Over the last few years we’ve seen many companies blindly follow various technology hypes like they’re the ultimate answer to making businesses future proof. Artificial intelligence (AI) and machine learning are currently receiving huge attention, but are they really such a game changer?
Companies often don’t have any idea how disruptive technology, like AI, could impact their customers’ experience and business, and they don’t have a clear understanding of its potential or its implications. That’s why we invited Igor Mikhalev from Firmshift, a data-driven technology development company, to answer a few questions about machine learning and AI.
After leading multiple scientific software development initiatives for industry leaders in knowledge extraction, scientific big data management and publishing, Igor Mikhalev completed his MBA at the University of Amsterdam (Amsterdam Business School). He now conducts his PhD research in collaboration with UvA and a number of organizations, all industry partners leading the way in data-driven R&D. As co-founder of development company Firmshift, Igor is also working on a number of transformational and AI-based development projects.
What drew you to the field of machine learning?
I love reinventing business models, ecosystems and customer experiences. AI and machine learning have exactly the potential to be the next level platform for the next wave of transformation.
The impact of these technologies is extremely profound in the societal sense and requires what we call the antidisciplinary approach to be successful. AI facilitates a natural, intimate, and timely transition in thinking across all domains I work with: technology, business, design, and science. It cuts across them.
AI and advanced machine learning allow us to work with high degrees of complexity, forms, and volumes of data across different disciplines to understand, learn, predict and then adapt. It enables our creations to act in ways that weren't explicitly programmed, which is critical for the new-generation solutions and research outcomes we’re delivering.
What excites you about AI?
Everything we have created as human beings, as a civilization, is the result of our intelligence. But at the same time, we have natural limitations in our reasoning and synthesis, and we tend to forget, become biased, and lose focus. What AI could do is essentially be a tool that helps us augment human intelligence, become more creative, and thus—hopefully—happier.
What’s your vision for the future of machine learning?
The field of AI and machine learning has been continuously evolving since it began in 1943. But, at the same time, machine learning—with its applied contribution to contemporary business—is yet to come of age. There’s so much excitement and interest, but also overheated expectations and confusion. It’s remarkable. Many of us have heard stories about startups and conferences getting decent funding just by mentioning a new “breakthrough” configuration of neural networks on a bunch of Powerpoint slides. However, I believe the amount of hype is proportional to the eventual outcome.
I don’t think I should be looking too far ahead, because it will most likely make any prediction of this kind pointless. However, talking about short to mid-term, I believe the focus will be on the ownership, sufficiency, and readiness of data, as well as organizational capabilities to nurture the creative process of working with internal and external data in the context of cross-functional business (model) innovation, supported by machine learning technology.
Data, the critical asset in this process, comes from ever-increasing amounts of content generated on the web by humans and connected devices: mobile, sensors, cameras, payment systems, and a huge array of other sources. Its value isn’t entirely understood yet, but I believe it will be one of the few ultimate competitive differentiators. Commonly accessible data will become increasingly commoditized, but value will likely reside with the owners of scarce data and the companies that interpret data in unique ways—and especially with providers of predictive analytics solutions that can develop successful models to transfer generated insights from one domain to others.
Do you think that, at some point, we’ll be able to trust AI entirely, or will the element of human control always be necessary?
You’re asking a very difficult question. I think we, as humans, only trust technology when it has been useful for a while, we understand how it works, and it causes us no distress. In the case of AI, it’s a bit different from conventional technology: it is, in a very general sense, an intelligent black box that in many cases prevents us from knowing the “how” part. So it gets very close to how humans operate and interact themselves. When do you trust another human? I suggest the trust is to be earned over time, just as in any personal relationship.
With this, AI challenges the relationship between human and machine. Not only because it lacks transparency and has a “mind of its own”, but also because it can take some of the existing activities and jobs traditionally performed by people. If we don’t know and do not explicitly control how it works, who takes responsibility for its mistakes? Its developers, data providers, or users? Should we even treat AI’s errors as mistakes? Maybe it’s “still learning”.
As with humans, AI systems should try to provide the best rationale as to how and why they arrived at a particular conclusion, so a human can assess it. A conversational interaction is probably the most suitable way of interrogating the machine learning model to explain a specific decision—that’s how it happens when you talk to a lawyer or a doctor.
We need to address the cultural, social, and ethical challenges that will arise as a direct result of the wider introduction of machine learning, probably by developing social and ethical roadmaps as impact analysis tools, and testing existing compliance laws. It opens a whole new dimension of legal and societal concerns, where the digital law and even digital philosophy can be the new disciplines that need development.
What advice would you give companies that want to start implementing AI in their business?
First of all, I want to share with you the results of our recent survey. Over 65% of participants communicated that machine learning is crucial in advancing the competitive capabilities of their companies—do or die!
However, generally speaking, companies have no idea where to start.