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How Machine Learning Will Transform the Way Employers and Candidates Connect - insideBIGDATA

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Even though you may not realize it, machine learning-powered matchmaking is present everywhere in our daily lives, from the type of content shown on our Facebook news feeds to the suggested TV shows that come up on Netflix, and even to the matches suggested on dating sites/apps like Match.com and Tinder. As machine learning continues to advance, it will start to make its way to the hiring process, driving efficiencies in connecting employers and candidates, especially for technical jobs. Analyzing large amounts of data on candidates will become increasingly important during the hiring process for many companies. Today, matching algorithms use strings and keywords in resumes to filter candidates. This enables companies to get more accurate results, quicker, during the hiring process.


Seven Ways to Avoid Bias in Your Data

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AI is taking off in all areas of business and in our daily lives – from improving agriculture and predicting where forest fires might erupt to determining who is likely to return to a hospital after discharge. With advanced GPUs that can crunch more data faster and growing demand from companies looking to increase competitive advantage, machine learning and other forms of AI are expected to become more pervasive. Today, many companies are relying on smart apps to provide the insight needed to make decisions that can affect people's lives, such as who qualifies for a mortgage or who will be insured. Because of this responsibility, it's more important than ever that data professionals don't inadvertently automate any biases into the AI algorithm because of the data they use or don't use, and how they use it. While AI should be regulated to ensure the fair and ethical use of data, particularly as it impacts decision-making and people's lives, unfortunately, we still have a long way to go before this happens.


Here's how to check in on your AI system, as COVID-19 plays havoc

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The machine learning approach works well when these new cases are similar to the examples in the training data. The ability of machine learning algorithms to identify subtle patterns in the training data can allow it to make a faster and possibly better predictions than a human. However, if the new cases are radically different from the training data, and especially if we are playing by a whole new rulebook, then the patterns in the training data will no longer be a useful basis for prediction. Some algorithms are designed to continuously add new training data and therefore update the algorithm, but with large changes this gradual updating will not be sufficient. To learn completely new rules, machine learning algorithms need large amounts of new data.


Beginner's Guide to Deep Learning Concepts

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Learning through experience, memorizing the things learnt are the skills which is taken care by our brain… So does anyone thought whether a machine can think like us, learn like us? Yes, Machines can think like us and more ever can think more than a human, learn like us by using some algorithms. This phenomenon is called "Machine Learning". Deep Learning is the subset of Machine Learning and Machine Learning is the subset of AI. Basically Deep Learning can be known as the improvement to Machine Learning.


5 Advantages Artificial Intelligence can Give Your SME - ReadWrite

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How fast do trends in the market advance? Specifically with regards to artificial intelligence. Businesses are taking advantage of artificial intelligence as a breakneck pace. According to a BI Intelligence report, 80% of companies will use AI chatbots by 2020. Here are five advantages artificial intelligence can give your SMEs.


How artificial intelligence is transforming the future of digital marketing

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Digital marketing relies on leveraging insights from the copious amounts of data that gets created every time a customer interacts with a digital asset. In 2020, we anticipate a significant uptick in the mainstreaming of AI and machine learning use cases in digital marketing across several areas. In the past year, online search has had several AI and machine learning developments. Google is leading the pack with exciting applications in information retrieval. For example, Google's BERT technology can process a word in the context of all the other terms in a sentence, rather than one-by-one in order.


Towards A More Transparent AI

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One cornerstone of making AI work is machine learning - the ability for machines to learn from experience and data, and improve over time as they learn. In fact, it's been the explosion in research and application of machine learning that's made AI the recent hot bed of interest, investment, and application that it is today. Fundamentally, machine learning is all about giving machines lots of data to learn from, and using sophisticated algorithms that can generalize from that learning for data that the machine has never seen before. In this manner, the machine learning algorithm is the recipe that teaches the machine how to learn, and the machine learning model is the output of that learning that can then generalize to new data. Regardless of the algorithm used to create the machine learning model, there is one fundamental truth: the machine learning model is only as good as its data. In many cases, these bad models are easy to spot since they perform poorly.


Sparse Matrix Representation in Python - KDnuggets

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Most machine learning practitioners are accustomed to adopting a matrix representation of their datasets prior to feeding the data into a machine learning algorithm. Matrices are an ideal form for this, usually with rows representing dataset instances and columns representing features. A sparse matrix is a matrix in which most elements are zeroes. This is in contrast to a dense matrix, the differentiating characteristic of which you can likely figure out at this point without any help. Often our data is dense, with feature columns filled up for every instance we have.


#futureofwork Twitter NodeXL SNA Map and Report for Thursday, 16 April 2020 at 08:47 UTC

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The graph represents a network of 5,864 Twitter users whose recent tweets contained "#futureofwork", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 10,000 tweets. The network was obtained from Twitter on Thursday, 16 April 2020 at 09:21 UTC. The tweets in the network were tweeted over the 3-day, 13-hour, 52-minute period from Sunday, 12 April 2020 at 18:54 UTC to Thursday, 16 April 2020 at 08:47 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.


Towards A More Transparent AI

#artificialintelligence

One cornerstone of making AI work is machine learning - the ability for machines to learn from experience and data, and improve over time as they learn. In fact, it's been the explosion in research and application of machine learning that's made AI the hot bed of interest, investment, and application that it is today. Fundamentally, machine learning is all about giving machines lots of data to learn from, and using sophisticated algorithms that can generalize from that learning to data that the machine has never seen before. In this manner, the machine learning algorithm is the recipe that teaches the machine how to learn, and the machine learning model is the output of that learning that can then generalize to new data. Regardless of the algorithm used to create the machine learning model, there is one fundamental truth: the machine learning model is only as good as its data. In many cases, these bad models are easy to spot since they perform poorly.