In typical machine learning projects, 80-90%
effort is on feature engineering
• E.g., flood prediction: history, current weather,
deforestation rate, change in landscape,
construction density, etc.
• A range of powerful classifiers: Random forests,
GBM, SVM, deep neural nets, etc.
• Try yourself on Kaggle.com!
11/12/2019
19
20. Current AI (deep learning): Mimic the brain
11/12/2019
20
andreykurenkov.com
21. DL basic 1: Repeat the trick, horizontally and vertically
Integrate-and-fire neuron
andreykurenkov.com
Feature detector
Block representation
11/12/2019
21
24. DL basic 4: Dual - guess and judge
11/12/2019
24Adapted from Goodfellow’s, NIPS 2014
25. Can you tell which one is real?
11/12/2019
25
Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of gans for improved quality,
stability, and variation. arXiv preprint arXiv:1710.10196.
26. [shutterstock: 567338095, Sarah Holmlund]. Credit: e3zine
What can AI/ML do, as a General Purpose Tech?
● Predict, aka slot filling
● Optimize, aka finding better places
● Uncover hidden factors & clusters
● Detect complex relationships
● Mimic the world
● Suggest actions with long-term rewards
● Reason about the world
● Be aware of its own limitations
30. What can AI/ML do to tackle climate change?
● Make systems more efficient
● Enable remote sensing and automatic monitoring
● Provide fast approximations to time intensive simulations
● Support interpretable or causal models (e.g. for understanding weather
patterns, informing policy makers, and planning for disasters).
AI/ML is only one part of the solution!
● It is a tool that enables other tools across fields
● Its performance improves with more data!
31. Rolnick, David, et al. "Tackling Climate Change with Machine Learning." arXiv
preprint arXiv:1906.05433 (2019).
Experimentation
Control systems
Predictive maintenance
Hybrid physical models
Forecasting
Human interaction
Remote sensing
System optimization
ML-enabled methodologies
Electricity system
Transportation
Building & cities
Industry
Farms & forests
CO2 removal
Climate prediction
Societal impacts
Solar geoengineering
Individual action
Collective decisions
Education
Finance
Actionable areas
32. Rolnick, David, et al. "Tackling Climate Change with Machine Learning." arXiv
preprint arXiv:1906.05433 (2019).
Computer vision
NLP
Causal inference
Interpretable ML
RL & control
Time-series analysis
Transfer learning
Uncertainty quant.
Unsupervised learning
Machine learning areas
Electricity system
Transportation
Building & cities
Industry
Farms & forests
CO2 removal
Climate prediction
Societal impacts
Solar geoengineering
Individual action
Collective decisions
Education
Finance
Actionable areas
34. Transportation
Problems
● Increased CO2 footprint
● Lost of time
● Health issues (physical and mental)
● Lost of productivity
● Increase transportation cost
AI/ML-driven solutions
● Predict traffic congestion, suggest
alternative route
● Optimize fuel consumption
● Detect route/traffic management
maintenance
Vietnam News
35. “Forecasting travel times helps improve
road safety and efficiency. Accurate
predictions help commuters make
informed decisions about when to travel
and on what routes. This helps to lower
intensity on problem arterials by
encouraging motorists to use
underutilised parts of the grid, and
where possible, by having them select
alternative times and modes of travel. ”
36. Smart homes and cities
Rolnick, David, et al. "Tackling Climate Change with Machine Learning." arXiv preprint arXiv:1906.05433 (2019).
37. Farms and forests
● Sensor network, automated sensing and optimization
Rolnick, David, et al. "Tackling Climate Change with Machine Learning." arXiv preprint
arXiv:1906.05433 (2019).
40. Source: mila
Climate prediction
● Predict effects of climate
change
● Extremely fast approximation
alternative to complex
simulation
41. Societal impacts
Rolnick, David, et al. "Tackling Climate Change with Machine Learning." arXiv preprint
arXiv:1906.05433 (2019).
42. Conservation effort
Norouzzadeh, Mohammad Sadegh, et al.
"Automatically identifying, counting, and
describing wild animals in camera-trap images with
deep learning." Proceedings of the National
Academy of Sciences 115.25 (2018): E5716-E5725.
45. Towards green AI
Strubell, Emma, Ananya Ganesh, and Andrew McCallum. "Energy and Policy Considerations for Deep Learning in NLP." arXiv
preprint arXiv:1906.02243 (2019).
46. South Vietnam, 2050
Credit: New York Times
Kulp, Scott A., and Benjamin H. Strauss. "New elevation data triple
estimates of global vulnerability to sea-level rise and coastal
flooding." Nature communications 10.1 (2019): 1-12.
Prediction model: Neural network
• 23 input features
• trained on US LIDAR-derived
elevation data
• Extrapolated over time and
space.
However, it has been criticized for
using inaccurate data for Vietnam.
51. Technology alone is never enough
“Technologies [to help fight
climate change] have largely not
been adopted at scale by society.
While we hope that ML will be
useful in reducing the costs
associated with climate action,
humanity also must decide to
act.”
Rolnick, David, et al. "Tackling Climate Change with Machine Learning." arXiv preprint arXiv:1906.05433 (2019).
52. First thing first: Speak collaborators’ languages
Information system Maths
Health informatics
Symbolic AI
Database
Probabilistic AI
Old machine learning Statistical machine learning
Hard core data mining
Current data mining
Clinical statistics
Theoretical statistics
Bioinformatics Statistical epidemiology
Biomedical engineering
Biostatistics
Deep learning
Molecular biology
Biochemistry
Quantum chemistry
Chemoinformatics Genetic statistics
Domain
CS/ML
Population genetics
(The case of biomedicine)
55. Source: opgal
To sum up
AI is a General-Purpose Technology (GPT)
● Just like electricity
Why AI for climate change?
● Automation, scalability, knowledge and data
integration
● Assisting in decision making
● Rational in an irrational world of politics.
● AI should be a green exemplar
Can AI fail?
● Yes. We are still learning.
● It is subject to misuse.
● It can be wrongly aligned with human values.