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) …
When you hear the phrase "artificial intelligence," what's the first thing that comes to mind? Depending on how you've seen AI, you might see it as a beautiful or scary thing. As the CEO of a startup that's built an AI-based marketing software tool, I believe it's the former. In fact, according to a 2019 study, "40% of marketing and sales teams say data science encompassing AI and machine learning is critical to their success as a department." It's easy to see why.
I am creating the web deployment for a book I am writing for Manning Publications on deep learning with structured data. The audience for this book is interested in how to deploy a simple deep learning model. They need a deployment example that is straightforward and doesn't force them to wade through a bunch of web programming details. For this reason, I wanted a web deployment solution that kept as much of the coding as possible in Python. With this in mind, I looked at two Python-based options for web deployment: Flask and Django.
Artificial Intelligence (AI) has progressed dramatically in the past decade, and one of the most useful products of this AI revolution is AI chatbots. They can help reduce the time taken to resolve queries of customers and also lessen the load on customer service agents. According to Gartner, nearly 25% of all customer service operations will use chatbots by 2020. A major reason for this is the fact that brands are investing in improving the customer experience. As many as 84% of organizations were expected to increase their investments in customer experience technology in 2017.
Today, Yusuf Mehdi, Corporate Vice President of Modern Life and Devices, announced the availability of new Microsoft 365 Personal and Family subscriptions. In his blog, he shared a few examples of how Microsoft 365 is innovating to deliver experiences powered by artificial intelligence (AI) to billions of users every day. Whether through familiar products like Outlook and PowerPoint, or through new offerings such as Presenter Coach and Microsoft Editor across Word, Outlook, and the web, Microsoft 365 relies on Azure AI to offer new capabilities that make their users even more productive. Azure AI is a set of AI services built on Microsoft's breakthrough innovation from decades of world-class research in vision, speech, language processing, and custom machine learning. What is particularly exciting is that Azure AI provides our customers with access to the same proven AI capabilities that power Microsoft 365, Xbox, HoloLens, and Bing.
Influencer marketing is the newest breakthrough to have shaken up the digital marketing landscape. Once an experimental channel, it has grown exponentially over the past few years – up to $6.5 billion in 2019. Now a mainstay of marketing organizations, the key success factor is figuring out how to get the most from it. Artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are revolutionising the way brands conduct influencer marketing. AI-powered influencer marketing tech is helping brands in three key areas: identifying the right creators, suggesting impactful workflow actions, and creating more relevant content.
I have been on both sides of the data science interview process; therefore, I know how stressful it is for the applicant, but also how important it is for the company to find the right candidate. Over the years, I have observed that anticipating audience needs is the most important factor at each interview stage; yet data science candidates often over-index on technical acumen, and neglect the fact that every evaluator is reviewing different attributes. Worse than that, data science candidates tend to go down rabbit holes such as Bayesian parameter estimation. Going this deep into a highly technical niche subject has two potential risks: Having an interviewer's eyes glaze over because they are not the right audience, or worse, the interviewer has a deeper technical understanding about that particular subject and will trip you up in your answer! In my 11 years building a data science career, I have found there to be four main stages to an interview process, each with distinct audiences: the initial phone screen, the technical evaluation, the "take home" assignment, and a behavioral assessment.
In the world of industrial automation, artificial intelligence (AI) plays a growing role. At first glance it may not seem very ubiquitous, but that's because the AI technology is largely hidden from view--embedded within a device or software to give it new capabilities. Though we may not yet be using AI as depicted in science fiction movies, it's no longer a rare feature in automation technologies and we are interacting with it at an increasing rate. To understand what I mean, think about the all ways AI is being used in automation today, for example: Advantech's work with Nvidia to bring AI to its cloud and edge computing products, Aveva's incorporation of AI into its Asset Performance Management Suite, Epicor adding AI technology to its ERP software, Festo's combination of AI and pneumatics to aid human/robot interactions, HPE's addition of AI to many of its enterprise computing products, Omron's new controller for predictive maintenance applications, PTC adding AI to its CAD software, Siemens adding a neural processing module to its S7-1500 controller, and the enablement of new capabilities in industrial inspection drones. This list is by no means exhaustive, but it does help illustrate the breadth of AI's application across a variety of technologies used in the discrete manufacturing and processing industries.
Artificial Intelligence is penetrating all corners of life. We've heard the Echo dots; we've met Sophia and we've asked Siri. But away from the every day AI that's rife in society, industries are starting to use the technology to their advantage, and the newsroom is no different. Organisations like Washington Post, Reuters and Press Association are all using their own forms of AI to improve the processes and systems that journalists are used to. At Forbes, their AI system'Bertie' has been programmed to give journalists first drafts and templates for stories.
Artificial Intelligence is a topic that has been around the science fiction community since possible the inception of science fiction. However, it has only been really taken seriously since machine learning as a concept started taking off and algorithms were able to actually follow the vision of what was once thought as an impossibility. Machine Learning (ML) is actually a subset of the greater Artificial Intelligence (AI) technology and it also includes deep learning (I may go further into this technology in a future article), which is itself a subset of machine learning. Machine learning is a huge topic right now due to so many technologies from autonomous vehicles to translation software using it. It is a very hot and growing industry for aspiring software developers to learn about and businesses to start implementing to stay ahead of competition.
Artificial Intelligence (AI) has been the major buzzword in the digital marketing world for the past few years, mainly due to the rapid advancements in machine learning technologies. AI is now a relatively familiar idea among marketers, and it's no longer a sci-fi term associated with the distant future. This is also true in the field of SEO. Machine learning technologies have now become a very important component of search engine algorithms. Meaning, if we can understand AI and how it can help SEO, we can further improve our SEO results.