Offshore wind farms are among the biggest machines we build--vast arrays of towers topped with slowly turning blades. They generate megawatts of power from their giant turbines, taking up miles of space. That means that, as green as they are, they still have an immense impact on the ecology around them, affecting birds, fish, and even the growth of kelp and other marine plants. Managing those turbines is a big issue. Instead, we need to consider them as part of a larger system, one that includes the environment they're part of.

The technology-driven process for analyzing data along with delivering actionable information to help executives make informed decisions is known as BI (business intelligence). Just simply going through that definition, one could say that BI is tailor-made for AI's intervention. Artificial intelligence today for many ventures is offering an incredible amount of use cases that involve improving business process, gathering insights, engaging customers, and building interactions with employees. The day isn't far where both will become inseparable from one another. According to a recent study published by AI Multiple, the AI industry is expected to grow and reach a worth of more than $15 trillion by 2030.

For years, the answer was by hard graft, with rangers checking burrows and nests for birds and eggs, and observers forced to sit for hours at a time armed with clipboards and no little patience. But in a marriage of nature and cutting edge technology, the arduous task of establishing the puffin population on the Isle of May is being carried out using artificial intelligence, machine learning, and image recognition software. Those behind the project believe it could help minimise disruption to birds' breeding and feeding habits, particularly when faced with developments such as offshore windfarms. The initiative uses four cameras placed in stainless steel boxes at various points of the island in the Firth of Forth in order to capture live footage of the puffins. Each box has a condensation heater as well as a backup power supply.

The amount of unstructured and structured data being generated within healthcare has increased significantly due to factors such as an aging population and the rise of telehealth -- virtual consultations -- as a method of delivering healthcare. This has only increased during the pandemic. In this article, we explore, through various use cases showing how healthcare organisations can leverage the increasing amount of data available using artificial intelligence (AI), and machine learning (ML) and analytics to improve the patient care experience and drive operational efficiencies. Unstructured data in healthcare refers to anything from clinicians handwritten notes to prescription forms and patient call center logs. This information is increasing in volume and new ways of capturing and analyzing this data are needed.

Conversational AI is not a single technology but rather a combination of multiple capabilities, including machine learning (ML), conversational user interfaces, natural language processing (NLP), and leading-edge design, to name a few. "Conversational AI interfaces--from text and chatbots to voice assistants--will leverage more robust machine learning technologies, making them more predictive and allowing businesses to serve customers more quickly and efficiently," said Philip Say, vice president of innovation product management at experience design agency Sutherland Labs. In the simplest terms, conversational AI "is really about giving a voice to brands," said Aaron Reich, global emerging tech lead at Avanade, an IT consulting firm. It can enable always-on support with the potential for an enhanced experience at a time when 51% of executives ranked ensuring a proper personality or a connection to the brand a top priority, according to Avanade's research. Enterprise adoption is being spurred by consumers now accustomed to interacting with voice-activated smart devices like Amazon Echo.

Data extraction from printed forms is by now a tried and true technology. Form Recognizer extracts key value pairs, tables and text from documents such as W2 tax statements, oil and gas drilling well reports, completion reports, invoices, and purchase orders. However, real-world businesses often rely on a variety of documents for their day-to-day needs that are not always cleanly printed. We are excited to announce the addition of handwritten and mixed-mode (printed and handwritten) support. Starting now, handling handwritten and mixed-mode forms is the new norm.

Enterprise seems to be entering a new era ruled by data. What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time. It's not a simple process for companies to incorporate machine learning into their existing business intelligence systems, though Skymind CEO and past Emerj podcast guest Chris Nicholson advises that it doesn't have to be daunting. "AI is just a box," he says.

Reach out if you want to talk about digital & AI ethics in your organization-- Email: e-bruce@manydoors.net If you work for an organization that uses data--and just about all organizations do, or will before long--even if your job isn't specifically about data, your ability to make decisions using data, and decision about data, is becoming more and more important. An important subset of these decisions that involves everyone--decisionmakers, employees, and customers alike--falls under the general category of digital ethics, which can encompass how data is collected, stored, used, and shared. To illustrate, lets look at two examples of digital ethics in action, one surprisingly successful, and one disastrous. My friend Aaron Reich is basically the futurist in residence at Avanade, the global technology consulting firm. From the vantage point of his high level insight into many of their consulting projects, last year he called out a few examples where companies achieved remarkable improvement in ways that they can help their customers using data and artificial intelligence.

Although we currently rely on real people to act as a point of reference, AI is on the rise in the decision-making field. One of the reasons is so-called'deep learning'. Here, an output is produced, but the logic used'behind the scenes' cannot be reproduced nor traced. Consequently, it is extremely difficult for human beings to determine whether the conclusions we are faced with are sound, or ethical. This is a topic of concern in the entire data and AI community, not just at Avanade.

Microsoft wants partners to put more focus into delivering the artificial-intelligence (AI) capabilities offered in its data platform and cloud portfolios. The company doesn't feel enough of its partners currently offer AI holistically to enable customers to transform their businesses. Setting the stage for how Microsoft might help enable partners to do that, the company recently announced a partnership with Accenture and its Avanade subsidiary. Collectively, they are creating preconfigured solutions geared toward helping clients in specific industries such as consumer-packaged goods, retail and telecommunications using AI to automate their business processes. Avanade's latest research found that 88 percent of global business and IT decision-makers say they don't know how to use AI -- and 79 percent encounter resistance.