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Microsoft and corporate activism

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BOSTON — Artificial intelligence needs to eat, breathe and sleep data to be effective. By that measure, the company…

— or country — with the most data should emerge victorious. But Brad Smith, president and chief legal officer at Microsoft, said the idea that data accumulators will rule the world and the rest of us will be powerless is a pessimistic take on what’s to come.

He prefers optimism. Indeed, Smith said there are ways to keep, say, China from becoming a global data dictator. His recommendation: Create “a global standard for ethical principles and for the protection of things like privacy so that the price for global admission is adherence to a global standard.”

Such a standard could create a classic data-silo problem for countries that don’t comply. For example, China may have the world’s largest population, but if it can’t access Europe’s data or the United States’ data, it will struggle to uncover patterns on a global, rather than national, scale.

That was one of the points Smith made at HUBweek, an innovation festival in Boston. In a fireside chat with Adi Ignatius, editor in chief of Harvard Business Review, Smith made it clear that Microsoft is participating in a new kind of corporate activism that targets broad societal issues — and it’s using this newly public corporate conscience as an advocacy and marketing tool.

For example, in December, Microsoft publicly supported a giving women and men the right to take a sexual harassment claim to court rather than keep the complaint in arbitration. When North Carolina a that restricted LGBT rights, Microsoft lent its voice to the opposition. More recently, the company launched the Defending Democracy Program, which is aimed at protecting campaigns from hacking, increasing political advertising transparency and defending against disinformation.

The political thread in the examples Smith provided is hardly accidental: Customers are increasingly turning to companies to take on issues they care about because of the dysfunction in Washington, D.C., Smith argued.

“We are living in a time when there are historically low levels of trust in government,” he said. “And so, whereas in the past, people would say, ‘I care about this. I’m going to go to government.’ They are less inclined to do that.”

But jumping into corporate activism and promoting a company’s moral compass can also create wrinkles. Smith said, given the gridlock in Washington, customers are beginning to ask Microsoft to help regulate the very technology it’s developing. One HUBweek attendee raised a question that has become common at tech events: Can government officials craft policy that effectively keeps up with the rapid pace of change in the tech industry? 

Smith shot down the idea, saying that any technology company’s involvement in regulation is inappropriate and restricts progress. “I don’t think it’s viable to ask tech to slow down,” he said. “It is not only appropriate, but it is right to ask government to move faster. And it’s incumbent on those of us in the technology sector to do what we can to share information so that governments can move faster.”

Plus, he said, “it’s important to remember that, in this country and in many others, people elect those who make the laws. People do not elect companies.”

‘Moneyball’ for movies

Legendary Entertainment is using analytics to develop films. Matt Marolda, chief analytics officer for the media company, referred to this as “Moneyball for movies,” referencing the story of how data helped transform a losing baseball team.

The use of data to develop movie products isn’t , but in the past, much of the data was collected through analog methods. These days, the data that Marolda’s applied analytics team uses for analysis comes from a variety of sources — from one-on-one conversations with viewers to digital data such as search queries and Twitter conversations.

The tried-and-true method of testing a film before a live audience is also used — but with a twist. While Marolda’s team tries “to be as unintrusive as possible” during the testing process, it uses iPads to capture facial expressions and wristbands to collect heart rate and other “various signals,” he said. That kind of data is used to determine what’s working and what’s not.

“We try and identify those moments where people are confused, people are rolling their eyes, where they’re really engaged,” Marolda said.

The data can also help pin down much bigger problems such as whether the chemistry between the two leads is believable. Marolda said filmmakers often have a gut answer to human chemistry questions like the latter, but the data can provide solid evidence.

“With that kind of evidence, entire storylines are removed from movies, reshoots might happen to redirect the plot,” he said. “Those indicators are a great way to understand what the opportunities are for improvement.”

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Source: https://searchcio.techtarget.com/news/252450811/Microsoft-and-corporate-activism

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If you did not already know

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Familia google


In the last decade, a variety of topic models have been proposed for text engineering. However, except Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), most of existing topic models are seldom applied or considered in industrial scenarios. This phenomenon is caused by the fact that there are very few convenient tools to support these topic models so far. Intimidated by the demanding expertise and labor of designing and implementing parameter inference algorithms, software engineers are prone to simply resort to PLSA/LDA, without considering whether it is proper for their problem at hand or not. In this paper, we propose a configurable topic modeling framework named Familia, in order to bridge the huge gap between academic research fruits and current industrial practice. Familia supports an important line of topic models that are widely applicable in text engineering scenarios. In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models. Simply through changing the data organization of Familia, software engineers are able to easily explore a broad spectrum of existing topic models or even design their own topic models, and find the one that best suits the problem at hand. With its superior extendability, Familia has a novel sampling mechanism that strikes balance between effectiveness and efficiency of parameter inference. Furthermore, Familia is essentially a big topic modeling framework that supports parallel parameter inference and distributed parameter storage. The utilities and necessity of Familia are demonstrated in real-life industrial applications. Familia would significantly enlarge software engineers’ arsenal of topic models and pave the way for utilizing highly customized topic models in real-life problems. …

Median Absolute Deviation (MAD) google


In statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample. Consider the data (1, 1, 2, 2, 4, 6, 9). It has a median value of 2. The absolute deviations about 2 are (1, 1, 0, 0, 2, 4, 7) which in turn have a median value of 1 (because the sorted absolute deviations are (0, 0, 1, 1, 2, 4, 7)). So the median absolute deviation for this data is 1. …

Temporal Recurrent Network (TRN) google


Most work on temporal action detection is formulated in an offline manner, in which the start and end times of actions are determined after the entire video is fully observed. However, real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, Temporal Recurrent Networks (TRNs), to model greater temporal context of a video frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS’14. The results show that TRN significantly outperforms the state-of-the-art. …

CDF2PDF google


CDF2PDF is a method of PDF estimation by approximating CDF. The original idea of it was previously proposed in [1] called SIC. However, SIC requires additional hyper-parameter tunning, and no algorithms for computing higher order derivative from a trained NN are provided in [1]. CDF2PDF improves SIC by avoiding the time-consuming hyper-parameter tuning part and enabling higher order derivative computation to be done in polynomial time. Experiments of this method for one-dimensional data shows promising results. …

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Source: https://analytixon.com/2021/06/13/if-you-did-not-already-know-1421/

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Predict Future Sales using XGBRegressor

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XGBRegressor |Predict Future Sales using XGBRegressor





















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Source: https://www.analyticsvidhya.com/blog/2021/06/predict-future-sales-using-xgbregressor/

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Decoding the Chi-Square Test - Use, Implementation and Visualization

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Chi-Square Test - Use, Implementation and Visualization





















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Source: https://www.analyticsvidhya.com/blog/2021/06/decoding-the-chi-square-test%e2%80%8a-%e2%80%8ause-implementation-and-visualization/

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Anonymous or Lambda Functions in Python: A Beginner’s Guide!

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Lambda Functions in Python: A Beginner’s Guide! – Analytics Vidhya





















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Source: https://www.analyticsvidhya.com/blog/2021/06/anonymous-or-lambda-functions-in-python-a-beginners-guide/

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