humans and big data
An evolving partnership: The future of man and machine
"When you’re finished changing, you’re finished." Business leaders likely weren't the intended audience when Benjamin Franklin uttered his now famous quote. However, his words likely resonate deeply with leaders in today's rapidly evolving marketplace who know that for their business to thrive, they must be prepared to adapt and embrace new technologies.
Artificial intelligence (AI) is at the forefront of digital technologies transforming business, but its advancement often raises a polarizing debate. On one side are those eager to embrace an increasing role for the machines; on the other, are those who fear that machines will push people out of jobs and could take over our world.
Regardless of what you believe the future will bring for humans and machines, there's no doubt that digital technologies will play an increasingly important role in our future. In fact, IDC predicts that global spending on AI systems will reach $52.2 billion by 2021. To fully embrace these technologies and understand how businesses can get the most out of them, leaders must first understand the possibilities and limitations. In that spirit, let's examine the roles humans and machines play in today's world of big data, how the relationship is likely to evolve, and what you as a leader, can do to cultivate the benefits.
Humans and machines today: An interdependent relationship
Try to remember all the things you've learned in your life. Can you do it? For most people, the answer is unequivocally no. And that's where machines play a starring role. Unlike humans, machines never forget. They have the ability to remember every data input and every pattern established. They also have the ability to process massive amounts of information at a rapid speed. For perspective, a new development from UCLA researchers processes data with such efficiency that it can identify an unfamiliar object at the speed of light.
It is this eternal memory of massive volumes of data that allows machines to identify patterns and make inferences most humans simply could never discover on their own. However, machines' exceptional capabilities only go so far—ultimately, human expertise and context are needed to achieve big data's full potential.
This is fundamentally true because AI utilizes what has been already learned. AI cannot create new knowledge or valid new scientific context, and cognitive advancement is limited by certainty of context. A human must create the necessary data structure and training routines to impart information to the machines, empowering its pattern-recognition capabilities.
Consider, for example, a designer examining the context of shoes. A machine without specialized training may never, with certainty, identify all relevant shoes versus other foot coverings because it cannot recognize the diversity of applications shoes fulfill—a search for shoes in reference to fashion could yield designer heels, in reference to protection could yield hiking boots, and in reference to comfort could yield slippers. All of these results are right, but a machine requires initial human insight as to this context to evolve its frame of reference. The machine perceives that these associations all fall within the realm of shoes and produces the best possible set of shoe findings.
Beyond implementing this ontology of relations, humans keep a machine apprised of newly evolving valid context and correct errors as they arise. Consider the shoes again. Once the machine understands the various terminology and ontological associations with shoes, it may infer that other things applied to a foot, such as hosiery, bandages or nail polish, are also relevant as shoes—but that's not the case. Consistent fine-tuning of the data structure and relationships advances the machine's capabilities and ensures greater accuracy.
Humans are also essential in optimal handling of real-time crises that machines aren't equipped to navigate alone. Take an aircraft, for example. Most commercial aircraft today operate frequently using autopilot, but when an unexpected issue arises, the human pilot steps in to troubleshoot. While the machine will inform and support the problem-solving process—such as, flagging a mechanical error—the pilot will ultimately manage the situation with judgement that the machine may not be able to provide without supervision. When unexpected situations arise, machines often lack sufficient context to be effective in unaided decision making.
This collaborative "give-and-take" is the cornerstone of the human-machine relationship today. Machines enable humans to process large volumes of information faster and solve more challenging problems by finding patterns in that data. Likewise, humans enable the technology to evolve and deliver the best possible results. But, will this relationship continue to be one of collaboration in the future?
Humans and machines tomorrow: A symbiotic partnership
There are many who fear that as machines get smarter, humans will be unnecessary. There's no question that machine learning capabilities will continue to advance. However, in the world of science, I truly believe the man-machine relationship will remain symbiotic, and in fact, become even more essential.
In the future, we will confront problems that don't even exist today. Our quest for new discoveries and innovation will become increasingly complex and the amount of data available will be unimaginable. Without a doubt, we will need machines more than ever to help us navigate and make sense of it. But ultimately, humans will continue to be essential to the process as well, setting new constructs that enable greater machine learning and applying machine-gleaned insights to drive new discoveries.
Leaders today and tomorrow: Never stop adapting
As leaders look to the future, Franklin's words will continue to ring true—a willingness to embrace change will be critical. So, how can you be adaptable and stay ahead of the curve?
First, companies must not only prioritize creating and implementing new technology, but also fostering a team of people with the necessary skills and expertise to get the most out of that technology. Long gone will be the days when technology ruled the investment plan; people must also be a top priority. This sentiment is reflected in a recent PwC survey of global CEOs in which more than 50% confirmed they are actively exploring the benefits of humans and machines working together. In the same survey, nearly 40% stated that they are considering the impact of artificial intelligence on future skill needs.
And that's not all. Future leaders—and leaders today—must also help shift the workplace mindset by fostering a culture that enables and encourages effective collaboration between humans and machines. "Either-or" won't work. At CAS, for example, we've spent more than a century curating and managing the highest quality scientific content. But the way we work today is very different from 111-years ago when CAS was founded. At that time, the primary mechanism we used for information storage was index cards. Since then, we've evolved our approach many times over, and today we marry specialized technologies with our team of hundreds of expert scientists to deliver a 360-degree view of global innovation and actionable insights to our users.
Are you and your organization ready to embrace new technologies to gain an information advantage? Learn how CAS can help your business accelerate its future with customized solutions that merge best-in-class data, technology and human insight.
CAS, a division of the American Chemical Society, partners with R&D organizations globally to provide actionable scientific insights that help them plan, innovate, protect their innovations, and predict how new markets and opportunities will evolve. Leverage our unparalleled content, specialized technology, and unmatched human expertise to customize solutions that will give your organization an information advantage.