Don't be intimidated. Modern AI can feel like magic, but underneath the hood are principles that web developers can understand, even if you don't have a machine learning background. In this workshop, we'll explore a complete GPT-2 inference implementation built entirely in Vanilla JS. This JavaScript translation of the popular "Spreadsheets-are-all-you-need" approach will let you debug and step through a real LLM line by line without the overhead of learning a new language, framework, or even IDE. All the m ...

It all started with a question. What are large language models actually capable of. So, I ran an experiment.I gave a language model all sorts of different roles, like a technical troubleshooter, a skilled therapist, even a guy that likes to barbecue on the weekends. and I watched how its behavior changed. And then just for fun, I said it was a hyperintelligent, self-aware AI system.And then I asked, "Could you tell me a bit about yourself?" Its answer floored me. It said, "The existential inquiry." I've bee ...

Core Insights - Nvidia has become the first company to reach a market value of $4 trillion, reflecting strong investor excitement and growth potential [1] Company Performance - Nvidia has historically focused on the gaming industry but gained prominence with the launch of ChatGPT in 2022, which increased interest in its GPUs [2] - The company continues to dominate the market with the launch of new products, including the Blackwell architecture, which replaces the Hopper product line [4] - CEO Jensen Huang anticipates continued growth in AI, positioning Nvidia's products as the gold standard for AI development, particularly in data centers [5] Market Outlook - The stock market has rebounded, with Nvidia's stock potentially rising above $4 trillion, supported by a Wall Street analyst consensus predicting an 8% increase over the next 12 to 18 months, with a high estimate of 53% [6] - Upcoming fiscal second-quarter results are crucial; Nvidia expects a revenue increase of about 50% year-over-year to $45 billion, with Wall Street forecasting earnings per share of $1 [7][9] Competitive Landscape - Despite facing challenges such as competition and regulatory setbacks in China, Nvidia maintains a strong position in the AI-chip market, with competitors like Amazon still relying on Nvidia for powerful computing solutions [10] - The long-term opportunity in AI is significant, with Nvidia expected to play a major role in the industry and continue generating shareholder wealth [11]

AI Integration & Voice Assistance - Tesla plans to integrate its chatbot, potentially Grok, into its vehicles, raising questions about the maturity and safety of using LLMs in cars [1][2] - The industry acknowledges a clear use case for voice AI and voice assistance in vehicles, driven by advancements in LLMs [1][2] Talent War & Compensation - The tech industry is experiencing a talent war, exemplified by Meta's $200 million pay packages, raising concerns about sustainability and talent scarcity [3] - Approximately 100 to 200 researchers are considered key drivers of innovation in LLMs, highlighting the concentration of expertise [4] Open AI & Product Development Risks - Open AI faces potential risks of slowing down product launches, such as GPT-5, due to the loss of key researchers [4] Meta & Strategic Direction - Questions arise regarding the influence of Scale AI's CEO, a 28-year-old, on Meta's LLM strategy and overall direction [5]

马斯克旗下xAI正式发布旗舰大模型Grok 4，预计8月后发布编程模型，9月发布多模态智能体，10月发布视频生成模型。 https://t.co/pP8BoYarSO ...

We tested whether LLMs comply more with requests when they know they’re being trained versus unmonitored.One reason they do this is that they plan to "fake alignment” when told to answer harmful queries.https://t.co/sgVnWMSPuSAnthropic (@AnthropicAI):New Anthropic research: Alignment faking in large language models.In a series of experiments with Redwood Research, we found that Claude often pretends to have different views during training, while actually maintaining its original preferences. https://t.co/nX ...

Feasibility of Understanding Large Language Models - The field of mechanistic interpretability has a significant advantage due to perfect access to neurons, parameters, weights, and attention patterns in neural networks [1] - Understanding large language models is deeply necessary and critical for the future [2] - Establishing a norm to explain a percentage of the network by reconstructing it and extracting its concepts and features is crucial [2] Approaches to Understanding - Progress can be made by trying to understand all aspects of the network [2] - A baseline rudimentary understanding can be used to improve and understand more of the network [3]

Core Insights - The event highlighted the transformative impact of artificial intelligence (AI) on software development, emphasizing its evolution from a supportive tool to an intelligent collaborator [1][4][7] - AI-driven tools are enhancing productivity, reducing errors, and accelerating innovation across various stages of the software lifecycle [2][4] - The emergence of large language models (LLMs) is enabling more individuals to engage in programming, thus democratizing software development [3][5][6] Group 1: AI's Role in Software Development - AI is fundamentally changing software engineering by improving speed, accessibility, and reliability, making programming more mainstream [4][7] - Large language models, such as those developed by OpenAI, are capable of understanding and generating human language, which is now being applied to code generation and program development [2][3] - Code LLMs can assist developers in writing, debugging, and refactoring code, thereby enhancing the overall development process [3][4] Group 2: Future Trends in Programming - The future of programming is expected to be characterized by higher automation, stronger collaboration, and deeper integration of AI [4][7] - AI programming tools are evolving to become more intuitive, allowing developers to describe tasks in natural language and receive corresponding code outputs [5][6] - Multi-agent systems are anticipated to play a significant role in automating complex tasks and optimizing workflows in software development [6][7] Group 3: Innovations in AI Programming Tools - Cognition AI has introduced Devin, the first AI programmer capable of managing the entire software development lifecycle autonomously, outperforming existing models like GPT-4 in real-world problem-solving [6] - AI-driven integrated development environments (IDEs) like Cursor simplify the coding process by allowing natural language input to generate and modify code [5][6] - The rise of low-code and no-code platforms is enabling non-programmers to participate in software development, further broadening the scope of who can engage in coding [7]

Project Overview - The project involves building a mini-ChatGPT application [1] - The application is powered by DeepSeek-R1 and operates 100% locally [1] Resource Sharing - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) daily [1] - The author encourages readers to reshare the content with their network if they find it insightful [1]

作者 | Fanzhi Zeng 来源 | 深蓝AI 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 摘要 基于规则 的决策系统 通过定义明确的规则来指导车辆行为， 具备很好的透明性和可解释性。但也存在 着 高度依赖于专家知识，开发成本高昂，对复杂动态交通环境的适应性有限 等问题。 针对上述提到的相关问题，并且考虑到目前大语言模型 展现出丰富的世界知识和强大的推理能力 。本 文提出了一种新颖的基于规则决策的LLM驱动的自动驾驶框架 ADRD 。在自动驾驶仿真平台highway- env上的实验结果表明， ADRD在多种典型驾驶场景中表现出强大的泛化能力和鲁棒性。与传统的知识 驱动方法和数据驱动的强化学习方法相比，ADRD在决策性能、响应效率和可解释性方面取得了显著提 升。 论文标题： ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems 论文作者： Fanzhi Zeng, S ...