Podcast Summary
AI at the Olympics and in the Military: The Olympics and military are integrating AI for athlete identification, training, judging, security, and military aircraft piloting, highlighting its growing importance and influence in our world.
AI is increasingly being integrated into various industries and aspects of life, from sports and entertainment to military and defense. The Olympics have announced plans to use AI for athlete identification, training, judging, and security, marking a significant role in the upcoming Paris Olympics. The US military has conducted the first-ever human versus AI dogfight, demonstrating progress in the use of machine learning in piloting military aircraft. Meta has released an open-source AI model called Llama 3. These developments underscore the growing importance and influence of AI in our world, and it's essential for individuals and businesses to stay informed and adapt to these advancements. Sign up for Everyday AI's free daily newsletter to stay updated on the latest AI news and learn how to leverage it for growth. So, whether you're interested in sports, technology, business, or just curious about AI, tune in to Everyday AI for practical advice and insights.
Meta's new LLAMA 3 language models outperform competitors: Meta's LLAMA 3 language models, including smaller and medium versions, have superior performance in benchmarks and human evaluations compared to competitors' models, with the largest yet-to-be-released model expected to be the biggest and potentially the best.
Meta's new large language model, LLAMA 3, which includes both smaller 8 billion and medium 70 billion parameter models, is now live and outperforming similar models from other companies in benchmark tests and human evaluations. Meta's CEO, Mark Zuckerberg, has released videos discussing the human evaluation scores, which are a widely regarded benchmark for measuring a model's ability to think, understand, and reason like a human. Meta's smaller and medium models are already benchmarking above leading models from Google, and the yet-to-be-released 400 billion parameter model is expected to be the biggest and potentially outperform the current leader, Quad 3 OPUS. Meta is making these models essentially open source, which is significant news in the AI community. The audience is encouraged to submit their AI-related questions for discussion on the show. One audience member, Doug, asked about the host's coffee preference, which was answered as an espresso.
ChatGPT's Memory Features: 'Memory' and 'Cross Chat Memory': ChatGPT is testing two memory-related features: 'memory' for recalling previous conversation info and 'cross chat memory' for sharing context between chats, but users have raised concerns about privacy and effectiveness.
ChatGPT, an advanced AI model from OpenAI, is currently testing two memory-related features: "memory" and "cross chat memory." The "memory" feature allows the AI to remember certain information from previous conversations and apply it to new ones. However, the user expressed concerns about the AI automatically committing information to memory without explicit consent, potentially leading to irrelevant or unwanted influences on future interactions. The "cross chat memory" feature, on the other hand, aims to share context windows between multiple chats, but its effectiveness is limited due to the current size of ChatGPT's memory capacity. It's important to note that not all users have access to these features yet, and their availability and functionality may change as OpenAI continues to develop and refine them.
Discussions on Copilot's accessibility and LLama 3's impact on AI: Discussions revolve around Copilot's potential sharing controls, the uncertain impact of LLama 3 on the AI market, and personal preferences for using AI applications.
There are ongoing discussions about the accessibility of custom GPTs in Microsoft 365 Copilot. While the speakers in the conversation are not currently using Copilot Pro, they believe that it may offer similar sharing controls as other AI applications, such as ChatGPT. Additionally, the topic of open-source AI, specifically LLama 3, was brought up. While LLama 3 is considered open source to some extent, it does not fully adhere to traditional open-source licenses. The impact of LLama 3 on the AI space is uncertain, but it could potentially challenge the dominance of closed, proprietary models like ChatGPT, Copilot, and Google's Bard (formerly known as Bardia). The speakers also shared their personal experiences with using AI applications, such as preferring larger screens for tasks and having limited apps on their phones.
Meta's power move in AI: Meta releases open-source large language models, leveraging community intelligence to improve models and potentially creating a moat in the AI space.
Meta's release of open-source large language models represents a significant power move in the tech industry. Meta, with its dominance in social media, sees an opportunity to create superior models and make competitors less relevant, even if the models are not immediately monetized. By making the models open source, Meta can leverage the collective intelligence of the global developer community to improve the models, creating a potential moat in the AI space. The uncertainty surrounding the monetization of large language models and the blurring lines between search and language models has other tech giants like Google and Microsoft concerned, but Meta seems unfazed. This strategy from Meta is fascinating and could lead to dedicated future discussions on its implications for the tech industry.
Evaluating the reasoning and understanding abilities of large language models with the MMLU test: The MMLU test is a comprehensive industry standard benchmark for assessing a language model's intelligence by testing its ability to apply knowledge to real-world tasks and reason like a human. Resources like Mistral, Llama, RAG, and the PPP course are available for those interested in creating or fine-tuning their own models.
The Multitask Multilingual Language Understanding (MMLU) test is an important industry standard benchmark for measuring the reasoning and understanding abilities of large language models, similar to how standardized tests like the SAT or ACT measure human intelligence. This test evaluates a model's ability to apply its knowledge from its dataset to real-world tasks, making it a comprehensive assessment of a model's intelligence. It's important because it tests the model's ability to understand and reason like a human, which is a crucial aspect of AI development. For those interested in creating or fine-tuning their own models, there are open-source models like Mistral and Llama, and third-party systems like RAG that make it easier to apply your own data. Additionally, NVIDIA RTX offers a built-in RAG system for running models locally. As for the best AI process, Jordan, the host of Everyday AI, recommends the free Prime Prompt Polish (PPP) course for improving prompting skills with ChatGPT. This course can help users get better results from the model by teaching them how to prime, prompt, and polish effectively. In summary, the MMLU test is an essential benchmark for evaluating the reasoning and understanding abilities of large language models, and there are resources available for those interested in creating or fine-tuning their own models. The PPP course is a valuable resource for improving prompting skills with ChatGPT.
Using AI tools for productivity and memory retention: AI tools like ChatGPT, Cast Magic, and Voila help improve workflow by enhancing productivity and memory retention through quick access to information and efficient recall of details.
AI tools like ChatGPT, Cast Magic, and Voila are essential for enhancing productivity and memory retention, particularly in the context of content creation and note-taking. The speaker, who spends several hours every day working on everyday AI, shares his process of using these tools to improve his workflow. For instance, he uses ChatGPT to chat with transcripts of interviews he conducts, allowing him to recall specific details more easily. Cast Magic and Voila serve similar purposes, providing quick access to key topics and information from transcripts or web pages. By using these tools, the speaker is able to recall important details more efficiently, reducing the amount of time spent searching for information and improving the overall quality of his work.
AI in education: Personalized exam prep: Consider starting an AI-powered exam preparation app or tutor for specific tests and exams to cater to niche markets in education.
Integrating AI into education through niche, personalized applications could be a successful startup idea. The speaker suggests creating a specific AI tutor for studying for particular exams or helping college students prepare for specific tests. Regarding tech stocks, the speaker mentions Meta as a potential investment opportunity, but it's essential to note that this is not financial advice. The speaker also expresses interest in Microsoft's Copilot Pro and plans to open a Copilot training for their team and community once they become a Microsoft Office team. Additionally, they plan to provide more in-depth, recorded trainings in their free community.
Detecting text-based disinformation and misinformation is challenging for AI: AI struggles to detect text-based disinfo/misinfo effectively, watermarking systems for media are being explored, but progress is slow, AI anxiety among pros is common, work-life balance is a challenge, VC investments in less promising AI startups may decrease, leading to industry consolidation
While AI is effectively used in cybersecurity to detect deepfakes and AI threats, detecting text-based disinformation and misinformation is more challenging. Companies are exploring watermarking systems for media, but progress on the tech side is slower. AI anxiety is common among professionals due to the rapid advancement of technology, and achieving work-life balance is a continuous challenge. Despite the overwhelming number of new AI tools and startups, it's expected that venture capital and private equity investments in less promising companies will eventually decrease, leading to consolidation in the industry. VCs and private equity firms should consider seeking expert advice, as many investments in AI startups may not yield the desired returns in the long run.
Landscape of acquisitions in generative AI has changed: Large tech companies now develop advanced AI models, potentially leaving VC/PE firms without returns, and copyrighted material used for training may lead to lawsuits
The landscape of acquisitions in the generative AI space has significantly changed compared to the past, with large tech companies developing their own advanced models and no longer relying on startups for innovation. Instead, venture capital and private equity firms may be losing their investments as these companies continue to collect and use vast amounts of data from the open and closed Internet for their models. This data, which includes copyrighted material, is used to train these models, leading to potential lawsuits against these companies for copyright infringement. These advanced language models have become sophisticated NLP prediction engines, using public discourse from the Internet as their training material. However, the application and meaning of copyright law in this context remain a topic of debate.
Exploring the Potential of AI Live Streams and Discussions: Weekly series with rotating categories and guests offer industry-specific insights. Sharing AI chats with team members or students expands knowledge, but real-time collaboration is not yet available.
As AI continues to advance, there's a growing need for specialized live streams and discussions to cater to various industries and topics. Douglas's idea of a weekly series with rotating categories and guests is an excellent example of this. Moreover, sharing AI chats and tools with team members or students who don't have access to the same account is a valuable collaboration method. However, it's important to note that the current sharing feature only allows access to the chat up until the point the sharer created it. Real-time collaboration between teams on the same chat is not yet available, although it's a promising direction for the future. Additionally, third-party tools that offer real-time collaboration come with limitations, as they don't allow access to additional features and integrations that enhance the capabilities of large language models.
A lively and informative 'Ask Me Anything' session on Everyday AI: The 'Ask Me Anything' freestyle Friday show on Everyday AI was an engaging and informative experience where hosts addressed various topics and answered questions from the audience, providing valuable insights and information.
The "Ask Me Anything" freestyle Friday show on Everyday AI proved to be an engaging and informative experience for both the hosts and the audience. The hosts addressed various topics and answered questions from the livestream audience, providing valuable insights and information. The audience showed appreciation for this informal Q&A session, and it was a departure from the regular planned shows. The hosts acknowledged that the show was all over the place but promised to answer some of the unanswered questions in the newsletter. The hosts encouraged the audience to visit everydayai.com for more AI-related news and updates. Overall, the "Ask Me Anything" freestyle Friday show was a success, and it might become a regular feature if the audience continues to enjoy it. The hosts expressed their gratitude for the audience's support and promised to keep delivering insightful content every day.