MongoDB is launching a new technology stack to enable customers to build AI applications. The MongoDB AI Applications Program (MAAP) will feature reference architectures, integrations with leading AI technology providers, and a support system for customers featuring access to experts and education. According to MongoDB, many customers have reported that they lack the multi-modal data … continue reading
Meta has announced the latest release of its open source AI model, Llama. According to Meta, with the release of Llama 3.1 405B, the company is trying to prove that open models can be just as capable as their closed counterparts, if not better. “Llama 3.1 405B is the first openly available model that rivals … continue reading
A number of companies have announced the formation of the Coalition for Secure AI (CoSAI), a group dedicated to addressing the security risks related to using AI. CoSAI was founded by Amazon, Anthropic, Chainguard, Cisco, Cohere, GenLab, Google, IBM, Intel, Microsoft, NVIDIA, OpenAI, Paypal and Wiz. It will be hosted at the standards body OASIS … continue reading
While the occurrence of software supply chain attacks just keeps getting worse every year, there appears to be a disconnect among leaders on the importance of securing those supply chains. According to research from IDC, there has been a 241% increase year-over-year in supply chain attacks, but a new survey from JFrog had only 30% … continue reading
The visual collaboration company Miro has announced the launch of Intelligent Canvas, an AI-based idea generation platform. “Miro’s Intelligent Canvas is built to support business processes well beyond the brainstorming phase,” Jeff Chow, CPTO of Miro. “It combines AI, composable workflows, and a more intuitive, interactive experience to drive higher team productivity. Miro’s canvas is … continue reading
The advent of DevOps, cloud-native computing, API use and now AI have made creating software way more complex for developers. These factors have also impacted the developers’ experience and productivity – and how productivity is measured. No longer do software engineers simply write code and run some tests. Now, they have to manage API integration … continue reading
Artificial intelligence (AI) continues to maintain its prevalence in business, with the latest analyst figures projecting the economic impact of AI to have reached between $2.6 trillion and $4.4 trillion annually. However, advances in the development and deployment of AI technologies continue to raise significant ethical concerns such as bias, privacy invasion and disinformation. These concerns … continue reading
GraphRAG is an open source research project out of Microsoft for creating knowledge graphs from datasets that can be used in retrieval-augmented generation (RAG). RAG is an approach in which data is fed into an LLM to give more accurate responses. For instance, a company might use RAG to be able to use its own … continue reading
Amazon hosted its annual AWS Summit today in NYC where it announced several updates related to its generative AI offerings. Here are the highlights from today’s event: AWS App Studio now in preview AWS App Studio is a no-code platform for building applications using generative AI, without having to have any software development knowledge. For … continue reading
Many development teams are beginning to experiment with how they can use AI to benefit their efficiency, but in order to have a successful implementation, they need to have ways to assess that their investment in AI is actually providing value proportional to that investment. A recent Gartner survey from May of this year said … continue reading
Anthropic’s developer Console now allows developers to generate, test, and evaluate AI prompts, allowing them to ultimately improve response quality. Claude 3.5 Sonnet introduced a built-in prompt generator that allows a user to describe a task and have Claude convert it into a high-quality prompt. For example, they could describe that they need to triage … continue reading
Until about two years ago, many enterprises were experimenting with isolated proofs of concept or managing limited AI projects, with results that often had little impact on the company’s overall financial or operational performance. Few companies were making big bets on AI, and even fewer executive leaders lost their jobs when AI initiatives didn’t pan … continue reading
