Jake Makler

Versus may be one of the few alternatives for creators to monetize content apart from advertisements.

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Our Call Score API - a very successful, business-ready application of GenAI techniques to measure and evaluate conversations - has a new set of features! You can try it out now, explainer and technical documentation below... We just launched the ability to define 'Custom Criteria' to evaluate any given conversation, and a 'Scorecard' that combines all these criteria together. This release continues our efforts to go beyond just the hype of Generative AI, and to build products that our customers need, want to use, and can integrate into their businesses and products from Day 1. Give it a try today! Blog: https://lnkd.in/gEvubpVt API Reference: https://lnkd.in/gmneKYCK.. Documentation: https://lnkd.in/gn_mr8VK.. #SymblDotAI #CallScore #CallScoring #ConversationUnderstanding #NLP #NLU #Conversation #ConversationAI #GenAI #GenerativeAI #NebulaLLM

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Interesting notes from Intuit's recent webinar on LLMs. What evaluation processes do you deploy? at tasq.ai our approach is to integrate the AI models with diverse, global human guidance, to support #responsibleai faster and near real time.

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Snowflake jumps into the Generative AI arena with its latest LLM announcement. The Arctic LLM. But, Do we need another LLM? Snowflake talks about unprecendent performance, unparalleled efficiency, etc. Typical sales jargon associated with a new product. But, Here is my take in English. Ecosystems of LLMs are evolving.  Models are being built across a wide array of use cases. GPT- 3: focus on Q&A, and content creation. DALL-E: Image generation LLaMA - summarization, transition, code generation T5: converting text from one form to another and a host of AWS LLMs. Each model and its iteration improves efficiency and performance.  So, Demand for specialized and effective LLMs will only grow from here. I made an example of how Snowflake may gain momentum with this investment, more importantly, how this will help a customer. Consider RetailSM, a retailer invested in a data-led journey. they are seeking to understand customer sentiment. In the past or current, their options are as follows. • Build a data lake. • Build a data mesh. • Unify all data. send Data scientists to town to build models. • Hope the models can drive the use case, and no one says your data is trash after 6 months of burning money. • Pray, the use case implementation (Sentiment analysis) journey yields results in under 6 months, with some $$$ returns. With this LLM model natively within Snowflake's realm, several benefits should come fast. #1 of that is to cut down a significant amount of time. Here are some others: 💡Gain deep insights from social media, customer interactions, and reviews through sentiment analysis. 💡 Tailor marketing campaigns with personalized recommendations and promotions.  💡Drive product development strategies based on customer feedback.  💡Enhance customer service by proactively addressing concerns. This mirrors an advance like Microsoft's Copilot. MS Copilot has the potential to revolutionize the modern workplace. Because Microsoft is the OG of the modern workplace. Snowflake is deep into the Enterprise data ecosystem. It can wield its analytics wizardry to empower businesses. In the end, Snowflake's Arctic LLM represents a powerful tool to harness the power of Gen AI. #SnowflakeArctic #GenerativeAI #DataAnalytics What do you think? Sridhar Ramaswamy

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2024 is the year of AI value, and time for companies to get out of PoC purgatory. “I really want to start driving use of AI at scale,” Luke G., head of analytics and AI at TD told The Wall Street Journal. “I think now is the time.” https://gag.gl/w2TUIR In his recent RevX keynote, Domino Data Lab's Nick Elprin detailed how companies do this today. Watch and learn how to accelerate AI impact: https://gag.gl/oN6hJo #AI #datascience #GenerativeAI

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Nate Nichols has been researching, building and deploying AI systems for 15 years. He does a great job breaking down the basics to help you with an AI roadmap to get started in your organization. Watch Nate and reach out if you want to learn more!

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MIT to CTO: Pioneering Tech Innovations in Ad Tech with Carl Livadas In this episode of Analytics Today, co-hosts Dr. Jeremy Roberts and Sameer Khan welcome Carl Livadas, a distinguished technology leader and CTO of Sojern. Hailing from a robust academic background at MIT and extensive experience in cybersecurity at Intel Research and BBN Technologies, Carl shares his journey from researcher to tech entrepreneur. Here are five key takeaways from this episode of "Analytics Today" with guest Carl Livadas: 1) Insights into Transitioning from Academia to Tech Leadership: Carl shares his journey from an academic background at MIT to becoming a CTO, highlighting the value of adapting research skills to meet the fast-paced demands of the tech industry. 2) Understanding the Role of AI and Machine Learning in Advertising: Carl explains how Sojern uses AI to optimize online advertising efficiency, offering a deep dive into cross-channel optimization and the balance between exploration and exploitation in ad spending. 3) The Importance of Privacy in Online Advertising: Discussion on how the advertising industry is evolving with privacy regulations and how technologies like AI can help balance effective marketing with consumer privacy. 4) Leadership Philosophies for Technology Managers: Carl emphasizes the importance of team dynamics, supporting individual growth within the team, and strategic planning for organizational development. 5) Career Advice for Aspiring Tech Leaders: Carl provides actionable advice for professionals aiming for C-level positions, stressing the importance of following one's passion, choosing the right environment, and building a supportive and effective team. Sell faster, smarter & better: Apollo AI Harnesses database of 250M+ contacts & sales engagement signals to create hyper-personalized AI msg Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you. Support the Show. Follow us on Twitter @sameerkhan or @jeremyaroberts or leave us a review on iTunes Click below to listen to our latest episode: https://lnkd.in/gAADm2Zu

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Check out my latest podcast with All Quiet: Second Front - Breaking Down Silos and Democratizing Data with Bryan Beal https://lnkd.in/gDKbjM92 or wherever you get your podcasts #ai #genai #snowflake

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For those who missed the Snowflake Data Summit keynote; here’s my GeneAI summarization :-) 1) Sridhar the new Snowflake CEO is smart as a pup and amazing academic and career pedigree, but has very little charisma or public speaking skills. Hope he improves on the latter over time. 2) Jensen zoomed in for his Keynote because he is still in Taiwan for Compudex. Jensen has labeled Taiwan the heart of GenAI Compute, all of Nvidia (and AMD)’s major suppliers are in Taiwan. It’s amazing this little island the size of California holds the future to AI. 3) Snowflake and Nvidia announced their partnership. Basically it’s combining Snowflake LLM Datastore with Nvidia LLM training and inference/generate. This is revolutionary. Snowflake pioneered the separation of compute from storage in massive EDWs, and now the circle is completely with bringing Nvidia compute training and inference/generation close to storage, eliminating bandwidth cost, latency and data policy/privacy issues. And as someone with Information Retrieval background, I see GenAI has come full circle with RAG - it’s just Precision/Recall, attributes vectorization as “embedding”, K-Nearest Neighbor, User Search Intent, DAG, ML algorithms, etc all over again. Except this time with supercharged GPU SoC that is improving at 16x Moore’s Law. We live in exciting times.

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“The AI revolution is actually a data revolution, and a company’s AI strategy is only as strong as its data strategy, with trust at its core.” Salesforce Chief Data Officer Wendy Batchelder 89% of UK technical + business leaders see data management as a crucial priority to capitalise on beneficial AI technologies. That's where Fullstory's Data Direct adds immediate value - we send clean, structured behavioural data straight to your warehouse (BigQuery, Snowflake, RedShift, Amazon S3)🔥. We capture every interaction you see—or don’t see 👀 https://lnkd.in/ep3UwP7t

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Whether you're technical or not, you should check this out. Ignore the title, even if it sounds like Charlie Brown's teacher. "Salesforce Announces GA of Data Cloud Vector Database, Unleashing the Power of Unstructured Data and Generative AI" (Link in Comments) What the heck does that mean? Businesses can now unlock insights from previously inaccessible unstructured data and take action across all channels and touchpoints, including sales, service, marketing, commerce, analytics, and other applications.😮 90% of your customer data is trapped in PDFs, emails, call transcripts, online customer reviews, and support tickets and other unstructured formats. You can now integrate these directly into customer profiles to gain a deeper understanding of customer needs and preferences, without having to rely on expensive and labor-intensive solutions. THINK ABOUT ALL THOSE EMAILS

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❄ Incredible video from Snowflake's enablement team on their usage of the Seismic Enablement Cloud. 📚 Enablement is about way more than the organisation and distribution of content, or assigning lessons for onboarding new hires. ⏰ Enablement is woven into the fabric of almost every activity of the revenue team, helping them save time and be more performant in their roles 👀 Have a watch of Vivi Michael's story and see what you think. #happycustomers #salesenablement #revenueenablement

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Super excited about the Databricks AI Summit next week! If you are there, come check out my session. I'll be talking with Mitchell Weiss (@mitchbweiss ) from Vail Resorts about all the unique needs Vail has as a company and how they are building a great modern architecture tailored to their needs, as well as how they are embracing a #lakehouse architecture and Amperity. "MODERNIZING DATA: LEVERAGING LAKEHOUSE ARCHITECTURE & DELTA SHARING" https://lnkd.in/g-n-YNVp

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This week, Jeff Skelly, Melissa Miller and I sat down with Julie L. Mohr, John Feldmeier, Jackie Rocca, Ema Patki, Craig Vaughan and Sean Crabtree, who represent various industries and roles, to discuss their perspectives and challenges in adopting AI. We all share a common desire to separate AI hype from reality, so we focused the discussion on creating tangible outcomes for our teams: 1. Quality data is crucial: AI is only as good as your data. Ensuring data integrity and completeness is a prerequisite for meaningful AI-powered experiences. Data quality initiatives and governance will become more critical across our organizations. 2. Experiment to identify use cases with the most productivity gains: For example, when we rolled out Virtual Agents internally, they assisted with 50% of requests in the first month of service. Six months later, we reduced the time it takes to resolve requests by 96%. Start with pilot projects, measure outcomes, and refine your approach based on feedback. 3. Joy, not just productivity: Today, Atlassian AI powers quick summaries, smarter search, custom virtual agents and more to free up time so teams can focus on the strategic and creative aspects of their jobs. While this can help productivity, it increases joyfulness for teams by letting them focus on the more enjoyable parts of their jobs. While this is not linearly measurable, this matters even more over the long run than productivity gains.

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Amazon Q goes GA today! We help customer on all layers of GenAI stack - custom chips, best Foundational models via Bedrock, and the application layer with apps like Amazon Q. Here is what Q does: 1/Q Developer (helps developers develop more quickly, especially when it comes to your AWS stack). 2/Q Business (helps the full organizations summarize documents and find patterns in data). 3/Q in Quicksight (helps the full organization summarize data patterns and build visualizations with natural language). Abhijeet Patil Ravi Kumar Keith Papsin Yogesh Bendre Check it out here:

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Business model innovation around Retrieval Augmented Generation from O'Reilly -- they have indexed all the content on Safari and are using it to provide answers grounded in vetted and credible content (their unique sauce). Another innovation here is to credit/compensate authors for the fraction of their work used in a generated answer. While this is progress, the more well-grounded the summaries look, the more dangerous the inevitable mistakes will be -- irrelevant documents will still lead to misleading summaries, and there will still be hallucinations because of interpolations with the text used to train Llama. At least based on the article, I don't see them employing self-checks or guardrails. I hope those are coming. https://lnkd.in/gqqHiR2D

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AI AGENTS ARE CO-WORKERS? Yes! And this article from Cloey Callahan and WorkLife makes a great case for why they could be important in the very near future. I am quoted talking about why it’s equally important to understand how we use all of this extra time these agents are going to provide us. The other perspective that is important is developing managers that understand how to lead these teams that will be a combination of AI agents and human beings. The managers that do this successfully will be exponentially more productive. Great read and thank you Cloey Callahan and WorkLife. #phenom #aiagents #aicoworker

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