This article presents a 5-level AI Capability Assessment Model (AI-CAM) and a related AI Capabilities Matrix (AI-CM) to assist practitioners in AI comprehension and adoption. These practical tools were developed with business executives, technologists, and other organisational stakeholders in mind. They are founded on a comprehensive conception of AI compared to those in other AI adoption models and are also open-source artefacts. Thus, the AI-CAM and AI-CM present an accessible resource to help inform organisational decision-makers on the capability requirements for (1) AI-based data analytics use cases based on machine learning technologies; (2) Knowledge representation to engineer and represent data, information and knowledge using semantic technologies; and (3) AI-based solutions that seek to emulate human reasoning and decision-making. The AI-CAM covers the core capability dimensions (business, data, technology, organisation, AI skills, risks, and ethical considerations) required at the five capability maturity levels to achieve optimal use of AI in organisations. The AI-CM details the related individual and team-level capabilities needed to reach each level in organisational AI capability; it, therefore, extends and enriches existing perspectives by introducing knowledge and skills requirements at all levels of an organisation. It posits three levels of AI proficiency: (1) Basic, for operational users who interact with AI and participate in AI adoption; (2) Advanced, for professionals who are charged with comprehending AI and developing related business models and strategies; and (3) Expert, for computer engineers, data scientists, and knowledge engineers participating in the design and implementation of AIbased technologies to support business use cases. In conclusion, the AI-CAM and AI-CM present a valuable resource for practitioners, businesses, and technologists, looking to innovate using AI technologies and maximise the return to their organisations.

In 2021, Spiceworks reported survey results that revealed, "Almost one-third (31%) of the professionals surveyed said their organizations are now using artificial intelligence (AI), and 43% are exploring the technology. About 34% reported their companies had not deployed any AI projects." This and other surveys show that most companies are in early stages of AI adoption -- and they most likely have not yet thought about change management for their AI systems, and what it's going to take to keep their AI systems up, running and relevant. In 2016, Microsoft developed a chatbot called Tay. Tay was designed to learn from human interactions on social media.

If you ask me, the hiring managers are not looking for the correct answers. They want to evaluate your work experience, technical knowledge, and logical thinking. Furthermore, they are looking for data scientists who understand both the business and technical sides. For example, during an interview with a top telecommunication company, I was asked to come up with a new data science product. I suggested an open-source solution and let the community contribute to the project.

AI is leading the march of technologies disrupting the enterprise. Industries like healthcare, ecommerce and banking are embracing artificial intelligence because AI has proven to enhance employees' productivity by simplifying monotonous office tasks. However, there is a longstanding controversy around AI-driven technologies that can understand and perform more than humans in business processes. This is the premise of a recent report from voice software specialist Red Box, called "Being Human: How and why machines are learning the art of conversation." The report surveyed a pool of business leaders in the U.K. and U.S. to learn how they used iterations of conversational AI, which comprises automated messaging and speech-enabled applications that personalize interactions with humans.

Across the enterprise ecosystem, employees are building a bottomless data lake, premised on the corporate mantra to "save everything, just in case," according to an article published in Gartner. Alan Dayley, a former research director at Gartner, notes that increased data growth over the past decade has created an unstructured data nightmare. "It's not just the cost to store it. Huge volumes of dark data make it harder to find what is useful and may mean we miss business opportunities," says Dayley. Mike Gualtieri, VP and principal analyst at Forrester notes in an article that between 60% and 73% of all data within an enterprise goes unused for analytics.

This immersive 2-part workshop will explore several plug-and-fly use cases for budget-friendly turn-key Drones when combined with off-the-shelve Artificial Intelligence solutions.

Dynam.AI, an artificial intelligence (AI) software development firm best known for full stack AI innovation, today announced the early commercial release of Vizlab, an AI/Machine Learning (ML) platform designed to address the complex needs of enterprise data scientists and solve the key problems with AI/ML applications in the market today. This customizable, intuitive, end-to-end AI/ML development solution enables ML data scientists to design, build, improve and deploy AI engines at scale. Vizlab empowers data scientists with necessary, in-demand tools to deploy explainable AI solutions with highly accurate analytic insights. Vizlab was initially built as an internal tool by the Dynam.AI team as a platform to support Dynam's consultative services, to automate and standardize AI pro-workflow and advance Dynam's core AI capabilities for customers with complex business use cases. Customers without internal data science teams seek out Dynam's end-to-end AI development services to learn more from their proprietary business and customer data to automate and improve processes and target more of their best customers.

In order to implement your artificial intelligence strategy, you need to understand how AI works, find out potential business use cases, gather high quality data and check whether your team is skilled. To make the most out of AI offerings and to drive competitive advantage, you should jumpstart your artificial intelligence strategy by following the guidelines mentioned in this blog post. As the pace of digital transformation goes up, business leaders should prepare to make faster, smarter, and more tangible decisions around the continuously growing data. Hence, companies need the assistance of artificial intelligence solutions that will allow them to make more, real-time, and accurate decisions for their business with maximum efficiency. But, for leveraging artificial intelligence in an organization, there should be an effective artificial intelligence strategy in place, which acts as a roadmap that guides business leaders to use the technology for the right business use cases.

When I mention AI (Artificial Intelligence) machines can smell now, my friends exclaim with the statement of "So What"! The best way is to explain to them the importance of smell in our lives. This article introduces considerable research to olfactory development in computer science and engineering at a high level and points out recent developments in the industry. I also touch on potential use cases and business value propositions. Let me give you a high-level background to digital scent technology as part of the technical literature review that I conducted reflecting olfactory progress in AI.

The following article was originally published by our sister site, Big Think Edge. Business leaders know they must prepare for technological upheavals in the years ahead. But keeping up-to-date on new technologies--to say nothing of understanding their complexities and forecasting those shifts--is an overwhelming task. To help organizations find their footing, the CompTIA Emerging Technology Community releases an annual list of the top 10 emerging technologies. What makes this list special is that it focuses on "which emerging technologies have the most potential for near-term business impact."