Artificial intelligence (AI) and machine learning (ML) are shifting from being business buzzwords toward wider enterprise adoption. The efforts around strategies and adoption are reminiscent of the cycle and tipping point for enterprise cloud strategies, when companies no longer had the option to move to the cloud and it only became a question of when, and how. AI and ML implementation strategies are in the same evolution mode as companies build their approaches. Below are some thoughts around the how. Forrester reported that almost two-thirds of enterprise technology decision-makers have either implemented, are currently implementing, or are expanding their use of AI.

"Al will not replace doctors but instead will augment them, enabling physicians to practice better medicine with greater accuracy and increased efficiency." Artificial intelligence (AI) and machine learning (ML) have received widespread interest in recent years due to their potential to set new paradigms in healthcare delivery. It is being said that machine learning will transform many aspects of healthcare delivery, and radiology & pathology are among the specialties set to be among the first to take advantage of this technology. Medical imaging professionals in the coming years will be able to use a rapidly expanding AI-enabled diagnostic toolkit for detecting, classifying, segmenting, and extracting quantitative imaging features. It will eventually lead to accurate medical data interpretation, enhanced diagnostic processes, and improved clinical outcomes.

A famous example would be Amazon's AI recruitment screening program. They were forced to abandon it due to gender discrimination. Their machine learning model had been trained to recognize the patterns and similarities from ten years of resumes submitted to Amazon. Many of these resumes came from men, since the technology field has been traditionally male dominated. The AI tool'learned' to reject females based upon this historical data.

"People think AI is magic, that data goes in and answers come out. From accounts to recruitment, drug discovery to financial products, AI implementation offers the promise to business leaders of automated decisions, innovative products and reduced OpEx through efficiency gains. The reality can be somewhat different. So where does the gulf between expectation and reality emerge? A new report from the Oxford Internet Institute (OII) published this month looks closely at why AI projects often fail. The report, AI @ Work, analyses themes in 400 reports about AI from January 2019-May 2020, focusing on how they covered AI in workplaces. The authors say they discovered a significant "evidence gap in how AI tools used and how people talk about what they are supposed to do." As Co-author Professor Gina Neff puts it: "Time and again, we see organisations making the same mistakes in the integration of AI into their decision-making: Over-reliance on the tech, poor integration into the larger data ecosystems, and lack of transparency about how decisions are made… the one takeaway that rings loud today is that AI systems often make binary choices in complex decision environments." As she told Computer Business Review: "As AI moves from the technology sector to more areas of our economy, it is time to take stock critically and comprehensively of its impact on workplaces and workers.

Despite the many unanswered questions that remain about the use of artificial intelligence (AI) in the workplace and in customer-facing and servicing departments, the growth of AI appears unstoppable. Even as early as two years ago, research from the UK-based digital marketing agency Big Rock found after interviewing 100 senior marketers globally, that AI applications, even at that stage had become one of the marketing departments mainstays. The interviews showed -- again at that stage -- that 55% of companies were either currently implementing or actively investigating some form of AI initiative within their marketing practices. Meaning, AI was already shaking things up in the industry. Unsurprisingly, the research read, this inevitable rise of AI technologies in marketing is causing a major shift in the way companies work.

Artificial Intelligence Application is a subject widely prevalent in the IT world for more than 4 decades and it was in the laboratory all through. AI is all about building intelligence into machines to enable it to perform human tasks with precision for the known operations. It also packs machines with a lot of data points for the machines to learn and handle unknown situations. AI simulates machines to work, learn, think and react like human beings. In this article let's see the applications developed using AI technologies.

In the digital-first world, the value of artificial intelligence (AI) is more evident than ever, and many CEOs and business leaders are witnessing the positive impact it's having on their organizations. So it's no surprise that enterprises plan to double their number of AI projects within the next year. But despite the clear advantages of AI, businesses are still struggling to find the right talent to successfully implement and fully utilize these technologies. What's more, the disparity between AI optimism across the C-suite, and trust at the employee level, adds yet another barrier. A recent study we conducted at EY of U.S. CEOs and business leaders shows that, while a majority (84%) of CEOs realize the value of AI and its importance to their company's success, nearly one in three (31%) view a lack of skilled talent as a top barrier to AI adoption.

As we indicated in our article on AI applications in Africa, keeping up with the changes in our current fast-paced world where tech solutions are developed by the day and requires formidable structures aimed at leveraging long term objectives and solutions. It's no doubt that AI offers remedies to Africa's most pervasive problems not just in healthcare but also in reducing poverty, elevating inclusion in societies, solutions to food crises, addressing sustainability challenges as well as enhancing the quality of education. Artificial intelligence is crucial in an African setting due to how it democratizes access to pioneering and productivity-boosting innovations that fuel the continent's growth towards sustaining its needs. In the past few years, several governments (as illustrated in our article mentioned above) across Africa have started mobilizing with the aim of promoting the growth of AI in the continent. Having a vibrant AI ecosystem requires integration of precise policies but most importantly, a coming together of progressive lawmakers, global technology partners, governmental institutions and civil society groups.

Amongst the analysts like Gartner, Forrester and IDC, Artificial Intelligence dominates their predictions for the future. You may well ask, 'what is artificial intelligence?'. The answer to this question is as under: "Artificial Intelligence is when a machine starts behaving like a human being" Technologically, machines do not have any intelligence and they just obey the commands given by their masters. However, when machines start displaying natural intelligence, then that is called artificial intelligence. In technical words, 'any machine that judges its environment and takes action to optimize its chance of achieving its goals.