Recently named by Fortune as one of the “World’s 50 Greatest Leaders,” Dr. Peter H. Diamandis has started over 20 companies in the areas of longevity, space, venture capital, and education. He’s the founder and executive chairman of the XPRIZE Foundation and the executive founder of Singularity University, an institution that counsels the world’s leaders on exponentially growing technologies. Below we discuss the opportunities and challenges found in global Artificial Intelligence (AI) development.
Mediaplanet: What are some of the factors shaping AI development globally?
Dr. Peter Diamandis: Today’s primary fuel source for AI development is, by far, an unprecedented explosion of data. While still primarily concentrated among the Googles, Alibabas, and Facebooks of the world, this abundance of data is now iteratively training countless machine learning models across everything from autonomous transit to strategy game simulations. Another key component to this data abundance is convergence: as proliferating sensors feed real-world data (beyond exclusively online or mobile user activity) through Internet of Things (IoT) networks, AI’s power is simultaneously amplified by rapid advances in computing — not to mention the exponential leap in AI’s ability to solve complex problems in the age of quantum computing. Beyond this convergence, however, other critical factors driving AI development include: machine learning expertise; an abundance of capital — from venture capitalists, sovereign wealth funds, tech behemoths funding home-grown applications, etc.; and a growing workforce of entrepreneurs who continue to identify new targets for AI solutions — whether in novel drug discovery or niche educational applications.
What are the main factors hindering AI development?
Unfortunately, the primary roadblock we’re witnessing now—and may continue to see—is hindered collaboration between today’s top AI superpowers, namely China and the U.S. On my most recent trip to China’s Greater Bay Area, one of the central discussions I had with several tech executives there concerned so-called technological “decoupling,” whereby Chinese and U.S. private sectors (and respective government AI regulation) could fundamentally diverge in their fostering of the AI sector and machine learning’s applications. Yet in order to derive maximum benefit from AI over the coming century, it’s vital that we achieve some level of consensus on everything from IP law standardization to transparency in cross-border tech sector M&A. More than ever, U.S.-China collaboration in both AI R&D and its commercial applications will help accelerate solutions to some of the most intractable global challenges—climate change, disease incidence and pharmaceutical obsolescence, water scarcity, an inefficient transportation sector, among others.
The other important piece of the puzzle is AI’s ability to explain its output and issues of bias. Especially now that AI is involved in facilitating legal decisions, or determining creditworthiness, it’s vital that we guarantee these systems’ equitability and transparency. Fortunately, however, we’re already seeing a wave of research—in everything from algorithm “fairness conditions” to techniques that ensure user privacy—starting to overcome these challenges.
Why is having a sound AI strategy crucial to a business’ success and competitiveness?
In the past few years, we’ve popularized the phrase, “Every company is now a technology company.” But the phenomenon we’re really beginning to see is that every company will soon be an AI company. Already, an explosion in Software as a Service (SaaS) AI platforms is enabling SMEs and big corporations to automate a large chunk of their business operations, widen profit margins, and free up significant percentages of their human capital for more creative, non-repetitive tasks. What companies can do with AI products today is such an important topic that I’m dedicating an entire module to SaaS at my annual Abundance 360 summit this January.
But beyond what’s already available in terms of business AI, the adoption of AI-driven decision-making in the next 3-5 years will be a make-or-break for businesses in the age of the smart economy. Whether you’re in retail, advertising, real estate, insurance, construction, media, you name it… AI’s use in problem-solving, optimization, client personalization, and a slew of other challenges will be the difference between staying (and leading) in business and getting disrupted by newly emerging business models.
Where are the main opportunities for growth and progress in AI development?
One of the areas I’m most excited about in convergence with AI is the longevity space. Take genomics for instance, where we’re seeing the dramatic demonetization of genome sequencing and correlations of genetic data with disease incidence. This is a perfect playground for AI, whose convergence with CRISPR will have powerful implications in the decade ahead. Or I often discuss the example of Insilico Medicine, a company I’ve invested in through Bold Capital that leverages generative adversarial networks (or GANs — a subset of AI) to discover novel molecules, rapidly test drug candidates, and output those ideal for drug development. It’s AI breakthroughs like these that are disrupting and fundamentally reshaping hundred-billion- or even trillion-dollar industries like big pharma. Thanks to convergences in machine learning and molecular biology, companies like Insilico are slated to do with 50 people what the pharmaceutical industry can barely do with 5,000, in a fraction of the time.
How can organizations future-proof themselves and take advantage of exponential technologies?
While there’s no one-size-fits-all answer here, one of the tools and thinking methodologies I often use is what has been coined the “futures wheel.” I first examine the convergence of one or more exponential technologies (say AI and sensors, for instance), and an industry (let’s take transportation). Then, I trace the first-order implications of that convergence. Using the example above, the first-order implications of AI and sensors’ convergence with transit could include autonomous ride-sharing, increased parking density, automated traffic law enforcement, working or sleeping during your commute, the elimination of parking lots, decimated traffic accidents, and so on. Next, based on these first-order implications, we can determine with fair certainty the second-order business opportunities.
By tracking the convergence of exponential technologies, predicting initial implications to industry, and then projecting the mid-term effects of those implications, every business in any industry will be able to position its products and services for a 2025 or 2030 scenario, future-proofing its business model and leveraging the right technological tools to do so.
How can leaders ensure that they are visionary, innovative, and inclusive when leveraging AI technologies?
One of my mantras is “Before something is truly a breakthrough, it’s a crazy idea.” Yet as companies grow, and become more bureaucratic, they tend to adopt a mentality of maintaining and protecting… taking fewer risks with their bread and butter products and seeking incremental, linear growth in revenue streams and the like. To maintain the risk-loving, innovative mentality of a startup, however, I often recommend that companies designate a “crazy idea” department. Think of Google’s X Development, which gives its employees the unbridled freedom to be visionary, explore, rapidly iterate upon, and kill (when necessary) crazy ideas, which have led to ground-breaking ventures like Waymo and Loon.
Who in your company are you giving the freedom to rapidly iterate on crazy ideas that could redefine your business model and lead to a breakthrough product or use of technology? How are you fostering a culture whereby your employees are rewarded for proposing wild, out-of-the-box solutions as opposed to being discouraged from norm-diversion?
What are some of the priorities and objectives that decision makers should address to build a culture of AI within their company?
As mentioned above, the implementation of SaaS products and newly emerging AI platforms—like those being deployed by Amazon Web Services (AWS)—is a critical first step for companies looking to embed AI in business processes and familiarize employees with algorithms for everyday use. In terms of company culture, however, it’s vital that businesses reduce the intimidation factor of AI for employees through online courses and discussion, and that C-suite executives invest in their own AI education. This includes learning what AI tools are available and applicable in your company today; what’s coming off the AI R&D conveyer belt in the next 6-12 months; and how machine learning might transform your business model—or launch a new division and revenue stream within your company—in the next 3-5 years. (While often overlooked, these questions are critical for any SME and large corporation alike, and are part of the reason I support a community of 3,000 entrepreneurs called Abundance Digital that focuses in these areas.) We currently stand at a major historical turning point, at which the rate of technological acceleration is itself accelerating. As a result, it’s more important than ever for employees to dedicate even a minimal percentage of their time to understanding the implications of technological change and how their industry will shift and adapt in the coming decade.
If you’re an exponentially and abundance-minded entrepreneur who would like coaching directly from Dr. Peter H. Diamandis, consider joining his Abundance 360 Mastermind (a Singularity University program), a highly selective community of 360 CEOs and entrepreneurs who Peter coaches for 3 days every January in Beverly Hills, Ca. Through A360, members are provided with context and clarity about how converging exponential technologies will transform every industry.
