Today in AI: Alibaba open-sources two AI models, AI-based HYRGPT eliminates the first two steps of hiring and more

The intensifying AI rivalry between the United States and China has reached a new level as Alibaba, the Chinese tech giant, releases two open-source AI models in response to Meta’s Llama 2. Alibaba’s cloud computing division has unveiled Qwen-7B and Qwen-7B-Chat, both categorized as Large Language Models (LLM), engineered to comprehend and engage in natural human language interactions.

These two models, Qwen-7B and Qwen-7B-Chat, are scaled-down versions of Alibaba’s larger language model, Tongyi Qianwen, which translates loosely to “exploring truth through a thousand questions.”

Furthermore, the landscape of recruitment is undergoing transformation with the introduction of HYRGPT, a generative AI-powered recruitment co-pilot developed by Personifwy, a Bengaluru-based candidate and employee engagement platform. This innovative tool streamlines the hiring process by negating the initial steps of manual CV scanning by recruiters. Instead, the software employs essential keywords and initiates contact with shortlisted candidates for preliminary interviews utilizing predetermined standard queries. Successful candidates proceed to an HR round, followed by a technical interview with the hiring manager.

The notable surge in share price for Isu Petasys Co., a company supplying multi-layered boards to prominent clients such as Alphabet, Nvidia, and Microsoft, has resulted in significant wealth growth for its Chairman, Kim Sang-Beom, and his family. Their stake’s value surpassed $800 million as of August 3, paralleling a broader trend in the AI sector. The rise in demand for AI investments has propelled individuals like Jensen Huang, CEO of Nvidia, whose fortune has nearly tripled this year to reach $39.9 billion, as documented by the Bloomberg Billionaires Index.

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Aihub Team

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AI and Data Privacy: Balancing AI advancements with privacy concerns and techniques for privacy-preserving AI.

AI and Virtual Assistants: AI-driven virtual assistants, chatbots, and voice assistants for personalized user interactions.

AI and Business Process Automation: AI-powered automation of repetitive tasks and decision-making in business processes.

AI and Social Media: AI algorithms for content recommendation, sentiment analysis, and social network analysis.

AI for Environmental Monitoring: AI applications in monitoring and protecting the environment, including wildlife tracking and climate modeling.

AI in Cybersecurity: AI systems for threat detection, anomaly detection, and intelligent security analysis.

AI in Gaming: The use of AI techniques in game development, character behavior, and procedural content generation.

AI in Autonomous Vehicles: AI technologies powering self-driving cars and intelligent transportation systems.

AI Ethics: Ethical considerations and guidelines for the responsible development and use of AI systems.

AI in Education: AI-based systems for personalized learning, adaptive assessments, and intelligent tutoring.

AI in Finance: The use of AI algorithms for fraud detection, risk assessment, trading, and portfolio management in the financial sector.

AI in Healthcare: Applications of AI in medical diagnosis, drug discovery, patient monitoring, and personalized medicine.

Robotics: The integration of AI and robotics, enabling machines to perform physical tasks autonomously.

Explainable AI: Techniques and methods for making AI systems more transparent and interpretable

Reinforcement Learning: AI agents that learn through trial and error by interacting with an environment

Computer Vision: AI systems capable of interpreting and understanding visual data.

Natural Language Processing: AI techniques for understanding and processing human language.