AI Trends Weekly Brief: Education for AI

Ashesh Shah
November 9, 2017
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Education for AI: Many Avenues Available to Qualify for Work in AI
Many avenues have opened up for those interested in getting the education and training required to work in the AI field. These range from higher education institutions – those with a history and tradition in AI and those expanding into AI as a new field of study – to free online courses such as from Coursera, to certificate programs from Udacity and others, to private company partnerships within the industry, such as the many recently announced by NVIDIA.
What’s the best way?
At the Computer Science Degree Hub website, skewed toward computer science, the suggestion is that candidates interested in pursuing jobs in AI require specific education based on foundations of math, technology, logic, and engineering perspectives. Written and verbal communication skills are also important to convey how AI tools and services are effectively employed within industry settings.
It seems clear the foundation courses are needed before branching into different fields of AI such as data science, machine learning, computer vision, self-driving cars or robotics.
This is echoed by David Ledbetter, a data scientist working at the Children’s Hospital, Los Angeles Pediatric Intensive Car Unit. Asked for suggestions for students or professionals interested in learning more about data science and AI, he points to fast.ai, run by Jeremy Howard and Rachel Thomas. However, he said standard course work is the foundation.
“To train up someone new in the role as a data scientist, I myself, might have a different take on the strengths needed to get someone to full capacity to become a contributing member of the team. I am looking for math, computer science, physics, chemistry and biology. Those strong fundamentals are so critical to every aspect of what we do. The specifics of coding in Python or constructing deep learning models with Keras, learning [Python] pandas to munge the data — those are easier to train. But the fundamentals are the foundation on which everything rests,” Ledbetter said in a 2017 interview with AI Trends.
Ledbetter’s own education background was a degree in math, experience at a company doing digital signal processing, detection theory, then machine learning work, then deep learning work. “And a lot of different detection analysis – on radar, sonar, optical data. Once you are thinking about it abstractly, pulling signals out of data, the transition to the medical field is not that extreme. When we get readings for patients in the ICU, we look for signals showing why they are sick and how they are going to get better. So many of the skills are the same, and being at Children’s Hospital Los Angeles, we get extremely high-fidelity data.”
A big key at his institution is the interaction between doctors and AI workers like himself. “We have a fellowship program in the pediatric ICU, a post-doctorate program for doctors, where 50% of their time is dedicated to research. We have a close collaboration with the doctors, to leverage their medical expertise and fold it into our data scientist expertise. We are both together trying to look at the same problem to come up together with the best overall solution.”
The best institutions of higher education for computer science and engineering are likely to be the leaders in AI-related fields of study.
Asked about certificate programs, Ledbetter said, “Many of the certificate programs provide a great primer for deep learning (such as Udacity or fast.ai). I feel like we’re actually in a really interesting period where there’s a great crop of eager junior-level data scientists, but probably not enough experienced senior/lead data scientists available to provide appropriate mentorship and guidance in real-world scenarios. Demands on a lead data scientist right now are so extreme (from actual coding, solution architecting, executive communication, mentorship) I feel like they’re currently the limiting factor, not new blood.”
He added that Children’s Hospital of LA is planning for a data science internship project to help provide mentorship to aspiring data scientists in the healthcare field. “The goal is to team data scientists up with research fellows from the hospital to help solve real clinical challenges,” Ledbetter said.
David Ledbetter will be speaking at AI World on December 11
NVIDIA Expands Deep Learning Institute
The private company partnerships have injected energy into the AI education field. The announcement in November of the expansion of NVIDIA’s Deep Learning Institute (DLI) is a prime example. The graphics processing unit (GPU) chip manufacturer offers training via DLI for developers, data scientists and researchers. DLI offers self-paced, online labs, instructor-led workshops, opportunities with business partners and alliances with higher education institutions.
DLI provides training on the latest techniques for designing, training and deploying neural networks across a variety of applications domains. Students learn widely-used open-source frameworks as well as NVIDIA’s GPU-accelerated deep learning platforms
The announced expansion includes:

New partnerships with Booz Allen Hamilton and deeplearning.ai to train thousands of students, developers and government specialists in AI.
New University Ambassador Program enables instructors worldwide to teach students critical job skills and practical applications of AI at no cost.
New courses designed to teach domain-specific applications of deep learning for finance, natural language processing, robotics, video analytics and self-driving cars.

“The world faces an acute shortage of data scientists and developers who are proficient in deep learning, and we’re focused on addressing that need,” said Greg Estes, vice president of Developer Programs at NVIDIA. “As part of the company’s effort to democratize AI, the Deep Learning Institute is enabling more developers, researchers and data scientists to apply this powerful technology to solve difficult problems.”
DLI – which NVIDIA formed last year to provide hands-on and online training worldwide in AI – is already working with more than 20 partners, including Amazon Web Services, Coursera, Facebook, Hewlett Packard Enterprise, IBM, Microsoft and Udacity.
DLI also announced a collaboration with deeplearning.ai, a new venture formed by AI pioneer Andrew Ng with the mission of training AI experts across a wide range of industries. The companies are working on new machine translation training materials as part of Coursera’s Deep Learning Specialization, which will be available later this month.
“AI is the new electricity, and will change almost everything we do,” said Ng, who also helped found Coursera, and was research chief at Baidu. “Partnering with the NVIDIA Deep Learning Institute to develop materials for our course on sequence models allows us to make the latest advances in deep learning available to everyone.”
DLI is also teaming with Booz Allen Hamilton to train employees and government personnel, including members of the U.S. Air Force. DLI and Booz Allen Hamilton will provide hands-on training for data scientists to solve challenging problems in healthcare, cybersecurity and defense.
To help teach students practical AI techniques to improve their job skills and prepare them to take on difficult computing challenges, the new NVIDIA University Ambassador Program prepares college instructors to teach DLI courses to their students at no cost. NVIDIA is already working with professors at several universities, including Arizona State, Harvard, Hong Kong University of Science and Technology and UCLA.
DLI is also bringing free AI training to young people through organizations like AI4ALL, a nonprofit organization that works to increase diversity and inclusion. AI4ALL gives high school students early exposure to AI, mentors and career development.
“NVIDIA is helping to amplify and extend our work that enables young people to learn technical skills, get exposure to career opportunities in AI and use the technology in ways that positively impact their communities,” said Tess Posner, executive director at AI4ALL.
In addition, DLI is expanding the range of its training content with:

New project-based curriculum to train Udacity’s Self-Driving Car Engineer Nanodegree students in advanced deep learning techniques as well as upcoming new projects to help students create deep learning applications in the robotics field around the world.
New AI hands-on training labs in natural language processing, intelligent video analytics and financial trading.
A full-day self-driving car workshop, “Perception for Autonomous Vehicles,” available later this month. Students will learn how to integrate input from visual sensors and implement perception through training, optimization and deployment of a neural network.

To increase availability of AI training worldwide, DLI recently signed new training delivery partnerships with Skyline ATS in the U.S., Boston in the U.K. and Emmersive in India.
IBM and MIT Announce Research Partnerships
Institutions of higher learning are also striking deals with technology companies to help fund research, which also of course is helpful to students.
For example, IBM and MIT in September announced that IBM plans to make a 10-year, $240 million investment to create the MIT–IBM Watson AI Lab in partnership with MIT. The lab will carry out fundamental artificial intelligence (AI) research and seek to propel scientific breakthroughs that unlock the potential of AI. The collaboration aims to advance AI hardware, software, and algorithms related to deep learning and other areas; increase AI’s impact on industries, such as health care and cybersecurity; and explore the economic and ethical implications of AI on society. IBM’s $240 million investment in the lab will support research by IBM and MIT scientists.
The new lab will mobilize the talent of more than 100 AI scientists, professors, and students to pursue joint research at IBM’s Research Lab in Cambridge, Massachusetts — co-located with the IBM Watson Health and IBM Security headquarters in Kendall Square.
The lab will be co-chaired by Dario Gil, IBM Research VP of AI and IBM Q, and Anantha P. Chandrakasan, dean of MIT’s School of Engineering. IBM and MIT plan to issue a call for proposals to MIT researchers and IBM scientists to submit their ideas for joint research to push the boundaries in AI science and technology in several areas, including:

AI algorithms: Developing advanced algorithms to expand capabilities in machine learning and reasoning. Researchers will create AI systems that move beyond specialized tasks to tackle more complex problems and benefit from robust, continuous learning. Researchers will invent new algorithms that can not only leverage big data when available, but also learn from limited data to augment human intelligence.
Physics of AI: Investigating new AI hardware materials, devices, and architectures that will support future analog computational approaches to AI model training and deployment, as well as the intersection of quantum computing and machine learning. The latter involves using AI to help characterize and improve quantum devices, and researching the use of quantum computing to optimize and speed up machine-learning algorithms and other AI applications.
Application of AI to industries: Given its location in IBM Watson Health and IBM Security headquarters in Kendall Square, a global hub of biomedical innovation, the lab will develop new applications of AI for professional use, including fields such as health care and cybersecurity. The collaboration will explore the use of AI in areas such as the security and privacy of medical data, personalization of health care, image analysis, and the optimum treatment paths for specific patients.
Advancing shared prosperity through AI: The MIT–IBM Watson AI Lab will explore how AI can deliver economic and societal benefits to a broader range of people, nations, and enterprises. The lab will study the economic implications of AI and investigate how AI can improve prosperity and help individuals achieve more in their lives.

In addition to IBM’s plan to produce innovations that advance the frontiers of AI, a distinct objective of the new lab is to encourage MIT faculty and students to launch companies that will focus on commercializing AI inventions and technologies that are developed at the lab. The lab’s scientists also will publish their work, contribute to the release of open source material, and foster an adherence to the ethical application of AI.
“The field of artificial intelligence has experienced incredible growth and progress over the past decade. Yet today’s AI systems, as remarkable as they are, will require new innovations to tackle increasingly difficult real-world problems to improve our work and lives,” says John Kelly III, IBM senior vice president, Cognitive Solutions and Research. “The extremely broad and deep technical capabilities and talent at MIT and IBM are unmatched, and will lead the field of AI for at least the next decade.”
“I am delighted by this new collaboration,” MIT President L. Rafael Reif says. “True breakthroughs are often the result of fresh thinking inspired by new kinds of research teams. The combined MIT and IBM talent dedicated to this new effort will bring formidable power to a field with staggering potential to advance knowledge and help solve important challenges.”
Both MIT and IBM have been pioneers in artificial intelligence research, and the new AI lab builds on a decades-long research relationship between the two. In 2016, IBM Research announced a multiyear collaboration with MIT’s Department of Brain and Cognitive Sciences to advance the scientific field of machine vision, a core aspect of artificial intelligence.
The collaboration has brought together leading brain, cognitive, and computer scientists to conduct research in the field of unsupervised machine understanding of audio-visual streams of data, using insights from next-generation models of the brain to inform advances in machine vision. In addition, IBM and the Broad Institute of MIT and Harvard have established a five-year, $50 million research collaboration on AI and genomics.
MIT researchers were among those who helped coin and popularize the very phrase “artificial intelligence” in the 1950s. MIT pushed several major advances in the subsequent decades, from neural networks to data encryption to quantum computing to crowdsourcing. Marvin Minsky, a founder of the discipline, collaborated on building the first artificial neural network and he, along with Seymour Papert, advanced learning algorithms.
Currently, the Computer Science and Artificial Intelligence Laboratory, the Media Lab, the Department of Brain and Cognitive Sciences, the Center for Brains, Minds and Machines, and the MIT Institute for Data, Systems, and Society serve as connected hubs for AI and related research at MIT.
For more than 20 years, IBM has explored the application of AI across many areas and industries. IBM researchers invented and built Watson, which is a cloud-based AI platform being used by businesses, developers, and universities to fight cancer, improve classroom learning, minimize pollution, enhance agriculture and oil and gas exploration, better manage financial investments, and much more.
In related comments, Anantha Chandrakasan, the dean of MIT’s School of Engineering, who led the effort to establish the agreement, said, “The project will support many different pursuits, from scholarship, to the licensing of technology, to the release of open-source material, to the creation of startups. We hope to use this new lab as a template for many other interactions with industry.”
He added, “The main areas of focus are AI algorithms, the application of AI to industries (such as biomedicine and cybersecurity), the physics of AI, and ways to use AI to advance shared prosperity.”
And, “The work on the physics of AI will include quantum computing and new kinds of materials, devices, and architectures that will support machine-learning hardware. This will require innovations not only in the way that we think about algorithms and systems, but also at the physical level of devices and materials at the nanoscale. To that end, IBM will become a founding member of MIT.nano, our new nanotechnology research, fabrication, and imaging facility that is set to open in the summer of 2018.”
What follows are some recent developments around AI education.
New Harvard Business Review Series Explores the Business of AI
Harvard Business Review in 2017 launched a two-week series, “Artificial Intelligence, For Real,” that offers a manager’s guide to AI. The program kicked off on July 18 with an article from MIT’s Erik Brynjolfsson and Andrew McAfee exploring the real potential of AI for businesses, its practical implications, and the barriers to adoption.
“The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transforms their core processes and business models to take advantage of machine learning,” write Brynjolfsson and McAfee. “The bottleneck now is in management, implementation, and business imagination.”
Online education firm Lynda.com extending to AI
Lynda.com, an online education company founded in 1995, recently added “Introduction to Python Recommendation System for Machine Learning,” one hour and 38 minutes, taught by Lillian Pierson, a professional engineer, book author and entrepreneur in the field of big data and data science. Lynda.com is now part of LinkedIn Learning, after being acquired in 2015 for $1.5 billion.
Lynda.com has more than 500 employees worldwide and offers instruction in English, German, French and Spanish. Basic courses start at $20/month; premium courses start at $30/month Lynda.com offers 158 courses in data science.
Lynda.com was founded by Lynda Weinman, a special effects animator and multimedia professor who founded a digital art school, as online support for books and classes. In 2002, the company began offering courses online. By 2004, it offered 100 courses. In 2008, the company began producing and publishing documentaries on creative leaders, artists, and entrepreneurs.
Clients include NBC Universal Autodesk and AllianceData.
Learn more at Lynda.com.
Middle schoolers can learn engineering at UC Berkeley camp
In the summer of 2017, fortunate middle school students in the Berkeley, Calif. area attended a camp for aspiring engineers. The Family 1st Architecture Camp, being held at UC Berkeley’s Wurster Hall, was founded by Jeremiah Tolbert and Camero Toler, alumni of the College of Environmental Design. The two founded the camp to expose underserved youth to architecture, engineering and construction.
The camp was launched In partnership with the AIA East Bay and 1st Family Foundation – a locally-focused nonprofit founded by Oakland natives and National Football League players Joshua Johnson, a quarterback with the Houston Texans, and Marshawn Lynch, a running back for the Oakland Raiders, who earned recognition for his performance with the Cal Bears before turning pro.
Savion Green of East Oakland enrolled in the camp three years ago when he was 11. He has lived in some tough neighborhoods, including Crenshaw and Hawthorne in LA and the Fillmore in San Francisco. He lost his father to homicide when he was a year old. Now, Savion is a mentor in the engineering camp. (In photo above, Savion Green (middle) is flanked by architect and volunteer instructor Omar Haque, (left) and teacher and volunteer Shalonda Tillman (right).)
He is mapping out a path to become an engineer, focusing on robotics and nanotechnology, in the hopes of making the world a better place. He credits his transformation to the summer engineering camp.
“I was kind of arrogant,” Savion told Berkeley News. “School came very easy for me and I was just bored with the stuff I was learning because I already knew it.”
Savion says neither of his choices that summer when he was 11 were appealing: summer classes or camp. He chose camp, even though he was clueless what architecture or engineering even meant. Once there, he learned to use computers to draw and design buildings and even cities. “I was so inspired,” he says.
Now Savion is supplementing his high school courses with local college courses, so that when he graduates, he will have close to an associate of arts degree. “I can go straight to MIT (Massachusetts Institute of Technology).” Second choice: Stanford University. Third: UC Berkeley.”
He aspires to earn a Ph.D. in nanotechnology and to find a cure for cancer. Meanwhile, today he works two jobs – one is counseling other youths about growing healthy foods and cooking nutritious meals, and the other is doing janitorial work for his uncle. This enables him to save money to build robots.
“I plan on starting my own company, selling my own robots, and just make life easier for people with this technology,” he says. “I really want to change the world.
Learn more at the Family 1st Architecture Camp.
Written and compiled by John P. Desmond, AI Trends editor
Source: AI Trends

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