Inefficiency in management of a business is one of the top reason for business failures. Efficient management in the business organization is one of the key reasons for success of the organization. While it is easier for large scale organizations to have multiple teams overlooking various departments, multiple levels of audit committees, the situation is quite different for Small and Medium Scale Organizations. Having a dedicated team for every department, allotting tasks through software, monitoring tasks automatically and keeping the employees motivated and automated incentivising based on performances is just a dream for startups and small-scale organizations. "OBIZCOIN creates Smart Process Automation BOT Based on Artificial Intelligence & Blockchain Technology to automate business operations" OBIZCOIN Bot will help organizations in designing the procedures and policies framework based on which the entire organization will function.
The first pieces of the brain's "inner GPS" started coming to light in 1970. In the laboratories of University College London, John O'Keefe and his student Jonathan Dostrovsky recorded the electrical activity of neurons in the hippocampus of freely moving rats. They found a group of neurons that increased their activity only when a rat found itself in a particular location.1 They called them "place cells." Building on these early findings, O'Keefe and his colleague Lynn Nadel proposed that the hippocampus contains an invariant representation of space that does not depend on mood or desire.
It is not an easy task to get into Machine Learning and AI. Given the enormous amount of resources that are available today, many aspiring professionals and enthusiasts find it hard to establish a proper path into the field. The field is evolving at a constant pace and it is crucial that we keep up with this rapid development. In order to cope with the speed of evolution and innovation that is today so overwhelming, a good way to stay updated and knowledgeable on the advances that have taken place in ML is to engage with the community by contributing to the many open-source projects and tools that are used daily by advanced professionals. Today, we discuss top 10 open-source projects on Python, Machine Learning and AI.
Today the data science community is still lacking good practices for organizing their projects and effectively collaborating. ML algorithms and methods are no longer simple "tribal knowledge" but are still difficult to implement, manage and reuse. To address the reproducibility we have build Data Version Control or DVC. This example shows you how to solve a text classification problem using the DVC tool. Git branches should beautifully reflect the non-linear structure common to the ML process, where each hypotheses can be presented as a Git branch. However, inability to store data in a repository and the discrepancy between code and data make it extremely difficult to manage a data science project with Git.
Right now our smartphone notifications are pretty "dumb" in the sense that as soon as something pops up, we are notified straight away, whether it be an email, message, a tag/mention on Facebook, a sales event, and so on. The onus is mostly on the user to manage their notifications and to choose what they want or don't want to see. However what if our phones could become smart enough to know what we usually respond to? That's something that two developers over in Taiwan are trying to do, where through the use of machine learning, our phones could start to learn our habits to determine what notifications it should display and which shouldn't. The developers, TonTon Hsien-de Huang and Hung-Yu Kao have dubbed this'Clicksequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation' or C-3PO, which like we said is an algorithm that learns what users respond to and which they don't.
In 2016, DeepMind's AlphaGo famously defeated Lee Sedol, an international Go champion, becoming the first computer program to beat a human world champion. In 2018, LawGeex, an AI contract review platform, pulled the same stunt on human lawyers. The AI system achieved a 94 percent accuracy rate at surfacing risks in non-disclosure agreements (NDAs). Experienced human lawyers average out at 85 percent accuracy for the same task. The study, conducted in collaboration with Duke and Stanford Law Schools, pitted AI against 20 top U.S.-trained lawyers with decades of experience specifically in reviewing NDAs, one of the most common agreements in law.
Have you ever wondered how to add speech recognition to your Python project? If so, then keep reading! It's easier than you might think. Far from a being a fad, the overwhelming success of speech-enabled products like Amazon Alexa has proven that some degree of speech support will be an essential aspect of household tech for the foreseeable future. If you think about it, the reasons why are pretty obvious. Incorporating speech recognition into your Python application offers a level of interactivity and accessibility that few technologies can match. The accessibility improvements alone are worth considering. Speech recognition allows the elderly and the physically and visually impaired to interact with state-of-the-art products and services quickly and naturally--no GUI needed! Best of all, including speech recognition in a Python project is really simple. In this guide, you'll find out how.
Many organizations are slow at adopting progressive methods. IT professionals need to prepare themselves for substantial change and a threat to jobs. This is because there is an accelerating and disruptive digital technology transformation in progress. It is referred to as the "digital revolution" which includes artificial intelligence. It can potentially adversely impact an organization's competitiveness and will be replacing employee jobs with computers.
The head of a Bermudian software engineering firm and a New York insurance lawyer will speak at a seminar on artificial intelligence and the insurance industry next week. Sandra De Silva, the founder, chief executive officer and chief software architect at Nova Ltd, a firm that has provided bespoke software development to the reinsurance industry since 2006, will talk on how AI and machine learning are impacting the insurance industry. Her fellow speaker will be Mina Matin, a partner at Norton Rose Fulbright in New York, who has extensive experience representing international insurers, reinsurers and captive insurance companies in litigation and arbitrations, in particular in the US, Bermuda and UK. The event, entitled --Artificial Intelligence and Insurance: A Glimpse of the Future--, is hosted by the Institute of Risk Management Bermuda Group and will take place at the offices of Deloitte at 20 Parliament Street, Hamilton next Wednesday from 4pm to 6pm. The IRM Bermuda Group stated: --This discussion will help those who are attending develop a better understanding on: what is AI; current uses of AI within insurance, and; what are the current issues within insurance coverages that may be impacted through the use of AI.--
Ah yes, the debate about which programming language, Python or R, is better for data science. In this series, I am considering machine learning and artificial intelligence as included in the term data science. This is almost the data science equivalent of tabs vs spaces for software engineers, at least at the time of this writing. This series is intended to be a somewhat definitive guide on this topic, including recommendations for languages and packages (aka libraries) applicable to different use cases, including data science in production and big data scenarios. This series is not intended to give side-by-side code comparisons, as there are plenty of other articles covering that. From my experience, which language to use is one of, if not the first question that someone interested in learning data science wants answered.