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The (Un)ethical Story of GPT-3: OpenAI's Million Dollar Model

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Back on October 12, 2019, the world witnessed a previously unimaginable accomplishment- the first sub-two-hour marathon was run in an incredible time of 1:59:40 by Kenyan native Eliud Kipchoge. He would later say in regards to the amazing achievement that he "expected more people all over the world to run under 2 hours after today" [1]. While Kipchoge set new records in long distance running, across the world a team of natural language processing (NLP) experts at OpenAI, the Elon Musk-backed AI firm, published a new transformer-based language model with 1.5 billion parameters that achieved previously unthinkable performance in nearly every language task it faced [2]. The main takeaway from the paper by many experts was that bigger is better-the intelligence of transformer models can dramatically increase with the scale of parameters. In March of 2020, this theory gained support with OpenAI's release of version three of the model or GPT-3 which encapsulates a staggering 175 billion parameters and achieved even more remarkable performance than version 2, despite sharing, quite literally, the same architecture [3].


Amazon makes Contact Lens generally available

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Last December during its re:Invent 2019 conference in Las Vegas, Amazon unveiled Contact Lens, a virtual call center product for Amazon Connect that transcribes calls while simultaneously assessing them. After a monthslong preview, Contact Lens today launched in general availability in the US East (N. Virginia), US West (Oregon), EU (Frankfurt), EU (London), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo) Amazon Web Services (AWS) regions, with rollouts in additional regions to come later this year. As customer representatives are increasingly ordered to work from home in Manila, the U.S., and elsewhere, companies including John Hancock, Capital One, Intuit, GE, Square, Fujitsu, and Dow Jones are turning to AI solutions like Contact Lens to bridge gaps in service. The solutions aren't perfect -- there's always going to be a need for human teams, even where chatbots are deployed -- but COVID-19 has accelerated the need for AI-powered contact center messaging.


What is Web Scraping: Introduction, Applications and Best Practices

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However, manually copy data from multiple sources for retrieval in a central place can be very tedious and time-consuming. "Web scraping," also called crawling or spidering, is the automated gathering of data from an online source usually from a website. While scraping is a great way to get massive amounts of data in relatively short timeframes, it does add stress to the server where the source hosted. However, as long as it does not disrupt the primary function of the online source, it is relatively acceptable. Despite its legal challenges, web scraping remains popular even in 2019.


Artificial Intelligence: 3 benefits for the insurance industry

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As the insurance sector competes to win market share, Henry Jinman at EBI.AI discusses three ways companies can benefit from the power of Artificial Intelligence The UK general insurance market continues to be fiercely competitive. While the battle for repeat business keeps downward pressure on pricing, a constantly changing regulatory agenda increases costs. Whatever the industry, successful companies know that building a business based on price alone is not sustainable. Customer service is what matters most. It's a sentiment that is reflected in the latest findings of multinational professional services company Ernst & Young (EY).


10 Machine Learning Projects to boost your Portfolio

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Getting a good job in the field of Machine Learning is getting very competitive. The best way to showcase your Machine Learning skills is in the form of Portfolio of Data Science and Machine Learning Projects. A good Portfolio of Projects will show that you can apply those Machine Learning skills in your work. Here are 10 Machine Learning Projects which will boost your Portfolio and will help you to get a job as a Data Scientist. Human activity recognition is the problem of classifying sequences of data recorded by specialized harnesses or smartphones into known well-defined Human activities.


AI 50 Founders Predict What Artificial Intelligence Will Look Like After Covid-19

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Transportation "can also become truly contactless if needed," says James Peng, who is CEO of ... [ ] self-driving startup Pony.ai. The first few months of 2020 have radically reshaped the way we work and how the world gets things done. While the wide use of robotaxis or self-driving freight trucks isn't yet in place, the Covid-19 pandemic has hurried the introduction of artificial intelligence across all industries. Whether through outbreak tracing or contactless customer pay interactions, the impact has been immediate, but it also provides a window into what's to come. The second annual Forbes' AI 50, which highlights the most promising U.S.-based artificial intelligence companies, features a group of founders who are already pondering what their space will look like in the future, though all agree that Covid-19 has permanently accelerated or altered the spread of AI. "We have seen two years of digital transformation in the course of the last two months," Abnormal Security CEO Evan Reiser told Forbes in May.


Here's How I Predicted Apple's Stock Price Using Natural Language Processing

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Stock market prediction refers to the act of attempting to determine the future value of a company's stock (or other financial instruments) that is traded on an exchange. Accurately predicting the stock market is like being able to see into the future. If one could do this, then they will undoubtedly engage in actions that will substantially benefit themselves. Imagine knowing that Apple's stock will increase from $300 per share by 80% tomorrow, and currently having the ability to buy 10 shares. That will guarantee a return of $2,400 in one day with possibly minimal effort.


AI 50 Founders Predict What Artificial Intelligence Will Look Like After Covid-19

#artificialintelligence

Transportation "can also become truly contactless if needed," says James Peng, who is CEO of ... [ ] self-driving startup Pony.ai. The first few months of 2020 have radically reshaped the way we work and how the world gets things done. While the wide use of robotaxis or self-driving freight trucks isn't yet in place, the Covid-19 pandemic has hurried the introduction of artificial intelligence across all industries. Whether through outbreak tracing or contactless customer pay interactions, the impact has been immediate, but it also provides a window into what's to come. The second annual Forbes' AI 50, which highlights the most promising U.S.-based artificial intelligence companies, features a group of founders who are already pondering what their space will look like in the future, though all agree that Covid-19 has permanently accelerated or altered the spread of AI. "We have seen two years of digital transformation in the course of the last two months," Abnormal Security CEO Evan Reiser told Forbes in May.


Global Big Data Conference

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The first few months of 2020 have radically reshaped the way we work and how the world gets things done. While the wide use of robotaxis or self-driving freight trucks isn't yet in place, the Covid-19 pandemic has hurried the introduction of artificial intelligence across all industries. Whether through outbreak tracing or contactless customer pay interactions, the impact has been immediate, but it also provides a window into what's to come. The second annual Forbes' AI 50, which highlights the most promising U.S.-based artificial intelligence companies, features a group of founders who are already pondering what their space will look like in the future, though all agree that Covid-19 has permanently accelerated or altered the spread of AI. "We have seen two years of digital transformation in the course of the last two months," Abnormal Security CEO Evan Reiser told Forbes in May. As more parts of a company are forced to move online, Reiser expects to see AI being put to use to help businesses analyze the newly available data or to increase efficiency.


Top 21 Datasets for Machine Learning and Statistics Projects

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Are you looking to build a machine learning and AI-based Intelligent app? You must need a huge amount of datasets to train your model. Mostly a machine learning project fails not because of the model and infrastructure but poor datasets . Especially the beginner who just started with data science wastes a lot of time in searching the best Datasets for machine learning projects. To help them out and save their valuable time, We have designed this article which includes a chain of data source links from where you can download Datasets for machine learning projects and start a machine learning project.