News from the AI & ML world
DeeperML - #aisoftware
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Clint Boulton,@Dell Technologies
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AI is rapidly transforming several key areas, including software development, AI security, and customer interactions. In software development, prompting GenAI systems to create code is reducing repetitive processes, accelerating production cycles and freeing up developers to focus on higher-value projects. Databricks and Noma are addressing critical AI inference vulnerabilities, while Impel is enhancing customer experiences in the automotive sector through fine-tuned AI models. Furthermore, agentic AI is enabling autonomous, goal-driven decision-making across the IoT, paving the way for smarter and more efficient smart environments.
Databricks and Noma Security are partnering to tackle AI inference vulnerabilities, helping CISOs confidently scale secure enterprise AI deployments. CISOs recognize that the vulnerable stage of AI deployment is inference, where live models encounter real-world data, leading to potential exposure to prompt injection, data leaks, and model jailbreaks. To combat these threats, Databricks Ventures and Noma Security are embedding real-time threat analytics, advanced inference-layer protections, and proactive AI red teaming directly into enterprise workflows. This joint approach is bolstered by a $32 million Series A funding round led by Ballistic Ventures and Glilot Capital, with strong support from Databricks Ventures. Impel is revolutionizing automotive retail by improving customer experience using fine-tuned LLMs on Amazon SageMaker. Their core product, Sales AI, provides personalized customer engagement 24/7, answering vehicle-specific questions and handling automotive trade-in and financing inquiries. By switching from a third-party LLM to a fine-tuned Meta Llama model on Amazon SageMaker AI, Impel achieved a 20% improvement in accuracy and greater cost control. Impel's Sales AI uses generative AI to provide instant responses around the clock to prospective customers through email and text, with features that provide consistent follow-up to engaged customers to help prevent stalled customer purchasing journeys and personalizes responses to align with retailer messaging and customer’s purchasing specifications.
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Sean Michael@AI News | VentureBeat
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Windsurf, an AI coding startup reportedly on the verge of being acquired by OpenAI for a staggering $3 billion, has just launched SWE-1, its first in-house small language model specifically tailored for software engineering. This move signals a shift towards software engineering-native AI models, designed to tackle the complete software development workflow. Windsurf aims to accelerate software engineering with SWE-1, not just coding.
The SWE-1 family includes models like SWE-1-lite and SWE-1-mini, designed to perform tasks beyond generating code. Unlike general-purpose AI models adapted for coding, SWE-1 is built to address the entire spectrum of software engineering activities, including reviewing, committing, and maintaining code over time. Built to run efficiently on consumer hardware without relying on expensive cloud infrastructure, the models offer developers the freedom to adapt them as needed under a permissive license. SWE-1's key innovation lies in its "flow awareness," which enables the AI to understand and operate within the complete timeline of development work. Windsurf users have given the company feedback that existing coding models tend to do well with user guidance, but over time tend to miss things. The new models aim to support developers through multiple surfaces, incomplete work states and long-running tasks that characterize real-world software development.
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@gradientflow.com
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AI agents are rapidly transforming the software development landscape, shifting traditional coding roles into collaborative partnerships between humans and artificial intelligence. According to Towards AI, AI agents are being integrated into coding tools, enabling developers to learn faster and perform tasks more efficiently. This evolution is not just about speed but also about improving the overall quality and effectiveness of the development process.
A dbt Labs report highlights the increasing adoption of AI within data teams, revealing that 80% of data professionals now use AI in their daily workflows, a significant increase from 30% the previous year. The report emphasizes that AI is not replacing human expertise but augmenting it, allowing professionals to focus on more specialized and strategic work. Specifically, 70% of respondents reported using AI for analytics development, automating routine tasks and fundamentally altering how data is delivered within organizations. As a result, organizations are increasingly valuing and trusting their data teams, with 75% of respondents agreeing that their contributions are highly regarded. However, the rise of AI agents is not without its challenges. Gradient Flow notes that while there is intense interest in AI agents, their presence in live enterprise environments remains relatively limited. There is confusion around the definition of "agent," with companies using the term broadly to describe everything from basic chatbots to sophisticated autonomous systems. Despite these challenges, technologists define a true agent as an autonomous software system that can perceive its environment, reason through complex problems, and take independent actions to achieve defined goals. These agents exhibit genuine autonomy, adapt to changing circumstances, maintain context across interactions, and employ multi-step reasoning to tackle problems, showcasing the potential for mature agents across various domains.
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