A survey of 2,700 AI researchers revealed varied opinions on AI risks. Notably, 58% foresee potential catastrophic outcomes, while others predict AI mastering tasks by 2028 and surpassing human performance by 2047. Immediate concerns like deep fakes and misinformation also trouble over 70% of researchers. Balancing both short-term and long-term AI risks is highlighted.
Generative AI has revolutionized AI, finding applications in text generation, code generation, summarization, and more. One evolving area is natural language processing (NLP) for intuitive SQL queries, aiming to make database querying more accessible to non-technical users. Key considerations include prompt engineering, architecture patterns, and optimization for efficient text-to-SQL systems using Large Language Models (LLMs).…
Using machine learning, NLP, and deep domain knowledge, Auchan Retail International achieved an impressive 18% reduction in out-of-stock items and overstock across national operations in just one year. Their dual-model strategy, extensive feature engineering, and close collaboration with stakeholders led to substantial operational improvements and efficiency in retail forecasting.
The paper discusses the superiority of Kalman Filter (KF) over neural networks in some cases and the need to optimize KF parameters. Despite its 60-year-old linear architecture, the KF outperformed a fancy neural network after parameter optimization. The study emphasizes the importance of optimizing KF and not relying on its assumptions, offering a simple training…
The article emphasizes the shift from creating traditional dashboards to storytelling with data, highlighting the need for more engaging and impactful communication of insights. It stresses the importance of framing questions, collecting relevant data, and structuring the data story in various engaging formats. The piece concludes with a call to embrace data storytelling for better…
Google and MIT researchers propose SynCLR, a novel AI approach for visual representation learning using synthetic images and captions. The method leverages generative models to synthesize large-scale training data, demonstrating superior performance to existing methods. The team highlights potential improvements and invites further research. For more details, refer to the original Paper and Github.
Vald is a cloud-native, open-source distributed vector search engine addressing challenges in large-scale similarity searches. Its features include distributed indexing, auto-indexing with backups, custom filtering, and horizontal scaling, making it resilient and versatile. Vald offers lightning-fast search on billions of vectorized data points, supporting multiple languages through gRPC. It’s a vital tool for advanced unstructured…
Microsoft is introducing an era of AI PCs with a new “Copilot” key on Windows 11 keyboards, set to debut on upcoming devices, including Surface products. The ribbon-like key directly accesses an AI chatbot via Bing, providing various capabilities like text work, app integration, and personal data security. Other computer manufacturers will also adopt the…
The text discusses techniques to improve the efficiency of large language models (LLMs) through prompt compression, focusing on methods such as AutoCompressors and LongLLMLingua. The goal is to reduce inference costs and enable faster and accurate responses. The article compares different compression methods and concludes that LongLLMLingua shows promise for prompt compression in applications like…
AutoRT, SARA-RT, and RT-Trajectory expand on our previous Robotics Transformers to improve robots’ decision-making speed, understanding, and navigation in diverse environments.
“Prompt Engineering, AI Agents, and LLMs: Kick-Start a New Year of Learning” sets the tone for the new year, introducing thought-provoking articles. Sheila Teo’s GPT-4 Competition win and Oren Matar’s ChatGPT review offer insights. Mariya Mansurova discusses LLM-Powered Analysts, while Heston Vaughan and others delve into AI agents and music AI breakthroughs. The newsletter also…
The researchers propose DL3DV-10K as a solution to the limitations in Neural View Synthesis (NVS) techniques. The benchmark, DL3DV-140, evaluates SOTA methods across diverse real-world scenarios. The potential of DL3DV-10K in training generalizable Neural Radiance Fields (NeRFs) is explored, highlighting its significance in advancing 3D representation learning. The work influences the future trajectory of NVS…
Microsoft recently added a new AI key to their keyboards for Windows 11 PCs. The key enables the use of Copilot, an AI tool for tasks like searching, email writing, and image creation. This move reflects Microsoft’s growing integration of AI in their products and partnerships with OpenAI. Yusuf Mehdi foresees AI transforming computer usage…
The development of Large Language Models (LLMs) like GPT and BERT presents challenges in training due to computational intensity and potential failures. Addressing the need for efficient management and recovery, Alibaba and Nanjing University researchers introduce Unicron, which enhances LLM training resilience through innovative features, including error detection, cost-efficient planning, and efficient transition strategies, achieving…
The text discusses the importance of spotting new trends and the various methods to identify them early. It covers tools such as Exploding Topics, utilizing YouTube, discovering mega trends through data, public domain opportunities, and sports industry trends. It emphasizes the need for a game plan to capitalize on trends and invites readers to join…
A new study proposes a three-step system to evaluate artificial intelligence’s ability to reason like a human, acknowledging the limitations of the Turing test due to AI’s capacity to imitate human responses.
In 2023, predictions about the future of AI, Big Tech, and AI’s impact on industries were partly accurate. Looking forward to 2024, specific trends include the rise of customized chatbots for non-tech users, advancements in generative video models, the spread of AI-generated election disinformation, and the development of robots with multitasking abilities.
A group of researchers led by Prof. Qu Kun has developed SPACEL, a deep-learning toolkit consisting of Spoint, Splane, and Scube modules, to overcome limitations in spatial transcriptomics analysis. By accurately predicting cell types, identifying spatial domains, and constructing 3D tissue architecture, SPACEL outperforms existing techniques, offering a powerful solution for comprehensive spatial transcriptomic analysis.
Large Language Models (LLMs) have revolutionized processing multimodal information, leading to breakthroughs in multiple fields. Prompt engineering, introduced by researchers at MBZUAI, focuses on optimizing prompts for LLMs. Their study outlines 26 principles for crafting effective prompts, emphasizing conciseness, context relevance, task alignment, and advanced programming-like logic to improve LLMs’ responses.
The article explores the intersection of philosophy and data science, focusing on causality. It delves into different philosophical theories of causality, such as deterministic vs probabilistic causality, regularity theory, process theory, and counterfactual causation. The author emphasizes the importance of understanding causality in data science to provide valuable recommendations.