Curriculum


How Will Artificial Intelligence Change Law Schools?

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Beyond the classroom curriculum, many law schools are designing experiential modes of introducing law students to artificial intelligence. At Georgia State University School of Law, for instance, the Legal Analytics and Innovation Initiative gives law students a chance to collaborate closely with computer science and business students at the same university to design complex technologies that solve previously unsolvable legal problems (such as predicting to a high degree of accuracy how a particular judge will rule in cases defined by a large set of parameters). This kind of work not only has the potential to be a flow-through to the legal practitioner space, but could over time become a mechanism for law schools to "spin out" the kinds of revenue-generating start-up businesses that are a common facet of life science departments at research universities. These programs have also been shown (according to the programs' own statistics) to help law students land jobs at higher rates than the overall student body, no doubt because the intersection of technology and law is a rare and valuable skillset in the eyes of employers.


Machine Learning: Lessons Learned from the Enterprise

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This article summarizes the lessons learned after two years of our team engaging with dozens of enterprise clients from different industries including manufacturing, financial services, retail, entertainment, and healthcare, among others. What are the most common ML problems faced by the enterprise? What is beyond training an ML model? How to address data preparation? How to scale to large datasets?


Bill Gates: If I were starting a company today, it would use AI to teach computers how to read

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If Bill Gates were to drop out of Harvard University and start a new company today, it would be one that focuses on artificial intelligence, he said in an interview on Monday. The perspective shows that the Microsoft co-founder hasn't lost interest in the technology industry where his company has operated for the past 44 years. "Given my background, I would start an AI company whose goal would be to teach computers how to read, so that they can absorb and understand all the written knowledge of the world. That's an area where AI has yet to make progress, and it will be quite profound when we achieve that goal," Gates told David Rubinstein at an Economic Club of Washington event in the nation's capital on Monday. Gates has invested in Luminous, a start-up developing silicon for AI.


What Does an AI Ethicist Do?

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Microsoft was one of the earliest companies to begin discussing and advocating for an ethical perspective on artificial intelligence. The issue began to take off at the company in 2016, when CEO Satya Nadella spoke at a developer conference about how the company viewed some of the ethical issues around AI, and later that year published an article about these issues. Nadella's primary focus was on Microsoft's orientation toward using AI to augment human capabilities and building trust into intelligent products. The next year, Microsoft's R&D head Eric Horvitz partnered with Microsoft's president and chief legal officer Brad Smith to form Aether, a cross-functional committee addressing AI and ethics in engineering and research. With these foundations laid, in 2018, Microsoft established a full-time position in AI policy and ethics.


7 Steps to Mastering Intermediate Machine Learning with Python -- 2019 Edition

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Are you interested in learning more about machine learning with Python? I recently wrote 7 Steps to Mastering Basic Machine Learning with Python -- 2019 Edition, a first step in an attempt to updated a pair of posts I wrote some time back (7 Steps to Mastering Machine Learning With Python and 7 More Steps to Mastering Machine Learning With Python), a pair of posts which are getting stale at this point, having been around for a few years. It's time to add on to the "basic" post with a set of steps for learning "intermediate" level machine learning with Python. We're talking "intermediate" in a relative sense, however, so do not expect to be a research-caliber machine learning engineer after getting through this post. The learning path is aimed at those with some understanding of programming, computer science concepts, and/or machine learning in an abstract sense, who are wanting to be able to use the implementations of machine learning algorithms of the prevalent Python libraries to build their own machine learning models.


7 Steps to Mastering Intermediate Machine Learning with Python -- 2019 Edition

#artificialintelligence

Are you interested in learning more about machine learning with Python? I recently wrote 7 Steps to Mastering Basic Machine Learning with Python -- 2019 Edition, a first step in an attempt to updated a pair of posts I wrote some time back (7 Steps to Mastering Machine Learning With Python and 7 More Steps to Mastering Machine Learning With Python), a pair of posts which are getting stale at this point, having been around for a few years. It's time to add on to the "basic" post with a set of steps for learning "intermediate" level machine learning with Python. We're talking "intermediate" in a relative sense, however, so do not expect to be a research-caliber machine learning engineer after getting through this post. The learning path is aimed at those with some understanding of programming, computer science concepts, and/or machine learning in an abstract sense, who are wanting to be able to use the implementations of machine learning algorithms of the prevalent Python libraries to build their own machine learning models.


The little robot that could

Robohub

"We're honored that we got to see a Wyss Institute technology go from its earliest stages to where we are today, with the opportunity to make a gigantic impact on the world," said Zivthan Dubrovsky, former Bioinspired Robotics Platform Lead at the Wyss Institute and co-founder of Root Robotics who is now the General Manager of Educational Robots at iRobot. "We're excited to see how this new chapter in Root's story can further amplify our mission of making STEM education accessible to students of any age in any classroom around the world." Root began in the lab of Wyss Core Faculty Member and Bioinspired Robotics Platform co-lead Radhika Nagpal, Ph.D., who was investigating the idea of robots that could climb metal structures using magnetic wheels. "Most whiteboards in classrooms are backed with metal, so I thought it would be wonderful if a robot could automatically erase the whiteboard as I was teaching – ironically, we referred to it as a'Roomba for whiteboards,' because many aspects were directly inspired by iRobot's Roomba at the time," said Nagpal, who is also the Fred Kavli Professor of Computer Science at Harvard's John A. Paulson School of Engineering and Applied Sciences (SEAS). "Once we had a working prototype, the educational potential of this robot was immediately obvious. If it could be programmed to detect ink, navigate to it, and erase it, then it could be used to teach students about coding algorithms of increasing complexity."


What if AI in health care is the next asbestos? - STAT

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Artificial intelligence is often hailed as a great catalyst of medical innovation, a way to find cures to diseases that have confounded doctors and make health care more efficient, personalized, and accessible. But what if it turns out to be poison? Jonathan Zittrain, a Harvard Law School professor, posed that question during a conference in Boston Tuesday that examined the use of AI to accelerate the delivery of precision medicine to the masses. "I think of machine learning kind of as asbestos," he said. "It turns out that it's all over the place, even though at no point did you explicitly install it, and it has possibly some latent bad effects that you might regret later, after it's already too hard to get it all out."


How 15 women in engineering discovered their passion for technology

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It's not hard to find a good story in the tech industry. The problem is that due to the industry's staggering gender gap, most of these stories center on the struggles and accomplishments of men. In this article, we aim to provide a platform for female technologists to share the stories of how they got into engineering, the biggest challenges they've faced, and their advice to the next generation of women in tech. You'll meet a former geologist turned product manager, an academic who fell in love with data science, a senior tech leader who discovered her dream job after the first two companies she worked for folded, and more. CCC's technology solutions are designed to increase connectedness among companies in the automotive industry, including insurance carriers, manufacturers, parts suppliers and collision repair shops. Ranjini Vaidyanathan was in academia and earned a PhD before realizing she had a passion for data science. While changing focuses wasn't always easy, Vaidyanathan said the transition was made easier by some simple, yet powerful, advice from her mentors. "When the going gets tough, what'll help you pull through is your passion for the technical work." How did you get into engineering? I studied applied science and mathematics before finally switching to data science after my PhD. It took me some time to decide what, exactly, I wanted to pursue. I had been doing pen-and-paper theory work as a student, but after a certain point, I realized I found applied problems more interesting. What's the biggest challenge you've faced in your career, and how have you worked to overcome it? Switching fields from academia to data science was challenging. I had to brush up industry-relevant skills like programming, and also adjust to the paradigm shift in thinking, both in terms of technical and soft skills.


Why AI talent is so hard to come by and what can be done to fill the gap

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Nearly every industry is using artificial intelligence in one way or another to improve business outcomes. AI holds great promise as new and exciting applications are discovered, but there is a catch. There aren't enough trained AI engineers capable of carrying out the work. Karen Roby talks with Sameer Maskey, a professor of AI at Columbia University and founder of Fusemachines, about the shortage and what can be done. The following is an edited transcript of the interview.