"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Human intelligence has been creating and maintaining complex systems since the beginnings of civilizations. In modern times, digital twins have emerged to aid operations of complex systems, as well as improve design and production. Artificial intelligence (AI) and extended reality (XR) – including augmented reality (AR) and virtual reality (VR) – have emerged as tools that can help manage operations for complex systems. Digital twins can be enhanced with AI and emerging user interface (UI) technologies like XR can improve people's abilities to manage complex systems via digital twins. Digital twins can marry human and AI to produce something far greater by creating a usable representation of complex systems. End users do not need to worry about the formulas that go into machine learning (ML), predictive modeling and artificially intelligent systems, but also can capitalize on their power as an extension of their own knowledge and abilities. Digital twins combined with AR, VR and related technologies provide a framework to overlay intelligent decision making into day-to-day operations, as shown in Figure 1. Figure 1: A digital twin can be enhanced with artificial intelligence (AI) and intelligent realities user interfaces, such as extended reality (XR), which includes augmented reality (AR) and virtual reality (VR). The operations of a physical twin can be digitized by sensors, cameras and other such devices, but those digital streams are not the only sources of data that can feed the digital twin. In addition to streaming data, accumulated historical data can inform a digital twin. Relevant data could include data not generated from the asset itself, such as weather and business cycle data. Also, computer-aided design (CAD) drawings and other documentation can help the digital twin provide context.
Since the last decade or so, the developments in information technology have been propelled by advancements in areas of Artificial intelligence and Machine learning. Recently, there is a healthy debate going on regarding potential advantages and disadvantages of same between two powerhouses -- Elon Musk of Tesla and Mark Zuckerberg. While the media is jumping on the bandwagon, it is important to understand some basic concepts of AI, ML and Deep Learning to get a better sense of What they do and How they can be useful. Refer to the picture below to get a better sense of co-relation between AI, ML and Deep Learning and how do Artificial Neural Networks work. How does Deep Learning work?
We as human may have often wondered if the intelligence of human can be copied and machines can work the same way as us. While it is still a distant dream but we are not very far away. In the path to artificial intelligence lets have an overview of what it really means and how data science is helping us achieve it. A) Artificial Intelligence: It is an important science that actually helps in daily activities nowadays. The end goal of any machine learning or deep learning algorithm is achieving artificial intelligence.
WASHINGTON (Reuters) - The White House Office of Management and Budget (OMB) said Friday that federal agencies will use artificial intelligence to eliminate outdated, obsolete, and inconsistent requirements across tens of thousands of pages of government regulations. A 2019 pilot project used machine learning algorithms and natural language processing at the Department of Health and Human Services. The test run found hundreds of technical errors and outdated requirements in agency rulebooks, including requests to submit materials by fax. OMB said all federal agencies are being encouraged to update regulations using AI and several agencies have already agreed to do so. Over the last four years, the number of pages in the Code of Federal Regulations has remained at about 185,000.
OpenAI recently launched Jukebox, a model that generates music with singing in the raw audio domain. As a generative model for music, Jukebox can handle the long context of raw audio using an autoencoder. Jukebox's autoencoder processes the audio files using a multiscale VQ-VAE to compress it to discrete codes and modeling those using autoregressive Transformers. Provided with a genre, artist, and lyrics as input, Jukebox can output a new music sample produced from scratch. This is a type of innovation that expands the boundaries of generative models to a new level.
This is a big question, and I'm not a particularly big person. As such, these are all likely to be obvious observations to someone deep in the literature and theory. What I find however is that there are a base of unspoken intuitions that underlie expert understanding of a field, that are never directly stated in the literature, because they can't be easily proved with the rigor that the literature demands. And as a result, the insights exist only in conversation and subtext, which make them inaccessible to the casual reader. Because I have no need of rigor to post on the internet, (or even a need to be correct) I'm going to post some of those intuitions here as I understand them.
Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.
In research published today in Patterns, a team of engineers led by Wang demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT. Wenxiang Cong, a research scientist at Rensselaer, is first author on this paper. Wang and Cong were also joined by coauthors from Shanghai First-Imaging Tech, and researchers from GE Research. "We hope that this technique will help extract more information from a regular single-spectrum X-ray CT scan, make it more quantitative, and improve diagnosis," said Wang, who is also the director of the Biomedical Imaging Center within the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer. Conventional CT scans produce images that show the shape of tissues within the body, but they don't give doctors sufficient information about the composition of those tissues.