Artificial Intelligence and Machine Learning explained

AI, or artificial intelligence, is technology that attempts to simulate human cognitive function. A  popular use case right now is ChatGPT, which allows you to conversationally ask questions to a chatbot and get back relevant information because the AI can understand what you’re asking in plain language. Beyond just answering questions, these AIs are capable of writing code, creating detailed plans based on your specifications, summarizing documents, and more.

AI has made its way into the software development space in a number of ways. AI can be baked into applications to improve end user experience by creating personalized recommendations and tailoring experiences to the end user. AI-assisted development tools can complete the piece of code you started writing, or even offer suggestions for how to improve your code. Generative AI chatbots, such as ChatGPT, can be used to ask for specific code snippets to perform a task, can explain what is happening in a piece of code, or can be used to troubleshoot why your code isn’t working as intended.

Read the articles below for more information on what you need to know about AI.

MongoDB launches new technology stack for building AI apps

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’s new Llama 3.1 model competes with GPT-4o and Claude 3.5 Sonnet

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

Coalition for Secure AI forms to address security risks of AI

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

Report: Execs and devs have different perceptions around supply chain security, AI use

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

Miro launches AI-based idea generation platform Intelligent Canvas

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

Developers, leaders disconnect on productivity, satisfaction

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

The impact of AI regulation on R&D

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 – SD Times Open Source Project of the Week

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

AWS Summit: AWS App Studio, Amazon Q Apps, and more

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

Q&A: Evaluating the ROI of AI implementation

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 adds prompt evaluation feature to Console

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

Working toward AIOps maturity? It’s never too early (or late) for platform engineering

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

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