Last week, we discussed the top five AI trends to watch for in 2018. But what are some of the AI predictions we’ve been anticipating, that well, won’t be happening this year?
We sat down for a chat with Conversica Chief Data Scientist, Dr. Sid Reddy, to get his take on AI “un-predictions” for 2018. Whether you plan to welcome our future robot overlords or resist anything AI-related for as long as possible, here’s what won’t be happening on any major scale this year:
- No self-driving cars…yet – While many are predicting a driverless future, we’re a long “road” away from autonomous vehicles that will take us to and fro while we read the news or otherwise spend our time behind the wheel. For 2018, human operators will continue to rule the roads because the discrete human judgments that are essential for driving will still require a person with all his or her faculties. Besides the technical aspects of autonomous vehicles that need improvement such as image recognition, inference engine, action translations, etc., humans tend to be more forgiving about mistakes made by human intelligence as opposed to those made by artificial intelligence.
- Getting replaced by bots – While it is entirely possible that AI agents have the ability to replace certain administrative tasks, the reality is that worker displacement by AI is over-hyped and unlikely. Even in an environment in which AutoML (Automated Machine Learning) helps machines to build machines through deep learning, don’t expect them to replace any complex aspect of your job any time soon. Thus, while AI will help automate various tasks that we most likely won’t want to do anyway, we’ll still need human knowledge workers for thinking, judgment and creativity. But, routine tasks beware: AI is coming for you!
- Receiving AI-powered medical diagnoses – Due to a lack of training data and continued challenges around learning diagnosis and prognosis decision-making through identifying patterns, AI algorithms will only be used on a limited basis to support diagnosis and treatment recommendations by humans. AI will be increasingly deployed against sporadic research needs in the medical arena, but as with fraud detection, pattern recognition by machines only goes so far. Human insight, ingenuity and judgment will continue to supersede the capabilities of today’s AI technologies. People are still better than machines at learning patterns and developing intuition about new approaches.
For companies looking to adopt AI
Despite what you might be hearing from AI solution vendors, businesses that want to adopt AI must first conduct a careful needs assessment. As part of this process, companies also must gain a realistic view of what benefits are being sought and how AI can be strategically deployed for maximum benefit. IT management, business users and developers should avoid being overly ambitious and carefully assess the infrastructure and data required to drive value from AI. Best practices and “buy versus build” analysis also should be part of the conversations about implementing AI applications.
Want to learn more about different types of AI technology? Check out this blog post for your basic guide to understanding AI, machine learning, and natural language processing.
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