Check out all the on-demand sessions from the Intelligent Security Summit here. Inflation and extended supply chain disruptions, among other geopolitical factors, will further complicate the world of supplier sourcing going into 2023. Now more than ever, it's critical that procurement leaders base their spend management and sourcing strategies on highly accurate data. Doing so is paramount to finding new, lower-cost or diverse suppliers. When implemented correctly, a diverse set of suppliers improves agility, helps navigate supply chain disruptions, improves brand reputation, facilitates innovation and increases competition.

Information created intentionally rather than as a result of actual events is known as synthetic data. Synthetic data is generated algorithmically and used to train machine learning models, validate mathematical models, and act as a stand-in for test production or operational data test datasets. The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance. Constraints when attempting to duplicate the complexity of the original dataset might lead to discrepancies. It is impossible to completely substitute accurate data because precise, accurate data are still needed to generate practical synthetic examples of the information.

Information created intentionally rather than as a result of actual events is known as synthetic data. Synthetic data is generated algorithmically and used to train machine learning models, validate mathematical models, and act as a stand-in for test production or operational data test datasets. The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance. Constraints when attempting to duplicate the complexity of the original dataset might lead to discrepancies. It is impossible to completely substitute accurate data because precise, accurate data are still needed to generate practical synthetic examples of the information.

Such is our faith in technology that we tend to overlook the'how' in favor of focusing solely on the result. In the case of artificial intelligence technologies, provided that the results are accurate enough, companies, institutions, and individuals are likely to trust them without caring how they were reached. Without data, there would be no artificial intelligence. With only a small amount of data, artificial intelligence would be, well, not so intelligent. The truth is that AI systems thrive on data, and the more of it we give them, the better they are able to formulate an accurate understanding of what they are being asked to quantify.

Computer vision technology is increasingly used in areas such as automatic surveillance systems, self-driving cars, facial recognition, healthcare and social distancing tools. Users require accurate and reliable visual information to fully harness the benefits of video analytics applications but the quality of the video data is often affected by environmental factors such as rain, night-time conditions or crowds (where there are multiple images of people overlapping with each other in a scene). Using computer vision and deep learning, a team of researchers led by Yale-NUS College Associate Professor of Science (Computer Science) Robby Tan, who is also from the National University of Singapore's (NUS) Faculty of Engineering, has developed novel approaches that resolve the problem of low-level vision in videos caused by rain and night-time conditions, as well as improve the accuracy of 3D human pose estimation in videos. The research was presented at the 2021 Conference on Computer Vision and Pattern Recognition (CVPR), a top ranked computer science conference. Night-time images are affected by low light and man-made light effects such as glare, glow, and floodlights, while rain images are affected by rain streaks or rain accumulation (or rain veiling effect).

Empowering application development teams with the best tools while creating a unified and highly flexible data layer still remains an operational challenge for the majority of businesses. Hence, data engineering is fast taking the center stage acting as a change agent in the way data is collated, processed and ultimately consumed. Not all AI/ML projects undertaken at an enterprise level are successful and this mainly happens due to lack of accurate data. Despite making generous investments in data analytics initiatives, several organizations often fail to bring them to fruition. Yet companies also end up spending significant time preparing the data before it can be used for decision modeling or analytics.

These days it's hard to find a public company that isn't talking up how artificial intelligence is transforming its business. From the obvious (Tesla using AI to improve auto-pilot performance) to the less obvious (Levis using AI to drive better product decisions), everyone wants in on AI. To get there, however, organizations are going to need to get a lot smarter about data. To even get close to serious AI you need supervised learning which, in turn, depends on labeled data. Raw data must be painstakingly labeled before it can be used to power supervised learning models.

Matillion, the leading provider of data transformation for cloud data warehouses (CDWs) announced that Uberflip has deployed Matillion ETL for Snowflake. By implementing cloud-native ETL, Uberflip reduced data preparation time from five weeks to just one day, helping their product, marketing, and sales teams to rapidly deliver better business value for customers. Matillion ETL delivered repeatable and scalable processes and models for data orchestration, and decreased required development time, freeing up valuable engineering resources. Uberflip is a leading content experience platform and software that enables marketers to create digital experiences with content for every stage of the buyer journey. To better serve internal teams and external customers, Uberflip's data scientists needed a solution that could help them rapidly extract, load, and transform data to scale analysis within the product, empower internal teams for self-service, and get real-time, accurate data from all sources.

The COVID-19 virus has caused millions of white-collar knowledge workers to work from home, and while this could be seen as a boon to productivity, unfortunately it is not. These remote workers battle hundreds of daily distractions from helping children to making dinner along with the usual online breaks. Over time, these non-company activities add up and can yield at least 3-4 hours of unproductive work time. Yet, these workers are worried about their jobs and want to hold on to them during a time of record unemployment. They want to show their organizations that they are as productive and valuable as ever to retain their coveted positions.

The rapid growth of digital technologies in the recent years has created a very distinct type of a customer-a customer that allots majority of his/her time on the internet be it via social media networks or mobile. We, our self can identify with them the way we spend most of our time online- and this is majorly affecting the way we communicate with customers. Statista says that as of 2018, "over 4 billion people are active internet users and 3.3 billion are social media users". Plus, out of this digital population, 79% of consumers demand real-time conversations and engagements with brands rather than phone or e-mail. The new type of customer is empowered with all the information and connectivity at their disposal.