I’m a dodo! This isn’t true! This isn’t me! He saw his chicken wings, his chicken feet. Oh my god, said the dodo, looking down at himself. Then the dodo caught sight of his own reflection in the glass.Īnd what he saw was a chicken staring back. And I’m here - I’m alive! Why don’t these people understand that? And below, it explained that the dodos were all dead.īut I’m a dodo! the dodo said. Near the end of the exhibit, the dodo came to a diorama - there were replicas of his ancestors behind glass. It was nothing that the dodo hadn’t always known before, but it seemed somehow he’d forgotten it. Where they were from and what they ate and all that. The dodo learned all about the history of dodos. So the dodo walked in and strolled around. And, in time, the dodo was very good at it.īut then, one day, the dodo walked by a museum and he saw a big banner out front. It wasn’t a very interesting existence, being a chicken, but it was better than being laughed at and scorned. He got pretty good at going bok-bok-bok-bok, and bobbing his head back and forth. So the dodo did some research into the whole chicken phenomenon, and then he started to practice. Whether it’s gypsum, lime, silica, alumina and iron oxide or it is context-specific AI to surpass and augment LLM generalization, the mixer is on.Maybe I’ll just pretend to be a chicken, he said. Looking at the context-based specifics of the company’s platform progression (as Databricks would surely insist that we do, based upon its adherence to context-based specifics when it comes to AI), there is a clear move here to provide organizations with some (almost simple sounding) extra tooling that in fact comes from deeply intelligent planning at the software architecture level.Īs we now build the walls of digital business with AI from sources such as Databricks, we will need a new form of cemented concrete to bind AI to more carefully precision-engineered workflow tasks. It alleviates time-strapped engineers, eases the burden of data management, and empowers employees to take advantage of the AI revolution without jeopardizing the company’s proprietary information.” Lakehouse expansionĭatabricks notes that it also continues to expand its Lakehouse Platform, recently announcing Lakehouse Apps and its Databricks Marketplace, plus a suite of data-centric AI tools for building and governing LLMs on the lakehouse. “Organizations can be confident that their employees will only have access to the data they are authorized to use, so increasing data accessibility doesn’t increase risk. “LakehouseIQ solves two of the biggest challenges that businesses face in using AI: getting employees the right data while staying compliant and keeping data private when it should be,” said CEO Ghodsi. LakehouseIQ does all of the while being governed by Unity Catalog, Databricks’ own solution for unified search and governance across data, analytics and AI. Databricks further claims that it is capable of generating additional insights that could spur new questions or lines of thinking. LakehouseIQ learns from these (above-referenced business-unique) signals within an organization using schemas, documents, queries, popularity rating measures, lineage, data science notebooks (not the laptop kind) and Business intelligence (BI) dashboards to become cumulatively smarter as it answers more queries.īecause this technology understands the specifics of an organization’s own business jargon in the context of where it is used (in terms of which applications and which digital services it exists in) it can interpret the intent of the question. “General purpose models don’t understand the unique language of every business: they cannot process jargon or internal acronyms they are not trained on the company’s unique data sets and they do not understand organizational charts or know which teams should have access to what information.” Large Language Models (LLMs) promised to fix this problem, but so far, the results have been disappointing,” proposes Ghodsi and team. This bottleneck prevents businesses from truly embracing data and AI. “Whether the CEO is trying to build quarterly sales forecasts or a marketer is attempting to analyze campaign performance, knowledge workers rely on a small team of over-worked data scientists and programmers to find and query the relevant data sets. The company says that its new search engine doesn't just find data, it interprets, aligns and presents it in an actionable, contextual format. LakehouseIQ is said to ‘significantly enhance’ Databricks’ in-product Search function. We know that when employees need access to internal data to help complete their tasks, many may find it tough to get what they need in terms to perform timely analytics.
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