Security News

While in last year's survey respondents cited company culture as the major bottleneck to enterprise AI adoption, lack of skilled people and difficulty hiring topped the list this year, noted by 19% of respondents. While it's not surprising that demand for AI expertise has exceeded supply, it's important to understand which specific skills and professional titles are most critical to AI adoption.

Founded in 2012 by former U.S. intelligence community AI and high-volume data processing experts, Koverse is a 16-person, Seattle-based company that delivers scalable, secure, and high-performing solutions to federal and commercial customers. Koverse solves one of the most difficult and time-consuming challenges in developing AI tools: organizing structured and unstructured data from multiple sources based on a user's individual attributes and permissions.

To date, hacking has exclusively been a human activity. Separately, AIs can engage in something called reward hacking.

Adversa has published comprehensive research on the security and trustworthiness of AI systems worldwide during the last decade. The research considers the impact of ongoing regulations concerning AI security in the EU and USA. "Building trust in the security and safety of machine learning is crucial. We are asking people to put their faith in what is essentially a black box, and for the AI revolution to succeed, we must build trust. And we can't bolt security on this time. We won't have many chances at getting it right. The risks are too high - but so are the benefits," said Oliver Rochford, Adversa Advisor.

IBM announced new capabilities for IBM Watson designed to help businesses build trustworthy AI. These capabilities further expand Watson tools designed to help businesses govern and explain AI-led decisions, increase insight accuracy, mitigate risks and meet their privacy and compliance requirements. The barriers to developing trustworthy AI and mitigating risk remain pervasive, with 82% of AI professionals surveyed saying their organization has been negatively impacted by problems, like bias, with data or AI models.

The EU unveiled a plan Wednesday to regulate the sprawling field of artificial intelligence, aimed at helping Europe catch up in the new tech revolution while curbing the threat of Big Brother-like abuses. There have been competing concerns over the plans from both big tech and civil liberties groups arguing that the EU is either overreaching or not going far enough.

GigaIO announced FabreX release 2.2, the native PCI Express Gen4 universal dynamic fabric, which supports NVMe-oF, GDR, MPI, and TCP/IP. This new release introduces an industry first in scalability over a PCIe fabric for AI workloads by enabling the creation of composable GigaPods and GigaClusters with cascaded and interlinked switches. Intel, WWT, and GigaIO will be discussing in an upcoming virtual roundtable on April 27th how the new scalability challenges for AI workloads can be met through next-gen high performance solutions like Optane and FabreX. By breaking the barrier of the server box, GigaIO's technology enables the entire rack to be treated as the compute unit.

Siemens intends to integrate Google Cloud's leading data cloud and artificial intelligence/machine learning technologies with its factory automation solutions to help manufacturers innovate for the future. While AI projects have been deployed by many companies in "Islands" across the plant floor, manufacturers have struggled to implement AI at scale across their global operations.

Lenovo Infrastructure Solutions Group announces the expansion of its partner ecosystem and launches five new ready-to-deploy artificial Intelligence solutions. Leveraging Lenovo's AI innovation centers, AI architecture and hardware expertise, Lenovo is helping move AI from concept to reality.

Grid.ai announced the general availability of Grid, a new platform that enables researchers and data scientists to train AI models on the cloud at scale, from a laptop with zero code changes. The availability of Grid enables AI researchers, machine learning engineers, and data scientists to do development and training at scale without requiring advanced skills in machine learning engineering or MLOps engineering.