HORTONWORKS BIG DATA MATURITY MODEL

whitePaper | January 10, 2020

Instead of following a set of rote, preprogrammed instructions, AI enables machines to learn, and then “act,” on information. Your voice-activated phone, technologies such as speech and image recognition, autonomous vehicles, the smart devices in your home—all of these advances were made possible by AI technology. AI also marks a fundamental advancement in critical, often time-consuming, business processes. It’s poised to dramatically enhance human decision-making and improve an organization’s ability to make predictions, automate business practices, and optimize logistics.

whitePaper | November 1, 2021

Robotics, automation, Internet of Things (IoT), and data are not new in manufacturing. Manufacturing is one of the most data-rich and first industries piloting artificial intelligence at the edge. It’s been estimated, however, that about 95% of all industrial data goes unused. The reason? Legacy equipment and operational systems that tend to be proprietary and siloed are incapable of communicating with each other.

whitePaper | November 24, 2022

High-profile examples of harm associated with algorithm failure and misuse have garnered significant public concern (e.g. manipulation of the democratic process, discriminatory practice in criminal justice sentencing, and bias against racial and gender categories in the labour market). The field of AI ethics refers to the literature and to the community of people interested in this space. One high-profile manifestation of this is the formation of specialist ethics councils and teams to deliberate on conundrums regarding the use, access and openness of platforms and technologies (such as social media and messaging apps, etc.). For instance, the banning of particular public figures as well as lay users for use of the services in a manner that is not acceptable has been a constant theme in the public debate.

whitePaper | November 30, 2022

The first-generation low-code platforms required considerable adaptations and months of development in the late 1990s and early 2000s. Gartner expects that by 2024, low-code/no-code automation will account for 65% of all app development.

whitePaper | June 28, 2022

Artificial intelligence (AI) has been accelerating across sectors. It has great potential to benefit society but the technology’s full potential can only be realized if it includes the diversity of the populations it represents throughout every step of its development. With growing concerns about bias, data privacy and lack of representation, it is critical to re-evaluate the way in which AI is both designed and deployed to ensure that all affected stakeholders and communities reap the benefits of the technology, rather than any harm.

whitePaper | January 20, 2022

Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing neural network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. To enable that, we present CMSIS-NN, an open-source library of optimized software kernels that maximize the NN performance on Cortex-M cores with minimal memory footprint overhead. We further present methods for NN architecture exploration, using image classification on CIFAR-10 dataset as an example, to develop models that fit on such constrained devices.