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) …
Mind AI is a venture-backed AI company that has more than a decade of research and development under its belt. This project is part of the third wave of AI, which has a focus on reaching human-level reasoning via crowdsourced artificial intelligence. The ecosystem and artificial intelligence engine in Mind AI were developed to create a new and an innovative approach to AI. Previous AI architectures rely on large quantities of data, supercomputers, and parallel processing. By contrast, Mind AI has a core reasoning engine that is based on new data structures, which are internationally patented and known as canonicals.
Data science and big data analytics are gradually making waves with advanced technologies like artificial intelligence (AI), machine learning (ML), and deep learning. Data science is essentially used to delve into huge datasets to extricate significant information. The experiences that data analysts and scientists acquire from huge volumes of data remain the mystery responsible for the swift transformation of the world around us. Companies and institutions across different divisions of the Industry are presently utilizing data science tools to control the changes driven by the recent innovations. Truth be told, about 55% of organizations have implemented huge big data analytics in 2017, which is a huge development from 18% in 2015.
As the Data Science and Machine Learning field evolve, there is a huge demand for a number of professionals who are skilled in this domain. When one starts with learning and implementing the techniques involved in building the models with the help of necessary libraries, it can be difficult to remember all the concepts. A flowchart or a cheat sheet will definitely help one to understand and remember the footsteps to build a robust model. In this article, we shall explore a couple of cheat sheets for machine learning tasks. For a given dataset, one can make use of the cheat sheets to handle various tasks with ease.
Programming is on its way to becoming an important life skill. Our grandchildren will likely learn to code as part of their regular school curriculum. Sure, typing in lines of code isn't exactly life-saving, but gaining coding proficiency can change your life and open you up to a world of possibilities. It transitions you from a mere consumer to a full-fledged creator as you can use your programming chops to put a roof over your head or build something that can change the world. Way back when, you would need to enroll in a computer science program and go into massive amounts of debt just to learn how to code.
Natural Language Processing (NLP) is the ability of a computer system to understand human language. Natural Langauge Processing is a subset of Artificial Intelligence (AI). There are multiple resources available online which can help you develop expertise in Natural Language Processing. In this blog post, we list resources for the beginners and intermediate level learners. A beginner can follow two methods i.e.
So does that guy selling sunglasses on the beach. It's why the funny old French bakery around the corner's been running for 15 years. Everyone's talking about it, but what is it? Forging a genuine connection and using that connection to inform your marketing decisions. At its most complex, conversational marketing has become synonymous with cutting-edge technologies for computer-based dialog processing.
Debugging: A post on ML debugging indicates how during the process of building machine learning models, one may run into a situation where a model is not working as well as you would like. Sometimes, the error rate is too high or the model works fine on the training data, but fails when applied on real-world data. By now, everybody knows that machine learning is known for its black box technique. In other words, machine learning algorithms suffer from high variance or high bias. This means the models are not good at generalising and when applied to other data or unseen data, they mail fail.
Data science has evolved into one of the most lucrative career options over the past few years. It will, therefore, come as no surprise that data scientists are one of the top paid professionals in the industry. In fact, they are hired after a lot of thought and diligence. This sometimes leaves them with very less room for mistakes. On the other hand, this is even applicable for beginners who wish to perfect data science.
Effect.AI published a new video blog informing the community about some of the latest developments regarding the project. Effect.AI's vision for its'Mechanical Turk', microtask platform is simply inspiring as its leadership is clearly choosing to build Effect.AI in a socially responsible way. Let's recap what the project is about, have a listen to what Polina Boykova has to say and connect some of the dots regarding the project's ambitions. Effect.AI has a three-staged approach with regards to building, what they call, the'Effect Network'. In the first and current stage, Effect.AI is aiming at disintermediating and decentralizing crowdsourced'micro-tasking'.