Problem definition: Traditionally, New York City's top 8 public schools have selected candidates solely based on their scores in the Specialized High School Admissions Test (SHSAT). These scores are known to be impacted by socioeconomic status of students and test preparation received in middle schools, leading to a massive filtering effect in the education pipeline. The classical mechanisms for assigning students to schools do not naturally address problems like school segregation and class diversity, which have worsened over the years. The scientific community, including policymakers, have reacted by incorporating group-specific quotas and proportionality constraints, with mixed results. The problem of finding effective and fair methods for broadening access to top-notch education is still unsolved. Methodology/results: We take an operations approach to the problem different from most established literature, with the goal of increasing opportunities for students with high economic needs. Using data from the Department of Education (DOE) in New York City, we show that there is a shift in the distribution of scores obtained by students that the DOE classifies as "disadvantaged" (following criteria mostly based on economic factors). We model this shift as a "bias" that results from an underestimation of the true potential of disadvantaged students. We analyze the impact this bias has on an assortative matching market. We show that centrally planned interventions can significantly reduce the impact of bias through scholarships or training, when they target the segment of disadvantaged students with average performance.

In this post, I will explore how the advent of large pre-trained language models (such as GPT3 [1]) are giving rise to the new paradigm of'prompt engineering' in the field of NLP. This new paradigm allows us to rapidly prototype complex NLP applications with little to no effort and based on very small amounts of data. I will present a case study where I used this technique during my summer internship at Waylay to create an application that makes industry-level automatization accessible to everyone using voice and text inputs (think of something like google assistant, but for IoT and on steroids!). Finally, I will conclude with some remarks on this new and exciting trend. If you don't feel like reading, you can watch this recording of the internal meeting where I presented my solution to the company.

Medical data is notoriously difficult to access. Before we see the true potential of predictive AI cooperation and legislation will be needed. After the events of 2020, many are looking to the power of artificial intelligence. We'll want to know when and where the next outbreak might be, helping business and society weather the disruption. Early in the COVID outbreak, AI indicated that something big was coming.

Implementing Artificial Intelligence (AI) in an organization is a complex undertaking as it involves bringing together multiple stakeholders and different capabilities. Many companies make the mistake of treating AI as a'pure play' technology implementation project and hence end up encountering many challenges and complexities peculiar to AI. There are three big reasons for increased complexity in an AI program implementation – (1) AI is a'portfolio' based technology (example, comprising sub-categories such as Natural Language Processing (NLP), Natural Language Generation (NLG), Machine Learning) as compared to many'standalone' technology solutions (2) These sub-category technologies (example, NLP) in turn have many different products and tool vendors with their own unique strengths and maturity cycles (3) These sub-category technologies (example, NLG) are'specialists' in their functionality and can solve certain specific problems only (example, NLG technology helps create written texts similar to how a human would create it). Hence, organizations need to do three important things – 'Define Ambitious and Achievable Success Criteria', 'Develop the Right Operating Rhythm', and'Create and Celebrate Success Stories' to realize the true potential of AI. Most companies have very narrow or ambiguous'success criteria' definition of their AI program.

Implementing Artificial Intelligence (AI) in an organization is a complex undertaking as it involves bringing together multiple stakeholders and different capabilities. Many companies make the mistake of treating AI as a'pure play' technology implementation project and hence end up encountering many challenges and complexities peculiar to AI. There are three big reasons for increased complexity in an AI program implementation – (1) AI is a'portfolio' based technology (example, comprising sub-categories such as Natural Language Processing (NLP), Natural Language Generation (NLG), Machine Learning) as compared to many'standalone' technology solutions (2) These sub-category technologies (example, NLP) in turn have many different products and tool vendors with their own unique strengths and maturity cycles (3) These sub-category technologies (example, NLG) are'specialists' in their functionality and can solve certain specific problems only (example, NLG technology helps create written texts similar to how a human would create it). Hence, organizations need to do three important things – 'Define Ambitious and Achievable Success Criteria', 'Develop the Right Operating Rhythm', and'Create and Celebrate Success Stories' to realize the true potential of AI. Most companies have very narrow or ambiguous'success criteria' definition of their AI program.

Each semester, Associate Professor Virginia Vassilevska Williams tries to impart one fundamental lesson to her computer-science undergraduates: Math is the foundation of everything. Often, students come into Williams' class, 6.006 (Introduction to Algorithms), wanting to dive into advanced programming that power the latest, greatest computing techniques. Her lessons instead focus on how algorithms are designed around core mathematical models and concepts. "When taking an algorithms class, many students expect to program a lot and perhaps use deep learning, but it's very mathematical and has very little programming," says Williams, the Steven G. (1968) and Renee Finn Career Development Professor who recently earned tenure in the Department of Electrical Engineering and Computer Science. "We don't have much time together in class (only two hours a week), but I hope in that time they get to see a little of the beauty of math -- because math allows you to see how and why everything works together. It really is a beautiful thing."

Meet four experts from Mowi, Wallenius Wilhelmsen, Gartner and Microsoft and listen to their advice on smart ways to accelerate your cloud journey. Hear their inside stories on how you can tap into the possibilities that lie in AI and analytics, for a variety of more innovative, efficient and customized solutions – driving both sustainability and profitability. Read more about how the companies realize business outcomes with cloud, AI and robotics. Cloud is no longer an opportunity for the few niche startups like Netflix, AirBnB and Uber. It is now the underpinning prerequisite for industry disruption across every industry.

Apple CEO, Tim Cook spoke up for privacy at a conference of European privacy commissioners in Brussels this morning. The themes of this year's conference is "Debating Ethics: Dignity and Respect in Data Driven Life", Cook is the first tech CEO to serve as the keynote speaker for the conference and was invited to speak. He talked about data, put in a bid for a bill of U.S. digital rights, slammed competitors for profiting while unleashing powerfully negative forces, and spoke up for a GDPR-style privacy protection in the U.S. What follows is the transcript of his speech. "It is an honor to be here with you today in this grand hall…a room that represents what is possible when people of different backgrounds, histories, and philosophies come together to build something bigger than themselves. "I am deeply grateful to our hosts.

What is IQT and how does it make all the connected things smarter? Emerging technologies like Internet of Things (IoT) are redefining the way businesses are generating and consuming data. As these technologies take shape and bring more data sources on line, one of the biggest challenges will be to understand and act on the data generated by anything that can be connected. This data holds paramount importance for the organisations. In fact, successful businesses thrive on the ability to convert data into insights.

As late as the mid 2000s, telephones were the primary tool for contact in case customers had any clarifications or grievances about the products or services they had procured. 'Customer care' for businesses was typically handled by employees of call centres, who could, by and large, be relied upon to respond to customers and sort out any issues they faced. With the limited communication options available then, companies were able to resolve issues impersonally and these services were mostly considered nothing more than an unavoidable expense. As a result, customers were often frustrated by rigid company policies that they weren't aware of, and add to that a lack of transparency and official procedures that took days or even weeks to resolve. Fast forward to today and the rise of e-commerce platforms, multiple communication channels, and the easy availability of smartphones has caused a gravitational shift in customer engagement methods.