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) …
Simply put, Artificial Intelligence (AI) is the level of intelligence exhibited by machines, in comparison to natural intelligence exhibited by human beings and animals. Therefore it is sometimes referred to as Machine Intelligence. When taught, a machine can effectively perceive its environment and take certain actions to better its chances of achieving set goals successfully. How can a machine be taught? The root of Machine learning involves writing codes or commands using a programming language that the machine understands.
Many times we listen to speak about machine learning, but it is important to know that there are other pipelines before machine learning which play a significant role in the study of Big Data. Some examples are ETL (extract, transform and load) or NLP(natural language processing). Nowadays, in particular, NLP pipeline is taking more and more space in Data Science. So, what is Natural Language Processing? Natural Language Processing is a process that permits Data Scientist or Data Analyst to extract important information from human language. For example, with NLP it is possible to find an important pattern by studying texts in posts or comments available on a social network.
Times have changed and caught most of us unprepared. It is always a part of Bolt's culture to move quickly and adapt -- and the crisis situation that is unfolding due to a pandemic definitely requires significant adaptation. This is a look from inside Bolt's data team -- data analysts, data engineers, data scientists -- as we share our experience and advice for times of crisis with all the similar teams out there. Most of the resources are thrown into surviving and, for some, even on seizing new opportunities. Data teams definitely have a role to play in this.
The first time I met Adam Rauh was earlier this year at the 2020 National Retail Federation (NRF) in New York. Adam is a lead solutions engineer at Tableau, a Salesforce company, and was at NRF with the purpose of demonstrating some of the most advanced analytics use cases in the retail industry. Rauh and I did a quick video demonstration of retailers can use analytics and machine learning to improve revenue, profitability, and customer experience. Our video had nearly 10K views at the conference. When data is visualized, everyone can take action.@tableau
The value of scientific digital-image libraries seldom lies in the pixels of images. For large collections of images, such as those resulting from astronomy sky surveys, the typical useful product is an online database cataloging entries of interest. We focus on the automation of the cataloging effort of a major sky survey and the availability of digital libraries in general. The SKICAT system automates the reduction and analysis of the three terabytes worth of images, expected to contain on the order of 2 billion sky objects. For the primary scientific analysis of these data, it is necessary to detect, measure, and classify every sky object.
Foodborne illness afflicts 48 million people annually in the U.S. alone. Over 128,000 are hospitalized and 3,000 die from the infection. While preventable with proper food safety practices, the traditional restaurant inspection process has limited impact given the predictability and low frequency of inspections, and the dynamic nature of the kitchen environment. Despite this reality, the inspection process has remained largely unchanged for decades. CDC has even identified food safety as one of seven "winnable battles"; however, progress to date has been limited.
When you think of the words "data" and "mine", no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining -- that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction. Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need.
AI has become the need of the hour and all the industries are now integrating analytics and AI to drive the decision-making process. Bhagirath Kumar Lader, who is the Chief Manager (Business Information System) at GAIL led us through a session briefing Artificial Intelligence essentials for business leaders in today's age. Lader is one of the key members of the digital transformation team at GAIL and carries huge knowledge about how AI, ML and DL are crucial to businesses. He gave us a quick overview of the motivation for AI, AI essentials, AI hype vs reality while taking us through use cases. While AI is a crucial part of businesses, one of the key drivers of its implementation is its ability to make the decision which is usually considered the forte of humans.
Singapore has kicked off efforts to develop a framework to ensure the "responsible" adoption of artificial intelligence (AI) and data analytics in credit risk scoring and customer marketing. Two teams comprising banks and industry players have been tasked to establish metrics that can assist financial institutions in ensuring the "fairness" of their AI and data analytics tools in these instances. The Monetary Authority of Singapore (MAS) said a whitepaper detailing the metrics would be published by year-end along with an open source code to enable financial institutions to adopt the metrics. These organisations then would be able to integrate the open source code into their own IT systems to assess the fairness of their AI applications, the industry regulator said in a statement Friday. It added that the open source code would be deployed on the online global marketplace and sandbox, API Exchange (APIX), which enabled fintech and FSI companies to integrate and test applications via a cloud-based platform.
Science's COVID-19 coverage is supported by the Pulitzer Center. Timothy Sheahan, a virologist studying COVID-19, wishes he could keep pace with the growing torrent of new scientific papers related to the pandemic. But there have just been too many--more than 5000 papers a week. "I'm not keeping up," says Sheahan, who works at the University of North Carolina, Chapel Hill. A loose-knit army of data scientists and software developers is pressing hard to change that.