How Artificial Intelligence Will Go To the Next Level

Continued broad development of AI technologies and concepts require new approaches to collaboration between industry organizations and academia.

Guest Commentary, Guest Commentary

October 10, 2018

4 Min Read

Industry and academia have collaborated in artificial intelligence research for decades, but in recent years the power balance in this relationship has shifted in ways that are detrimental to AI progress and the sustainability of the field.

Most existing arrangements between industry and academia are either “work for hire,” which often is too narrowly defined to attract the brightest minds in academia to participate, or “buy the lab,” which effectively end collaborations by hiring researchers away from academia and prevent the next generation of AI talent from receiving the education and research opportunities that will lead to AI progress in the future, cannibalizing the future pipeline to serve the needs of the present.

A new working model between industry and academia is needed, one in which stable, long-term industry-academic partnerships enable continued AI advancement while preserving our society’s capacity to conduct fundamental research and train future generations of AI experts.

In a long-term partnership, academic and industry researchers must work collaboratively as equals, rather than industry merely sponsoring research or pulling faculty or students out of academia.

Instead of traditional top-down or single-organization decision-making, successful partnerships should be guided by more inclusive decision-making approaches – for example, through joint committees, with equal representation of academic and industry members, each of whom feels a strong responsibility to the collaboration and to the advancement of AI.

We believe our MIT-IBM Watson AI Lab collaboration offers a new model for engaging between academia and industry. Below are five key advantages to such a model, and an explanation of why it’s the surest path to transformational progress in AI research.

Complementary strengths

AI is exploding with new and expanding subfields, and conducting rapid and meaningful AI research demands cross-disciplinary knowledge, along with intense focus. Strong long-term partnerships between academia and industry are positioned to integrate a broad range of academic disciplines -- from computer science, mathematics and logic to biology, linguistics, economics and even the arts -- with industry’s real-world perspective, domain knowledge, and access to data. Furthermore, advances in AI demand new ideas and a creative, ambitious workforce, along with substantial computational and financial resources. With academia being a fertile source for the former and industry uniquely positioned to provide the latter, unifying the two takes full advantage of their complementary strengths.

Diverse representation

Because expansion of AI has broad implications for all people and communities, its creation and development should reflect a diversity of backgrounds and viewpoints. Part of the value of a peer research approach is in the variety of perspectives, expertise, and experience levels it offers. Students bring fresh ideas and eagerness to immerse and learn quickly, to experiment and take risks, to deeply focus on a novel problem or solution, and to earn a scientific reputation (and a degree) for themselves. Experienced academic and industry researchers share deep expertise in their chosen areas that comes from years, potentially decades, of focus, failures, and breakthroughs; scientific rigor and principled approaches; and an understanding of the broader context in which technology can be brought into service.


One of the greatest accelerators in AI progress is the openness with which academic and industry players have shared the fruits of their research. Yet it is not uncommon that when talented AI researchers leave academia and join industry, their research becomes more closed and less accessible to the field, slowing the overall development of AI as a field. We recognize that there is substantial value in an open ecosystem in which industry and academia work in close collaboration with one another, sharing their results and technologies with the wider AI community. By publishing in top scientific conferences and journals, and open-sourcing data and code, we can feed the research ecosystem and accelerate rather than stifle the development of AI.

Radical ideas and growth

Scientific discoveries are sparked by creativity and curiosity as much as rigor and discipline. In AI, this calls for an entrepreneurial research model that welcomes new projects, sets them up for success by establishing milestones, and iterates on promising work. Our collaboration with MIT is designed to nurture radical ideas, encouraging them to take root and grow into breakthroughs.

Market opportunities

Identifying and meeting marketplace needs are key to the success of a business endeavor, as those familiar with start-ups know well. Why? Because targeting market needs leads to investment, which is essential to further develop emerging technologies. By pursuing opportunities to commercialize AI discoveries and inventions, we can encourage a healthy growing climate for AI research.

Bringing industry and academia together creates the ideal environment for incubating the new breakthroughs needed to realize broad AI for enterprise and the increases in value and productivity it promises. Now is the time to align across sectors and the entire AI ecosystem to accelerate our progress. AI has unprecedented potential to benefit enterprises and the societies they serve. We must work together to fuel the innovations that will deliver on that potential.

David Cox is director of the MIT-IBM Watson AI Lab, IBM Research.

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Guest Commentary

Guest Commentary

The InformationWeek community brings together IT practitioners and industry experts with IT advice, education, and opinions. We strive to highlight technology executives and subject matter experts and use their knowledge and experiences to help our audience of IT professionals in a meaningful way. We publish Guest Commentaries from IT practitioners, industry analysts, technology evangelists, and researchers in the field. We are focusing on four main topics: cloud computing; DevOps; data and analytics; and IT leadership and career development. We aim to offer objective, practical advice to our audience on those topics from people who have deep experience in these topics and know the ropes. Guest Commentaries must be vendor neutral. We don't publish articles that promote the writer's company or product.

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