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) …
In this blog post, I will explain how machine learning fits into the broader landscape of data and computer science. This means understanding how machine learning interrelates with parent fields and sister disciplines. This is important, as these are the terms you will see time and again when searching for relevant study materials and hear mentioned ad nauseam in machine learning books. Relevant disciplines can also be difficult and confusing to tell apart at first glance, such as'machine learning' and'data mining.' The lineage of machine learning can be understood by first examining its forefathers.
While the headline seems a lot intriguing, it's certainly interesting to note about how would things function if Robotic Process Automation (RPA) would be playing a pivotal role in hyper automation in the post COVID era. Hyperautomation refers to the use of a combination of technologies to automate, simplify, discover, design, measure, and manage workflows and processes across the enterprise. Although, times have been quite difficult in the current scenario, it has been noticed that leaders who have been slow in adopting automation technologies -- such as Robotic Process Automation ( RPA), Artificial Intelligence ( AI), and Machine Learning (ML)--have started to leverage them as a way of cutting costs during economic turmoil, providing faster customer service, and revamping their distributed work operations. Under all such circumstances, Robotics is playing a key role which is paving the way for a brighter world in the upcoming days. These days hospitals are rapidly deploying new technologies to support their staff better, and they also have been facilitating a lot of changes that concern automation.
LONDON (Reuters) - When banks were flooded with loan requests from businesses struggling with the fallout of the coronavirus pandemic, hastily built robots helped several lenders cope with the deluge. The bots were one of many quick technology changes deployed across the industry during the crisis, a contrast to the slow progress it's made in the past two decades to improve technology in the face of increasing competition from fintech rivals. Now the jolt from the COVID-19 pandemic has accelerated the process even though banks globally are having to cut IT spending this year for the first time since 2009, based on data from research company IDC. "Bots allowed us to process a much higher volume of applications than we would have been able to do before. It meant the timelines didn't get longer with the massive volume," said Simon McNamara, chief administrative officer at Britain's NatWest, which has granted more than 13 billion pounds ($16.90 billion) of state-backed loans.
The ELI5 definition for Reinforcement Learning would be training a model to perform better by iteratively learning from its previous mistakes. Reinforcement learning provides a framework for agents to solve problems in case of real-world scenarios. They are able to learn rules (or policies) to solve specific problems, but one of the major limitations of these agents are that they are unable to generalize the learned policy to newer problems. A previously learned rule would cater to a specific problem only, and would often be useless for other (even similar) cases. A good meta-learning model on the other hand, is expected to generalize to new tasks or environments that have not been encountered by the model in training.
Like them, loathe them, fear them, but robots are bedding down in the legal profession. The number of patents filed globally with the World Intellectual Property Organisation for "lawtech" leapt by 34 per cent last year to a record high of 1,369, figures from a report published this week reveal. The figures also show that the number of patents filed has increased by nearly 65 per cent over the past two years, up from 831 in 2017. According to researchers at Thomson Reuters, which has exclusively shown the results with The Times, the increase in global patents filed "demonstrates how quickly the legal profession has turned to technology to revolutionise how it operates".
In the previous article here, we have gone through the different methods to deal with imbalanced data. In this article, let us try to understand how to use imbalanced-learn library to deal with imbalanced class problems. We will make use of Pycaret library and UCI's default of credit card client dataset which is also in-built into PyCaret. Imbalanced-learn is a python package that provides a number of re-sampling techniques to deal with class imbalance problems commonly encountered in classification tasks. Note that imbalanced-learn is compatible with scikit-learn and is also part of scikit-learn-contrib projects.
People used to associate the term "artificial intelligence" with images of science fiction without even thinking that one day it could come to life. The concept of AI has been elevated from the realm of sci-fi to reality. There are many ways AI is used behind the scenes in everyday life that we don't even realize. These solutions are sometimes inaccessible, and they still need extensive training and expertise to become commonplace. As an entrepreneurial executive with more than 20 years of work experience in the software development and technology industry, and as the founder and CEO of one of the leading AI companies in Armenia, which builds deep tech innovations based on artificial intelligence and machine learning, this is a topic I deal with on a daily basis.
Customer support is an integral part of every business; without offering support services, it is difficult to achieve maximum customer satisfaction. To ensure the same, businesses hire professionals who work round the clock to deliver support services. No matter how efficiently a business handles this segment, they might have to face problems such as "delay in responding customers' queries" or "making a customer wait to connect with the support professionals", and more. A Conversational AI is a perfect solution to this most common challenge that manufacturing, FMCG, retail, e-commerce, and other industries are facing. Never heard of this term?
Gartner has revealed in a recent survey that 60% of Australia and New Zealand CIOs have no interest in blockchain technology. Instead, the 2021 Gartner CIO Agenda survey revealed business intelligence and data analytics is on top of the list when it comes to technology priorities for Australian and New Zealand CIOs next year. This was followed by artificial intelligence and machine learning and investments for the digital workplace. For two-thirds of the same group of CIOs, they expect investment in technology will increase in 2021, as budgets are expected to grow 1.9% on average. Gartner added the same survey indicated that 60% of ANZ respondents believe the CIO and CEO relationship strengthened during the COVID-19 pandemic, with 70% reporting there was increased engagement with their CEO, especially ad hoc, informal interactions during the crisis. At the same time, Gartner has forecast enterprise IT spending across all sectors in Australia will grow 3.6% and reach a total value of around AU$96 billion in 2021.