Saskana AI, a Japanese startup founded by former Google researchers, has raised $100 million in a series A funding round. They aim to become a world-class AI lab and are taking a unique approach by using a swarm of small AI models to generate complex results. They are also building their models on native Japanese data sets, which could provide interesting perspectives. With the funding, they plan to scale up their nature-inspired approach to AI development and build a talent-dense AI research organization. Their partnership with Nvidia will provide infrastructure support and help Japan regain a technological edge in AI.

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**AI vs Human: The Future of Translation [11:48 ]

In this podcast episode, Jaeden Schafer discusses Smart Cat, an AI-powered translation platform that recently secured $43 million in funding. He explores the advantages of AI translations over traditional methods, the company's business model, and its position in the market. The conversation also touches on the importance of human review in translation and the future prospects for Smart Cat in the growing machine translation industry.

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** The High Cost of AI Talent Acquisition [ 10:12 ]

In this episode, Jaeden Schafer discusses the recent surge in OpenAI's valuation, which is now set at $150 billion. He explores the funding strategies being employed, including a potential $6.5 billion equity financing round and a $5 billion credit line. The conversation also delves into the upcoming innovations from OpenAI, specifically the Q-Star and Strawberry models, which are expected to enhance AI capabilities. Finally, Jaeden highlights the significant costs associated with acquiring and maintaining top AI talent, emphasizing the financial implications of these developments.

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OpenAI has made updates to their API to make it easier for developers to improve AI assistants and agents. The new feature allows developers to inspect file search results and modify the ranker's behavior to prioritize relevant results. This update is a step towards creating fully autonomous AI agents. Developers have responded positively to the update, expressing excitement about the new features. AI agents are expected to take over tasks with minimal input, and big companies like Google and Salesforce are already working on agent platforms. However, there is still room for improvement in terms of accuracy and benchmarking metrics for AI agents.

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Google and Gmail are introducing a new AI integration called Gemini chat, which allows users to interact with their emails. The feature provides options to show unread emails, get the status of recent orders, and ask questions about emails. While some find it useful, others find it creepy. The integration also works with Google Drive files, making it even more convenient. However, there are concerns about trusting the AI to provide accurate information. The rollout of Gemini chat has had some issues, but it is expected to be a useful tool for email management.

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OpenAI is in talks to close a new funding round at a $100 billion valuation, which would be one of their biggest infusions of capital. They have already raised $86 billion and allowed employees to sell shares in the company. OpenAI is on track to lose about $5 billion by the end of the year, despite generating $3.4 billion in annualized revenue. They have burned $8.5 billion in AI training and staffing. Thrive Capital and Microsoft are expected to participate in the funding round, and there are rumors that Nvidia and Apple may also contribute.

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Google Gemini has made major upgrades to its AI model, including the introduction of Gems, Assistants, and Imagen 3. Gems are custom AI experts that can be used for various tasks. Gemini is known for its enterprise use cases and developer-friendly features. Imagen 3 enhances image generation capabilities, although there are restrictions to address concerns about deep fakes. Google has implemented Synth ID watermarking technology to verify the authenticity of generated images. Gemini faces competition from similar products by OpenAI, Microsoft, Meta, and Hugging Face. While Google's image generator is unique, open source models used by competitors may offer similar capabilities.

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Mid Journey has announced that they are working on hardware, but the exact details of the hardware are still unknown. There have been rumors and speculation, with some suggesting it could be a new Oculus or Vision Pro-like headset. Mid Journey has been engaging with people on social media, responding to comments and teasing the nature of the hardware. The CEO mentioned an 'orb' in a previous tweet, but it's unclear if this is related to the upcoming hardware. The AI hardware market has seen some flops in the past, but there have been successful collaborations like Meta's Ray-Ban glasses. Overall, the announcement has generated excitement and curiosity.

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Bland AI is a company that has recently raised $22 million in funding for their customizable phone calling agent. The agent can talk in any language or voice, handle millions of calls simultaneously, and be used for various use cases. Bland AI aims to fix the way businesses handle their phone communications by providing an AI solution that can work 24/7 and be trained to the company's liking. This technology has the potential to disrupt the massive industry of phone communications and improve efficiency across the board.

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XAI's AI model Grok 2 received a significant speed boost after developers rewrote the inference code stack using SG Lang in just three days. This open-source system executed complex language model programs and achieved higher throughput. The developers, Liam Zhang and Sied Melik, accomplished this feat, resulting in Grok 2 being two times faster and slightly more accurate. The success of this update sets a precedent for improving AI models without the need for retraining or expensive infrastructure. Grok 2 has also secured the number two spot on the Limsis chatbot arena leaderboard, with impressive user ratings.

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00:00 Introduction to XAI and Grok

02:14 Code Rewrite and Speed Boost with SG Lang

03:13 SG Lang: Efficient Language Model Execution

05:34 Grok 2 Ranks Second on LimSis Leaderboard

07:26 XAI's Progress with Grok and Image Generation