Francis Dougherty

Cohere's newest #foundationmodel, Rerank 3, purpose built to enhance #enterprisesearch and Retrieval Augmented Generation (RAG) systems. "A primary challenge when building semantic search and RAG systems is determining how to best chunk your data. Our Rerank 3 model now boasts a 4k context length allowing customers to pass in larger documents for ranking. " "Rerank 3 shows up to 2x lower latency compared to Rerank 2 for shorter document lengths and up to 3x improvements at long context lengths." #AI #EnterpriseAI https://lnkd.in/edyrQhCm

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We’ve seen countless buzzwords come and go. “AIOps” is the latest in a long line of catchy but ultimately misguided terms that fail to capture the true potential of AI in the world of IT ops and observability. The observability market has been fragmented for years, with leading vendors like Datadog, New Relic, and Splunk rarely capturing more than 20% market share. Why? Because fundamentally, observability has been treated as a big data problem rather than an intelligence problem. Why is there an opportunity to change this paradigm now? LLMs offer a unified approach to data understanding. Their ability to process and correlate heterogeneous data types – logs, metrics, and traces – in their raw formats breaks down some of the silos that have plagued observability. The transformer architecture underlying LLMs excels at capturing long-range dependencies, crucial for understanding system-wide patterns. LLMs also bring the power of zero-shot and few-shot meaning they can adapt to new failure modes without extensive retraining, addressing the perennial issue of concept drift in rapidly evolving systems. Beware the technical challenges: LLMs currently struggle with both tabular and time series data, common formats in observability. Although we anticipate that innovations in newer architectures, multimodality, and multi-agent systems will mitigate some of these challenges over time, near-term solutions will require creative workarounds from builders. Read more on our point of view below! Foundation Capital https://lnkd.in/grqHXNMF

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Since launching last Fall, its been incredible to see all the many workflows and integrations that companies are building with Lucid's Developer Platform. We've seen companies build automated processes and deeply linked templates that help with Sales, Marketing, Operations, and Engineering teams get the most out of Lucid. Come learn how to get started building from two of our engineers on the API teams who help engineers build these integrations every day.

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Geoffrey Hinton, the "godfather of AI" and 2024 Nobel laureate in Physics, has sounded the alarm on the technology he helped create. His concerns echo those of the Keurig cup inventor a decade ago, who regretted the environmental impact of his innovation. Both cases highlight a crucial lesson: groundbreaking technologies can evolve beyond their creators' intentions. Hinton warns that AI could pose an existential threat to humanity, potentially manipulating people and even replacing biological intelligence24. As he puts it, "It's quite conceivable that humanity is just a passing phase in the evolution of intelligence." These cautionary tales remind us of the importance of responsible innovation. As technology advances at breakneck speed, we must ensure it benefits rather than harms our future. It's time to heed the warnings of pioneers like Hinton and work towards aligning AI with human values and interests. Source - https://lnkd.in/gxH-KDFu

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AI leaders have a crucial chance to address risks now and strengthen governance practices before playing catch-up to regulations, notes Kjell Carlsson, Ph.D. of Domino Data Lab. In a recent VentureBeat article, he shares his perspective on California's AI bill veto and why proactive governance is key to the future of AI innovation. Learn more about this addressing this opportunity: https://gag.gl/4WCTkP #AI #AIGovernance #AIInnovation #DataScience

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Santana Row shopping center in San Jose has implemented a ticketless parking system that eliminates the need for drivers to take a ticket upon entry and pay at a pay station upon exit. Instead, drivers can simply scan a QR code (see photo) posted throughout the garage using their phones, enter their license plate number and credit card information on the website that pops up, and a camera will read their license plate and open the gate as they exit. While this system may seem convenient and efficient, I think there is a significant security concern! There is a risk that perpetrators could replace the posted QR code with their own, which would redirect drivers to a fake website where they would unknowingly enter their credit card information. This could lead to unauthorized charges and potential identity theft. It is crucial for the shopping center to address this security concern by taking appropriate measures to protect drivers' personal and financial information. They must ensure that the QR codes posted are authentic and that there are no vulnerabilities that could allow perpetrators to manipulate the system. As an alternative solution, pay stations could be installed for shoppers to use instead of relying on their own phones.

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It was great to hear Robert Wong Julie Willem Angie McAllister, Ph.D. and Markus Kunesch on the recent episode of the @Ed-Technical podcast -- the first of a three-part series about how we're applying Google AI to our work in education and learning: https://lnkd.in/e7hQi6a2. This episode delves deep into LearnLM, how we're making Gemini tuned for pedagogy and how we're integrating this technology into our mainstream products to improve learning outcomes, and evolve how people learn on Google products. It explains inspiring new product ideas, and the steps we take across the company to understand and enhance the journey of learning in a responsible and innovative way. #LearnLM #AI #Education #Google

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Come check out this talk if you want to learn how we built distributed queries for Iceberg + Python!

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This is another huge step forward for Appvance IQ. Since we introduced our AI Coverage Map in 2020, it has become a key feature in how AI Automation Engineers and their Dev counterparts strategically deploy unlimited AI testing resources targeting the most business-critical and vital user flows. The new coverage maps in 5.2 bring even more insight into how enterprise applications are really working and where defects are lurking, where performance is lagging, and where risks are undetected. Let me know if you'd like to learn more.

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Interested to see OpenAI acquire Rockset today. Rockset is a realtime search and analytics database built by the team who funded Hadoop Distributed File System (HDFS) at Yahoo and created RocksDB, the open source embedded storage engine used by CockroachDB. I imagine this will form the basis of OpenAI's RAG at scale solution for enterprise data. https://lnkd.in/eDSG2AjX

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AI leaders have a crucial chance to address risks now and strengthen governance practices before playing catch-up to regulations, notes Kjell Carlsson, Ph.D. of Domino Data Lab. In a recent VentureBeat article, he shares his perspective on California's AI bill veto and why proactive governance is key to the future of AI innovation. Learn more about this addressing this opportunity: https://gag.gl/4WCTkP #AI #AIGovernance #AIInnovation #DataScience

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Thats the exact explanation I gave to a group of investors last week. (… it’s just Precision/Recall, attributes vectorization as “embedding”, K-Nearest Neighbor, User Search Intent, DAG, ML algorithms, etc all over again …). LOL and they asked what’s new this time around. And I was like not much, ML is still the backbone, and AI is the inference and actions based on the model. It works better when there is a feedback loop in a closed system. But the current state of the art like ChatGPT is based on scraping the Internet (garbage in, garbage out). Kinda like early search engines. What is a better analogy is that NVIDIA/Snowflake etc are selling tools, aka pickaxes to prospectors. The gold is up to you to find. And the opportunity right now is to monetize industry specific (healthcare, travel, financial services) clean data to build models with (aka a treasure map for the prospectors). From an enterprise perspective, what’s out there today aka generative AI isn’t very useful. Too much hype. To build useful engines will require an effort similar to search revolution 2 decades ago. From

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At the core of effective engineering leadership is the ability to stay connected with your team. This is perhaps the most challenging aspect because retaining the respect of your people is difficult unless you remain technically involved yourself. In my journey as an engineering leader, staying true to the individual contributor role that originally earned their respect has kept me close. This approach extends beyond technical expertise; it's about maintaining a deep connection with the core activities that propel product development forward at Gearflow. One of my biggest professional fears is becoming irrelevant or disconnected from my team and our work. This fear motivates me to continually reinvent and retool in response to market demands. Next month, I'll be sharing about my adventures generating synthetic data recently. And while I'm no expert when I start preparing for these talks, the process is incredibly valuable. It's less about delivering a perfect talk and more about how the preparation expands my thinking and exposes me to new capabilities. This helps me connect the dots to opportunities at Gearflow that bring real innovation to our customers. Speaking regularly holds me accountable and pushes me to learn ahead of the curve, ensuring I remain relevant and impactful day-to-day. Every talk is a journey of discovery, helping me to lead with humility, communicate clearly, and inspire a culture of innovation through a deep, technical understanding of our work.

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Interesting point of view on Apple's approach to AI. I believe that Apple approach is pretty thoughtful for the consumer space (that is generally grossly overhyped). I strongly believe that the real next frontier for LLM value creation is in developing LLMs for enterprise use cases that offer concrete practical value. This is very much an (yet) unsolved area! #GenerativeAI #largelanguagemodels #artificialintelligence #airevolution

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My first boss at Amazon was Anurag Gupta. When he was leaving Amazon he asked me to write two papers: one that describes the “new” Amazon Redshift after we completely changed the architecture of the system, and one that presents how a typical cloud data warehouse workload looks like. We did write the Amazon Redshift ReInvented paper that presents the “new” Redshift and published it at SIGMOD’22 (https://lnkd.in/gK3J_CZQ), but we didn’t get to write the second one. I am pleased to say that Tim Kraska and his team, Dominik Horn, Pascal Pfeil et al, wrote the second one and they published it at the upcoming VLDB’24 conference: https://lnkd.in/gC3kcWsZ . This paper is an important scientific contribution as it anaylzes the production workload of a major analytics system, such as Amazon Redshift, and quantitatively compares the real world against the standard industry benchmarks (TPC-DS and TPC-H). There are so many interesting insights from this paper. For example, in the real world only a little less than 50% of the executed statements are read-only SELECTs, whereas in the benchmarks the SELECTs are between 75% to 80%. If you are attending VLDB’24 make sure to attend the presentation of this paper. To further help the scientific community, along with the paper the team is releasing the Redset, which is a dataset containing three months worth of user query metadata that ran on a selected sample of instances in the Amazon Redshift Provisioned and Serverless fleet: https://lnkd.in/gCcyyhZT . Looking forward to seeing how the community will use these traces. #vldb24 #redset #redshift #realworkloads #tpc

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This is a fantastic partnership, bringing Gretel's synthetic data generation to all Microsoft Azure customers and users. Synthetic data is a safer, more effective and more scalable to create insights, marketplaces and data exchanges.

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MyPOV: Amazon is ‘laying out the foundation’ for this growing tech sector, expert says Fox Business Network Varney and CO Amazon Web Services (AWS) #ReInvent #AWSreInvent Salesforce Marc Benioff Andy Jassy Matt Garman https://lnkd.in/gQT9r4Nn

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