Improving the efficiency of Transformer-based language pre-training is an important task in NLP, especially for the self-attention module, which is computationally expensive. In this paper, we propose a simple but effective solution, called \emph{LazyFormer}, which computes the self-attention distribution infrequently. LazyFormer composes of multiple lazy blocks, each of which contains multiple Transformer layers. In each lazy block, the self-attention distribution is only computed once in the first layer and then is reused in all upper layers. In this way, the cost of computation could be largely saved. We also provide several training tricks for LazyFormer. Extensive experiments demonstrate the effectiveness of the proposed method.

Artificial intelligence is a disruptive technology that finds more applications each day. But with each new innovation in artificial intelligence technologies like machine learning, deep learning, neural network, the possibilities to scale a new horizon in tech widens up. In the past few years, a form of neural network that is gaining popularity, i.e., Transformers. They employ a simple yet powerful mechanism called attention, which enables artificial intelligence models to selectively focus on certain parts of their input and thus reason more effectively. The attention-mechanism looks at an input sequence and decides at each step which other parts of the sequence are important.

Artificial intelligence is a branch in computer science that deals with the simulation of intelligent behavior. It gives computers an ability to think and perform different tasks, such as humans and animals, while learning through the errors during this process. Artificial Intelligence is usually an algorithm built in such a way that permits the computer to perform tasks efficiently while making nominal errors. It uses personified knowledge by applying deep learning and machine learning algorithms while performing several tasks. Drug discovery is the preliminary step in the process of a novel drug identification and its therapeutic target. Artificial intelligence (AI) is commonly used in the healthcare industry for drug discovery.

AI can do a lot of things extremely well. One thing that it can do just okay -- which, frankly, is still quite extraordinary -- is write college term papers. That's the finding from EduRef, a resource for students and educators, which ran an experiment to determine if a deep learning language prediction model known as GPT-3 could get passing marks in an anonymized trial. Everything you need to know about OpenAI's breakthrough AI language program "We hired a panel of professors to create a writing prompt, gave it to a group of recent grads and undergraduate-level writers, and fed it to GPT-3 and had the panel grade the anonymous submissions and complete a follow up survey for thoughts about the writers," according to an EduRef post. The results were a surprising demonstration of the natural-language prowess of AI.

The IBM Watson Studio platform provides an end-to-end data science solution that quickly puts AI to work, helping your organization stay ahead of fraudsters. As a single platform for collaborative model development, Watson Studio makes it possible for cross-functional business and technical teams to work together quickly and seamlessly. Watson Studio enables different sets of users to work together regardless of the types of users they are. It supports teams with easy tooling to help automate tasks and with more advanced tools such as deep learning and neural networks. To learn more, watch this video showing IBM Watson Studio at work in a fraud prediction scenario.

Learn how to solve challenging machine learning problems with TensorFlow, Google's revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up.

We have seen an explosion in developer tools and platforms related to machine learning and artificial intelligence during the last few years. From cloud-based cognitive APIs to libraries to frameworks to pre-trained models, developers make many choices to infuse AI into their applications. AI engineers and researchers choose a framework to train machine learning models. These frameworks abstract the underlying hardware and software stack to expose a simple API in languages such as Python and R. For example, an ML developer can leverage the parallelism offered by GPUs to accelerate a training job without changing much of the code written for the CPU. These frameworks expose simpler APIs that translate to complex mathematical computations and numerical analysis often needed for training the machine learning models. Apart from training, the machine learning frameworks simplify inference -- the process of utilizing a trained model for performing prediction or classification of live data.

I remember the first time I created a simple machine learning model. It was a model that could predict your salary according to your years of experience. And after making it, I was curious about how I could deploy it into production. If you have been learning machine learning, you might have seen this challenge in online tutorials or books. You can find the source code here if you are interested.

In 2007, some of the leading thinkers behind deep neural networks organized an unofficial "satellite" meeting at the margins of a prestigious annual conference on artificial intelligence. The conference had rejected their request for an official workshop; deep neural nets were still a few years away from taking over AI. The bootleg meeting's final speaker was Geoffrey Hinton of the University of Toronto, the cognitive psychologist and computer scientist responsible for some of the biggest breakthroughs in deep nets. He started with a quip: "So, about a year ago, I came home to dinner, and I said, 'I think I finally figured out how the brain works,' and my 15-year-old daughter said, 'Oh, Daddy, not again.'" Hinton continued, "So, here's how it works."

Last July, GPT-3 took the internet by storm. The massive 175 billion-parameter autoregressive language model, developed by OpenAI, showed a startling ability to translate languages, answer questions, and – perhaps most eerily – generate its own coherent passages, poems, and songs when given examples to process. As it turns out, experts were captivated by these abilities, too: captivated enough, in fact, that researchers from OpenAI and a number of universities met several months ago to discuss the technical and sociopolitical implications of the platform. The summit, helmed by OpenAI in partnership with Stanford's Institute for Human-Centered Artificial Intelligence, convened in October. Apart from those two institutions, the remainder of the participants are currently unknown by the public, as the meeting was held under the Chatham House Rule, whereby a meeting's information is public but its participants are secret.