Latest news with #LatentSpace
Finextra
10-07-2025
- Business
- Finextra
FIA hails InnovateIN48 champion Latent Space
Global financial technology leader FIS (NYSE: FIS), has announced the winners of its 12th annual InnovateIN48 innovation competition, with competitors pushing the boundaries of AI to help solve operational challenges faced by today's businesses. 0 The competition comes at a crucial time, with many businesses now making significant investments in AI and automation technologies. New research from FIS' landmark report, 'The Harmony Gap: Finding the Financial Upside in Uncertainty,' found that 55% of companies are investing in innovative solutions such as generative AI and machine learning to help meet their strategic objectives, while 78% of organizations that have already integrated AI reported measurable improvements in fraud detection and risk management. However, 73% cited the high cost of implementation and maintenance as an obstacle to their firm's adoption of AI and automation, as well as struggling with a lack of in-house expertise (64%). To help address the expertise gap, InnovateIN48 competitors developed early-stage prototypes and conceptual AI frameworks, pushing the boundaries of how AI can help organisations accomplish their strategic goals. These included automated code vulnerability detection, intelligent regulatory compliance checking and AI-powered treasury operations. The winners were team Latent Space, who developed a solution that helps confirm that software projects follow important internal and external AI guidelines, with the solution's potential to improve AI compliance impressing judges. Ramkumar Narayanan, Head of FIS India and Philippines Technology and Services Organization said, "This year's InnovateIN48 focused on showcasing innovative use cases for employing AI to solve real-world business issues. While AI as a technology is transformational, the knowledge of how to use AI is a fundamental component of a firm's success. We're working to develop the in-house talent that the fintech industry needs to realize AI's potential while fostering innovative thinking that could lead to breakthrough solutions."
Forbes
18-04-2025
- Business
- Forbes
Everyone's Getting Better At Using AI: Thoughts On Vibe Coding
Female freelance developer coding and programming. Coding on two with screens with code language and ... More application. As we look at the landscape around the second quarter of 2025, we see amazing developments. You can look at what's happening in conferences and at trade shows. You can ask engineers what they're doing, or consult with a CEO. Everywhere you look, things are changing at breakneck speed. What's the difference between people who actually use the keyboard to write code, and others who manage people and processes at an abstract level? Well, in the AI age, that gap is getting smaller quickly. But there's still an emphasis on people who know how to code, and especially, people who know how to engineer. Coding is getting automated, but engineering is still a creative component of the human domain - for now. I was listening to a recent episode of AI Daily Brief, where Nathaniel Whittemore talked to Shawn Wang, professionally known as 'Swyx,' about valuing the engineering role. 'It has always been valuable for people who are involved to keep the pulse on what builders are building,' Swyx said. The two conceded, though, that right now, 'building' is becoming a vague term, as it's getting easier to develop project on a new code basis. You just tell AI what you want, and it builds it. Having said that, in putting together events for the engineering community, Swyx sees the effort as vital to the industry itself. 'The people who have hands on a keyboard also need a place to gather,' he said, noting that for some of these events, attendees have to publish or otherwise prove their engineering capabilities. Later on, in thinking about how this works logistically, the two talked about a new tool called Model Context Protocol, which lives on GitHub, and how it's being used. MCP connects LLMs to the context that they need. This utility involves prebuilt integrations, a client server architecture, and APIs, as well as environments like Claude Desktop. The 'hosts' are LLMs, the 'client' provides a 1:1 server connection, and servers handle context, data and prompts. The system utilizes the transport layer for various communication events including requests, results and errors. 'You're not stuck to one model,' Swyx pointed out in illustrating how versatile these setups can be. Noting an 'S curve' for related technology, Swyx discussed timing of innovations, invoking Moore's law. 'If you're correct, but early, you're still wrong,' he said. Mentioning how companies are 'moving away from a cost plus model to one where you deliver outcomes.' Paraphrasing Shakespeare, he suggested that at companies like Google, execs are asking: 'To MCP, or not to MCP?' And there's another question for implementers: 'How much of my job can you do?' As for a timeline for MCP, Swyx cited the work of Alex Albert, also at Latent Space. 'The immediate reaction was good,' he said. 'There was a lot of immediate interest. I don't think there was a lot of immediate follow through.' Later on, Swyx brought up the contributions of Lilian Wang, who he said defined an AI agent as 'LLM + memory + planning + tool use.' He also laid out his own definition based on the acronym IMPACT, noting that he sees a lot of this type of work as disordered or unstructured, and that people should really ideally be able to define agent engineering well. The 'I', he said, stands for intent and intensity, goals, and evaluations. 'M' is memory; 'P' is planning. 'A' is authority. 'Think of (the agent) as like a real estate agent,' he said, suggesting that the agent should have specialized knowledge. 'C' is control flow, and 'T' is tool use, which he said everyone can agree on. Swyx called for a 'tight feedback loop' and processes that 'organically take traction' in enterprise. This part of the conversation was absolutely fascinating to me as a clear eyed assessment of the different ways people use the term 'vibe coding.' I've written about how figures like Andrej Karpathy and Riley Brown define this practice of working with AI that can craft code. But there are two interpretations of this phrase, and they're radically different. One that the duo mentioned is that the human programmer can get the vibe of the code and analyze it as a professional, where they need to already have some knowledge of what code is supposed to look like. But then there's the other definition. 'Vibe coding gets taken out of context,' Swyx said. In this latter interpretation, you don't need expertise, because you just evoke the vibe of the code and let the AI figure it out. But this way, he said, you can get into trouble and wasted dollars. As for best practices in vibe coding, Swyx suggested dealing with legacy code issues, having the appropriate skepticism about the limitations of vibe coding, and sampling the space There's something here,' he said, displaying enthusiasm for the democratization of code. 'I don't know if vibe coding is the best name for it.' In addition to all of the above, people are going to need some form of expertise, whether they are leaders, or builders, or both. Regardless of which way you view the new coding world, there's little question that reskilling for humans is going to be a piece of the puzzle. This resource from Harvard talks about tackling the challenge: 'As new technologies are integrated into organizations, with greater frequency, transforming how we work, the need for professionals to adapt and continue to learn and grow becomes more imperative.' I agree. All of this is quite instructive at this point in time when companies are looking for a way forward. Let's continue with this deep analysis of business today, as AI keeps taking hold throughout the rest of the year.
