One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
Artificial intelligence (AI) is taking over just about every aspect of communication. From Siri answering your vocalized questions to Amazon recommending products based on your browsing history, AI has permeated our lives in ways we don't even think about anymore. AI is developing faster than ever -- experts have projected the global AI market value to reach $190 billion by 2025. It's not time to panic about Skynet taking over just yet, but it is important to understand how AI is currently impacting and will continue to impact writers and other professions. Human writers will always have a place in content creation.
Most of the modern-day NLP systems have been following a pretty standard approach for training new models for various use-cases and that is First Pre-train then Fine-tune. Here, the goal of pre-training is to leverage large amounts of unlabeled text and build a general model of language understanding before being fine-tuned on various specific NLP tasks such as machine translation, text summarization, etc. In this blog, we will discuss two popular pre-training schemes, namely, Masked Language Modeling (MLM) and Causal Language Modeling (CLM). Under Masked Language Modelling, we typically mask a certain % of words in a given sentence and the model is expected to predict those masked words based on other words in that sentence. Such a training scheme makes this model bidirectional in nature because the representation of the masked word is learnt based on the words that occur it's left as well as right.
AI (Artificial Intelligence) refers to creation of unique systems with the help of software which can perform certain tasks without human intervention and instructions. AI comprises integration of various technologies like machine learning, reasoning, perception, natural language processing. AI is primary used in healthcare sector for diagnosis and treatment. Increase in applications of AI (artificial intelligence) in healthcare industry is the key driving factor which is expected to boost the global artificial intelligence in diagnostics market growth. Furthermore, rise in demand for reducing diagnostic costs, reducing machine downtime and improving patient care will accelerate the usage of artificial intelligence in diagnostics and which is expected to propel the market growth during this forecast period.
The Future of AI: Probably, you are aware of Artificial intelligence and even interact with this technology daily. Many people still think that AI is a science fiction story, but it is becoming more commonplace in our daily lives. Amazon Alexa, Google Echo, and personalized social media feeds are some common AI examples. In business, artificial intelligence has a wide range of applications and usages. From boring to exciting, AI is already disrupting virtually every business process in every industry.
Facebook AI Research (FAIR) open-sourced Expire-Span, a deep-learning technique that learns which items in an input sequence should be remembered, reducing the memory and computation requirements for AI. FAIR showed that Transformer models that incorporate Expire-Span can scale to sequences of tens of thousands of items with improved performance compared to previous models. The research team described the technique and several experiments in a paper to be presented at the upcoming International Conference on Machine Learning (ICML). Expire-Span allows sequential AI models to "forget" events that are no longer relevant. When incorporated into self-attention models, such as the Transformer, Expire-Span reduces the amount of memory needed, allowing the model to handle longer sequences, which is key to improved performance on many tasks, such as natural language processing (NLP).
Since their introduction three years ago, transformer architectures have become the de-facto standard for natural language processing (NLP) tasks and are now also seeing application in areas such as computer vision. Although many transformer architecture modifications have been proposed, these have not proven as easily transferable across implementations and applications as hoped, and that has limited their wider adoption. In a bid to understand why most widely-used transformer applications shun these modifications, a team from Google Research comprehensively evaluated them in a shared experimental setting, where they were surprised to discover that most architecture modifications they looked at do not meaningfully improve performance on downstream NLP tasks. The researchers began by reimplementing and evaluating a variety of transformer variants on the tasks where they are most commonly applied. As a baseline, they used the original transformer model with two modifications: applying layer normalization before the self-attention and feedforward blocks instead of after, and using relative attention with shared biases instead of sinusoidal positional embeddings. The researchers employed two experimental settings to evaluate each modification's performance: transfer learning based on T5, and supervised machine translation on the WMT'14 English-German translation task.
If you're a city inhabitant or just live in an older building, you're probably no stranger to window AC units. Although at times they can feel totally retro, there are ways to upgrade them and have them work better for you. With the Cielo Breez Eco, you can control your AC unit directly by using your smartphone. All you need is a ductless air conditioning system, such as a mini-split, portable, or the aforementioned window unit with an IR remote control. You can even use it on a heat pump system in your home.
In honor of Juneteenth, Google Assistant has an important new feature. "Hey Google, what happened today in Black history? On Saturday morning, Google unveiled the addition of a Black history function, available to users of any Assistant-enabled smart speaker, smart display, or phone. Just ask "Hey Google, what happened today in Black history?" and the voice assistant will recite daily history content curated by Google with the help of civil rights activist and scholar Dr. Carl Mack. The facts are intended to highlight important Black cultural events and leaders as the United States continues its racial reckoning. The feature is one of numerous initiatives being taken not just by Google, but also countless other companies as many of them honor Juneteenth for the first time. On Wednesday, President Biden officially made the day, which commemorates the emancipation of enslaved people, a federal holiday, recognized in all 50 states. You can read more about Juneteenth here. At Google, the company also released a new Doodle from Detroit-based artist Rachelle Baker, honoring Black joy and artistic contributions. In a Google press release sent to Mashable, Baker described her process creating the Doodle, saying, "I looked at tons of photos and art illustrating some of the first ever Juneteenth celebration, as well as celebrations, parades, and festivities from recent years.
Xilinx has introduced its Kria programmable chips and boards for holding AI applications at the edge of the network. This should come in handy for visual applications like smarter cameras. San Jose, California-based Xilinx, which is in the process of being acquired by Advanced Micro Devices (AMD) for $35 billion, has a group of products dubbed the Kria portfolio of adaptive system-on-module offerings for AI at the edge. These are production-ready small form factor embedded boards that enable rapid deployment in edge-based applications. Coupled with a complete software stack and prebuilt, production-grade accelerated applications, Kria adaptive modules are a new method of bringing adaptive computing to AI and software developers.