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Classifying Toxic Comments with Natural Language Processing

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Regardless of whether you have a Medium account, Youtube channel, or play League of Legends, you have probably seen toxic comments somewhere on the internet before. Toxic behavior, which includes rude, hateful, and threatening actions, is an issue that stops a productive comment thread, and turns it into a battle. Needless to say, developing and artificial intelligence to identify and classify toxic comments would greatly help many online groups and communities. The data for this project can be found on Kaggle. This data set contains hundreds of thousands of comments, each labelled with some of the following traits: toxic, severe toxic, obscene, threat, insult, and identity hate. Here are two examples of a toxic comment, and a non-toxic comment with their labels.


Jumping the hurdles to post-pandemic AI automation - Write side up - by Freeform Dynamics

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In the post-pandemic, post-Brexit world, businesses of all sorts face a range of new challenges – and many will be wondering if AI-based automation could help them win through. From adding more self-service capabilities for hotel guests through modernising e-commerce fulfilment to replacing missing workers in farming, the opportunities are many, but so are the pitfalls. Given all this, some research that we carried out last year on attitudes to AI – and in particular its subset, machine learning (ML) – is looking even more relevant now than it was then. It gives a picture not just of where AI could add value, but of key routes to get there and of hurdles that must be overcome along the way. As well as asking how our respondents perceived AI and ML, and hearing a lot of weariness with the noise and hype, we asked how well their organisations understood "the AI imperative".


AI continues to flourish in business despite the pandemic and a turbulent economy

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Nearly three-quarters of businesses now consider artificial intelligence (AI) critical to their success, and AI continues to grow in importance across companies of various sizes and industries, according to a new report. And despite turbulent times, more than two-thirds of respondents to Appen Limited's 2020 State of AI Report do not expect any negative impact from the COVID-19 pandemic on their AI strategies. Nearly half of companies have accelerated their AI strategies, 20% doing so "significantly," betting their AI projects will have a positive impact on their organization's resiliency, efficiency, and innovation, according to the annual report. SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium) Yet almost half (49%) of respondents feel their company is behind in their AI journey, suggesting a critical gap exists between the strategic need and the ability to execute among business leaders and technologists, Appen said. Surprisingly, respondents are not that leery of AI: The report also found that only 25% of companies said unbiased AI is mission-critical.


PyTorch for Beginners - Building Neural Networks

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Deep learning and neural networks are big buzzwords of the decade. Neural Networks are based on the elements of the biological nervous system and they try to imitate its behavior. They are composed of small processing units – neurons and weighted connections between them. The weight of the connection simulates a number of neurotransmitters transferred among neurons. Mathematically, we can define Neural Network as a sorted triple (N, C, w), where N is set of neurons, C is set {(i, j) i, j N} whose elements are connections between neurons i and j, and w(i, j) is the weight of the connection between neurons i and j.



A self-contained robot with ultraviolet light tubes: MIT's idea to disinfect rooms of coronaviruses and …

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It is the CSAIL (Computer Science and Artificial Intelligence Laboratory) of MIT that has developed this robot together with Ava Robotics, who have …


SpaceX Vs Blue Origin: Who Wins The Space Race

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The space projects have been dominated by government bodies until we saw the ambitious companies such as SpaceX and Blue Origin diving into this diverse area. These two are the most prominent names in the private space community and are often put on a face-off due to the similarity of its founders in other areas as well. Owned by two of the most powerful businessmen of all time -- Elon Musk and Jeff Bezos, they have been on the competition radar for their interest in the area of autonomous vehicles. Similarly, in the space segment, while the two companies might look quite similar in its attempts to explore space, the ideology and the approach of these companies vary quite significantly. But one thing cannot be denied that they both are developing large, reusable vehicles capable of carrying people and satellites across space. While we have often heard about SpaceX's missions and launches over the past few years, Blue Origin does not come out to be so ambitious in gaining traction.


Why Facial Recognition Providers Must Take Consumer Privacy Seriously

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Consumer privacy has made big headlines in the recent years with the Facebook Cambridge Analytica Scandal, Europe's GDPR and high-profile breaches by companies like Equifax. It's clear that the data of millions of consumers is at risk every day, and that companies that wish to handle their data must do so with the highest degree of protection around both security and privacy of that data, especially for companies that build and sell AI-enabled facial recognition solutions. As CEO of an AI-enabled software company specializing in facial recognition solutions, I've made data security and privacy among my top priorities. Our pro-privacy stance goes beyond mere privacy by design engineering methodology. We regularly provide our customers with education and best practices, and we have even reached out to US lawmakers, lobbying for sensible pro-privacy regulations governing the technology we sell.


Most Learning Is Slow In The Field Of Machine Learning

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The first day of The Rising 2020 started with an informal session with Sara Hooker, a researcher at Google Brain where she shared some of her personal reflections on how to navigate in the field of machine learning and why we need to celebrate failures as well as success. Sara started her session with a simple story where she shared her childhood dream of being featured in the magazine, The Economist. In fact, she mentioned that "one of my goals was to eventually be an economist." However, when that happened in 2016, it wasn't a pleasing feeling for Sara; instead, it was a feeling of "unease" and seemed problematic. A lot of this could be attributed to the article that The Economist did, which profiled the efforts of fast.ai, a course that's run by Jeremy Howard and Rachel Thomas, and utilised Sara as an example of their success.


NVIDIA AI Lets You See What Your Pet Would Look Like If It Were A Meerkat

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One of NVIDIA's many different artificial intelligence projects (and by far the best one to date) lets you envision what your pet might look like it it were a meerkat. In case you didn't know, NVIDIA has its own research group dedicated solely to research into AI, and that includes developing new AI systems and agents which can do some pretty neat things. As the researchers say, although they take AI research very seriously, there's still no excuse not to have some fun with the products of their labors. It's the name given to an AI system they developed around a year ago which can generate a selection of images that are sorts of translations of your own pet's face into what said pet might look like if they were other types of animals. "With GANimal, you can bring your pet's alter ego to life by projecting their expression and pose onto other animals," explain the developers.