CIO Business Intelligence
APRIL 25, 2023
A transformer is a type of AI deep learning model that was first introduced by Google in a research paper in 2017. It is simply unaware of truthfulness, as it is optimized to predict the most likely response based on the context of the current conversation, the prompt provided, and the data set it is trained on.
CIO Business Intelligence
APRIL 24, 2023
How we got here The most notable enabling technologies in generative AI are deep learning, embeddings, transfer learning (all of which emerged in the early to mid-2000s), and neural net transformers (invented in 2017). One of the most important of such architectures, the “transformer,” was developed in 2017.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
O'Reilly on Data
FEBRUARY 18, 2020
Up until 2017, the ML+AI topic had been amongst the fastest growing topics on the platform. In 2019, as in 2018, Python was the most popular language on O’Reilly online learning. After several years of steady climbing—and after outstripping Java in 2017—Python-related interactions now comprise almost 10% of all usage.
Domino Data Lab
FEBRUARY 10, 2019
Humans likely not even notice the difference but modern deep learning networks suffered a lot. But apparently, models trained on text from 2017 experience degraded performance on text written in 2018. Images off the web tend to frame the object in question. We might expect that.
The Unofficial Google Data Science Blog
NOVEMBER 17, 2020
The first is that they are straightforward to optimize using traditional gradient-based optimizers as long as we pre-specify the placement of the knots. There is a robust set of tools for working with these kinds of constrained optimization problems. Other deep learning models can also be written in this form.
Domino Data Lab
MARCH 16, 2021
Lately, however, there is very exciting research emerging around building concepts from first principles with the goal of optimizing the higher layers to be human-readable. Instead of optimizing for pure accuracy, the network is constructed in a way that focuses on strong definitions of high-level concepts. Saliency Maps.
Domino Data Lab
JULY 2, 2019
SQL optimization provides helpful analogies, given how SQL queries get translated into query graphs internally , then the real smarts of a SQL engine work over that graph. Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. Software writes Software?
Expert insights. Personalized for you.
109 
Let's personalize your content