Currently, pre-trained models can be considered the default choice for a wide range of NLP tasks. Despite their SoTA results, there is practical evidence that these models may require a different number of computing layers for different input sequences, since evaluating all layers leads to overconfidence in wrong predictions (namely overthinking). This problem can potentially be solved by implementing adaptive computation time approaches, which were first designed to improve inference speed. Recently proposed PonderNet may be a promising solution for performing an early exit by treating the exit layer's index as a latent variable. However, the originally proposed exit criterion, relying on sampling from trained posterior distribution on the probability of exiting from the $i$-th layer, introduces major variance in exit layer indices, significantly reducing the resulting model's performance. In this paper, we propose improving PonderNet with a novel deterministic Q-exit criterion and a revisited model architecture. We adapted the proposed mechanism to ALBERT and RoBERTa and compared it with recent methods for performing an early exit. We observed that the proposed changes can be considered significant improvements on the original PonderNet architecture and outperform PABEE on a wide range of GLUE tasks. In addition, we also performed an in-depth ablation study of the proposed architecture to further understand Lambda layers and their performance.

Donald Trump granted clemency to 237 people during his administration. Some of the pardons--particularly those related to drug offenses--fit within the norms of the office. But a much larger portion were favors done for wealthy people who could access Trump through top-dollar lawyers, golf clubs, rich South Floridian social circles, and family. We revisited these pardons four years later to see what they could tell us about Trump's 2024 campaign. The biggest takeaway had to do with the shadowy political operatives--including Steve Bannon, Michael Flynn, and Roger Stone--who have spent the past four years pushing dangerous and wild election conspiracy theories in hopes they will be rewarded once more.

Currently, pre-trained models can be considered the default choice for a wide range of NLP tasks. Despite their SoTA results, there is practical evidence that these models may require a different number of computing layers for different input sequences, since evaluating all layers leads to overconfidence in wrong predictions (namely overthinking). This problem can potentially be solved by implementing adaptive computation time approaches, which were first designed to improve inference speed. Recently proposed PonderNet may be a promising solution for performing an early exit by treating the exit layer's index as a latent variable. However, the originally proposed exit criterion, relying on sampling from trained posterior distribution on the probability of exiting from the i -th layer, introduces major variance in exit layer indices, significantly reducing the resulting model's performance. In this paper, we propose improving PonderNet with a novel deterministic Q-exit criterion and a revisited model architecture.

Currently, pre-trained models can be considered the default choice for a wide range of NLP tasks. Despite their SoTA results, there is practical evidence that these models may require a different number of computing layers for different input sequences, since evaluating all layers leads to overconfidence in wrong predictions (namely overthinking). This problem can potentially be solved by implementing adaptive computation time approaches, which were first designed to improve inference speed. Recently proposed PonderNet may be a promising solution for performing an early exit by treating the exit layer's index as a latent variable. However, the originally proposed exit criterion, relying on sampling from trained posterior distribution on the probability of exiting from the $i$-th layer, introduces major variance in exit layer indices, significantly reducing the resulting model's performance. In this paper, we propose improving PonderNet with a novel deterministic Q-exit criterion and a revisited model architecture. We adapted the proposed mechanism to ALBERT and RoBERTa and compared it with recent methods for performing an early exit. We observed that the proposed changes can be considered significant improvements on the original PonderNet architecture and outperform PABEE on a wide range of GLUE tasks. In addition, we also performed an in-depth ablation study of the proposed architecture to further understand Lambda layers and their performance.

The slightest mention of AI and machine learning (ML) was enough to strike fear into the hearts of many not so long ago. To be fair it's not surprising, particularly given the cultural references we've been fed over the years – see Skynet (Terminator), Hal (2001: A Space Odyssey), and Ava (Ex-Machina) – it's little wonder there's been a smidgen of anxiety. However, in recent times, technologists have started to acknowledge that AI and ML are actually good at automating the laborious processes we as humans and businesses can't be bothered with – most of the time they do this more accurately too. The question still remains though, do we really have anything to worry about when it comes to automating our working lives via ML and AI? Taken on the findings of a recent PwC report, there's still some substantial negativity among the general population when it comes to automation, with 60% of people believing that it will take their job (and there are further concerns raised when you start to mention the AI elements). That is really two questions in one though: can we automate everything, and can we make that a full end-to-end process?

How do you explain quantum computing? Think of that vintage telephone at the General Store on Petticoat Junction. Put it next to your iPhone. Every day, it seems like there is news of yet another breakthrough in computing. Technology experts debate exactly when we will all have quantum computers instead of Echo Dots, but the day is coming.

Bias is everywhere, including in your data. A little skew here and there may be fine if the ramifications are minimal, but bias can negatively affect your company and its customers if left unchecked, so you should make an effort to understand how, where and why it happens. "Many [business leaders] trust the technical experts but I would argue that they're ultimately responsible if one of these models has unexpected results or causes harm to people's lives in some way," said Steve Mills, a principal and director of machine intelligence at technology and management consulting firm Booz Allen Hamilton. In the financial industry, for example, biased data may cause results that offend the Equal Credit Opportunity Act (fair lending). That law, enacted in 1974, prohibits credit discrimination based on race, color, religion, national origin, sex, marital status, age or source of income.

Every moment of your waking life and whenever you dream, you have the distinct inner feeling of being "you." When you see the warm hues of a sunrise, smell the aroma of morning coffee or mull over a new idea, you are having conscious experience. But could an artificial intelligence (AI) ever have experience, like some of the androids depicted in Westworld or the synthetic beings in Blade Runner? The question is not so far-fetched. Robots are currently being developed to work inside nuclear reactors, fight wars and care for the elderly.

Every moment of your waking life and whenever you dream, you have the distinct inner feeling of being "you." When you see the warm hues of a sunrise, smell the aroma of morning coffee or mull over a new idea, you are having conscious experience. But could an artificial intelligence (AI) ever have experience, like some of the androids depicted in Westworld or the synthetic beings in Blade Runner? The question is not so far-fetched. Robots are currently being developed to work inside nuclear reactors, fight wars and care for the elderly.