Philips' industry-first Tube for Life guarantee minimizes lifetime operating costs and provides reliability to help ensure efficient operation Amsterdam, the Netherlands and Chicago, USA – Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, today announced new additions to its CT imaging portfolio at the Radiological Society of North America (RSNA) annual meeting (November 28 – December 2, Chicago, USA). The new CT 5100 – Incisive – features CT Smart Workflow [1], a comprehensive suite of artificial intelligence* (AI) enabled capabilities designed to accelerate CT workflows, enhance diagnostic confidence, and maximize equipment up-time, helping imaging services to enhance patient outcomes, improve department efficiency, reduce operational costs, and meet ambitious financial objectives. CT 5100 – Incisive – with CT Smart Workflow [1] includes Philips' Tube for Life guarantee, which over the lifetime of the scanner can potentially lower operating expenses by an estimated USD 420,000 [2][3]. This newest CT innovation from Philips also provides access to Philips' Technology Maximizer program, which provides users with the latest software and hardware updates as they are released. "With the combination of CT 5100 – Incisive – and CT Smart Workflow, we have embedded AI into the tools that radiology departments use every day so they can apply their expertise to the patient, rather than unnecessary distractions associated with the CT imaging itself," said Frans Venker, General Manager of Computed Tomography at Philips.

Attacking image recognition systems with carefully-crafted adversarial images has been considered an amusing but trivial proof-of-concept over the last five years. However, new research from Australia suggests that the casual use of highly popular image datasets for commercial AI projects could create an enduring new security problem. For a couple of years now, a group of academics at the University of Adelaide have been trying to explain something really important about the future of AI-based image recognition systems. It's something that would be difficult (and very expensive) to fix right now, and which would be unconscionably costly to remedy once the current trends in image recognition research have been fully developed into commercialized and industrialized deployments in 5-10 years' time. Before we get into it, let's have a look at a flower being classified as President Barack Obama, from one of the six videos that the team has published on the project page: In the above image, a facial recognition system that clearly knows how to recognize Barack Obama is fooled into 80% certainty that an anonymized man holding a crafted, printed adversarial image of a flower is also Barack Obama.

While 2021 was an exciting year for AI regarding innovations and new inventions, it was immune to controversies and scandals. In this article, we take a look at some of the most prominent ones that grabbed headlines. From the range of announcements made at the Tesla AI Day 2021, one that caught the fancy of a lot of people was the humanoid robot. Introduced in a unique manner, a human dressed in a white bodysuit and shiny mask did the news reveal during the event. Called Optimus, this humanoid robot, standing five feet eight inches and weighing 125 pounds, would be capable of performing repetitive tasks; the first prototype is likely to be released next year.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The whole idea of the Internet of Things is to extend the capability of the Internet beyond computers and smartphones to electronic, mechanical devices, sensors, etc.

Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. While such models can provide a powerful score for matching and subsequent zero-shot tasks, they are not capable of generating caption given an image. In this work, we repurpose such models to generate a descriptive text given an image at inference time, without any further training or tuning step. This is done by combining the visual-semantic model with a large language model, benefiting from the knowledge in both web-scale models. The resulting captions are much less restrictive than those obtained by supervised captioning methods. Moreover, as a zero-shot learning method, it is extremely flexible and we demonstrate its ability to perform image arithmetic in which the inputs can be either images or text and the output is a sentence. This enables novel high-level vision capabilities such as comparing two images or solving visual analogy tests.

Saliency methods have been widely used to highlight important input features in model predictions. Most existing methods use backpropagation on a modified gradient function to generate saliency maps. Thus, noisy gradients can result in unfaithful feature attributions. In this paper, we tackle this issue and introduce a {\it saliency guided training}procedure for neural networks to reduce noisy gradients used in predictions while retaining the predictive performance of the model. Our saliency guided training procedure iteratively masks features with small and potentially noisy gradients while maximizing the similarity of model outputs for both masked and unmasked inputs. We apply the saliency guided training procedure to various synthetic and real data sets from computer vision, natural language processing, and time series across diverse neural architectures, including Recurrent Neural Networks, Convolutional Networks, and Transformers. Through qualitative and quantitative evaluations, we show that saliency guided training procedure significantly improves model interpretability across various domains while preserving its predictive performance.

A drastic rise in potentially life-threatening misinformation has been a by-product of the COVID-19 pandemic. Computational support to identify false information within the massive body of data on the topic is crucial to prevent harm. Researchers proposed many methods for flagging online misinformation related to COVID-19. However, these methods predominantly target specific content types (e.g., news) or platforms (e.g., Twitter). The methods' capabilities to generalize were largely unclear so far. We evaluate fifteen Transformer-based models on five COVID-19 misinformation datasets that include social media posts, news articles, and scientific papers to fill this gap. We show tokenizers and models tailored to COVID-19 data do not provide a significant advantage over general-purpose ones. Our study provides a realistic assessment of models for detecting COVID-19 misinformation. We expect that evaluating a broad spectrum of datasets and models will benefit future research in developing misinformation detection systems.

Background: COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity. Objective: This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter. Methods: We collected 31,100 English tweets containing COVID-19 vaccine–related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia. Results: Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion. Conclusions: Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines.

In SEO, the focus is so often on Google. 'How do I rank higher in the Google SERPs?', or'How can I get more rich snippets on Google?' Of course, Google is one of the most popular search engines, but it's certainly not the only one. Different search engines have different audience demographics and different pros and cons, so when you're optimizing your website, you don't want to miss out on a significant share of a certain market. In this article, you will find a complete list of all top internet search engines, their pros and cons, and whether Google really is the most popular. We made a list of the top ten search engines widely used today.

The U.N.'s Educational, Scientific, and Cultural Organization (UNESCO) this week approved a series of recommendations for AI ethics, which aim to recognize that AI can "be of great service" but also raise "fundamental … concerns." UNESCO's 193 member countries, including Russia and China, agreed to conduct AI impact assessments and place "strong enforcement mechanisms and remedial actions" to protect human rights. "The world needs rules for artificial intelligence to benefit humanity. The recommendation[s] on the ethics of AI is a major answer," UNESCO chief Audrey Azoulay said in a press release. "It sets the first global normative framework while giving States the responsibility to apply it at their level. UNESCO will support its … member states in its implementation and ask them to report regularly on their progress and practices."