Artificial Intelligence (AI) and its sub-field Machine Learning (ML) have taken the world by storm. We are moving towards a world enhanced by these recent upcoming technologies. It's the most exciting time to be in this career field! The global Artificial Intelligence market is expected to grow to $400 billion by the year 2025. From Startups to big organizations, all want to join the AI and ML bandwagon to acquire cutting edge technology.
It's hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing). A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you. So what is this course all about, and how have things changed since then? In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.
COVID-19 pandemic has profoundly influenced the health, financial, and social texture of countries. Recognizable proof of individual-level susceptibility factors may help individuals in distinguishing and dealing with their emotional, psychological, and social well-being. In March 2020, the episode of the Covid illness 2019 (COVID-19) arrived in all nations of the Western world. To decrease the speed of its spread, numerous nations hindered their economies and upheld articulated limitations on public life. After calamities, the vast majority are resilient and don't surrender to psychopathology.
Under the umbrella of data science fields, natural language processing (NLP) is one of the most famous and important subfields. Natural language processing is a computer science field that gives computers the ability to understand human -- natural -- languages. Although the field has gained a lot of traction recently, it is -- in fact -- a field as old as computers themselves. However, the advancement of technology and computing power has led to incredible advancements in NLP. Now, speech technologies are becoming as famous as written text technologies.
With the advancement of machine translation, there is a recent movement towards large-scale empirical techniques that have prompted exceptionally massive enhancements in translation quality. Machine Translation is the technique of consequently changing over one characteristic language into another, saving the importance of the info text. The ongoing research on Image description presents a considerable challenge in the field of natural language processing and computer vision. To overcome this issue, multimodal machine translation presents data from other methods, for the most part, static pictures, to improve the interpretation quality. Here, we will cover the absolute most well-known datasets that are utilized in machine translation.
Google asserts that the patent industry stands to benefit from AI and machine learning models like BERT, a natural language processing algorithm that attained state-of-the-art results when it was released in 2018. In a whitepaper published today, the tech giant outlines a methodology to train a BERT model on over 100 million patent publications from the U.S. and other countries using open-source tooling, which can then be used to determine the novelty of patents and generate classifications to assist with categorization. The global patent corpus is large, with millions of new patents issued every year. Patent applications average around 10,000 words and are meticulously wordsmithed by inventors, lawyers, and patent examiners. Patent filings are also written with language that can be unintelligible to lay readers and highly context-dependent; many terms are used to mean completely different things in different patents.
Decentralized Autonomous Organizations, unlike traditional hierarchical organizations or for the matter of fact even agile organizations are an interconnected network of individuals which work in a self-enforcing manner with self- defined protocols and are not necessarily bound by legal contracts but rather an ecosystem of trust. The unique idea about these organizations is that they are governed by incentive networks in the sense groups of people from disparate disciplines work together on a project which they feel to be highly instrumental for the progress of humanity and science,then at the culmination of the project rewards are distributed in proportions stipulated in the smart contracts those which at the genesis of the project the peers had consented upon. Artificial General Intelligence a discipline created by Ben Goertzel an avid AI researcher, it can be understood and envisaged as an intelligent system possessing the ability to harbor a plethora of mental cognitive states and action capabilities like and more than Humans, possibly it could be a digital twin that leverages on high speed computational advantages. An AGI in contrast with Narrow AI which comprises the current systems in Machine learning discipline, can transcend the barriers that Narrow AI faces because of specialized algorithms that are built for specific use cases whereas AGI can be understood as a general purpose learner constituting in its cognitive organization multiple intertwined yet independent systems the likes of reinforcement learning for grasping new concepts without training data with a purpose of maximizing rewards through trial and error, Natural language processing for deriving important inputs from human interaction, it could go on depending on how incessantly this field is researched with a long term goal of Using AI for better good of Humankind. The plausible form it could manifest is that of Collective General intelligence.
"What do data-rich models know that models with less pre-training data do not?" The performance of language models is determined mostly by the amount of training data, quality of the training data and choice of modelling technique for estimation. Pretrained language models like BERT use massive datasets on the order of tens or even hundreds of billions of words to learn linguistic features and world knowledge, and they can be fine-tuned to achieve good performance on many downstream tasks. General-purpose pre-trained language models achieve strong performance on NLU tasks through pretraining on billions of words. But what exact knowledge, ask the researchers at NYU, do these models learn from large scale pretraining that they cannot learn from less data? To understand the relation between massiveness of data and learning in language models, the researchers adopted four probing methods -- classifier probing, information-theoretic probing, unsupervised relative acceptability judgment, and fine-tuning on NLU tasks and plotted to learn curves (shown above) for the four probing methods.
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Digital transformation is accelerating in many areas of business, but with people forced to stay at home and social distancing likely to become a long-term feature of daily life, it is the interaction between services and people that is accelerating fastest. In the first few months of the pandemic, use of online and mobile banking channels skyrocketed and this level is expected to continue far after it subsides. In fact, up to 45% of consumers are expecting to cut back on branch visits following the end of the crisis. As consumers use digital banking services more, they also expect more. In many ways, digitisation is creating an expectation economy – brands who meet or pass expectations in the delivery of digital services will perform well.