Artificial Intelligence has been around as an academic discipline since the 1950s. And a number of the machine learning techniques that are in vogue at the moment are a product of the 20th century. So why is this just now a big deal? Largely because it wasn’t easy to build a whole lot of real-world applications with the limited ability to process information. We’re talking about an era when access to computing power was limited to a few well-funded corporations. And “large-scale data” was in megabytes, not terabytes and petabytes.
The ability to store, move and compute a large amount of data is what lends a whole lot of predictive capability to the algorithms. Pete Johnson, who leads Big Data and AI Initiatives at the global financial powerhouse Metlife, corroborates this view and cites three significant developments enabling AI in enterprises:
- Previously expensive hardware has now become a commodity, particularly when using the cloud
- Integration of multiple sources of data – visual, textual, structured and others – enhancing the richness of information
- Scaled up algorithms and techniques like Deep Learning have enhanced our analytical ability
To these, I’d add two recent developments: the prevalence of GPUs (Graphics Processing Unit) and “everything as a sensor”. With its massively parallel processing power, GPUs are the rocket fuel that allows big data to be crunched quickly and effectively. And with every machine that we interact with capturing information about us, we’re producing data at an unprecedented rate. Our phones are always with us, and we provide everything from behavioral data, purchase patterns, interests, political affiliations, and demographic information through every click on our computers. It comes as no surprise then, that in a survey of executives from the largest technology companies in the world by New Vantage Partners, 98% of leaders say that they are investing heavily in AI and Big Data, and three-quarters of the respondents indicated that the availability of larger and more frequent sets of data was driving AI and cognitive learning investments.
Hand-in-hand with the giddy anticipation comes some sobering concerns as well. More data means a greater need to secure it all. And the more advanced our algorithms get, the harder it gets to understand what’s happening under the hood. And how do we get the people in our organizations to stay up to date? These are some of the big and urgent challenges facing our companies as we jump on the AI & Big Data bandwagon.
Data Privacy and Security: We’re collecting data everywhere. And most of us are either collecting or releasing data that we don’t even realize. Do we know which sites use cookies, which apps track our location, and how long our financial and health information is stored (and how securely)? Probably not. We’re at the infancy of privacy and security at scale. Enterprises have historically dealt with these issues by building fortresses within which the data is contained, sheltered from prying eyes. But as even enterprises start to embrace the cloud, APIs, interconnected services and SaaS, this is going to get harder and harder.
Interpretability of algorithms: The power of machine learning models lie in their ability to take big data as input, and produce predictions through a series of convoluted steps. The more data we throw at this process, and the more we tweak the models to produce better results, the harder it gets for us to understand what exactly is going on. This might be the reason stringent financial regulators are wary of certain models when making customer or financial decisions. They are wary of a number of real problems with the outputs of these models – including bias and victimization. There is more research now around interpretability of models, but product designers, analytics professionals and business leaders must all become as aware of the dangers of this technology as they are of their power.
Data-Driven organizations: Perhaps the biggest adaptation that companies have to go through is one of educating their teams. With this level of attention, the executives responding to the New Vantage Partners survey are unanimous in their agreement that data-centricity needs to permeate every level of their organizations. AI and Big Data cannot be things that small, isolated teams work on and worry about. Everybody needs to be aware – so product designers, marketers, lawyers, and leaders are all thinking about these issues every day.
“With great power comes great responsibility”. Never has this adage been truer than in the case of mass adoption of Big Data and AI. It’s’ opening hitherto unknown doors to us, and allowing us to mine an incredible amount of value for companies and individuals alike. I hope we also take the time to educate ourselves and learn to be responsible in the use of these powerful technologies.
