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AI In Academia: Much Potential, Much Resistance

By Keith Rajecki There’s much debate in higher education circles over the appropriate role of artificial intelligence in academia.

Colleges and universities have started using virtual assistants, chatbots, and other intelligent software tools to augment or replace student interactions with advisers or counselors and to provide some institutional services.

But incoming classes of students increasingly expect virtual services, such as a chatbot branded as the university mascot giving out real-time campus directions, or a virtual assistant offering initial guidance on course selection during registration, or a machine-learning engine recommending supplemental reading to improve course performance during the semester.

The chatbot service, which took four weeks to implement, handled 2,100 phone conversations on its first day, and the average hold time dropped from 40 minutes to 90 seconds during the most recent results process compared with the previous year.

Institutions must employ AI-infused applications to help drive the registration and course-selection process, to help students select dormitories and find internships, to help professors understand where their students are excelling and where they’re falling behind in individual courses.

Rajecki: Because adaptive intelligence helps increase revenue and lower costs by more closely targeting prospects and improving processes, it’s pretty much a prerequisite as part of today’s business applications.

It costs a public university $536, on average, to recruit an undergraduate student,according to a recent report, while a private institution spends about three times that amount.

It’s also hoped that AI will power unique, differentiated services that address the needs and interests of students and produce better outcomes, including recruitment, retention, graduation, and even employment.

As colleges and universities go down this AI path, they need to invest in development and training for professors, instructional designers, and advisers to help them identify the value of AI and apply it in their professional practice in order to benefit students.

Artificial Intelligence and the Military: Technology Is Only Half the Battle

Editor’s Note: As 2018 comes to a close, War on the Rocks is publishing a series of year-end reflections on what our editors and contributors learned from the publication’s coverage of various national security topics.

War on the Rocks articles discussed issues ranging from the different ways international competitors and military services are pursuing AI to the challenges AI applications present to current systems of decision-making, trust, and military ethics.

Connor McLemore and Hans Lauzen identified broader areas where AI may have the greatest strategic impact: those where machines have an edge over human speed, agility, and labor intensity and where machines can effectively identify patterns.

Along these lines, multiple War on the Rocks writers have suggested that technical challenges to strategic AI development could pale in comparison to the task of organizing a military acquisitions complex to achieve it.

Nailing down long-term strategic goals may be second-order to making the unsexy reforms — prioritizing enterprise-wide data labeling, cloud-building, and software developer-user teaming — that will enable military AI no matter its specific applications.

New systems to develop and manage the information — and human expertise — generated in AI testbeds like the Athena platform and others will require further innovation in resource organization.

Managing Humans to Manage AI As Jacquelyn Schneider noted, the Third Offset counterintuitively turns the expected ascendency of machine over man on its head: “In contrast to the first or second offsets, in which the United States was able to double down on the development of physical components of technology… the autonomy arms race is all about talent and manpower.” If battlefield AI is to be revolutionary, War on the Rocks writers argued this year, the U.S. military may need to make even larger changes in how it recruits, trains, and keeps the humans required to build and operate AI systems.

If cognitive bias leads humans to struggle to trust their AI-enabled smartphone GPS, how will it play out when commanders ask unit-level operators to rely on specific AI applications in life or death situations?

While this debate was about remotely piloted systems, not AI, it nonetheless raises important questions about how the level of human trust in new technologies will vary as militaries adopt more advanced systems.

Both militaries and the civilian AI industry want AI applications that work effectively, requiring extensive development and testing to address the challenges of spoofing, hacking, and appropriate usage.

Safety may be a concern shared with other nations also developing military AI, and might thus be a productive area for diplomacy and confidence building in the absence, as Alexandra Bell and Andrew Futter note, of verification standards for AI-related arms control.

Yet, as Charles Rybeck, Lanny Cornwell, and Philip Sagan note, U.S. policymakers and budgeteers have yet to fully grasp the political implications of competing with powers like Russia and China, comfortable with “informatization” of everyday citizens’ livelihoods in the name of national security.

40 Must-Read Machine Learning, AI, and Deep Learning Blogs

To keep up to date on the latest industry news, machine learning blogs are a valuable, yet sometimes overlooked, resource.

In the world of artificial intelligence, there is an abundance of blogs that offer illuminating perspectives on recent trends, new products, and industry news.

For any budding machine learning engineer, spending time in the AI blogosphere will not only help your prospects for career advancement but also keep you connected to the broader AI community.

The site’s non-technical guide to artificial intelligence is also particularly useful for those who are interested in jumping into the world of machine learning but don’t have the technical background.

The Spectator Shakir Mohamed, a research scientist at DeepMind, writes about machine learning tips and tricks on his blog, named after an English publication that circulated in the early 18th century.

Many of Mohamed’s articles, including a recent post on making artificial intelligence truly global, are thought-provoking and consider the broader implications of AI on the world around us.

Domino Data’s machine learning blog Data science company Domino offers up a bounty of AI-related content on its ML blog, covering industry best practices and subjects like machine learning models and captive learning.

Distill aims to present AI research in a more user-friendly way, incorporating reactive diagrams and compelling graphics that help the reader to more easily understand the research.

Springboard’s AI / machine learning blog If you’re looking for first-hand insight and tips on breaking into the field of AI, you’re right where you belong!

Springboard’s AI and machine learning blog posts include practical guides, tutorials and other bits of career advice on how to transition into a machine learning engineer role.

Drawing on the expertise of industry influencers, O’Reilly’s blog posts show how companies are utilizing AI technology, highlighting the latest ideas, tools, and solutions in the industry.

The Google AI Blog In this company blog, researchers and engineers at Google provide a riveting look at how the tech giant is incorporating AI and ML technology into its products, like its remarkable translation tools.

MIT’s artificial intelligence news This news stream provides the latest on what’s happening in AI at the Massachusetts Institute of Technology, which is considered one of the leading academic institutions in AI research.

Berkeley Artificial Intelligence Research (BAIR) Blog You’ll find BAIR research findings, AI perspectives, and other content on this blog written by students, post-docs, and faculty of this highly rated UC Berkeley program, which brings together researchers across the areas of computer vision, machine learning, natural language processing, planning, and robotics.

On this company blog, DeepMind team members share information on their research, important milestones, and other company news in a clear and compelling fashion.

Scroll through this site and you’ll find a host of intriguing articles on how AI technology is being deployed, as well as educational posts explaining machine learning and other related topics.

news outlet dedicated to covering data science and AI, insideBIGDATA features news stories, white papers, and reports, as well as interviews with data scientists and AI professionals.

Natural Language Processing Blog Hal Daume III, presently a principal researcher at Microsoft, was inspired to create this blog in 2005 after noticing that, while there were machine learning research blogs at the time, he couldn’t find an open forum dedicated to natural language processing.

Analytics Vidhya Analytics Vidhya’s site provides a vast toolbox of educational content for data science professionals, including lots of material focused on AI, machine learning, and deep learning.

Nvidia’s deep learning blog California tech company Nvidia shares its corporate news and announcements, along with other deep learning-related stories on this company blog.

His posts are engaging, easy to understand, and he sometimes incorporates real-life happenings into his blog posts—like applying machine learning to March Madness, for instance—to help explain complex ideas.

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