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) …
Keeping up with artificial intelligence (AI) and data privacy can be overwhelming. While there's loads of promise and opportunity, there are also concerns about data misuse and personal privacy being at risk. As we evaluate these topics and as the Fourth Industrial Revolution unfolds, questions arise about the promise and peril of AI, and how can organizations put steps in place to better realize the value of it. Integrating "ethics" into technology products can feel abstract for engineers and developers. While many technology companies are independently working on initiatives to do this in concrete and tangible ways, it is imperative that we break out of those silos and share best practices.
Many companies are swimming in data, and they are spending millions to collect more. But even with new tools and algorithms to analyze and make predictions based on consumer data, it's often still not being used effectively. Customer Analytics for Growth is for business leaders who want to cultivate an analytics-based mindset throughout their organization, and gain a deep understanding of emerging AI technologies that are rapidly changing businesses today. In Customer Analytics for Growth, you will explore the upside -- and the downside -- of complex data models, and understand the importance of transparency in data collection and analysis. A distinctive highlight of Customer Analytics for Growth is engaging in discussions with expert practitioners from a range of industries who have experience with both business-to-consumer and business-to-business customer models.
Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models. Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. It is the practice of collaboration between data science and IT teams designed to accelerate the entire machine lifecycle across model development, deployment, monitoring, and more.
Successful artificial intelligence (AI) implementations rarely hinge on the unique innovation of a specific algorithm or data science technique. Those are important factors, but even more foundational to successful AI enablement are the core data operations and enabling platforms. These act as the fuel and chassis of the AI machine that a business must build and evolve for continued competitive advantage. Successful digital transformations focus on evolving and optimizing business operations through the better use of data assets combined with modern technologies such as machine learning (ML), AI, and robotics. These paradigm shifts result in the creation of new operating patterns rather than simply more efficient legacy operations.
Nearly a decade after co-founding Cyberjustice Laboratory, a unique hub that analyses the impact of technologies on justice while developing concrete technological tools that are adapted to the reality of justice systems, Karim Benyekhlef and Fabien Gélinas have set their sights on artificial intelligence. The Autonomy through Cyberjustice Technologies (ACT), the latest brainchild of the Cyberjustice Laboratory, is the largest international multidisciplinary research initiative that seeks to leverage artificial intelligence to increase access to justice while providing justice stakeholders with a roadmap to help them develop technology that is better adapted to justice. "The main objective behind the initiative is to ensure that individuals know their rights, understand their legal situation regarding their problems and improve access to justice – and AI may help accomplish those goals," said Benyekhlef, the head of Cyberjustice Laboratory and a law professor at the Université de Montréal. "There's a good chance that our reflections and work on areas such as privacy, data management, data governance could easily be used in other realms such as in public administration. But we must be careful. We cannot play the sorcerer's apprentice. These are tools that are not yet mature. There's work to be done."
Communication is an integral part of businesses, not only internally, but also externally, in how they communicate with the customers and partners. Consequently, it's essential to work with a communication system in place to achieve this successfully. Having the correct communication system will consequently create effective communication between employees, clients, and stakeholders, improving customer service and as a result, customer engagement. However, with time and growth of the business comes new challenges. Customer queries start piling up and even having a successful communication system sometimes is not enough to manage the new flood of enquiries.
In 2020, the nature of customer engagement will change as personalisation - how marketing and customer value management actually engage with customers - rapidly matures. This means a change will be required in legacy campaign and loyalty programme management solution architectures (i.e. a move from relational databases of static customer data and batch processes to a real-time online customer profiling and engagement triggering). Those Communications Service Providers (CSPs) who lead the way will tap the real benefits that can be achieved by moving to CE 3.0. Net Promoter Scores in the telecoms industry are low; yet to date there's been relatively little analysis of why. One change lies in clearer answers to the question "Does my operator give me value for my money?".
Andrew Ng, known as the man behind Google Brain, released this completely free book in 2018. Drawing from his experiences leading the Google Brain team he introduces a framework for embedding machine learning into an organisation. This book covers how to develop a machine learning strategy, the correct way to test machine learning models and how to build a "superhero" team. This is a must-read for both data scientists and business leaders looking to begin using or scaling machine learning in the enterprise. Even if you are already deploying machine learning this book will provide tips on how you can improve existing processes.
It's been a heck of a year, hasn't it? Even if you're still knee-deep in holiday and end-of-year promotions, it makes sense to take time to pause. Now's the time to reflect on the challenges, opportunities, and accomplishments of 2019--before the crazy starts up again. With that in mind, we're revisiting the big lessons drawn from our most popular pieces on digital marketing and landing pages. For each, we'll talk about how you can best apply these lessons in 2020 and beyond.
Machine learning is a highly effective tool for developing trading systems for Bitcoin and other cryptocurrencies. This post will explore some of the concepts that apply, potential issues you may encounter, and the competencies you'll need to develop your own machine-learning based trading system. Crypto-ML.com is a trade alert platform built on machine learning. We'll discuss the learnings and strategies it uses as well. Want to see the details of how we implement these concepts in our latest models? There is a lot of confusion as to what machine learning really is.