News from the AI & ML world
DeeperML - #genai
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@cloud.google.com
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AI & Machine Learning
Alpian, a pioneering Swiss private bank, is revolutionizing the financial services industry by integrating Google's generative AI into its core operations. As the first fully cloud-native private bank in Switzerland, Alpian is embracing digital innovation to offer a seamless and high-value banking experience, balancing personal wealth management with digital convenience. This strategic move positions Alpian at the forefront of the digital age, setting a new benchmark for agility, scalability, and compliance capabilities within the tightly regulated Swiss financial landscape. Alpian's partnership with Google, leveraging tools like Gemini, enables developers to interact with infrastructure through simple conversational commands, significantly reducing deployment times.
Alpian faced the challenge of innovating within the strict regulatory environment of the Swiss banking system, overseen by FINMA. The integration of generative AI required meticulous attention to compliance and security. By implementing a platform that utilizes generative AI, Alpian has created a defined scope where engineers can autonomously interact with IT elements using a simplified conversational interface. This approach allows teams to focus on innovation rather than repetitive tasks, accelerating deployment times from days to hours and empowering them to develop cutting-edge services while adhering to stringent compliance standards. The benefits of this generative AI integration extend beyond internal workflows, directly enhancing the client experience. Faster deployment times translate into quicker access to new features, such as tailored wealth management tools and enhanced security measures. Furthermore, Google’s NotebookLM, which now allows users to publicly share notebooks with a link, can be used to provide clients with AI-generated research summaries or briefing documents. This initiative not only optimizes internal operations but also establishes a new benchmark for operational excellence in the banking sector, showcasing the transformative potential of AI in redefining private banking for the 21st century. Recommended read:
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staff@insideAI News
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IBM has launched watsonx AI Labs, a developer-first innovation hub located in New York City. The new lab is designed to accelerate the adoption of AI at scale by connecting IBM's enterprise resources and expertise with AI developers focused on building AI applications for business. Located in Manhattan at IBM's new offices at One Madison, watsonx AI Labs aims to connect IBM’s network of engineering labs, bringing together IBM researchers and engineers in a collaborative hub for co-creating and advancing agentic AI solutions.
The watsonx AI Labs is intended to co-create generative AI solutions with IBM clients, nurture AI talent within New York City, and advance enterprise AI implementations. IBM plans to work with startups, scale-ups, and enterprises to discover AI value through this initiative. New York City has a growing AI ecosystem, with more than 2,000 AI startups and an AI workforce that grew by almost 25% from 2022 to 2023. Since 2019, over 1,000 AI-related companies in New York City have collectively raised $27 billion in funding. As part of its investment in AI and commitment to the local startup ecosystem, IBM also announced the acquisition of Seek AI. Seek AI is a New York City-based startup that specializes in building AI agents that leverage enterprise data, providing businesses with a natural language interface to query and analyze corporate data stores. Seek AI's expertise will be integrated into watsonx AI Labs, helping businesses leverage agentic AI to extract value from their data and improve data analysis and summarization capabilities. Recommended read:
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@learn.aisingapore.org
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Amazon is expanding its AI capabilities, focusing on both customer-facing and internal operational improvements. A key development is the enhanced Amazon Q Business, a generative AI-powered assistant now supporting anonymous user access. This feature allows businesses to create public-facing applications, such as Q&A sections on websites, documentation portals, and self-service customer support, without requiring user authentication. This provides guest users with AI-driven assistance to quickly find product information, navigate documentation, and troubleshoot issues.
The anonymous Amazon Q Business applications can be integrated into websites using either an embedded web experience via an iframe or through customized interfaces built with Chat, ChatSync, and PutFeedback APIs. Amazon offers a consumption-based pricing model for these anonymous applications, charging based on the number of Chat or ChatSync API operations. This allows businesses to offer powerful AI assistance to a wider audience while maintaining control over costs and deployment. In addition to AI-powered customer service, Amazon is also enhancing its warehouse operations with the introduction of the Vulcan robot. Equipped with gripping pincers, built-in conveyor belts, and a pointed probe, Vulcan is designed to handle 75% of the package types in Amazon's fulfillment centers. This robot represents a significant advancement in robotics, as it can "feel" objects, enabling it to handle a variety of items with the necessary strength and agility. Amazon says this "touch" capability is a fundamental leap forward, differentiating Vulcan from previous robots that lacked the ability to sense contact. Recommended read:
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@www.techmeme.com
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Ken Yeung
, venturebeat.com
According to a new Amazon Web Services (AWS) report, generative AI has become the top IT priority for global organizations in 2025, surpassing traditional IT investments like security tools. The AWS Generative AI Adoption Index, which surveyed 3,739 senior IT decision makers across nine countries, reveals that 45% of organizations plan to prioritize generative AI spending. This shift signifies a major change in corporate technology strategies as businesses aim to capitalize on AI's transformative potential. While security remains a priority, the broad range of use cases for AI is driving the accelerated adoption and increased budget allocation.
The AWS study highlights several key challenges to GenAI adoption, including a lack of skilled workforce, the cost of development, biases and hallucinations, lack of compelling use cases, and lack of data. Specifically, 55% of respondents cited a lack of skilled workers as a significant barrier. Despite these challenges, organizations are moving quickly to implement GenAI, with 44% having moved beyond the proof-of-concept phase into production deployment. The average organization has approximately 45 GenAI projects or experiments in various stages, with about 20 of them transitioning into production. In response to the growing importance of AI, 60% of companies have already appointed a dedicated AI executive, such as a Chief AI Officer (CAIO), to manage the complexity of AI initiatives. This executive-level commitment demonstrates the increasing recognition of AI’s strategic importance within organizations. Furthermore, many organizations are creating training plans to upskill their workforce for GenAI, indicating a proactive approach to address the talent gap. The focus on generative AI reflects the belief that it can drive automation, enhance creativity, and improve decision-making across various industries. Recommended read:
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Noor Al-Sibai@futurism.com
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Duolingo, the popular language-learning application, is shifting to an "AI-first" model, initiating a restructuring of its operations to focus on generative AI for content creation and process automation. This move includes a gradual reduction in reliance on contractors, with AI taking over tasks where possible. CEO Luis von Ahn conveyed this strategic shift in an internal memo, emphasizing the need to proactively respond to technological changes, similar to the company’s successful early adoption of a "mobile first" strategy in 2012. He noted that AI is already transforming how work is accomplished within the company.
The primary objective of this transition is to accelerate content delivery and increase its scale. Duolingo views manual content creation as no longer viable for meeting its needs, emphasizing that replacing slow, manual processes with AI-driven solutions is key to providing the desired amount of content for learners in a fraction of the time. Von Ahn stated that without AI, producing new materials would take decades, and that AI integration will also support new features, including video calls. He made sure to note that one of the best decisions the company made recently was replacing a slow, manual content creation process with one powered by AI. Following the announcement of its "AI-first" strategy, Duolingo launched 148 new language courses created with generative AI. CEO Luis von Ahn stated the company was able to develop more courses in less than a year than it had in the previous twelve years combined. The expansion primarily focuses on making seven popular non-English languages – Spanish, French, German, Italian, Japanese, Korean, and Mandarin – available across all 28 of Duolingo's supported interface languages, aiming to dramatically expand access for speakers of languages that previously had limited learning options, particularly in Asia and Latin America. Recommended read:
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Noor Al-Sibai@futurism.com
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Duolingo is making a significant shift to an AI-first model, restructuring its operations to focus on generative AI for content creation and process automation. CEO Luis von Ahn announced plans to gradually reduce the company's reliance on contractors, aiming to automate tasks wherever possible. This transition marks a fundamental cultural shift, with leadership emphasizing the transformative power of AI in reshaping how work is accomplished. This mirrors the company's early adoption of a "mobile-first" strategy in 2012 which led to significant recognition.
This strategic move is driven by the need to deliver app content more quickly and at a greater scale. Duolingo states that manual content creation is no longer viable for meeting the company's needs. Replacing slow, manual processes with AI-driven solutions allows for the faster provision of content for learners. The company reported that AI has enabled them to build more courses in one year than in the previous twelve years combined. A large content expansion was recently launched by the company, releasing 148 new language courses which were all created using generative AI. The implementation of AI extends beyond content creation, with plans to integrate it into hiring processes and employee performance reviews. Teams will be encouraged to prioritize automation before requesting additional resources. CEO Luis von Ahn stated that the changes are not intended to reduce the company's focus on employee well-being, adding that the move is not about replacing employees with AI but removing bottlenecks. Instead, the goal is to empower employees to focus on creativity, accelerating Duolingo's mission to deliver language instruction globally. Recommended read:
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staff@insideAI News
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MLCommons has released the latest MLPerf Inference v5.0 benchmark results, highlighting the growing importance of generative AI in the machine learning landscape. The new benchmarks feature tests for large language models (LLMs) like Llama 3.1 405B and Llama 2 70B Interactive, designed to evaluate how well systems perform in real-world applications requiring agentic reasoning and low-latency responses. This shift reflects the industry's increasing focus on deploying generative AI and the need for hardware and software optimized for these demanding workloads.
The v5.0 results reveal significant performance improvements driven by advancements in both hardware and software. The median submitted score for Llama 2 70B has doubled compared to a year ago, and the best score is 3.3 times faster than Inference v4.0. These gains are attributed to innovations like support for lower-precision computation formats such as FP4, which allows for more efficient processing of large models. The MLPerf Inference benchmark suite evaluates machine learning performance in a way that is architecture-neutral, reproducible, and representative of real-world workloads. Recommended read:
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Nazy Fouladirad@AI Accelerator Institute
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hiddenlayer.com
, AI Accelerator Institute
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As generative AI adoption rapidly increases, securing investments in these technologies has become a paramount concern for organizations. Companies are beginning to understand the critical need to validate and secure the underlying large language models (LLMs) that power their Gen AI products. Failing to address these security vulnerabilities can expose systems to exploitation by malicious actors, emphasizing the importance of proactive security measures.
Microsoft is addressing these concerns through innovations in Microsoft Purview, which offers a comprehensive set of solutions aimed at helping customers seamlessly secure and confidently activate data in the AI era. Complementing these efforts, Fiddler AI is focusing on building trust into AI systems through its AI Observability platform. This platform emphasizes explainability and transparency. They are helping enterprise AI teams deliver responsible AI applications, and also ensure people interacting with AI receive fair, safe, and trustworthy responses. This involves continuous monitoring, robust security measures, and strong governance practices to establish long-term responsible AI strategies across all products. The emergence of agentic AI, which can plan, reason, and take autonomous action to achieve complex goals, further underscores the need for enhanced security measures. Agentic AI systems extend the capabilities of LLMs by adding memory, tool access, and task management, allowing them to operate more like intelligent agents than simple chatbots. Organizations must ensure security and oversight are essential to safe deployment. Gartner research indicates a significant portion of organizations plan to pursue agentic AI initiatives, making it crucial to address potential security risks associated with these systems. Recommended read:
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Sean Michael@AI News | VentureBeat
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AI News | VentureBeat
, www.computerworld.com
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Gartner, an analyst firm, released a report forecasting that global generative AI spending will reach $644 billion in 2025. This figure represents a 76.4% year-over-year increase from 2024. Despite high failure rates among early generative AI projects, organizations are still expected to invest heavily, with the lion's share of spending going towards services. GenAI services are projected to grow by 162% this year, following a 177% increase last year. According to Gartner Analyst John-David Lovelock, the shift from software to generative AI is becoming a "tidal wave of money."
The surge in spending is primarily driven by vendor investments in the technology. Hyperscalers are making massive capital expenditures on GPU infrastructure, and software vendors are rushing to deploy generative AI tools. Enterprises, however, are pulling back on in-house AI projects and increasingly opting for off-the-shelf solutions. "CIOs are no longer building generative AI tools, they’re being sold technology," Lovelock stated, emphasizing that vendors are offering solutions that meet enterprise needs. Recommended read:
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