Netflix released at least one movie a week over the past two years – I challenge you to name them all! According to Bloomberg, the streaming giant is restructuring its movie division and releasing fewer movies overall. Despite the sheer number of titles Netflix previously released, only a few had won accolades, attained significant hours of streaming, or had the kind of cultural impact some of the biggest blockbusters had achieved. Netflix ramped up its film development after studios started building their own streaming services instead of licensing their movies to the company. This restructuring will combine the team working on small projects with a budget of $30 million or less and the unit that produces mid-budget films that cost $30 million to $80 million.

A group of Tesla workers in New York has sent company chief Elon Musk a letter stating their intention to unionize, according to Bloomberg. It could end up being the first Tesla union if successful, seeing as previous attempts fizzled out before organizers could petition for a vote. The employees involved in the campaign are in charge of labeling data for Tesla's Autopilot technology at the company's Buffalo, New York facility. Bloomberg says the group is asking for better pay, job security and a better work environment that eases the production pressures placed on them. Workers told the news organization that they've been skipping bathroom breaks, since Tesla keeps a close eye on their every move.

This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematical models for the Black-Scholes equation to analyze data for 92,846 companies. We solve the Black-Scholes (BS) equation forwards in time as an ill-posed inverse problem, using the Quasi-Reversibility Method (QRM), to predict option price for the future one day. For each company, we have 13 elements including stock and option daily prices, volatility, minimizer, etc. Because the market is so complicated that there exists no perfect model, we apply ML to train algorithms to make the best prediction. The current stage of research combines QRM with Convolutional Neural Networks (CNN), which learn information across a large number of data points simultaneously. We implement CNN to generate new results by validating and testing on sample market data. We test different ways of applying CNN and compare our CNN models with previous models to see if achieving a higher profit rate is possible.

This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the follow-up project of the research "Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting". First, the project included an application of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to provide a novel way of predicting stock option trends. Additionally, it examined the dependence of the ML models by evaluating the experimental method of combining multiple ML models to improve prediction results and decision-making. Lastly, two improved trading strategies and simulated investing results were presented. The Binomial Asset Pricing Model with discrete time stochastic process analysis and portfolio hedging was applied and suggested an optimized investment expectation. These results can be utilized in real-life trading strategies to optimize stock option investment results based on historical data.

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The company that owns Tinder will pay $441 million to the popular dating app's founders to settle a dispute over the valuation of stock options, documents showed Wednesday. The suit filed in New York in 2018 contended that Tinder owner Match Group, and its then parent firm InterActiveCorp, schemed to dramatically drive down the value of stock options and then eliminate them altogether. Co-creators Sean Rad, Justin Mateen and Jonathan Badeen alleged Match and IAC relied on bogus figures to arrive at a valuation of $3 billion in 2017 -- when Tinder was actually worth more than four times that. Tinder's owner is paying the app's founders millions to settle a lawsuit Photo: AFP / Aamir QURESHI Created in 2012, Tinder now has more than 10 million paying users who can quickly scroll through possible romantic matches, and then swipe left or right to signal interest. With options on about 20 percent of Tinder's stock, the founders and their early employees felt they had been shortchanged by several billion dollars.

As the transformative potential of AI has become increasingly salient as a matter of public and political interest, there has been growing discussion about the need to ensure that AI broadly benefits humanity. This in turn has spurred debate on the social responsibilities of large technology companies to serve the interests of society at large. In response, ethical principles and codes of conduct have been proposed to meet the escalating demand for this responsibility to be taken seriously. As yet, however, few institutional innovations have been suggested to translate this responsibility into legal commitments which apply to companies positioned to reap large financial gains from the development and use of AI. This paper offers one potentially attractive tool for addressing such issues: the Windfall Clause, which is an ex ante commitment by AI firms to donate a significant amount of any eventual extremely large profits. By this we mean an early commitment that profits that a firm could not earn without achieving fundamental, economically transformative breakthroughs in AI capabilities will be donated to benefit humanity broadly, with particular attention towards mitigating any downsides from deployment of windfall-generating AI.

But a limited pool of talent has allowed viable candidates who smell the desperation to put a high price on their skills. The number of job postings featuring "artificial intelligence" or "machine learning" in their description has doubled since 2015, according to global jobs site Indeed.com, Search entries for AI and machine learning positions have risen 182% in the past three years. But more job searches don't mean more qualified applicants, as industries struggle to find sufficiently skilled candidates. Meanwhile, the lucrative salaries being offered on public platforms such as Indeed.com

Three of Tinder's founders and a handful of current executives say the popular dating app's parent companies cheated them out of as much as $2 billion by manipulating financial information to undermine its valuation, according to a lawsuit filed Tuesday. The co-founders and executives claim that Match Group Inc. MTCH -0.21% and IAC/InterActiveCorp . IAC 0.18% hid projections of Tinder's rapid growth in order to reduce payments to the holders of stock options, which were based on the company's valuation. The suit, filed by 10 plaintiffs in New York Supreme Court, also says that Greg Blatt, a longtime executive of IAC who served as interim chief executive of Tinder, groped and sexually harassed Tinder's vice president of marketing and communications, Rosette Pambakian, during the Los Angeles-based company's 2016 holiday party. Mr. Blatt didn't immediately respond to a request for comment.

Researchers in artificial intelligence can stand to make a ton of money. But this week, we actually know just how much some A.I. experts are being paid -- and it's a lot, even at a nonprofit. OpenAI, a nonprofit research lab, paid its lead A.I. expert, Ilya Sutskever, more than $1.9 million in 2016, according to a recent public tax filing. Another researcher, Ian Goodfellow, made more than $800,000 that year, even though he was only hired in March, the New York Times reported. As the publication points out, the figures are eye-opening and offer a bit of insight on how much A.I. researchers are being paid across the globe.