Scott Jenson’s Post

Another component of next Wednesday's "Keep Pace with Enterprise AI" is the use of reddit as the vehicle to discuss/comment on each episode. While I have a great starting list of topics - I am curious as to what you are encountering (Positive OR Negative) - about Enterprise AI Adoption Just comment on the following link - I look forward to the discussion ! https://lnkd.in/eKNyWrgC

“Levels of Investment in the Development of AI Avatars for Call Centers” by Perplexity 2:55 pm PDT, Sunday, July 7, 2024 Major call centers and customer service providers are investing heavily in developing advanced AI avatars and automated systems, though exact spending figures are not widely disclosed. Here are some key insights: Character.AI has launched a feature called Character Calls that enables two-way voice conversations with AI avatars in multiple languages[3]. This represents a significant investment in natural language processing and voice synthesis technologies. ServiceNow is collaborating with NVIDIA to develop generative AI digital customer service avatars aimed at enhancing employee and customer interactions[3]. While specific costs aren't mentioned, partnerships between major tech companies like this typically involve multi-million dollar investments. The global chatbot market, which includes AI avatars for customer service, was valued at $1 billion in 2023 and is projected to reach $2 billion by 2026[4]. This indicates substantial and growing investment in the sector. Zoom's CEO has hinted at developing advanced AI avatars that can replicate a worker's image and characteristics[3]. For a company of Zoom's size, this likely represents a major R&D investment. While precise figures aren't available, these developments suggest that big call centers and tech companies are spending tens to hundreds of millions of dollars on AI avatar technology. The goal is to improve efficiency and customer experience, though human agents are still expected to play a critical role in handling complex issues and providing empathy in customer interactions[1][2]. Sources [1] How much longer before AI takes over call center jobs? - Reddit https://lnkd.in/gH7Ajjns [2] Will AI Dominate the Call Center This Year? - CMSWire.com https://lnkd.in/gZVwTK7s [3] Look Who's Talking! Character.AI Launches Two-Way Avatar ... https://lnkd.in/gf9iSdX2 [4] AI in Call Centers: What Future for Call Center Agents? - LinkedIn https://lnkd.in/g2_df2ak [5] AI in Contact Centers: Leveraging Lessons From the Past https://lnkd.in/gY_zrE26

Meta has just released an updated variant of their most recent open-source model, version 3.1! Some reports indicate that it is already outperforming OpenAI's GPT-4o (at the 405B size), which is quite impressive for an open-source model! See here for more: https://lnkd.in/eaBWwS_b And it looks like 3.1 also expands on how conversations can be customized through the system message and added tags: https://lnkd.in/ea9gaCan Definitely check it out and request access to Meta-Llama-3.1-8B-Instruct via Hugging Face here: https://lnkd.in/eZxDkphT If you had told me a year ago that open-source models, even at sizes of 8B or lower, would rival ChatGPT, I would not have believed it. The rapid advancement in the field of open-source AI is truly remarkable. I'm very excited about how these models have grown in both seriousness and development, with so many different releases coming along. The community's collaborative efforts have led to significant improvements in performance and accessibility, making cutting-edge AI technology available to a broader audience. I will definitely be trying out Meta's latest release, version 3.1, and comparing it not just to its previous version but also to Google's latest open-source release, Gemma-2. This hands-on comparison will help me understand the nuances and strengths of each model. It's an exciting time to be involved in AI, as the competition between these models drives innovation and pushes the boundaries of what's possible.

https://lnkd.in/eRFzvBUU Look at the code and shed some light to improve further! Krish Naik iNeuron.ai iNeuron.ai #AI #DeepLearning #ContinuousLearning #KrishNaik #KnowledgeSharing

Last night I went to a set of talks on AI hosted by friends Ross Hale and Charlotte Onsrud at Artium, Artium's AI Builders Series. There were some great talks on using AI to solve customer problems, to solve UX problems, to bring people together, and to design really cool stuff. At the end, we were talking and Dr. Sam Khoze of FBRC.ai mentioned his interest in AGI and how close we are to making a digital version of reality possible. Which is crazy. And it made me think about The Singularity-Simulation Hypothesis, that "the closer we are to living through the technological singularity, the more likely we are to be living in a simulation." I haven't seen much discussion on this since Elon brought it up a few years ago. But if we are (or when we are), as our simulated selves discover the technology to create simulations do we end up with a bunch of nested simulations? Anyway, here's a great discussion. https://lnkd.in/gE6CVwTn #innovation #ai #simulation #singularity #elonmusk

Lots of love lately for Cohere’s Command-R on Reddit, for RAG use cases especially: https://lnkd.in/eJT9ih7r #generativeai #llms #ai #search #datasciencs #cohere

Interesting read on Bill Gate's thoughts on AI and Agents (yes, from reddit). [And here is what ChatGPT drafted with more excitements and emojis ---🚀 Exciting Read on AI and Agents! 🤖 Bill Gates recently shared a thought-provoking article on how Artificial Intelligence is set to revolutionize our daily lives. 🌐 The article delves into the transformative impact AI agents will have on our interactions and experiences. 🤯 Check it out for a glimpse into the future! 👉 Read the article here #AI #ArtificialIntelligence #Innovation]

I work closely with my clients to help them understand the fidelity and reliability of responses that come from large language models like ChatGPT. The hype associated with Generative AI tends to overshadow the fact that these models don’t ‘think’, rather, they assemble responses based on a statistical representation of the information they’ve been trained on. Depending on how a prompt is structured, the reliability of the resulting response may be compromised, i.e., wildly wrong. It doesn’t necessarily matter how large or complex the model is. A recent Reddit thread brings this point into sharp focus. Various large-scale models were asked to clarify why '1 is a prime number but not 2,' a statement that is mathematically incorrect. Surprisingly, many models provided inaccurate explanations, endorsing the erroneous premise. This conversation is crucial for several reasons: Trust in AI - As AI becomes increasingly integrated into decision-making processes, understanding its limitations is vital. Inaccurate information can have real-world consequences. Data Quality - This instance also underscores the importance of reliable data for training AI models, errors can propagate if not carefully managed. User Education - The scenario serves as a reminder that while AI can be a powerful tool, it's not a replacement for human education and expertise. It is critical to always double-check information and consider the source. It's a good reminder that while this technology has advanced rapidly, it's not foolproof and should not be treated as such. #ArtificialIntelligence #DataScience #MachineLearning #NaturalLanguageProcessing #ChatGPT #Llama #DecisionMaking #DataQuality #TrustInAI #AIethics #TechnologyLimitations https://lnkd.in/gXjf7g5b

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Interesting thought on whether AI could be called natural evolution. I don’t think so. I am am not aware of anything a species has created, including AI that’s better than the species itself in all respects. It may be better in some specific respects, but not overall. But evolution always progresses forward to produce something better eventually. Based on these observations, AGI or ASI will very likely to be unattainable . #ai #agi #evolution #nature https://lnkd.in/g-x8qBfQ