If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Do you remember what Steve Jobs said about'making a dent in the universe?' Well, the way Artificial Intelligence and Big Data are improving lives it seems it would be much easier to do so with these technologies. Be it fraud prevention, automation, security, banking, and now forecasting climate change, AI and data-driven technologies are making rapid progress. Take the finance sector, for instance, AI has been serving it for years by automating and streamlining the customer experience. Additionally, AI-driven identity verification systems are detecting fraud, eliminating fraudsters, and helping banks through automation.
We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We offer a multi-resolution multi-task (MRGP) framework that allows for both inter-task and intra-task multi-resolution and multi-fidelity. We develop shallow Gaussian Process (GP) mixtures that approximate the difficult to estimate joint likelihood with a composite one and deep GP constructions that naturally handle biases. In doing so, we generalize existing approaches and offer information-theoretic corrections and efficient variational approximations. We demonstrate the competitiveness of MRGPs on synthetic settings and on the challenging problem of hyper-local estimation of air pollution levels across London from multiple sensing modalities operating at disparate spatio-temporal resolutions.
The use of artificial intelligence (AI) and machine learning to drive innovation across all industries has increased significantly in recent years. Indeed, the proliferation of data science applications from genome sequencing for better disease diagnosis and prevention, to advances in leading edge engineering for autonomous driving, and climate modelling to combat Climate Change, has led to an exponential demand for High Performance Computing (HPC). AI for sustainability is one of the most promising new fields of study, with a recent report by PwC and Microsoft reporting that using AI for environmental applications in four key sectors could reduce global greenhouse gas emissions by 4% in just 10 years' time. Recent efforts include international non-profit organisation, Global Fishing Watch, using AI and satellite data to prevent overfishing, and wind companies using AI to get each turbine's propeller to produce more electricity per rotation by incorporating real time weather and operational data. But alongside worries about AI bias or human jobs being replaced by machines, concerns about the environmental impact of AI itself should be at the fore.
Over the 20 years to 2017, the network of actors spreading scientific misinformation about climate change has been increasingly integrated into US political philanthropy. That's according to a study that used natural language processing to analyse connections between the two fields. "The study introduces a new and broader pathway through which climate change misinformation travels, beyond the tendency of research to narrowly focus on the activities of think-tanks and fossil-fuel interests, often in isolation from mainstream American institutions like philanthropy," writes Justin Farrell of Yale University, US, in Environmental Research Letters (ERL). "Yet, as this study also shows, the impact of funding from fossil-fuel sources still plays an important role, revealing that the strength of the relationship between the misinformation network and philanthropy is strongest for people and organizations directly tied to such funding." Farrell employed novel machine learning capabilities to recognise and classify repeating themes and links in lists of attendees and speakers at philanthropic meetings, millions of words of written materials, and lists of board members and lifetime achievement award winners.
Smart technology, artificial intelligence and machine learning are grabbing headlines every day, and those familiar buzzwords are now inescapable. Algorithms are being enhanced and scientists are coming up with new ways to train and teach these models. According to VentureBeat, machine learning is also shaping business and society. The publication spoke to five leading artificial intelligence experts for their input on what we'd see happen in machine learning in the new year. PyTorch creator Soumith Chintala, University of California professor Celeste Kidd, Google AI chief Jeff Dean, Nvidia director of machine learning research Anima Anandkumar, and IBM Research director Dario Gil said great strides were made in several fields in 2019, like natural language-based models and reinforcement learning, but the five AI experts were essentially unanimous in predicting an even more exciting 2020.
Climate change is the most existential threat humanity faces today, and AI can solve problems in unprecedented ways. It only makes sense that we use the latter to work on the former. That was a significant sub-theme of NeurIPS 2019, highlighted in particular by the Tackling Climate Change workshop, and talk from some prominent leaders in machine learning suggests that the ML field can and should focus on it. There are two thrusts: One is about urging machine learning practitioners to use their research to work on solving climate change problems. The other is about ensuring that performing the research itself doesn't ironically contribute to climate change.
Nearly half of the tasks currently undertaken by humans could already be automated, even at current levels of technology. Within the next decade it is likely large sections of society will be looking for new jobs. People are calling it the fourth industrial revolution or "industry 4.0". The first industrial revolution used steam power to mechanise production. The second used electric power to mass produce products while the third introduced computers to automate production.
Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this into account, we developed Smell Pittsburgh, a system that enables community members to report odors and track where these odors are frequently concentrated. All smell report data are publicly accessible online. These reports are also sent to the local health department and visualized on a map along with air quality data from monitoring stations. This visualization provides a comprehensive overview of the local pollution landscape. Additionally, with these reports and air quality data, we developed a model to predict upcoming smell events and send push notifications to inform communities. We also applied regression analysis to identify statistically significant effects of push notifications on user engagement. Our evaluation of this system demonstrates that engaging residents in documenting their experiences with pollution odors can help identify local air pollution patterns, and can empower communities to advocate for better air quality. All citizen-contributed smell data are publicly accessible and can be downloaded from https://smellpgh.org.
Throughout the last 650,000 years, there have been seven ice ages. The most recent, around 7,000 years ago, marked the beginning of the modern climate era and human civilization. This current shift in the earth's environment is significant, as it's more than 95 percent likely induced by human activity, such as the burning of fossil fuels. The gases released by these resources trap heat in the atmosphere and cause the global temperature to rise. Not only will this increase lead to more intense heat waves, but it will destroy ice caps and warm ocean waters, affecting natural habitats.
Automated decision-making is becoming increasingly accepted. Machine learning now allows management to make decisions about workers at a more granular level than ever before, based on comprehensive information preselected by algorithms. Given the cutting edge nature of the technologies used, it is important to look at the occupational safety and health issues arising as well as benefits posed for workers today. The term'artificial intelligence' (AI) came into being in the 1950s, at an academic conference where scientists set out to make a machine behave in ways that would seem intelligent if a human'were so behaving'. 'Intelligence' at this time was linked to the use of language, formation of concepts, and the ability to improve oneself, as well as to solve problems originally'reserved for humans' (McCarthy, J., Minsky, M. L., Rochester, N., Shannon, C. E., 1955, 'A proposal for the Dartmouth Summer Research Project on Artificial Intelligence').