loader image

MIT’s recent billion-dollar commitment to its new AI-focused school, the Stephen A. Schwarzman College of Computing, represents an essential advance, not for its magnitude but for its plans to infect the rest of the university with AI.

Announced earlier this month, MIT’s new school’s mission includes engaging across MIT to explore how AI might impact research across fields from engineering and social sciences to the humanities. MIT’s president, Rafael Reif, explained the purpose of the school is to “educate bilinguals of the future.”

If MIT remains true to this vision— academic territoriality can be profoundly dysfunctional— it could generate breakthroughs. As Reif advises, “to educate bilinguals, we have to create a different structure.”

While a significant advance, connecting university-wide isn’t enough. Businesses and investors are playing increasingly engaged, even pivotal, roles in AI’s evolution. To remain relevant and prosper, businesses and universities must reconsider how they engage. The business-university nexus presents implications across research fields and technologies, but AI most starkly and urgently presents this challenge.

The AI Escaped, And It’s Coming For You

While hype obscures reality, AI’s long-term impact will be profound, from commerce, security and governance to research and the arts. Yet AI research at universities generally remains sequestered within the field of computer science. AI emerged from– and is impossible without– computer science, but marketplace opportunities and challenges long ago transcended this narrow realm.

We’ve seen this before. Steam engines arose in the 18th century in response to draining water from mines. While technical developments remained the province of mechanical engineering and thermodynamics, the implications of this motive force propelled one of history’s most dramatic transformations.

Partly as a result of steam power, the modern corporate form rose. Timezones laid upon Earth in response to logistics challenges of rail transportation. Artists like Dickens and Turner and theorists from Marx to Nietzsche (who rose outside of universities) gathered inspiration and urgency. Clearly such broad implications weren’t best left to mechanical engineers.

How might universities best generate and diffuse knowledge to help society garner the benefits and navigate the challenges of AI? What roles might businesses and investors play?

Dangerous Liaisons: Business + Academia 

In the scramble for research resources, corporations and investors play rising roles for funding, conduits to market and opportunities for graduates.

Current demand for AI talent far overtakes supply. Talent attracts stunning private sector offers from Googles and Facebooks. Top graduates who might have considered research and teaching roles increasingly take commercial paths.

This is understandable, but as businesses require great talent, they thus require great teachers. While many corporations are investing in AI training, training only goes so far. Progress requires exceptional research beyond the limits of practice. Corporations should approach university relationships more as partnerships than simply as talent and technology acquisition.

According to Venture Beat, AI startup acquisitions by leading tech companies soared from 22 in 2013 to 115 in 2017. As university labs rise, firms quickly circle to partner— or to acquire entire research teams. Leading AI institutions like CalTech and Carnegie Mellon University are already facing such trials.

Both businesses and universities should consider talent strategies in terms of affiliation, instead of as acquisition or retention. Provide flexible platforms for the best talent to pursue their interests in ways that advance your organization’s objectives, rather than as an exclusive employer. Enable flow between academic, government and commercial environments and you’re likely to have better access, wider market intelligence and contributions from exceptional individuals unlikely to linger with any single employer. The best people always have options. Build optionality into your approach.

Better Questions, Better Outcomes

Universities must also reconsider how they engage commercial partners. They’re more than a source of jobs and funding. Businesses face challenges translatable into research questions. According to a study by my Kellogg colleagues Ben Jones and Mohammad Ahmadpoor published in Science, research focused on applied challenges not only more likely leads to commercialization (as expected), but also more likely leads to significant impact on theory.

Fundamental research exploring applied challenges – known as “Pasteur’s Quadrant” after Louis Pasteur, a luminary in both scientific and commercial realms—appears likely to accelerate progress of both theory and practice. Business leaders and researchers should start by become better at sharing challenges and co-defining questions.

Element AI represents one effort to better engage. Founded by deep learning pioneer Yoshua Bengio of the University of Montreal and serial entrepreneur Jean-François Gagné, Element AI tackles “problems from multiple fields and industries that require us to push the boundaries of what existing science and technology can achieve.” As their SVP of Research, Philippe Beaudoin, asserts, “we give the best talent opportunities to flow between research and practice. Established corporate and university structures don’t suffice.”

Value In Ivory Towers

As a business school professor, entrepreneur and angel investor, I’m keen on commercialization. However, we shouldn’t overplay application. Ivory towers present both perils (ungrounded wheel spinning) and essential homes for discovery.

Some avowedly non-applied theoretical investigations rise years later with unanticipated relevance. As Albert Einstein agonized over formalizing his general theory of relatively, he turned to his friend, mathematician Marcel Grossmann. Grossmann recommended elliptic geometry, introduced over 50 years earlier by Bernhard Riemann. Elliptic geometry became a representational key that unlocked 20th century physics. Practical implications, from computing to telecommunications, reverberate today. (See Ben Jones’s discussion of this case.)

Ignoring for now their ills, ivory towers can be contemplative and meditative, characteristics abjectly lacking in the marketplace. These rarified worlds insulate researchers from some of the biases practitioners (academia’s word for everyone else) face. Researchers suffer their own biases– that enable but also circumscribe exploration— but there is value to society in exercising diverse perspectives.

Researchers explore what’s possible. Corporations, investors and entrepreneurs seek marketplace impact and profits. At their best, technologists become masters of the art of the possible— articulating what technology might enable. Business leaders should be masters of the art of the valuable. Just because something is possible, doesn’t mean anyone will pay for it.

Preventing An AI Enron

Or even that it should be done. This market-driven dynamic— from possible to valuable— generates unparalleled prosperity. However, nowhere in this dance are ethical questions necessarily addressed. Ethics require commitment.

Market-based ethical checks do exist. As Milton Friedman articulated, customers tend to eschew companies that violate ethical norms and expectations. Ethical failings can lead to collapse (think Enron). But marketplace controls are often a delayed, blunt instrument. As AI presents fundamental implications for everyone, all sectors must better engage to envision the worlds we desire, and those we don’t.

Into this fray we must eagerly welcome the social sciences, humanities and the arts. What do these transitions mean for individuals, communities and societies? To what lives do we aspire and why? Better to be shocked by George Orwell or a Black Mirror episode than blindsided by reality.

AI is an experiment on humanity itself. As MIT’s president explained in a university-wide email, AI is “creating ethical strains and human consequences our society is not yet equipped to control or withstand.” As MIT builds its new college, we each should consider the rise of AI and our responsibilities therein.

*This article originally appeared in Forbes on Oct. 27, 2018.

“8Ps” of StrategyOpportunity
for Disruption
Recommended Leverage Points
Position- The farmers, individual and corporate, that you are targeting.

- The need of the agricultural industry that you seek to fill.
3- What technologies do you control that can help you tap into market
segments that you previously thought unreachable?

- What are the potential business alliances you could think about with key players in the segment to serve your customers with integrated solutions? (Serving customers with more integrated solutions example: serving farmers with fertilizers, crop protection and other).
Product- The products you offer, and the characteristics that affect their value to customers.

- The technology you develop for producing those products.
8- What moves are your organization taking to implement Big Data and analytics to your operations? What IoT and blockchain applications can you use?

- What tools and technology could you utilize or develop to improve food quality, traceability, and

- How can you develop a more sustainable production model to accommodate constraints on arable

- What is the future business model needed to serve new differentiated products to your customers?
Promotion- How you connect with farmers and consumers across a variety of locations and industries.
- How to make consumers, producers, and other stakeholders aware of your products and services.
8- How are you connecting your product with individual and corporate farms who could utilize it?
- How could you anticipate market and customer needs to make customers interested in accessing your differentiated products?
PriceHow consumers and other members of the agricultural supply chain pay for access to agricultural products.7- What elements of value comprise your pricing? How do each of those elements satisfy the varying needs of your customers?
Placement- How food products reach consumers. How the technologies, data, and services reach stakeholders in the supply chain.9- What new paths might exist for helping consumers access the food they desire?
- How are you adapting your operations and supply chain to accommodate consumers’ desire for proximity to the food they eat?
- How could you anticipate customer expectation to make products more
accessible to customers/agile supply chain?
- Have you considered urbanization as a part of your growth strategy?
- How your food satisfies the needs and desires of your customer.
- How the services you provide to agribusiness fulfill their needs.
9- Where does your food rate on a taste, appearance, and freshness
- Could the services you provide to companies and farms in the agriculture industry be expanded to meet more needs?
- What senses does your food affect besides hunger? How does your
customer extract value from your food in addition to consumption?
Processes- Guiding your food production operations in a manner cognizant of social pressure.8- How can you manage the supply chain differently to improve traceability and reduce waste?
- How can you innovate systems in production, processing, storing, shipping, retailing, etc.?
- What are new capabilities to increase sustainability (impact on the environment, or ESG) components?
People- The choices you make regarding hiring, organizing, and incentivizing your people and your culture.- How are you leveraging the agricultural experience of your staff bottom-up to achieve your vision?
- How do you anticipate new organizational capabilities needed to perform your future strategy (innovation, exponential technologies needed, agile customer relationship, innovative supply chain)?
- How do you manage your talents to assure suitable development with exposure in the agrifood main challenges/allowing a more sustainable view of the opportunities/cross-sectors?