How Using A.I. Today Is Different Than Using A.I. Tomorrow—A.I. 2018 Predictions


Will Steven Spielberg's movie, A.I. Artificial Intelligence, ever happen in real life? Will Mecha, the advanced cybertronic humanoid, replace humans? Will Robots and A.I. take our jobs? PwC said in its report--2018 AI predictions--those scenarios might happen, but not in 2018. 

Michael Baccala, PwC’s assurance innovation leader, and Michael Shehab, the firm’s tax technology process leader, talked about how A.I. is being used today and where the technology is going in an exclusive interview with Bloomberg Tax. 

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More Practical, More Realistic

When it comes to A.I., companies tend to have high expectations on what it can do and the return it can generate. But this year, A.I. will come more down to earth. Baccala said that A.I. will become more practical, begin to address some more specific areas, and there will be an actual return on investment.

“Companies know that the A.I. technology is there, but have had a hard time figuring out how they could benefit from it.” Baccala said, “They sometimes struggle to come up with the questions they are trying to answer. But this year, they [companies] are getting more specific about what they are trying to accomplish and are starting to look at different areas inside their organization where A.I. can be used.”

Compared with two or three years ago, when companies were investing in tons of unknowns and were having return on investment (ROI) expectations that were unrealistic, “they are now really thinking through the questions they are trying to answer, and the ROI they are trying to achieve.” Shehab said, companies are “more realistic and practical about the capabilities of the technology today.”

A.I. in Coping With the New Tax Law 

While companies are scratching their heads and assessing the impact of the new tax law, Shehab said that some are already leveraging A.I. technology to run analysis and to come up with “best practices.”  

“Tax reform has created significant changes in the business environment and business climate, and has added a lot of complexity in the international and global front,” Shehab said, “We are building, and utilizing A.I. technology to help global and multinational clients evaluate the tax reform impact on them.”

The firm is using A.I. and cloud computing to run different scenarios that help clients figure out where their next plant, their next research lab, and their next factory or production facility is going to be. By using A.I., “those multinational organizations have reimagined their supply chain.” Shehab said.

Learning from Machine Learning

Machine learning is a type of technology that gives computers the ability to learn without being explicitly programmed. However, for machine learning to produce accurate information, a computer must be fed data.

Baccala said that when utilizing machine learning, it is easy to introduce bias into the algorithm based on the data provided. “We have a client who has 10,000 real estate leases. We were feeding the machine with real estate leases. But we couldn’t just take that model and use it for another client, who has mostly equipment leases. We had to go backward and adjust the model.”

A.I. Will Be for ‘Dummies’ 

By “dummies,” we mean that A.I. will be for those who don’t have a computer science or traditional tech background. For those “non-techies,” A.I. will be more accessible. A.I. will empower current employees to add more value to existing enterprises.

Baccala said that A.I. specialists probably aren’t functional experts. Once the A.I. is up and running, it is up to the economists, analysts, and traders to continuously customize, tweak, and make adjustments and improvements to it.  

A.I. is becoming more user friendly. “More and more of the capabilities are becoming more accessible to non-techie individuals.” Baccala said, “It needs domain experts to ‘train’ the A.I. model and machine learning. Over time, you will have this concept of citizen data scientists.” 

“We are incredibly bullish and excited about A.I. and the possibilities around it.” Shehab concluded.

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