Apple could unveil Gemini-powered Siri in Feb. Gemini 3 is now Google's default model for AI Overviews Plus, you can start an AI Mode conversation directly from a summary. The Google logo and lettering can be seen on the façade of the company's Munich headquarters building in Munich (Bavaria). Google has begun rolling out two upgrades for Search. Starting today, Gemini 3 is the default model powering AI Overviews. When the company debuted its new family of AI systems last November, it first deployed Gemini 3 in AI Overviews through a router that was programmed to direct the most difficult questions to the new system.

Given a default distribution $P$ and a set of test data $x^M=\{x_1,x_2,\ldots,x_M\}$ this paper seeks to answer the question if it was likely that $x^M$ was generated by $P$. For discrete distributions, the definitive answer is in principle given by Kolmogorov-Martin-L\"{o}f randomness. In this paper we seek to generalize this to continuous distributions. We consider a set of statistics $T_1(x^M),T_2(x^M),\ldots$. To each statistic we associate its maximum entropy distribution and with this a universal source coder. The maximum entropy distributions are subsequently combined to give a total codelength, which is compared with $-\log P(x^M)$. We show that this approach satisfied a number of theoretical properties. For real world data $P$ usually is unknown. We transform data into a standard distribution in the latent space using a bidirectional generate network and use maximum entropy coding there. We compare the resulting method to other methods that also used generative neural networks to detect anomalies. In most cases, our results show better performance.

The introduction of transformers has made a huge impact on Artificial Intelligence, especially in the Natural Language Processing domain. Transformers paved way for the most awaited success of transfer learning in Natural Language Processing. As a result, many large language models came into existence, and now we are able to build beneficial applications on top of these cutting-edge models. A transformer is, in simpler language, an encoder-decoder architecture with a self-attention mechanism on both sides. The encoder block takes input and converts it into numerical form, and the decoder block takes that numerical form and converts it to text.

Basic safety needs in the paleolithic era have largely evolved with the onset of the industrial and cognitive revolutions. Robots don't have the same hardwired behavioral awareness and control, so secure collaboration with humans requires methodical planning and coordination. You can likely assume your friend can fill up your morning coffee cup without spilling on you, but for a robot, this seemingly simple task requires careful observation and comprehension of human behavior. Scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have recently created a new algorithm to help a robot find efficient motion plans to ensure physical safety of its human counterpart. In this case, the bot helped put a jacket on a human, which could potentially prove to be a powerful tool in expanding assistance for those with disabilities or limited mobility.

We propose a novel hybrid approach to lemmatization that enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. During training, the enhanced lemmatizer learns both to generate lemmas via a sequential decoder and copy the lemma characters from the external candidates supplied during run-time. Our lemmatizer enhanced with candidates extracted from the Apertium morphological analyzer achieves statistically significant improvements compared to baseline models not utilizing additional lemma information, achieves an average accuracy of 97.25% on a set of 23 UD languages, which is 0.55% higher than obtained with the Stanford Stanza model on the same set of languages. We also compare with other methods of integrating external data into lemmatization and show that our enhanced system performs considerably better than a simple lexicon extension method based on the Stanza system, and it achieves complementary improvements w.r.t. the data augmentation method.

Decomposing models into multiple components is critically important in many applications such as language modeling (LM) as it enables adapting individual components separately and biasing of some components to the user's personal preferences. Conventionally, contextual and personalized adaptation for language models, are achieved through class-based factorization, which requires class-annotated data, or through biasing to individual phrases which is limited in scale. In this paper, we propose a system that combines model-defined components, by learning when to activate the generation process from each individual component, and how to combine probability distributions from each component, directly from unlabeled text data.

You may hear about "no free lunch" (NFL) theorem, which indicates that there is no best algorithm for every data. One algorithm may perform well in one data but perform poorly in other data. That is why there are so many machine learning algorithms available to train data. How do we know which machine learning model is the best? We cannot know until we experiment and compare the performance of different models.

This app does object detection using the SSD Mobilenet Caffe model, the Intel Movidius Neural Compute Stick 2, OpenVINO Toolkit R3 and the Intel RealSense depth camera. It first detects an object in the video frame and then uses the depth stream to detect how far the object is using the Intel RealSense depth camera (tested with Intel RealSense D415). The default model used in this sample uses the PASCAL Voc dataset and detects up to 20 classes. Please see the networks/ssd_mobilenet_caffe sample for more information. Note: All development and testing has been done on Ubuntu 16.04 on an x86-64 machine.

--Automated machine learning has gained a lot of attention recently. Building and selecting the right machine learning models is often a multi-objective optimization problem. General purpose machine learning software that simultaneously supports multiple objectives and constraints is scant, though the potential benefits are great. In this work, we present a framework called Autotune that effectively handles multiple objectives and constraints that arise in machine learning problems. Autotune is built on a suite of derivative-free optimization methods, and utilizes multilevel parallelism in a distributed computing environment for automatically training, scoring, and selecting good models. Incorporation of multiple objectives and constraints in the model exploration and selection process provides the flexibility needed to satisfy tradeoffs necessary in practical machine learning applications. Experimental results from standard multi-objective optimization benchmark problems show that Autotune is very efficient in capturing Pareto fronts. These benchmark results also show how adding constraints can guide the search to more promising regions of the solution space, ultimately producing more desirable Pareto fronts. Results from two real-world case studies demonstrate the effectiveness of the constrained multi-objective optimization capability offered by Autotune. There has been increasing interest in automated machine learning (AutoML) for improving data scientists' productivity and reducing the cost of model building. A number of general or specialized AutoML systems have been developed [1]- [7], showing impressive results in creating good models with much less manual effort. Most of these systems only support a single objective, typically accuracy or error, to assess and compare models during the automation process.

First off, you do have to accept that it is harder to get named entities right in lowercase or inconsistently cased English text than in formal text, where capital letters are a great clue. Nevertheless, there are things that you must do to get CoreNLP working fairly well with lowercase text – the default models are trained to work well on well-edited text. If you are working with properly edited text, you should use our default English models. If the text that you are working with is (mainly) lowercase or uppercase, then you should use one of the two solutions presented below. If it's a real mixture (like much social media text), you might use the truecaser solution below, or you might gain by using both the cased and caseless NER models (as a long list of models given to the ner.model property).