News from the AI & ML world
DeeperML - #responsibleai
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@pub.towardsai.net
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pub.towardsai.net
DeepSeek's R1 model is garnering attention as a potential game-changer for entrepreneurs, offering advancements in "reasoning per dollar." This refers to the amount of reasoning power one can obtain for each dollar spent, potentially unlocking opportunities previously deemed too expensive or technologically challenging. The model's high-reasoning capabilities at a reasonable cost are seen as a way to make advanced AI more accessible, particularly for tasks that require deep understanding and synthesis of information. One example is the creation of sophisticated AI-powered tools, like a "lawyer agent" that can review contracts, which were once cost-prohibitive.
The DeepSeek R1 model has been updated and released on Hugging Face, reportedly featuring significant changes and improvements. The update comes amidst both excitement and apprehension regarding the model's capabilities. While the model demonstrates promise in areas like content generation and customer support, concerns exist regarding potential political bias and censorship. This stems from observations of alleged Chinese government influence in the model's system instructions, which may impact the neutrality of generated content. The adoption of DeepSeek R1 requires careful self-assessment by businesses and individuals, weighing its strengths and potential drawbacks against specific needs and values. Users must consider the model's alignment with their data governance, privacy requirements, and ethical principles. For instance, while the model's content generation capabilities are strong, some categories might be censored or skewed by built-in constraints. Similarly, its chatbot integration may lead to heavily filtered replies, raising concerns about alignment with corporate values. Therefore, it is essential to be comfortable with the possible official or heavily filtered replies, and to consider monitoring the AI's responses to ensure they align with the business' values. Recommended read:
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Ryan Priem@AI Accelerator Institute
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AI Accelerator Institute
Google is significantly enhancing its Customer Engagement Suite with the addition of human-like AI agents, marking a new era in AI-powered customer interactions. Announced at Cloud Next 2025, the update focuses on creating more interactive and personalized experiences through its Conversational Agent product. These enhancements include improved voice comprehension, emotional intelligence, and streaming video support, enabling the bots to adapt better to real-time conversations and even "see" service tickets presented via customer devices. This move aims to make AI agents more accessible and easier to deploy, transforming how businesses engage with their customers across all touchpoints.
Google's upgraded Conversational Agents now leverage the latest Gemini models, including Gemini 2.5 Flash, to achieve a more human-like sound, higher comprehension levels, and the ability to understand emotion. To further streamline the agent development process, Google is introducing an AI assistant with a no-code interface and an Agent Development Kit. The suite also includes new connector tools that allow the software to perform specific tasks like product lookups, adding items to shopping carts, and processing checkouts through API calls. These additions reflect Google's commitment to providing organizations with the resources needed to build and deploy advanced conversational AI agents effectively. Launched in September 2024, Google’s Customer Engagement Suite is positioned as an AI-powered platform designed to help organizations deliver better customer experiences. With these new enhancements, Google intensifies its competition with other customer experience (CX) players such as Salesforce, Zendesk, Intercom, and Amazon Web Services, all of whom are integrating AI to improve customer service through various channels like chat, voice, and video. Duncan Lennox, Google’s vice president and general manager of applied AI, highlighted the transformative potential of AI agents, stating that they enable new levels of hyper-personalized, multimodal conversations with customers, ultimately improving customer interactions across all touchpoints. Recommended read:
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Ryan Daws@AI News
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Anthropic's AI assistant, Claude, has gained a significant upgrade: real-time web search. This new capability allows Claude to access and process information directly from the internet, expanding its knowledge base beyond its initial training data. The integration aims to address a critical competitive gap with OpenAI's ChatGPT, leveling the playing field in the consumer AI assistant market. This update is available immediately for paid Claude users in the United States and will be coming to free users and more countries soon.
The web search feature not only enhances Claude's accuracy but also prioritizes transparency and fact-checking. Claude provides direct citations when incorporating web information into its responses, enabling users to verify sources easily. This feature addresses growing concerns about AI hallucinations and misinformation by allowing users to dig deeper and confirm the accuracy of information provided. The update is meant to streamline the information-gathering process, allowing Claude to process and deliver relevant sources in a conversational format, rather than requiring users to sift through search engine results manually. Recommended read:
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Amber Hoak,@Microsoft Research
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Source Asia
, Microsoft Research
Microsoft Research is pioneering a new data science approach called Semantic Telemetry to gain a deeper understanding of how users interact with AI systems. This innovative project aims to classify human-AI interactions and understand user behavior, which is crucial for developing and supporting increasingly high-value use cases for generative AI. Semantic Telemetry is a new way to measure how people interact with AI systems.
Semantic Telemetry employs a unique methodology using large language models (LLMs) to generate meaningful categorical labels from chat log data. This process involves iteratively prompting an LLM to generate summaries of conversations and refine classification labels. The resulting classifications are then used to label new, unstructured chat log data, enabling a comprehensive analysis of user interactions and the value derived from AI tools. Recommended read:
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