Rick Osterloh casually dropped his laptop onto the couch and leaned back, satisfied. It's not a mic, but the effect is about the same. Google's chief of hardware had just shown me a demo of the company's latest feature: computational processing for video that will debut on the Pixel 6 and Pixel 6 Pro. The feature was only possible with Google's own mobile processor, which it's announcing today. He's understandably proud and excited to share the news.

Recently there has been a huge interest in dialog systems. This interest has also been developed in the field of the medical domain where researchers are focusing on building a dialog system in the medical domain. This research is focused on the multi-turn dialog system trained on the multi-turn dialog data. It is difficult to gather a huge amount of multi-turn conversational data in the medical domain that is verified by professionals and can be trusted. However, there are several frequently asked questions (FAQs) or single-turn QA pairs that have information that is verified by the experts and can be used to build a multi-turn dialog system.

How can I tell if my machine learning model is working for me? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. Businesses must eventually sustain themselves beyond external funding sources by turning profits. What's talked about less than ML itself is how one can leverage machine learned models to generate profits. I've got a whole sub-section of the book that shows how to account for the revenues and costs of building a machine learning system, using traditional accounting concepts. Essentially, data learning loops bring about profit and create investment opportunities for the AI-First vendor: better predictions can lead to more automation, which lowers operating costs, which in turn means more gross profit that can be invested in research and development (models and data), leading to better predictions, and so on.

If you have ever used Google Maps on your phone without fiddling with the location settings, it goes without saying that the tech giant knows everywhere you've been. The really bad news is that even if you have previously tried to stop Google tracking your every movement, the company may have done so anyway. On Friday the Australian Competition and Consumer Commission (ACCC) won a legal action in the federal court, which ruled that, thanks to a peculiar set-up that required a user to check "No" or "Do Not Collect" to both "Location History" and "Web & App Activity" on some Android and Pixel phones, someone who ticked "No" to just one would still end up being tracked. We asked Dr Katharine Kemp, a legal academic from the University of New South Wales whose focus is consumer law, and the Australian cryptographer Vanessa Teague for their thoughts on the significance of the decision and how a person might go about securing their devices. Kemp, an Apple user herself, says that for many consumers, today's decision may not actually mean much, as the decision only related to Android users and Google has since updated the settings that formed the basis of the ACCC's complaint.

Frequently asked questions (FAQs) sound like a harmless annoyance from an outsider's perspective. Nearly every website has a page dedicated to the most common and mundane asks from curious visitors. But, from the inside of an organization, FAQs are anything but harmless. FAQs can have a paralyzing impact on internal support functions by creating helpdesk backlogs of unwanted low-value workloads that are being assigned to higher cost channels. This problem can be resolved with a combination of culture-focused strategy and smart technology.

Google Colab is a project from Google Research, a free, Jupyter based environment that allows us to create Jupyter [programming] notebooks to write and execute Python [1](and other Python-based third-party tools and machine learning frameworks such as Pandas, PyTorch, Tensorflow, Keras, Monk, OpenCV, and others) in a web browser. A programming notebook is a type of shell or kernel in the form of a word processor, where we can write and execute code. The data required for processing in Google Colab can be mounted into Google Drive or imported from any source on the internet. Project Jupyter is an open-source software organization that develops and supports Jupyter notebooks for interactive computing [4]. Google Colab requires no configuration to get started and provides free access to GPUs.

In the last few years, three major topics received increased interest: deep learning, NLP and conversational agents. Bringing these three topics together to create an amazing digital customer experience and indeed deploy in production and solve real-world problems is something innovative and disruptive. We introduce a new Portuguese financial domain language representation model called BERTa\'u. BERTa\'u is an uncased BERT-base trained from scratch with data from the Ita\'u virtual assistant chatbot solution. Our novel contribution is that BERTa\'u pretrained language model requires less data, reached state-of-the-art performance in three NLP tasks, and generates a smaller and lighter model that makes the deployment feasible. We developed three tasks to validate our model: information retrieval with Frequently Asked Questions (FAQ) from Ita\'u bank, sentiment analysis from our virtual assistant data, and a NER solution. All proposed tasks are real-world solutions in production on our environment and the usage of a specialist model proved to be effective when compared to Google BERT multilingual and the DPRQuestionEncoder from Facebook, available at Hugging Face. The BERTa\'u improves the performance in 22% of FAQ Retrieval MRR metric, 2.1% in Sentiment Analysis F1 score, 4.4% in NER F1 score and can also represent the same sequence in up to 66% fewer tokens when compared to "shelf models".

Google Colab is a project from Google Research, a free, Jupyter based environment that allows us to create Jupyter [programming] notebooks to write and execute Python [1](and other Python-based third-party tools and machine learning frameworks such as Pandas, PyTorch, Tensorflow, Keras, Monk, OpenCV, and others) in a web browser. A programming notebook is a type of a shell or kernel in the form of a word processor, where we can write and execute code. The data required for processing in Google Colab can be mounted into Google Drive or imported from any source on the internet. Project Jupyter is an open-source software organization that develops and supports Jupyter notebooks for interactive computing [4]. Google Colab requires no configuration to get started and provides free access to GPUs.

Frequently asked question (FAQ) retrieval, with the purpose of providing information on frequent questions or concerns, has far-reaching applications in many areas, where a collection of question-answer (Q-A) pairs compiled a priori can be employed to retrieve an appropriate answer in response to a user\u2019s query that is likely to reoccur frequently. To this end, predominant approaches to FAQ retrieval typically rank question-answer pairs by considering either the similarity between the query and a question (q-Q), the relevance between the query and the associated answer of a question (q-A), or combining the clues gathered from the q-Q similarity measure and the q-A relevance measure. In this paper, we extend this line of research by combining the clues gathered from the q-Q similarity measure and the q-A relevance measure and meanwhile injecting extra word interaction information, distilled from a generic (open domain) knowledge base, into a contextual language model for inferring the q-A relevance. Furthermore, we also explore to capitalize on domain-specific topically-relevant relations between words in an unsupervised manner, acting as a surrogate to the supervised domain-specific knowledge base information. As such, it enables the model to equip sentence representations with the knowledge about domain-specific and topically-relevant relations among words, thereby providing a better q-A relevance measure. We evaluate variants of our approach on a publicly-available Chinese FAQ dataset, and further apply and contextualize it to a large-scale question-matching task, which aims to search questions from a QA dataset that have a similar intent as an input query. Extensive experimental results on these two datasets confirm the promising performance of the proposed approach in relation to some state-of-the-art ones.

Artificial intelligence (AI) systems face a set of conflicting goals: being accurate (consuming large amounts of computational power and electrical power) and being accessible (being lower in cost, less computationally intensive, and less power-hungry). Unfortunately, many of today's AI implementations are environmentally unsustainable. Improvements in AI energy efficiency will be driven by several factors, including more efficient algorithms, more efficient computing architectures, and more efficient components. It's necessary to measure and track the energy consumption of AI systems to identify any improvements in energy efficiency. One example of the increasing awareness of the importance of energy consumption in AI systems is having is reflected in the fact that the ULPMark (ultra-low power) benchmark line from EEMBC is now adding ML inference and developing a new benchmark, the ULPMark-ML.