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) …
Based on our interactions and the results of this study, we expect to see organizations not only adopt AI--but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.
Artificial intelligence (AI) relies on big data and machine learning for myriad applications, from autonomous vehicles to algorithmic trading and from clinical decision support systems to data mining. The availability of large amounts of data is essential to the development of AI. But the scandal over the use of personal and social data by Facebook and Cambridge Analytica has brought ethical considerations to the fore - and it's just the beginning. As AI applications require ever greater amounts of data to help machines learn and perform tasks hitherto reserved for humans, companies are facing increasing public scrutiny, at least in some parts of the world. Tesla and Uber have scaled down their efforts to develop autonomous vehicles in the wake of widely reported accidents.
Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for real-world, reproducible active learning research. This paper details the challenges of building the system and demonstrates its capabilities with several experiments.
You don't need to be a rocket scientist to understand AI and its impact, but you should develop foundational knowledge about AI in order to help you vet new opportunities. To get started, check out this glossary to find simple definitions of common AI terms. When it's time to start thinking about AI-based projects, you can use use-cases to see what's already been done with the technology. To find the right use cases, you'll need to evaluate all the tasks you do in a day, then assess which ones are valuable to automate with AI. There are plenty of AI technologies you can start getting your hands dirty with, starting today, and experimentation is one of the best ways to develop your knowledge.
In November, the musician Grimes made a bold prediction. "I feel like we're in the end of art, human art," she said on Sean Carroll's Mindscape podcast. "Once there's actually AGI (Artificial General Intelligence), they're gonna be so much better at making art than us." Her comments sparked a meltdown on social media. The musician Zola Jesus called Grimes the "voice of silicon fascist privilege."
Learning Management Systems (LMS) and Educational Data Mining (EDM) are two important parts of online educational environment with the former being a centralised web-based information systems where the learning content is managed and learning activities are organised (Stone and Zheng,2014) and latter focusing on using data mining techniques for the analysis of data so generated. As part of this work, we present a literature review of three major tasks of EDM (See section 2), by identifying shortcomings and existing open problems, and a Blumenfield chart (See section 3). The consolidated set of papers and resources so used are released in https://github.com/manikandan-ravikiran/cs6460-Survey. The coverage statistics and review matrix of the survey are as shown in Figure 1 & Table 1 respectively. Acronym expansions are added in the Appendix Section 4.1.
Deep learning offers the promise of bypassing the procedure of manual feature engineering by learning representations in conjunction with statistical models in an end-to-end fashion. In any case, neural network architectures themselves are ordinarily designed by specialists in a painstaking, ad hoc fashion. Neural architecture search (NAS) has been touted as the way ahead for lightening this agony via automatically identifying architectures that are better than hand-planned ones. Machine learning has given some huge achievements in diverse fields as of late. Areas like financial services, healthcare, retail, transportation, and more have been utilizing machine learning frameworks somehow, and the outcomes have been promising.
Uber has been one of the most active companies trying to accelerate the implementation of real world machine learning solutions. Just this year, Uber has introduced technologies like Michelangelo, Pyro.ai and Horovod that focus on key building blocks of machine learning solutions in the real world. This week, Uber introduced another piece of its machine learning stack, this time aiming to short the cycle from experimentation to product. PyML, is a library to enable the rapid development of Python applications in a way that is compatible with their production runtime. The problem PyML attempts to address is one of those omnipresent challenges in large scale machine learning applications.
While most executives at financial institutions agree that artificial intelligence (AI) is important to their organization's success, few have fully implemented AI projects. In a recent Cognizant survey of 230 financial services executives, three-quarters said AI is extremely or very important to the success of their organizations. However, only 61% of those were aware of an AI project at their company. Even more telling, only 29% were aware of a project that had been fully implemented. Clearly, AI is quickly becoming a competitive requirement, creating the risk that those who are not implementing or updating AI capabilities will fall behind.
Deploying machine-, and in particular deep-learning, (ML/DL) solutions in industry-strength, production quality contexts proves to challenging. This requires a structured engineering approach to constructing and evolving systems that contain ML/DL components. In this paper, we provide a conceptualization of the typical evolution patterns that companies experience when employing ML/DL well as a framework for integrating ML/DL components in systems consisting of multiple types of components. In addition, we provide an overview of the engineering challenges surrounding AI/ML/DL solutions and, based on that, we provide a research agenda and overview of open items that need to be addressed by the research community at large.