A great deep dive by @manuelsoria_ and @RLanceMartin on text-to-SQL and all the steps involved!
DATA
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Training Data Influence Distributions Follow Heavy-Tailed Power Laws
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The influence distributions are heavy-tailed, with the tail approximately following a power law. Most influence is concentrated in a small fraction of training sequences. Still, the influences are diffuse, with any particular sequence only slightly influencing the final outputs.
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Training Sequences Shape Generalization in Scaled Language Models
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Identifying the most influential training sequences revealed that generalization patterns become much more sophisticated and abstract with scale. For example, here are the most influential sequences for 810 million and 52 billion parameter models for a math word problem:
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Influence Functions: Measuring Training Data Impact on Models
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Influence functions are a classic technique from statistics. They are formulated as a counterfactual: if a copy of a given training sequence were added to the dataset, how would that change the trained parameters (and, by extension, the model’s outputs)?
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User behavior analytics: choosing the right platform
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Figure out where people are getting stuck, what keeps them coming back, what they like to use and what they don’t like to use. We tried out tons of products on the market. I remember Flurry, Google Analytics, Adobe, Kissmetrics, and others. And none of them were able to…
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CRM analytics gap: Why deals fail remains hidden from view
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all of the knowledge about what was working and not working was hidden inside people’s heads: ‘The CRM was showing me stuff, but it wasn’t anything meaningful. Yes, you didn’t close that deal, but why?’ He left Sisense and looked around to see if anybody was addressing this.
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AI System Captures Sales Intelligence for Organization
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I was on sabbatical doing nothing, learning deep learning, so we figured: let’s try to come up with a system that takes the stuff from salespeople’s heads, captures the information, and gives visibility and guidance to the rest of the organization.”
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Gong CEO shares startup origin story solving sales analytics
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As one example, the origin story for @Gong_io from @eilonreshef (CEO): “Amit, now my co-founder, ran a company in the BI space called Sisense. He ran into a problem—when sales didn’t work well—and it was very hard to understand why. He realized that essentially…
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DNA Hacking Risks in Biometric Cryptocurrency Authentication Systems
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“What are the risks if your DNA, something that uniquely identifies you, gets hacked?” asks HAI fellow @kingjen
. A new cryptocurrency requires unique biometric data for access – raising concerns about the risks of stolen DNA credentials. https://
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Data and Machine Learning as Industry 4.0 Driving Force
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Cracking the #Data #MachineLearning as the driving force #Industry40 #Automation #DigitalTransformation #BigData #AI #DataScience #ML #DigitalTwins #AILabPage #VinsLens @AITimeJournal @esterliquori @FrRonconi @ingliguori @BigDataGal @mclynd @KirkDBorne vinodsblog.com/2016/12/31/cr…
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