News from the AI & ML world
DeeperML - #aisolutions
|
Alexey Shabanov@TestingCatalog
//
AI agents are rapidly transforming how work gets done by automating and streamlining a variety of workflows. These intelligent systems are designed to handle tasks ranging from managing schedules, emails, and notes, as exemplified by Genspark's new AI Secretary feature, to providing personalized customer engagement in the automotive retail sector, demonstrated by Impel's use of fine-tuned LLMs. The core advantage of agentic AI lies in its capacity for autonomous decision-making and enhanced customer experiences powered by AI-driven solutions. Impel, for instance, optimizes automotive retail customer connections through personalized experiences at every touchpoint, utilizing Sales AI to provide instant responses and maintain engagement during the car-buying journey.
The development of agentic AI extends to the realm of IoT, where these agents are poised to enable autonomous, goal-driven decision-making. This is particularly relevant in smart homes, cities, and industrial systems, where AI agents can proactively address network issues, strengthen security, and improve overall productivity. Agentic AI marks a structural shift from traditional AI, transitioning from task-specific and supervised models to autonomous agents capable of real-time decisions and adaptation. These agents possess memory, autonomy, task awareness, learning, and reasoning abilities, allowing them to operate with minimal human intervention. However, the effectiveness of AI agents hinges on accurate monitoring strategies and their ability to navigate complex tasks. To ensure reliability in real-world scenarios, benchmarks like WebChoreArena are being developed to challenge agents with memory-intensive and reasoning-intensive scenarios. Building robust conversational AI agents also requires overcoming limitations in existing frameworks. The Rasa platform offers an alternative approach through process calling, enabling the creation of reliable, process-aware, and easily debuggable conversational agents. This method addresses issues such as loss of conversational context and poor adherence to business processes, ensuring that AI agents can consistently guide users through predetermined workflows.
Share:
References :
Classification:
@futurumgroup.com
//
Snowflake Summit 2025, held in San Francisco, showcased Snowflake's intensified focus on AI capabilities, built upon a unified data foundation. Attracting approximately 20,000 attendees, the event underscored the company’s commitment to making AI and machine learning more accessible and actionable for a broad range of users. Key themes revolved around simplifying AI adoption, improving data interoperability (especially for unstructured and on-premises data), enhancing compute efficiency, embedding AI into data governance, and empowering developers with richer tooling.
Major announcements highlighted the expansion of the Snowflake AI Data Cloud's capabilities. Cortex AI was a central focus, with the introduction of Cortex AISQL, which embeds generative AI directly into queries to analyze diverse data types. This allows users to build flexible pipelines using SQL, marking a significant evolution in the intersection of AI and SQL across multimodal data. Another notable launch was SnowConvert AI, an automation solution designed to accelerate migrations from legacy platforms by automating code conversion, testing, and data validation, reportedly making these phases 2-3 times faster. Snowflake also introduced Cortex Knowledge Extension which allows information providers to create a Cortex Search service over their content without copying or exposing the entire information corpus. This enables a "share without copying" model paid via Snowflake. Furthermore, Snowflake's acquisition of Crunchy Data, a provider of Postgres managed services, signals the growing importance of relational databases in supporting AI agents. These updates and acquisitions position Snowflake to meet the increasing demands of enterprises seeking to leverage AI for data-driven insights and operational efficiency.
Share:
References :
Classification:
|
BenchmarksBlogsResearch Tools |