Managing scientific names in ontologies that represent species taxonomies is challenging due to the ever-evolving nature of these taxonomies. Manually maintaining these names becomes increasingly difficult when dealing with thousands of scientific names. To address this issue, this paper investigates the use of ChatGPT-4 to automate the development of the :Organism module in the Agricultural Product Types Ontology (APTO) for species classification. Our methodology involved leveraging ChatGPT-4 to extract data from the GBIF Backbone API and generate OWL files for further integration in APTO. Two alternative approaches were explored: (1) issuing a series of prompts for ChatGPT-4 to execute tasks via the BrowserOP plugin and (2) directing ChatGPT-4 to design a Python algorithm to perform analogous tasks. Both approaches rely on a prompting method where we provide instructions, context, input data, and an output indicator. The first approach showed scalability limitations, while the second approach used the Python algorithm to overcome these challenges, but it struggled with typographical errors in data handling. This study highlights the potential of Large language models like ChatGPT-4 to streamline the management of species names in ontologies. Despite certain limitations, these tools offer promising advancements in automating taxonomy-related tasks and improving the efficiency of ontology development.

But true data intelligence is about more than establishing the right data foundation. Organizations are also wrestling with how to overcome dependence on highly technical staff and create frameworks for data privacy and organizational control when using generative AI. Specifically, they are looking to enable all employees to use natural language to glean actionable insight from the company's own data; to leverage that data at scale to train, build, deploy, and tune their own secure large language models (LLMs); and to infuse intelligence about the company's data into every business process. In this next frontier of data intelligence, organizations will maximize value by democratizing AI while differentiating through their people, processes, and technology within their industry context. Based on a global, cross-industry survey of 600 technology leaders as well as in-depth interviews with technology leaders, this report explores the foundations being built and leveraged across industries to democratize data and AI.

By Aditya Malik, Founder, ValueMatrix.ai What oil was in the 18th century, data is in the 21st century – immensely valuable and influential. With the run towards AI supremacy, businesses and organizations possessing qualified and unbiased data win. However, the data openly available is marred with conscious and subconscious bias, largely due to woke or un-woke nature of humans. We don't know what we don't know – right or wrong is only temporary, as in always waiting to be judged with time.

Stijn "Stan" Christiaens is Founder and Chief Data Citizen at Collibra. With all the incredible results in today's generative AI, is this when robots finally take our jobs? Historically, new technologies become a tool for people to change and improve the jobs that need doing, and AI is no different. Many organizations are investing in AI-powered solutions to enable faster problem-solving and better decision making, yet many struggle to see positive results. Done right, AI can make companies more efficient by unlocking data-driven insights to save costs or help generate more revenue.

Regression branch of Machine Learning purely focuses on prediction of continuous values. The supervised learning branch has many regression based methods with parametric and non-parametric learning models. In this paper we aim to target a very subtle point related to distance based regression model. The distance based model used is K-Nearest Neighbors Regressor which is a supervised non-parametric method. The point that we want to prove is the effect of k parameter of the model and its fluctuations affecting the metrics. The metrics that we use are Root Mean Squared Error and R-Squared Goodness of Fit with their visual representation of values with respect to k values.

Artificial intelligence has a vocabulary all its own. Just within the field of machine learning, you've got a bevy of terms and concepts to sort out: supervised versus unsupervised ML, deep learning and neural networks, and black box versus explainable AI. If you need to brush up on the lingo fast, try our AI cheat sheet. For a deeper dive, try the executive's guide to real-world AI. Because of this – and because of the outsized enthusiasm and hype that engulfs AI – there is some fundamental confusion around AI and related technologies.

With the evolution of technology, we've been able to make our daily lives easier and more convenient. However, the downside of it is that the more we continue to improve our lives, the more likely it'll backfire at some of our professionals. The notary has always been considered one of the safest professions from an economic point of view and able to guarantee a decent status quo. With the advent of the Blockchain, it will be useless to register a change of ownership, the foundation of a new company, or any other deed. Because thanks to this technology, a subject that determines the validity of a document will no longer be needed, but everything will be guaranteed automatically.

Over the past two years, there's been an acceleration of the digital landscape in large part due to COVID-19. As a result, digital transformation has become front of mind for many businesses. Then it's important to understand that the key to success is linking commercial impact to data. This process includes connecting data across systems to generate a better user experience and then integrating that data to enable the business to measure and analyse effectiveness at scale. Done correctly, it provides visibility and allows data driven decision making at a velocity that generates business growth but also competitive advantages.

According to a top consulting report, if the Industry gets it right, linking the physical and digital worlds could generate up to $11.1 trillion a year in economic value by 2025. These have resulted in the exponential growth of the data generated through the IoT devices, which has created a requirement to bring computational power at individual device levels using edge computing rather than sending data to the cloud for analysis. Edge computing can move parts of the service-specific processing and data storage from the central cloud/datacenter to edge network nodes; when combined with Artificial Intelligence (AI), it can bring intelligence at the device level. This help to build a smart/intelligent connected network of edge devices called Edge AI or Edge AIoT (Artificial Intelligence of Things) or Intelligent Internet of Things. To know more about Edge AI please check out our blog on Edge AI: The Era of Distributed AI Computing.

The new partnership between Mindtree and Databricks will look to support use of the Databricks platform from implementation throughout the entire customer journey. Customers will now be able to gain insights from larger data sets using AI, with Databricks offering a unified solution for data engineering, collaborative data science, full-lifecycle machine learning, and business analytics through a lakehouse architecture. With Databricks, enterprises can build rich data sets and optimise machine learning at scale, as well as streamlining workflows across teams, reducing infrastructure complexity, and delivering superior customer experiences. For AI to be effective in aiding accurate insights, organisations require complete access to analytics on data lakes, which often proves to be the largest data source at their disposal. Clint Hook, director of Data Governance at Experian, looks at how organisations can automate data quality to support artificial intelligence and machine learning.