Batch normalization is implemented (if desired) as outlined in the original paper that introduced it, i.e. after the Dense linear transformation but before the non-linear (ReLU) activation. The output layer is just a standard Dense layer with 1 neuron and a sigmoid activation function (that squishes predictions to between 0 and 1), such that our model is ultimately predicting 0 or 1, fake or true. Batch normalization can help speed up training and provides a mild regularizing effect. Both the Keras- and spaCy-embedded models will take a good amount of time to train, but ultimately we'll end up with something that we can evaluate on our test data with. Overall, the Keras-embedded model performed better– achieving a test accuracy of 99.1% vs the spaCy model's 94.8%.

AWS recently released a new feature in SageMaker (AWS Machine Learning Service) JumpStart to incrementally retrain machine-learning (ML) models …

… Microsoft, Google, IBM, Oracle and Alibaba Cloud who are leading with machine learning and artificial intelligence products for developers.

Enter Secondmind, a machine learning company based in the UK.

Using machine learning, semantic mapping and novelty detection, the robot can independently identify and flag security-relevant anomalies like …

We don't have much reason to think that they have an internal monologue, the kind of sense perception humans have, or an awareness that they're a being in the world. Over the weekend, the Washington Post's Nitasha Tiku published a profile of Blake Lemoine, a software engineer assigned to work on the Language Model for Dialogue Applications (LaMDA) project at Google. LaMDA is a chatbot AI, and an example of what machine learning researchers call a "large language model," or even a "foundation model." It's similar to OpenAI's famous GPT-3 system, and has been trained on literally trillions of words compiled from online posts to recognize and reproduce patterns in human language. LaMDA is a really good large language model.

A few decades ago, tech innovations like artificial intelligence, cloud computing, and machine learning were more fiction than reality.

In 2020, OpenAI's machine learning algorithm GPT-3 blew people away when, after ingesting billions of words scraped from the internet, …

RADAR journalists use a tool called Arria Studio, which offers a glimpse of what writing automated content looks like in practice. The author writes fragments of text controlled by data-driven if-then-else rules. For instance, in an earthquake report you might want a different adjective to talk about a quake that is magnitude 8 than one that is magnitude 3. So you'd have a rule like, IF magnitude 7 THEN text "strong earthquake," ELSE IF magnitude 4 THEN text "minor earthquake." Tools like Arria also contain linguistic functionality to automatically conjugate verbs or decline nouns, making it easier to work with bits of text that need to change based on data.

SAN FRANCISCO, June 23, 2022 (GLOBE NEWSWIRE) -- Tecton, the enterprise feature store company, today announced a partnership with Databricks, the Data and AI Company and pioneer of the data lakehouse paradigm, to help organizations build and automate their machine learning (ML) feature pipelines from prototype to production. Tecton is integrated with the Databricks Lakehouse Platform so data teams can use Tecton to build production-ready ML features on Databricks in minutes.