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Artificial Intelligence (AI) is the number one strategic technology according to research firm Gartner. Here are our predictions for AI in the coming year.
Artificial intelligence (AI) has officially gone mainstream. Industry research firm Gartner named AI as its number one strategic technology for a second year in a row. The acquisitions race among giants like Google, IBM, Salesforce and Apple to purchase private AI companies keeps heating up — 2016 alone saw 40 AI-related acquisitions and our own research found that 62% of large enterprises will be using AI-technologies by 2018.
Since everyone seems to be talking about AI broadly, we focused our predictions this year on what we see happening with communications and AI.
As a leader in this area, we are working with enterprises to close the communication gap between man and machine. For 2017, our predictions are related to how we’ll communicate with computers and other devices, how AI systems will communicate with each other, and how we’ll communicate with each other about AI.
The recent, combined efforts of a number of innovative tech giants point to a coming year when interacting with technology through conversation becomes the norm. Are conversational interfaces really a big deal? They’re game-changing. Since the advent of computers, we have been forced to speak the language of computers in order to communicate with them and now we’re teaching them to communicate in our language.
Search engines like Google and Bing have already made big moves enabling search queries via spoken word while Facebook launched an AI-effort, DeepText, to understand individual users’ conversational patterns and interests. Meanwhile, the move toward natural language interfaces has already picked up steam with the explosion of companies focused on enabling chatbots, digital assistants and even messaging apps eclipsing social networks in monthly activity. Beyond 2017, think of a future when we can casually ask our personal devices for information regardless of subject – “How much money do I have in checking?”, “When was my last physical?” or “What restaurant within a 10-minute driving distance has an open table for 2 people?”
If people don’t trust AI, they won’t use it. In the next year, designers will begin to apply knowledge of human interaction, specifically in the area of how we earn trust and respect, to AI systems. Elements of communication like tone, sentiment, timing, visual cues and word choice combined with AI technologies like natural language generation that increase transparency into how these systems operate will play a role in helping users trust and rely on AI systems.
Stanford’s recent study on AI’s impact over the next 100 years states it well, “Design strategies that enhance the ability of humans to understand AI systems and decisions (such as explicitly explaining those decisions), and to participate in their use, may help build trust and prevent drastic failures, it’s critical that engineers and designers create systems that communicate freely about how they work.” In other words, if my AI-powered home monitoring system unlocks my home for an unscheduled visitor in the middle of the day, it better be able to explain why.
In the next year, efforts will begin to create universal standards for AI to AI interactions. Without standards, AI technologies will increasingly become siloed or worse, interfere negatively with each other when multiple AI systems are involved in determining a single outcome. Imagine driverless cars on a collision course without the means to communicate with each other or an enterprise with multiple siloed AI systems that has a predictive analytics system moderating decisions about production levels but another AI system with a different data source that has indicated production needs to change. 2017 will be the beginning of talks among the tech giants, relevant industry associations and governmental bodies to establish universal AI standards.
In 2016, examples that reflected the multiple sources of bias that can occur within AI systems. Some of these sources include the data used to train systems, users’ interactions with the systems, similarity bias and the bias of conflicting goals. Most of this bias currently goes unnoticed but as AI usage grows and increasingly impacts people’s lives, recommendations need to be established for acknowledging and addressing systems’ biases or AI will take a major hit impeding future progress.
Companies will begin looking for demonstrable value and ROI proof points from AI technologies. While funding for AI-related startups keeps increasing – in the last 5 years alone, investments in AI have grown tenfold from $94M in 2011 to $1049M in 2016 – we’ve seen few real commercial applications surface. Most often these technologies are piloted by Innovation teams or an R&D department. 2017 will be the tipping point when companies start questioning their investments and AI will have to grow up.
It’s pretty amazing to think that just two years ago we were talking about AI and robots coming to kill us. Tech luminaries were proclaiming AI would bring upon the apocalypse and now, some of these same people are founding organizations to push AI to its limits. So much has been accomplished in a short span of time, and we’re now starting to realize the benefits of partnering with AI versus fearing it. I’m already looking forward to next year when I can review my predictions to see how we fared – or more likely, I’ll be asking my intelligent system to tell me about my hits and misses. Hopefully, I’ll do well.