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Applying Linearly Scalable Transformers to Model Longer Protein Sequences

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In a bid to make transformer models even better for real-world applications, researchers from Google, University of Cambridge, DeepMind and Alan Turing Institute have proposed a new transformer architecture called "Performer" -- based on what they call fast attention via orthogonal random features (FAVOR). Believed to be particularly well suited for language understanding tasks when proposed in 2017, transformer is a novel neural network architecture based on a self-attention mechanism. To date, in addition to achieving SOTA performance in Natural Language Processing and Neural Machine Translation tasks, transformer models have also performed well across other machine learning (ML) tasks such as document generation/summarization, time series prediction, image generation, and analysis of biological sequences. Neural networks usually process language by generating fixed- or variable-length vector-space representations. A transformer however only performs a small, constant number of steps -- in each step, it applies a self-attention mechanism that can directly model relationships between all words in a sentence, regardless of their respective position.


Google claims its new TPUs are 2.7 times faster than the previous generation

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Google's fourth-generation tensor processing units (TPUs), the existence of which weren't publicly revealed until today, can complete AI and machine learning training workloads in close-to-record wall clock time. That's according to the latest set of metrics released by MLPerf, the consortium of over 70 companies and academic institutions behind the MLPerf suite for AI performance benchmarking. It shows clusters of fourth-gen TPUs surpassing the capabilities of third-generation TPUs -- and even those of Nvidia's recently released A100 -- on object detection, image classification, natural language processing, machine translation, and recommendation benchmarks. Google says its fourth-generation TPU offers more than double the matrix multiplication TFLOPs of a third-generation TPU, where a single TFLOP is equivalent to 1 trillion floating-point operations per second. It also offers a "significant" boost in memory bandwidth while benefiting from unspecified advances in interconnect technology.


Recurrent Neural Networks -- Part 1

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These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!


GPT-3: The First Artificial General Intelligence?

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If you had asked me a year or two ago when Artificial General Intelligence (AGI) would be invented, I'd have told you that we were a long way off. Most experts were saying that AGI was decades away, and some were saying it might not happen at all. The consensus is -- was? -- that all the recent progress in AI concerns so-called "narrow AI," meaning systems that can only perform one specific task. An AGI, or a "strong AI," which could perform any task as well as a human being, is a much harder problem. It is so hard that there isn't a clear roadmap for achieving it, and few researchers are openly working on the topic. GPT-3 is the first model to shake that status-quo seriously. GPT-3 is the latest language model from the OpenAI team.


Top 10 Best FREE Artificial Intelligence Courses

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Most of the Machine Learning, Deep Learning, Computer Vision, NLP job positions, or in general every Artificial Intelligence (AI) job position requires you to have at least a bachelor's degree in Computer Science, Electrical Engineering, or some similar field. If your degree comes from some of the world's best universities than your chances might be higher in beating the competition on your job interview. But looking realistically, not most of the people can afford to go to the top universities in the world simply because not most of us are geniuses and don't have thousands of dollars, or come from some poor country (like we do). No with the high demand of skilled professionals from these fields, there are exceptions being made, so we can see that people who don't come from these fields, are learning and adjusting themselves in order to get that paycheck. In this article, we are going to list some of the free Artificial Intelligence courses that come from Harvard University, MIT University, and Stanford University that anyone can attend, no matter where they live.


Deep learning to translate between programming languages

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Migrating a codebase from an archaic programming language such as COBOL to a modern alternative like Java or C is a difficult, resource-intensive task that requires expertise in both the source and target languages. COBOL, for example, is still widely used today in mainframe systems around the world, so companies, governments, and others often must choose whether to manually translate their code bases or commit to maintaining code written in a language that dates back to the 1950s. We've developed TransCoder, an entirely self-supervised neural transcompiler system that can make code migration far easier and more efficient. Our method is the first AI system able to translate code from one programming language to another without requiring parallel data for training. We've demonstrated that TransCoder can successfully translate functions between C, Java, and Python 3. TransCoder outperforms open source and commercial rule-based translation programs.


The Hottest Artificial Intelligence Technologies and Algorithms

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Artificial Intelligence is the theory that computer systems are able to perform tasks that normally require human intelligence. AI has advanced its way into our lives by modernizing industries for healthcare, financial, education, transportation, and many more. An example is that artificial neural networks are used as clinical support decision systems for medical diagnosis in EMR software. From speech recognition to machine learning platforms, AI will soon be everywhere you look. Currently, in the market, there are different kinds of AI technologies that are helping people with everyday life and soon, AI is bound to be everywhere and in everything that we use.


Applications Of Natural Language Processing (NLP)

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Natural Language Processing is among the hottest topic in the field of data science. Companies are putting tons of money into research in this field. Everyone is trying to understand Natural Language Processing and its applications to make a career around it. Every business out there wants to integrate it into their business somehow. Because just in a few years' time span, natural language processing has evolved into something so powerful and impactful, which no one could have imagined.


Why RNN's are great for machine translation

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Neural networks are a fascinating field of computer science that attempts to model the brain in a mathematical sense. Unfortunately, they are nowhere near the level of complexity as the human brain, and will most likely not be in the near future. However, while there is no single network that can match the brain in its complexity and accuracy, there are specific networks that can rival the accuracy and speed of the human mind, though they often lag in the accuracy department. One such example of this is the recurrent neural network, typically shortened to RNN. The RNN is significantly more accurate than a typical neural network, specifically when it comes to data that enter in sequences, such as sound and videos.


What I learned from looking at 200 machine learning tools - KDnuggets

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To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. After filtering out applications companies (e.g., companies that use ML to provide business analytics), tools that aren't being actively developed, and tools that nobody uses, I got 202 tools. Please let me know if there are tools you think I should include but aren't on the list yet! I categorize the tools based on which step of the workflow it supports. I don't include Project setup since it requires project management tools, not ML tools.