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You.com showcases the state of AI today
The story of you.com is multi-faceted and telling in many ways. You.com was founded in 2020 by Richard Socher, one of the leading NLP (Natural Language Processing) researchers in the world, to offer a better search experience to users and compete with Google.
With a startup exit and a Chief Data Scientist stint at Salesforce, Socher got the experience, network and backing he needed to pursue his long-time ambition of taking on Google. That's something few people have tried, with moderate success.
Socher diagnosed early enough that the way to success is by carving a niche for you.com. You.com focuses on serving knowledge workers in "complex informational / action searches": elaborate queries, and queries that are really about accomplishing a task, respectively.
In 2022, in the pre-ChatGPT era, Socher set out a course for you.com based on AI, apps, privacy, and personalization. In 2024, you.com is staying the course, but a few things have changed. In the GenAI era the competition is growing, and borrowing pages from you.com’s book.
Language model providers such as OpenAI and Anthropic now offer services similar to you.com. Upstarts such as perplexity.ai have sprung up, and Google itself is embracing the AI approach to search.
You.com is making progress too. Since launching in November 2021, you.com has served 1 billion queries and has millions of active users, including from Fortune 500. The company's ARR has grown by 500% since January 2024.
Today, you.com announced a $50 million Series B funding round, as well as a new team plan called Multiplayer AI. We caught up with Socher, talked about the news, and took you.com for a spin.
Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/09/04/you-com-raises-50m-to-lead-ai-for-knowledge-workers/
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"Data Rules" is a book about data, but not just about big data crunching. A book about the relationship of data with economic institutions and society, but also about the interplay with data technologies by which data are being generated and processed. A book that is critical, but not ideological.
This is how Jannis Kallinikos describes "Data Rules: Reinventing the Market Economy", a book co-authored by himself and Cristina Alaimo and recently published by The MIT Press.
Jannis Kallinikos is Full Professor of Organization Studies and the CISCO Chair in Digital Transformation and Data Driven Innovation at LUISS University, Rome.
This is where we met to talk about the key concepts in "Data Rules":
Understanding data generation and useHow data is breaking boundariesPlatforms and choiceThe illusion of objectivityAlgorithms, agency and surveillanceFrom market and design rules to data rulesArticle published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/07/01/data-rules-from-interoperability-to-commensurability/
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Saknas det avsnitt?
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What is a universal semantic layer, and how is it different from a semantic layer? Are there actual semantics involved? Who uses that, how, and what for?
When Cube Co-founder Artyom Keydunov started hacking away a Slack chatbot back in 2017, he probably didn't have answers to those questions. All he wanted to do was find a way to access data using a text interface, and Slack seemed like a good place to do that.
Keydunov had plenty of time to experiment, validate, and develop Cube, as well as get insights along the way. We caught up and talked about all of the above, as well as Cube's latest features and open source core.
Article published on Orchestrate all the Things.
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From better together to full native integration, Neo4j is creating an ecosystem around all major cloud platforms to provide graph-powered features for Generative AI and beyond.
As Neo4j just announced its partneship with Microsoft, we met with Chief Product Officer Sudhir Hasbe to talk about:
What this partnership means for users and how it worksHow graph-powered generative AI aligns with cloud platform AI strategiesSimilarities and differences across themHow Neo4j's strategy is shaping up, and when Databricks and Snowflake integration are coming.For additional analysis and a writeup of the conversation, you can read the article published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/03/27/neo4j-partners-with-microsoft-unfolds-strategy-to-power-generative-ai-applications-with-cloud-platforms-and-graph-rag/
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If we look at the current status quo in AI as a case of demand and supply, what can we do to close the gap between the exponentially growing demand on the side of AI models and the linearly growing supply on the side of AI hardware?
This formulation was the premise on which Yonatan Geifman co-founded Deci in 2019.
Today, with the generative AI explosion in full bloom, demand is growing faster than ever, and Deci is a part of this by contributing a number of open source models.
Join us as we explore:
How AI models are different than traditional software and what open source means in AIChoosing between GPT-4, Claude 3 and open source LLMsCustomizing LLMs and fine-tuning vs. RAGEvaluating LLMsMarket outlookArticle published on Orchestrate All the Things: https://linkeddataorchestration.com/2024/03/06/evaluating-and-building-applications-on-open-source-large-language-models/
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There’s more to AI chips than NVIDIA: AMD, Intel, chiplets, upstarts, analog AI, optical computing, and AI chips designed by AI.
The interest and investment in AI is skyrocketing, and generative AI is fueling it. Over one-third of CxOs have reportedly already embraced GenAI in their operations, with nearly half preparing to invest in it.
What’s powering it all - AI chips - used to receive less attention. Up to the moment OpenAI’s Sam Altman claimed he wants to raise up to $7 trillion for a “wildly-ambitious” tech project to boost the world’s chip capacity. Geopolitics and sensationalism aside, however, keeping an eye on AI chips means being aware of today’s blockers and tomorrow’s opportunities.
According to a recent study by IMARC, the global AI chip market is expected to reach $89.6 Billion by 2029. The demand for AI chips has increased substantially over time. Growth in AI technology, rising demand for AI chips in consumer electronics and AI chip innovation all contribute to this forecast.
Few people have more insights to share on AI hardware than Tony Pialis, CEO and co-founder of Alphawave. In an extensive conversation, Pialis shared his insider’s perspective on the AI chip landscape, the transformative rise of chiplets, specialized hardware for training and inference, emerging directions like analog and optical computing, and much more.
Article published on Orchestrate All the Things: https://linkeddataorchestration.com/2024/02/13/the-future-of-ai-chips-leaders-dark-horses-and-rising-stars/
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For many organizations today, data management comes down to handing over their data to one of the "Big 5" data vendors: Amazon, Microsoft Azure and Google, plus Snowflake and Databricks.
But analysts David Vellante and George Gilbert believe that the needs of modern data applications coupled with the evolution of open storage management may lead to the emergence of a "sixth data platform".
The sixth data platform hypothesis is that open data formats may enable interoperability, leading the transition away from vertically integrated vendor-controlled platforms towards independent management of data storage and permissions.
It's an interesting scenario, and one that would benefit users by forcing vendors to compete for every workload based on the business value delivered, irrespective of lock-in. But how close are we to realizing this?
To answer this question, we have to examine open data formats and their interoperability potential across clouds and formats, as well as on the semantics and governance layer.
We caught up with Peter Corless and Alex Merced to talk about all of that.
Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/01/11/data-management-in-2024-open-data-formats-and-a-common-language-for-a-sixth-data-platform/
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What is a skills-based economy and how is LinkedIn moving from vision to implementation?
As LinkedIn Director of Engineering Sofus Macskássy shares, there's AI, taxonomy, and ontology involved in building the Skills Graph that powers the transition.
We discuss the process of extracting skills from text, building a skills graph, and leveraging it for various product lines within LinkedIn.
We cover aspects related to explicit and implicit skill provenance, credibility, depth and interoperability.
Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2023/12/13/how-linkedin-is-moving-towards-a-skills-based-economy-with-the-skills-graph
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Amazon Neptune, the managed graph database service by AWS, makes analytics faster and more agile while introducing a vision aiming to simplify graph databases.
It's not every day that you hear product leads questioning the utility of their own products. Brad Beebe, the general manager of Amazon Neptune, was all serious when he said that most customers don't actually want a graph database. However, that statement needs contextualization.
If Bebee had meant that in the literal sense, the team himself and Amazon Neptune Principal Product Manager Denise Gosnell lead would not have bothered developing and releasing a brand new analytics engine for their customers. We caught up with Bebee and Gosnell to discuss Amazon Neptune new features and the broader vision.
We cover where Amazon Neptune fits in the AWS vision of data management, and how the new analytics engine provides a single service for graph workloads, high performance for graph analytic queries and graph algorithms, and vector store and search capabilities for Generative AI applications. We also share insights on the One Graph vision, the road from serverless to One Graph via HPC, as well as vectors and Graph AI.
Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2023/11/29/amazon-neptune-introduces-a-new-analytics-engine-and-the-one-graph-vision/
00:00:00 Introduction
00:01:44 Amazon Neptune & AWS vision of data management
00:05:35 The Importance of Graph Databases
00:08:55 Amazon Neptune Use Cases
00:13:13 Introduction to Amazon Neptune Analytics
00:15:20 Key Features of Neptune Analytics
00:17:40 Use Cases for Neptune Analytics
00:21:10 Preparing Data for Generative AI Applications
00:23:37 Neptune Analytics Use Cases and Deployment
00:26:43 Pricing and Roadmap Q&A
00:48:46 Conclusion
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“Graph database growth is going strong through the Trough of Disillusionment.” And “Graph Analytics go big and real-time.” These were two of the headlines of the Spring 2023 update of the Year of the Graph newsletter. In combination, they seem like an appropriate summary of the reasoning behind a new entry in the graph database market: Aerospike Graph, which Aerospike officially unveiled in June 2023.
We caught up with the company’s Chief Product Officer Lenley Hensarling to discuss this long journey that started about three years ago, as well as Aerospike's differentiation in a very densely populated market.
Article published on Orchestrate all the Things.
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LinkedIn is a case study in terms of how its newsfeed has evolved over the years.
LinkedIn's feed has come a long way since the early days of assembling the machine learning infrastructure that powers it.
Recently, a major update to this infrastructure was released. We caught up with the people behind it to discuss how the principle of being people-centric translates to technical terms and implementation.
Article published on Orchestrate all the Things
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Would you leave a Google Staff Research Engineer role just because you want your TV to automatically pause when you get up to get a cup of tea? Actually, how is that even relevant, you might ask. Let's see what Pete Warden, former Google Staff Research Engineer and now CEO and Founder of Useful Sensors, has to say about that.
Although naturally much of what he did was based off things others were already working on, Warden is sometimes credited as having kickstarted the TinyML subdomain of machine learning. Either way TinyML is getting big, and Warden is a big part of it.
Useful Sensors is Warden's latest venture. They just launched a product called AI in a Box, which they dubs an "offline, private, open source LLM for conversations and more". Even though it's not the first product Useful Sensors has created, it's the first one that's officially launched. That was a good opportunity to catch up with Warden and talk about what they are working on.
Article published on Linked Data Orchestration
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In an era of dried-up funding and Data Lakehouse vendor supremacy, Redpanda is going against the grain.
The company just secured a $100 million Series C funding round to execute on an unconventional strategy.
Redpanda Founder and CEO Alex Gallego explains how things work for the company.
Article published on Orchestrate all the Things
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The EU Parliament just voted to bring the EU AI Act regulation into effect. If GDPR is anything to go by, that's a big deal.
Here's what and how it's likely to effect, its blind spots, what happens next, and how you can prepare for it based on what we know.
Article published on Orchestrate all the Things
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From a consumer-oriented application, Foursquare has evolved to a data and product provider for enterprises. The next steps in its evolution will be powered by the Foursquare Graph
If the name Foursquare rings a bell, it means you were around in the 2010s. Your only resort to plausible deniability would be if you are a data professional - although that's not an either/or proposition.
In the 2010s, Foursquare was a consumer-oriented mobile application. The premise was simple: people would check in at different locations and get gamified rewards. Their location data would be shared with Foursquare and used for services such as recommendations.
Facebook and Yelp got the lion's share of that market, but Foursquare is still around. In addition to having 9 billion-plus visits monthly from 500 million unique devices, Foursquare's data is used to power the likes of Apple, Uber and Coca-Cola.
Today the company announced Foursquare Graph, what it dubs the industry’s first application of graph technology to geospatial data.
I caught up with Vikram Gundeti, Distinguished Engineer at Foursquare, to learn more about what kind of data Foursquare deals with, what it does with that data, and how using graph is going to help.
Article published on Orchestrate all the Things
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On AI-generated content, writing, new, old, and broken media, platforms, models, audiences, and body parts.
An update from the host on launching the Orchestrate All The Things Newsletter, and some insights on Technology, Data, Media, AI, Writing, and Content.
A new type of podcast episode: AI-generated article narrations.
Article published on Orchestrate All The Things.
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Is incremental change a bad thing? The answer, as with most things in life, is "it depends". In the world of technology specifically, the balance between innovation and tried and true concepts and solutions seems to have tipped in favor of the former. Or at least, that's the impression reading the headlines gives. Good thing there's more to life than headlines.
The ScyllaDB team is one of those who work with their garage doors up and are not necessarily after making headlines. They believe that incremental change is nothing to shun if it leads to steady progress.
Compared to the release of ScyllaDB 5.0 in ScyllaDB Summit 2022, what ScyllaDB Summit 2023 brought could be labeled “incremental change.” But this is just the tip of the iceberg, as there's more than meets the eye here.
We caught up with ScyllaDB CEO and co-founder Dor Laor to discuss what kept the team busy in 2022, how people are using ScyllaDB, as well as trends and tradeoffs in the world of high performance compute and storage.
Article published on The New Stack
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The last couple of years have been an AI model arms race.
The assumption is that the larger the model the better it will perform. But that may not always be the case.
FAR AI Research Scientist Ian McKenzie is a key member of the team organizing the Inverse Scaling Challenge, an initiative set up to investigate scaling laws. We discuss:
Large Language Models and how they are trainedThe scaling laws and how they are being revised as research and development progressesThe Inverse Scaling Challenge and its findingsArticle published on Orchestrate all the Things.
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Can technology, and real-time technology in particular, help companies achieve savings during economic hardship? Alok Pareek thinks it can.
Pareek is the Co-founder and EVP of products of Striim, a vendor whose goal and motto is to "help companies make data useful the instant it’s born".
Depending on which angle you look at it, you could say that Pareek is either biased or in the know. Either way, it was not so long ago that real-time data, or streaming data as this market is also called, was estimated to be worth billions.
But then again, as the recent wave of layoffs and market capitalization losses goes to show, many projections around technology are off the mark.
Could real-time data be different? Where does cloud modernization come into play and how does Striim's offering relate to that? As Striim today announced the availability of its fully managed Striim Cloud service on Amazon Web Services (AWS), we connected with Pareek to discuss.
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