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✨ 我们的跨链交换页面改变了游戏规则! 不再有桥梁🌉,只有 #ApeBond 🐵💛 提供无缝跨链代币交换。 LI.FI 使之成为可能 🔥 立即尝试➡️ apebond.click/swap $BTC $ETH $BNB

✨ 我们的跨链交换页面改变了游戏规则!

不再有桥梁🌉,只有 #ApeBond 🐵💛

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LI.FI 使之成为可能 🔥

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Impressive working video from OpenAI-supported humanoid robot company 1X 1X, a humanoid robot company supported by OpenAI, focuses on chaining tasks with its robot called Eve. A robot that can perform sequential tasks is approaching full autonomy. Just like the artificial intelligence developments experienced after the release of ChatGPT in November 2022, it seems that a similar development has occurred on the humanoid robots side. The first ChatGPT or similar ones before it worked only in question and answer format. However, today we can give them a task and make them fulfill the requirements of that task. That's exactly what humanoid robot firm 1X, backed by #ChatGPT developer #OpenAI , is trying to incorporate into its robot, Eve. While #1X wants to provide physical labor through safe, smart androids, the steps it takes also serve this purpose. The new video published by the company shows the humanoid robot Eve's ability to complete autonomous tasks one after the other. However, the company also points out that this is just the beginning of the journey. The company had previously developed an autonomous model that can combine many tasks into a single target conditional neural network. However, when multitask models were small (<100M parameters), adding data to correct the behavior of one task often negatively affected the behavior in other tasks. Although increasing the number of parameters comes to mind as the first solution for this, this time the training takes longer and delays which indicators need to be collected to improve the robot behavior. So how can we quickly iterate over data while creating a general robot that can perform many tasks with a single neural network? 1X's answer to this is quite clever. The firm says it separates the ability to quickly improve task performance from the process of combining multiple capabilities into a single neural network. To achieve this, the firm created a voice-controlled natural language interface to chain short-term capabilities into longer ones across multiple smaller models. $BTC $ETH
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Important statement from NVIDIA CEO before COMPUTEX 2024! NVIDIA CEO Jensen Huang addressed industry leaders, press members, entrepreneurs and technology enthusiasts ahead of #COMPUTEX2024 . Huang said that Taiwan is the home of NVIDIA's most valuable partners and everything #NVIDIA does starts from here. He stated that they created the world's artificial intelligence infrastructure together with their partners in Taiwan. He touched on three main topics in his speech: What generative artificial intelligence is and its impact, the roadmap for the future, and what's next. Huang noted that the computer industry is about 60 years old and has experienced only a few major technological changes in that time. Today, we are on the verge of such a change. He said the performance growth of central processing units (CPUs) is slowing down, but our processing needs are increasing exponentially. This leads to computing inflation. The solution lies in accelerated computing, which they have been working on for 20 years. NVIDIA's #CUDA platform supports CPUs, accelerating workloads where dedicated processors can perform much better. Huang predicted that every application and data center will be accelerated in the future. Accelerated computing is a solution that is both logical and sustainable. Huang showed that 25 times more performance/Watt ratio can be achieved with only a 3-fold increase in power consumption and a 50% increase in costs, while providing 100-fold acceleration. Accelerated computing is no easy task. Software needs to be completely rewritten and algorithms restructured. #NVIDIA has been developing domain-oriented libraries for 20 years to meet this challenge. These include the deep learning library cuDNN, the artificial intelligence physics library, the 5G radio library Ariel, the chip manufacturing library cuLitho, the gene sequencing library Parabricks, the combinatorial optimization library cuOpt, and cuQuantum for quantum computer simulation. $BTC $ETH $BNB
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