AI: Beyond the Hype and Into Reality - Dataconomy

Buzzwords are part of what makes the internet go ’round, and you’d be hard-pressed to find a more popular and controversial term today than Artificial Intelligence (AI). Once just an ethereal concept that interested the nerdiest among us, AI has become a very real obsession in all corners of the tech world. Read the headlines and you’ll hear about the variety of super-smart devices coming our way. Are we ready?

The problem with these headlines, though, is that they assume a zero-sum game: “humans vs. AI-powered systems.” The truth is far different: we need AI, and AI needs us. Just as the industrial revolution shifted the burden of drudge work and large-scale heavy lifting onto the backs of machines, the AI revolution will allow humans to slough off the drudge work associated with computing, enabling them to focus on contextualization and reasoning – the kinds of things humans are good at.

Furthermore, unlike the news stories that hint at an AI takeover, the technology’s real presence is not based on conjecture or predicted trends (after all, if we lived in a future in which trends dictated our lives, we’d all be traveling byhoverboard). There just so happens to be a major worker shortage in the economy today that is hurting profits and holding the economy back from its full potential.

With the help of AI systems, we will be able to fill the skills gap, ensuring a brighter future.

The best way to approach AI is to examine exactly what it can and cannot do. There are dozens of technologies that fit under the rubric of AI, among them chatbots, neural networks, machine learning, natural language processing, swarm intelligence and sentiment analysis. The more we examine AI, the more we realize that it needs a guiding human hand to fulfill its potential.

Let’s examine several AI technologies and discuss how they can help us achieve our goals.

Sentiment analysis: A technology that seeks to understand general attitude, meaning or opinion behind text or other types of content (videos, articles, social media posts, etc.). Sentiment analysis is used for many purposes, such as gaining insights into customer preferences, political preferences or even looming security threats, as social media posts of prospective criminals or terrorists are parsed.

This form of AI does work that humans are capable of, however, remains inherently unscalable if it should be seen as more of an assistive technology. As it exists now, sentiment analysis needs human guidance in order to know what to look for.

Chatbots: An application that uses natural language processing, sentiment analysis and other AI tricks to talk to you online. Chatbots became famous in mid-2016 when Facebook backed the technology for use on its Messenger platform. Since then, thousands have come online. A customer could query a retail chatbot for information or a service (“Show me black loafers, size 10.”). The chatbot would either respond by performing the request, or asking follow-up questions (“Are you interested in penny loafers or drivers?”).

2016 was clearly the year of the chatbot, but experience has reduced the ardor of many –including Facebook – for the tech this year. Critics complain that few chatbots lived up to their hype. They may have a role online, but a far more limited one than their advocates foresaw. Once again, the most successful application of this technology entails a human-AI partnership – with people supervising the chatbot, ensuring the customers get what they want.

Machine learning (ML): A system that can self-improve with exposure to useful data. ML can, on the basis of existing data, determine patterns, build models and even make predictions. An ML-equipped piece of software or robot could theoretically be programmed to do a job by itself, and get better and more efficient as time goes on. ML-powered robots are just a few years from being able to fold your laundry without help, and a few decades from performing surgery by themselves.

While it’s tempting to believe AI will put humans out of work, history tells us another story. Machines such as automobiles, sewing machines and the printing press did not put people out of business, despite peoples’ worries at the time. Instead, they actually created new jobs and industries, freeing humans up from drudge work and allowing them to blossom in higher-level, more creative jobs. Without the human touch, those machines could not have done their jobs as effectively.

The same result is likely in the machine learning era. A study has shown that a big data analytics program with an end-to-end active learning system (i.e. machine learning) to detect network security risks performed 3.41 times better when supervised by a human than when it ran by itself, with false positives reduced fivefold. The “analyst-in-the-loop security system,” with analyst intuition backing ML, is much more effective, the study shows – demonstrating that even in the big data present and future, it’s likely to be a human-machine partnership that elevates the quality of services and products.

Meanwhile, even with all the technology, the number of jobs available has grown exponentially. Between 1960 and 2005, for example,total employment in OECD countries rose by 452 million jobs, representing a 76% increase in the proportion of the population employed. Many of those jobs involve a human-tech partnership.

Technology gives humans much greater ability to work efficiently by allowing them to concentrate on the creative aspects of work that help businesses thrive. Of course, nobody knows what the future will bring, but experience, as well as experts who have good records of predicting trends, tell us: The best applications and jobs are yet to come.

Like this article? Subscribe to our weekly newsletter to never miss out!