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ChatGPT added the DALL·E 3 image editing function, which can visually edit AI-generated images to solve the problem of accurate content control.

Now, the ChatGPT memory feature is available to all Plus users! 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅

The organization behind the Large Model Arena, LMSYS, has launched the next generation benchmark, Arena-Hard, which has attracted widespread attention. The latest references also reveal the true capabilities of the two instruction-fine-tuned versions of Llama 3. Compared to the previous MT Bench, where everyone's scores were similar, the differentiation of Arena-Hard has increased from 22.6% to 87.4%, making the strengths and weaknesses clear at a glance. Arena-Hard is built using real-time human data from the arena, with a high consistency rate with human preferences of 89.1%. Besides achieving SOTA on the above two metrics, there is an additional benefit: The real-time updated test data includes new prompts thought up by humans, which AI has never seen during training, reducing potential data leakage. Furthermore, after the release of a new model, there is no need to wait about a week for human users to participate in voting; results can be obtained quickly by running the testing pipeline for just $25. 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂 😂

According to a survey conducted by McKinsey, generative AI is set to bring a market opportunity worth between $2.6 to $4.4 trillion globally. This extensive survey included enterprises with over five thousand employees. The results revealed that 80% of surveyed CEOs believe that generative AI will disrupt all industries within the next 18 months. Additionally, 30% of these enterprises have already started investing in generative AI today. 😍 This underscores a significant trend: generative AI is not just anticipated to have a major impact, it is already shaping industries across the board. Businesses and leaders are recognizing the transformative potential of this technology and are moving quickly to integrate it into their operations. This shift highlights the importance of staying ahead in the rapidly evolving tech landscape, where generative AI plays a pivotal role. 😎

Recently, Nature published a paper that expanded the full functionality of dual-comb spectroscopy into the near-ultraviolet spectrum and low-light power conditions, opening new applications in precision spectroscopy, biomedical sensing, and environmental atmospheric detection. Using a photonic energy-level interferometer, Xu Bingxin and others overcame the challenges posed by low nonlinear frequency conversion efficiency. This research lays a solid foundation for extending dual-comb spectroscopy to shorter wavelengths. In the fields of atomic electron transitions, molecular vibrational transitions, fundamental physics, validation of quantum electrodynamics, determination of fundamental constants, precision measurement, optical clocks, atmospheric chemistry, astrophysical spectroscopy, and strong-field physics, ultraviolet spectroscopy holds crucial research significance. The main significance of this achievement focuses on ultraviolet precision spectroscopy and fundamental physics research. Additionally, because it operates at very low power levels, this achievement is also applicable to biomedical sensing and scenarios where samples are susceptible to radiation damage. (Source: Nature)

"Liquids are now 'smart' and programmable? Recently, a new type of multifunctional programmable metamaterial called 'Metafluid' was featured in Nature. It was developed by a research team from the Harvard University School of Engineering and Applied Sciences (SEAS) and is said to freely adjust its elasticity, optical properties, and viscosity." Even able to switch between Newtonian and non-Newtonian fluids. Researchers say that with these buff attributes, the fluid has tremendous potential applications in programming hydraulic robots, smart dampers, and optical equipment. 😄 😀 😄 😀 😄 😀 😄 😀 😄 😀 😄 😀 😄 😀 😄 😀 😄 😀 😄 😀

Developing Chatbots in AI Companies Step 1: Purpose Definition Before initiating any coding, clearly identify the functions your bot is intended to fulfill. Whether it's fielding customer questions or facilitating site navigation, a well-defined goal will drive the entire development process. Step 2: Selecting a Language Model Choose a large language model that aligns with your requirements. For instance, GPT-3 is an excellent choice for chatbots designed to simulate human-like conversations. Step 3: Model Training Proceed to train your model using an extensive dataset that is relevant to the intended functions of your chatbot. During this phase, the model will acquire and adapt patterns based on the data provided. Step 4: System Integration Once training is complete, embed your model into the application environment where it will operate, such as a website, mobile app, or social media platform. Step 5: Testing and Refinement Following integration, rigorously test the chatbot and refine its responses based on user feedback. This stage involves continuous monitoring and adjustments to enhance functionality and user experience. 😀 😁 😄 😃 😍

How AI Companies Develop Chatbots Step 1: Define the Purpose of Your Bot Before you start coding, it is crucial to define the tasks you want your bot to perform. Whether it's answering customer queries or helping users navigate your website, having a clear objective will guide your development process. Step 2: Choose a Large Language Model Select a large language model that suits your needs. For example, if you want your chatbot to generate human-like text, GPT-3 would be a good choice. Step 3: Training The next step is to train your model. For this, you will need a large dataset relevant to your chatbot's theme. The model learns patterns and information from these data. Step 4: Integration Once your model is trained, integrate it into the platform where it will be used. This can be a website, mobile application, or social media platform. Step 5: Testing and Refinement After integration, it is time to test your bot and refine it based on user feedback. This is an iterative process that requires continuous monitoring and adjustments. 😄 😀 😁 😊 😍