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) …
This is a part of a series of blogs where I'll be demonstrating different aspects and the theory of Machine Learning Algorithms by using math and code. This includes the usual modeling structure of the algorithm and the intuition on why and how it works, using Python code. Logistic Regression is one of the first algorithms that is introduced when someone learns about classification. You probably would have read about Regression and the continuous nature of the predictor variable. Classification is done on discrete variables, which means your predictions are finite and class-based like a Yes/No, True/False for binary outcomes.
Converting dates to numbers is important because while time is essential for a model's consideration, it cannot handle datetime objects. Instead, time can be represented as an integer. The majority of a data science project comprises of data cleaning and manipulation. Images created by author unless explicitly stated otherwise. Missing values often plague data, and given that there are not too many of them, they can be imputed (filled in).
Gartner, Inc. identified the top 10 data and analytics (D&A) technology trends for 2020 that can help data and analytics leaders navigate their COVID-19 response and recovery and prepare for a post-pandemic reset. "To innovate their way beyond a post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts," said Rita Sallam, distinguished research vice president at Gartner. AIBy the end of 2024, 75% of organizations will shift from piloting to operationalizing artificial intelligence (AI), driving a 5 times increase in streaming data and analytics infrastructures. Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures.Other smarter AI techniques such as reinforcement learning and distributed learning are creating more adaptable and flexible systems to handle complex business situations; for example, agent-based systems that model and simulate complex systems. Dynamic data stories with more automated and consumerized experiences will replace visual, point-and-click authoring and exploration. As a result, the amount of time users spend using predefined dashboards will decline.
Almost most of the major automakers are developing autonomous cars of some kind. Some, like Tesla's Autopilot and Google's Waymo, already are in use, though they're maybe not fully autonomous yet. Tesla and Waymo, like so many other automakers in the autonomous car race, remain ironing out the kinks. In the meantime, one of the biggest debates surrounding driverless cars is how they'll impact the insurance industry. If human error causes virtually all car accidents, then in theory, self-driving cars would be the solution.
Experts from MIT and IBM held a webinar this week to discuss where AI technologies are today and advances that will help make their usage more practical and widespread. Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and IBM's director of the Watson AI Lab said during a webinar this week. Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like "why" and "how" versus "what," the panel said. The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said. The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning.
A team of scientists has developed a technique that automatically makes written sentences more polite. Why it matters: As the authors themselves note in the paper, it is "imperative to use the appropriate level of politeness for smooth communication in conversations." And what better to determine the appropriate level of politeness than an unfeeling machine-learning algorithm? What's new: In a paper presented this week at the annual meeting of the Association for Computational Linguistics, researchers from Carnegie Mellon University analyzed a dataset of 1.39 million sentences, each of which was labeled with a politeness score. Of note: The researchers used the "Enron Corpus" as a dataset -- hundreds of thousands of emails exchanged by Enron employees and preserved by the federal government during its investigation of the now-defunct energy firm.
The human brain has advanced over time in countering survival instincts, harnessing intellectual curiosity, and managing authoritative ordinances of nature. When humans got an idea about the dynamics of the environment, we started with our quest to replicate nature. While the human brain discovers ways to go beyond our physical capabilities, the combination of mathematics, algorithms, computational methods, and statistical models accumulated momentum after Alan Mathison Turing built a mathematical model for biological morphogenesis, and published a seminal paper on computing intelligence. Today, AI has developed from data models for problem-solving to artificial neural networks, a computational model predicated on the structure and functions of human biological neural networks. The brain, customarily perceived as an organ of the human body, should be understood as a biologically predicated form of artificial intelligence (AI).
Digital generated image of data. Lemonade is one of this year's hottest IPOs and a key reason for this is the company's heavy investments in AI (Artificial Intelligence). The company has used this technology to develop bots to handle the purchase of policies and the managing of claims. Then how does a company like this create AI models? Well, as should be no surprise, it is complex and susceptible to failure.
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'The machines are taking over' has become a long drawn concept and a proverb now. To cite some examples following the herd would be Amazon, DHL, CIG, Siemens, Uber, and Tesla. This doesn't come off as a surprise when it has already been established that automation has fruitful results in the long term. Canon India seems to walk the same path, and have really robust plans for the future, relying on automation. Express Computer's Gairika Mitra gets into an invigorating chat with K Bhaskhar, Senior Vice President, Business Imaging Solutions (BIS), Canon India, to gain further clarity on this.