Wall Street went into panic mode about two weeks ago after the Chinese startup DeepSeek released an artificial intelligence system that appeared to be radically more efficient than what its American competitors had built. The investors who had pumped trillions of dollars into tech stocks over the past few years worried whether the tens of billions of dollars that tech companies were spending on new data centers suddenly looked like comic overkill. But the biggest tech companies made clear in recent earnings reports that they believe there may be no such thing as overkill when it comes to new data centers.

Large language models are meticulously aligned to be both helpful and harmless. However, recent research points to a potential overkill which means models may refuse to answer benign queries. In this paper, we investigate the factors for overkill by exploring how models handle and determine the safety of queries. Our findings reveal the presence of shortcuts within models, leading to an over-attention of harmful words like 'kill' and prompts emphasizing safety will exacerbate overkill. Based on these insights, we introduce Self-Contrastive Decoding (Self-CD), a training-free and model-agnostic strategy, to alleviate this phenomenon. We first extract such over-attention by amplifying the difference in the model's output distributions when responding to system prompts that either include or omit an emphasis on safety. Then we determine the final next-token predictions by downplaying the over-attention from the model via contrastive decoding. Empirical results indicate that our method has achieved an average reduction of the refusal rate by 20\% while having almost no impact on safety.

This work offers a novel theoretical perspective on why, despite numerous attempts, adversarial approaches to generative modeling (e.g., GANs) have not been as popular for certain generation tasks, particularly sequential tasks such as Natural Language Generation, as they have in others, such as Computer Vision. In particular, on sequential data such as text, maximum-likelihood approaches are significantly more utilized than GANs. We show that, while it may seem that maximizing likelihood is inherently different than minimizing distinguishability, this distinction is largely artificial and only holds for limited models. We argue that minimizing KL-divergence (i.e., maximizing likelihood) is a more efficient approach to effectively minimizing the same distinguishability criteria that adversarial models seek to optimize. Reductions show that minimizing distinguishability can be seen as simply boosting likelihood for certain families of models including n-gram models and neural networks with a softmax output layer. To achieve a full polynomial-time reduction, a novel next-token distinguishability model is considered.

Deep learning models require a very large amount of data in order to perform better than other techniques. Also, according to Tatman, just the compute of a simple image generation model in deep learning can cost around $60,000. This cost will increase with the complexity of the data models. It additionally requires expensive GPUs and hundreds of machines which will again deepen the cost to the user. Many less skilled people also find it difficult to adopt deep learning, as there is no standard theory available for learning about deep learning tools.

For years, the semiconductor world seemed to have settled into a quiet balance: Intel vanquished virtually all of the RISC processors in the server world, save IBM's POWER line. Elsewhere AMD had self-destructed, making it pretty much an x86 world. Suddenly only ATI, now a part of AMD, remained. It boasted just half of Nvidia's prior market share. On the newer mobile front, it looked to be a similar near-monopolistic story: ARM ruled the world.

So what were the answers popping in your head? Now some of you might laugh and say how on earth can you predict so far ahead, predicting things 100 yrs into future is crazy. What makes you say Regression Will continue to be used in 2118? Well the answer is Lindy effect. Yes, the heuristic I am using to predict this is Lindy Effect.

So what were the answers popping in your head? Random forest, SVM, K means, Knn or even Deep Learning and its variants? Now some of you might laugh and say how on earth can you predict so far ahead, predicting things 100 yrs into future is crazy. Well the answer is Lindy effect. Yes, the heuristic I am using to predict this is Lindy Effect.

The recent revelation that Google is using machine learning to help process some of its search results is attracting interest and questions about this field within artificial intelligence. What exactly is "machine learning" and how do machines teach themselves? Here's some background drawn from those involved with machine learning at Google itself. Yesterday, Google held a "Machine Learning 101" event for a variety of technology journalists. I was one of those in attendance.

I've been making some TensorFlow examples for my website, fomoro.com, While it's fresh in my head, I wanted to write up an end-to-end description of what it's like to build a machine learning app, and more specifically, how to make your own reverse image search. For this demo, the work is ⅓ data munging/setup, ⅓ model development and ⅓ app development. At a high-level, I use TensorFlow to create an autoencoder, train it on a bunch of images, use the trained model to find related images, and display them with a Flask app. In the last post, I talked model development and training.

A customer benchmarking engine is an emerging technology which uses an artificial intelligence approach to automate the reasoning that underlies data-driven benchmarking. Its benefits are discussed here, there, and elsewhere. Briefly, it uncovers comparative insights on customers which empower customer-focused employees to be more proactive, or which are shown directly to those customers as a premium information service. The business benefits include churn reduction, market differentiation, extra revenue, and deeper customer relationships. But, automated customer benchmarking doesn't always make sense.