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) …
A smart city is a municipality that uses information and communication technologies (ICT) to increase operational efficiency, share information with the public and improve both the quality of government services and citizen welfare. While the exact definition varies, the overarching mission of a smart city is to optimize city functions and drive economic growth while improving quality of life for its citizens using smart technology and data analysis. Value is given to the smart city based on what they choose to do with the technology, not just how much technology they may have. Several major characteristics are used to determine a city's smartness. A smart city's success depends on its ability to form a strong relationship between the government -- including its bureaucracy and regulations -- and the private sector.
Introduced in 2019, by IBM, Brazil has launched the largest research facility, that focuses on artificial intelligence, through a collaboration between the private and public sector. The Artificial Intelligence Center (C4AI) is supported by investments made by IBM along with the São Paulo Research Foundation (FAPESP) and the University of São Paulo (USP). This AI centre -- C4AI has been established to tackle five significant challenges that are related to health, the environment, the food production chain, the future of work and the development of NLP technologies in Portuguese. Along with this, it will also aid in projects relating to human wellbeing improvement as well as initiatives focused on diversity and inclusion. The total investment in the AI centre will reach $20 million over the next ten years, which will be split among the investors. The USP will contribute $1 million to cover costs related to the physical set-up of the space, as well as over 70 lecturers and staff to run the centre.
It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version. Tensorflow is Google's library for deep learning and artificial intelligence. Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow.
Artificial intelligence, the latest facet of information technology, has gained increasing momentum and been widely applied in various sectors with tremendous potential, thus becoming a driving force of scientific and technological development during China's 13th Five-Year Plan (2016-20) period. It has also injected new impetus into the digital economy and played a key role in bolstering high-quality development and accelerating the nation's push for industrial upgrading, experts said. According to the 13th Five-Year Plan, the country called for developing AI, with a focus on fostering the industrial ecology of AI and promoting the integration and application of AI into key industries and fields. In July 2017, the State Council, China's Cabinet, issued a plan that set benchmarks for the country's AI sector, with the value of core AI industries predicted to exceed 1 trillion yuan ($150 billion) and making the country one of the global leaders in AI innovation by 2030. China has made tremendous strides in AI over the past five years as it has outpaced the United States in the number of worldwide AI-related patent applications, said a report from a Ministry of Industry and Information Technology research unit. The report also pointed out that AI is considered an important direction for industrial upgrading, and the country's strategic plan for AI offers a broad space for the research and development of AI technologies and related industries.
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?
Imagine that before you could make dinner, you first had to rebuild the kitchen, specifically designed for each recipe. You'd spend way more time on preparation, than actually cooking. For computational biologists, it's been a similar time-consuming process for analyzing genomics data. Before they can even begin their analysis, they spend a lot of valuable time formatting and preparing huge data sets to feed into deep learning models. To streamline this process, researchers from the Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC) developed a universal programming tool that converts a wide variety of genomics data into the required format for analysis by deep learning models.
Named Entity Recognition is one of the most important and widely used NLP tasks. It's the method of extracting entities (key information) from a stack of unstructured or semi-structured data. An entity can be any word or series of words that consistently refers to the same thing. Every detected entity is classified into a predetermined category. For example, a NER model might detect the word "India" in a text and classify it as a "Country".
We also especially encourage students from underrepresented minorities to participate. Hands-on programming labs are a core part of our curriculum, so having some programming knowledge (specifically Python) will help participants get more out of the workshop. However, programming knowledge is not required; the workshop will include a track for participants who are completely new to programming. Experience with typical undergraduate math (calculus, linear algebra) and statistics (intro probability) is also helpful, but not required. The workshop will be run on Eastern Time, though students from outside this timezone are welcome to apply.
There is an interminable interest in artificial intelligence (AI). According to the AI Index 2019 Annual Report published by the University of Stanford, the volume of peer-reviewed AI papers has grown by more than 300% between 1998 and 2018. In over 3,600 global news articles on ethics and AI identified by the Human-Centered AI Institute at Stanford between mid-2018 and mid-2019, topics such as possible frameworks and guidelines on the ethical use of AI, use of face recognition applications, data privacy, the role of big tech, and algorithm bias dominated. This highlights the importance of understanding how bias can slip into data sets and raise awareness when working towards mitigating bias. AI strikes humanity where it hurts most: It uncovers how preconceived notions affect the outcome of well-intentioned applications.