Photo Credit: Possessed Photography
It’s becoming easier than ever to predict the future.
Artificial Intelligence (AI) refers to a computer’s ability to simulate human intelligence to accomplish tasks that could previously only have been taken on by human beings. Over the last couple of decades, AI has become a critical part of establishing insight-driven business practices and a key driver in new product and service innovation. As the world becomes more dependent on technology to better understand and cater to the people that inhabit it, machine learning and predictive analytics are helping businesses personalize solutions for their customers at a speed that was previously just not possible.
According to Forbes, 95% of business leaders who indicated they were comfortable using big data to solve business problems also use AI technology to help guide the development of those solutions. When properly leveraged, AI can provide a variety of solutions that can scale and improve over time, creating substantial benefits for businesses, customers and employees.
Vladisav Jelisavcic, a member of Horn and Mane’s Global Thought Leadership Board and founder of Zmaitech, a Belgrade-based firm that specializes in Artificial Intelligence, explains how AI works. “Until recently, some jobs were reserved solely for human beings. Because making rational decisions requires identifying patterns and rules. At least on a subconscious level,” he says. His examples? “Stop driving when a pedestrian is crossing the street; recommend a book based on someone’s taste; design a product or service to address a specific gap.”
Jelisavcic adds, “Some of those rules come naturally to us – like keeping our balance while walking or understanding speech or reading; others require years of experience like driving, using complex machinery or analyzing financial markets.”
Today, AI makes it possible to find patterns and understand rules like a human can. This is the “learning” component of artificial intelligence: given enough data, an algorithm is now capable of achieving, and sometimes even surpassing, human expertise. Even more interesting, is that we can teach an AI vessel the same way we teach children – through examples. And repetition in behaviour can eventually drive predictive analytics that can accurately forecast future behaviours.
Recently, it’s also been noted that artificial intelligence is capable of exhibiting a high level of creativity. Exposure to vast amounts of data has enabled it to generate models that are now capable of identifying and understanding abstract notions (e.g., objects in images, tones in text, etc.), and then enabling it to develop meaningful data sets in return (i.e., new images, responsive phrases, etc.) that can come across as very human.
AI is already quite prevalent in our day to day lives. You’ve likely come across it when shopping on-line, interacting with chatbots, using mobile maps and messaging friends on social media. And you’ll continue to see it fuel all kinds of innovation in the not-so-distant future. Here’s a brief look at some current applications across various digital channels.
Facebook uses AI technology to help it detect faces within pictures. When you upload your photos, the platform can recognize faces in your images and make suggestions for people you might want to tag or acknowledge.
In addition, machine learning has made it possible for our mobile devices to understand the sentiment behind emojis. Instagram can now make recommendations based on what you’re writing. Emoji-to-text-translation is now a basic expectation.
Google uses location-based data collected via mobile phones to inspect shifting traffic patterns at various points throughout the day. It can also incorporate user-reported traffic facts, like stalled cars, construction and accidents, to factor in estimated travel times. This ongoing data collection is what makes it possible for users to identify the fastest possible route for travel, which in turn can help reduce commuting times.
AI technology has helped make banking more convenient than ever before. Customers can now deposit cheques via smartphone apps without leaving the comfort of their home. The transaction takes less than 5 minutes from start to finish.
There are many media outlets out there, like Bloomberg and Forbes, that leverage bots to help journalists identify new stories and sources and help them determine what they might want to write about next, based on audience engagement with previous materials, headlines and images.
AI can be used to complete repetitive tasks and clerical work like invoicing and reporting, which can save businesses a lot of time and money. Automation, in a lot of cases, can also help with improving accuracy of work generated.
Chatbots have become a popular go-to for customer service teams all around the world. They can respond to pre-programmed keywords and basic queries quickly, addressing basic needs faster than traditional call centres. The best out there are applying advanced AI models that can make it feel as though you’re actually interacting with a human.
Companies likes Spotify, Amazon and Netflix have continued to perfect the art of personalization using AI. Machines can learn quickly that users who watch Movie A are likely to watch Movie B. Netflix uses the viewing history of other app users with the same preferences to suggest new titles you would be most likely to watch. Because of this personalization, you’re more likely to stay engaged, come back for more and continue to use your monthly subscription.
Talent Acquisition & Retention
AI applications can also use existing data, with pre-set parameters, to help identify employees within a company that are likely to become high performers. Not only can this aid in eliminating bias within talent pools, it can also help businesses sustain robust talent pipelines and clear succession strategies.
We asked Vladisav Jelisavcic to give us his top three predictions for AI in the coming year – here’s what he had to say.
Multimodal learning is the way of the future.
Currently, a lot of active research effort is being done on the so-called multimodal learning. Being able to effectively cope with various data sources (images, sound, video, text, sensor signals, etc.) can become the next enabler for many start-ups in the AI space.
AI for scientific discovery will reach new heights.
With the global pandemic we have seen an increase in AI-backed health-oriented businesses, and the recent breakthrough in Protein Folding from DeepMind can certainly have a great impact on the future of medicine.
We’ll be able to fight bias within data sources.
Finding more effective ways to identify and fight bias (social, gender, political, etc.) that is almost always present in large unmoderated data sources (such as social networks or the world wide web) can enable broader adoption of AI in sensitive policy making domains. This was already a hot topic (and albeit controversial one) in the previous year with no indication of not being so in the near future as well.
Jelisavcic stresses that it’s important to understand that AI is not a one-size fits all and is most effectively leveraged when trying to generate a very specific solution to a clearly outlined business problem. “You don’t need to be an expert on AI to benefit from it,” he says. “ All you need to really understand is what problem you’re trying to solve. The rest can be left to the data experts to figure out.”
If you’d like to explore how artificial intelligence can help you leverage data to predict future customer behaviours and develop insight-driven solutions to complex problems, get in touch today to set up a consultation.