CyberGuy explains how ChatGPT's functions can help you in your day to day life. Romanian Prime Minister Nicolae Ciuca has made the controversial decision to officially employ an artificial intelligence assistant to help inform him of what voters want for the future of the country. This decision has been met with mixed reactions, with some applauding the prime minister for embracing new technology to improve his government, while others express major concern over the potential risks of relying on AI to make critical decisions. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER Romanian Prime Minister Nicolae Ciuca is using AI to inform him of what voters want. The AI adviser has been named Ion (Romanian for John), and it is built within a long, mirror-like structure that has a moving graphic at the top to show that it is listening at all times.

Humanizing AI systems also stokes our fears, and scared people are more vulnerable to believe and spread wrong information, said Wardle of Brown University. Thanks to science-fiction authors, our brains are brimming with worst-case scenarios, she noted. Stories such as "Blade Runner" or "The Terminator" present a future where AI systems become conscious and turn on their human creators. Since many people are more familiar with sci-fi movies than the nuances of machine-learning systems, we tend to let our imaginations fill in the blanks. By noticing anthropomorphism when it happens, Wardle said, we can guard against AI myths.

Google Bard is the company's answer to ChatGPT: an AI chatbot using LaMDA, the company's in-development language model. We've been testing it, and what's immediately clear are all the company's warnings, whether it's the experiment label or the regular reminders that Bard "will not always get it right." Even the example entries, when you boot up Bard, include what the chatbot can't do. The big difference between Google and Bing's integration is the alternative responses that Bard throws up alongside the conversation. You can click the dropdown arrow next to "View other drafts" at the top left of each chat bubble to see some other suggestions.

Journalists had fun last year asking the shiny new AI chatbot ChatGPT to write their columns, most concluding that the bot was not good enough to take their jobs. But many commentators believe journalism is on the cusp of a revolution where mastery of algorithms and AI tools that generate content will be a key battleground. The technology news site CNET perhaps heralded the way forward when it quietly deployed an AI program last year to write some of its listicles. It was later forced to issue several corrections after another news site noticed that the bot had made mistakes, some of them serious. But CNET's parent company later announced job cuts that included editorial staff -- though executives denied AI was behind the layoffs.

Don't miss out on ET Prime stories! Get your daily dose of business updates on WhatsApp. India's largest electric two-wheeler company Ola Electric Mobility is planning to raise a fresh round of $250-300 million in growth equity to expand two-wheeler operations and fund its planned battery facility, said people with knowledge of the matter. Punjab National Bank (PNB), Indian Overseas Bank (IOB) and DCB Bank may have to negotiate higher funding costs as these lenders seek to sell bonds amid a global turmoil caused by the Swiss regulator's write-down of nearly $17 billion of Additional Tier 1 (AT-1) instruments in the Credit Suisse bailout. Indian equities rose on Tuesday, tracking the overnight rebound in the US markets, on hopes that the banking crisis might be eased for now following Credit Suisse's acquisition by UBS.

This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification and sentiment analysis of product reviews. A "cross-context" setting is enabled using test sets that are distinct from the training data. Specifically, in the news classification task, the models are developed on local news from India and tested on the local news from China. In the sentiment analysis task, the models are trained on movie reviews and tested on customer reviews. This comparison is aimed at exploring the limits of the representative power of today's Natural Language Processing systems on the path to the systems that are generalizable to real-life scenarios. The models are fine-tuned and fed into a Feed-Forward Neural Network and a Bidirectional Long Short Term Memory network. Multinomial Naive Bayes and Linear Support Vector Machine are used as traditional baselines. The results show that, in binary text classification, DistilBERT is significantly better than ELMo on generalizing to the cross-context setting. ELMo is observed to be significantly more robust to the cross-context test data than both baselines. On the other hand, the baselines performed comparably well to ELMo when the training and test data are subsets of the same corpus (no cross-context). DistilBERT is also found to be 30% smaller and 83% faster than ELMo. The results suggest that DistilBERT can transfer generic semantic knowledge to other domains better than ELMo. DistilBERT is also favorable in incorporating into real-life systems for it requires a smaller computational training budget. When generalization is not the utmost preference and test domain is similar to the training domain, the traditional ML algorithms can still be considered as more economic alternatives to deep language representations.

Siamese network based trackers develop rapidly in the field of visual object tracking in recent years. The majority of siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making the similarity response map sensitive to background influence and hence challenging to focus on the target region. Additionally, there are no structural links between the classification and regression branches in these trackers, and the two branches are optimized separately during training. Therefore, there is a misalignment between the classification and regression branches, which results in less accurate tracking results. In this paper, a Target Highlight Module is proposed to help the generated similarity response maps to be more focused on the target region. To reduce the misalignment and produce more precise tracking results, we propose a corrective loss to train the model. The two branches of the model are jointly tuned with the use of corrective loss to produce more reliable prediction results. Experiments on 5 challenging benchmark datasets reveal that the method outperforms current models in terms of performance, and runs at 38 fps, proving its effectiveness and efficiency.

Considering a conversation thread, stance classification aims to identify the opinion (e.g. agree or disagree) of replies towards a given target. The target of the stance is expected to be an essential component in this task, being one of the main factors that make it different from sentiment analysis. However, a recent study shows that a target-oblivious model outperforms target-aware models, suggesting that targets are not useful when predicting stance. This paper re-examines this phenomenon for rumour stance classification (RSC) on social media, where a target is a rumour story implied by the source tweet in the conversation. We propose adversarial attacks in the test data, aiming to assess the models robustness and evaluate the role of the data in the models performance. Results show that state-of-the-art models, including approaches that use the entire conversation thread, overly relying on superficial signals. Our hypothesis is that the naturally high occurrence of target-independent direct replies in RSC (e.g. "this is fake" or just "fake") results in the impressive performance of target-oblivious models, highlighting the risk of target instances being treated as noise during training.

Although other pooling mechanisms have been studied in order to take recency effects into account, in most studies Current state-of-the-art video quality models, such as VMAF, mean pooling achieved the best performance [6, 7]. VMAF is give excellent prediction results by comparing the degraded the only of those metrics that include a temporal component, video with its reference video. However, they do not consider which considers temporal masking effects. However, video temporal distortions (e.g., frame freezes or skips) that transmitted during VC calls can be affected by a number of occur during videoconferencing calls. In this paper, we temporal distortions that are perceived as frame freezes, frame present a data-driven approach for modeling such distortions skips, frame rate variations (e.g., video is played back faster automatically by training an LSTM with subjective after delayed packets arrive), or generally low frame rate.

A positive phrase or a sentence with an underlying negative motive is usually defined as sarcasm that is widely used in today's social media platforms such as Facebook, Twitter, Reddit, etc. In recent times active users in social media platforms are increasing dramatically which raises the need for an automated NLP-based system that can be utilized in various tasks such as determining market demand, sentiment analysis, threat detection, etc. However, since sarcasm usually implies the opposite meaning and its detection is frequently a challenging issue, data meaning extraction through an NLP-based model becomes more complicated. As a result, there has been a lot of study on sarcasm detection in English over the past several years, and there's been a noticeable improvement and yet sarcasm detection in the Bangla language's state remains the same. In this article, we present a BERT-based system that can achieve 99.60\% while the utilized traditional machine learning algorithms are only capable of achieving 89.93\%. Additionally, we have employed Local Interpretable Model-Agnostic Explanations that introduce explainability to our system. Moreover, we have utilized a newly collected bangla sarcasm dataset, BanglaSarc that was constructed specifically for the evaluation of this study. This dataset consists of fresh records of sarcastic and non-sarcastic comments, the majority of which are acquired from Facebook and YouTube comment sections.