Artificial intelligence is pervasive; every major category of technology now incorporates AI techniques and the trend is growing. Although AI offers many benefits, risks and ethical issues abound. Despite having an enormous potential impact on society, jobs, and the economy, policymaking and educational planning have not kept pace with changes in technology, nor are we close to adopting updated legal frameworks. Also: 13 AI trends that will reshape the economy in 2018 TechRepublic Dr. Shirley Malcom is a respected and prominent educator who handles education policy at the American Association for the Advancement of Science (AAAS), which is the world's largest general scientific association and is best known for publishing Science magazine. Among her many honors, Shirley is a Regent of Morgan State University and on the Board of Trustees at Caltech. Education reform and worker re-training in the era of AI are crucial priorities for her.

A programming language textbook might not be the first thing you'd expect to see when walking into a correctional facility. The creators of the Brave Behind Bars program are hoping to change that. Founded in 2020, Brave Behind Bars is a pandemic-born introductory computer science and career-readiness program for incarcerated women, based out of The Educational Justice Institute at MIT (TEJI). It's taught both online and in-person, and the pilot program brought together 30 women from four correctional facilities across New England to study web design. One of the co-founders, Martin Nisser, a PhD student from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), explains the digital literacy and self-efficacy focused objectives: "Some of the women haven't had the opportunity to work with a computer for 25 years, and aren't yet accustomed to using the internet. We're working with them to build their capabilities with these modern tools in order to prepare them for life outside," says Nisser.

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. Our approach is to (a) develop a methodology for an external audit of stability of predictions made by algorithmic personality tests, and (b) instantiate this methodology in an audit of two systems, Humantic AI and Crystal. Crucially, rather than challenging or affirming the assumptions made in psychometric testing -- that personality is a meaningful and measurable construct, and that personality traits are indicative of future success on the job -- we frame our methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. In our audit of Humantic AI and Crystal, we find that both systems show substantial instability with respect to key facets of measurement, and so cannot be considered valid testing instruments. For example, Crystal frequently computes different personality scores if the same resume is given in PDF vs. in raw text format, violating the assumption that the output of an algorithmic personality test is stable across job-irrelevant variations in the input. Among other notable findings is evidence of persistent -- and often incorrect -- data linkage by Humantic AI.

Testing with quiz questions has proven to be an effective way to assess and improve the educational process. However, manually creating quizzes is tedious and time-consuming. To address this challenge, we present Leaf, a system for generating multiple-choice questions from factual text. In addition to being very well suited for the classroom, Leaf could also be used in an industrial setting, e.g., to facilitate onboarding and knowledge sharing, or as a component of chatbots, question answering systems, or Massive Open Online Courses (MOOCs). The code and the demo are available on GitHub.

In an effort to serve its consumers more directly, Nike has released a plan of action that details an innovative approach to transforming its supply chain. The company issued a statement listing four key components to help make its goal successful. First, Nike intends to open several regional distribution centers across the US and Europe, in addition to having its own dedicated train – the Nike "Sole Train" – to increase capacity and speed, and help power long term growth. This step would transform the brand's central distribution centers in Memphis, TN, into omni-channel facilities. Second, the brand will leverage technology by using AI and machine learning to deliver products faster and more precisely.

We would all love it if we could say we knew everything. Unfortunately, that is not possible. Sometimes I tell myself "I don't know anything", to push myself to learn and continuously improve. Data Science is one of those sectors which require constant learning and there is always room for improvement. It's difficult to keep on top of things and feel a sense of accomplishment in the world of Data Science.

The graph represents a network of 1,107 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 21 January 2022 at 11:47 UTC. The requested start date was Friday, 21 January 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 8-hour, 23-minute period from Tuesday, 18 January 2022 at 16:30 UTC to Friday, 21 January 2022 at 00:53 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 1,219 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 21 January 2022 at 12:06 UTC. The requested start date was Friday, 21 January 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 8-hour, 51-minute period from Tuesday, 18 January 2022 at 15:53 UTC to Friday, 21 January 2022 at 00:45 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 1,201 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 21 January 2022 at 11:27 UTC. The requested start date was Friday, 21 January 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 2-hour, 34-minute period from Tuesday, 18 January 2022 at 22:19 UTC to Friday, 21 January 2022 at 00:53 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.