The Next Phase of AI

Posted by Accenture on Oct 9, 2019 1:54:40 PM
Accenture

 

There is a lot of discussion about what AI can do right now, but for Marc Carrel-Billiard, at Accenture, it’s important to also look into the future of AI and consider what the next phase of this world-changing technology might be. “There are a lot of clients that talk about AI, but very few that talk about AI at scale; there’s a big difference between doing AI pilots and then doing AI at scale Carrel-Billiard explains.

The R&D Labs at Accenture focus on applied research with a timeframe of 3-5 years, with a focus on working on research applicable to current and upcoming business problems. Carrel-Billiard isn’t short in examples: “One in particular is in the automotive industry. There is one thing I believe will be really big in the future and is already growing hugely and is something we’ve increased  capacity in at the lab and that’s Industry 4.0. How are things are going to be manufactured in the future and how it will be experienced by end-users? One aspect that we are very interested in  is quality control. Think for examples at detecting defects on leather seats which are manufactured in the automotive industry? Nowadays there’s a quality controller who will check those, but this is difficult to spot - I mean the seats are soft they’re not made of a hard material - so sometimes it’s not even a defect it might be the leather itself. For this example we use high resolutions cameras and machine learning to help operators in their quality control tasks .We've been able to improve drastically the level of quality control that's come out of this process. Another example could be in life sciences, for example in tumour detection, using it also for classification of amoebas, viruses et cetera with petri boxes, and we can then couple this with a robot who can decide where exactly these should be stored and classified.”

 

Thinking about the future of AI isn’t just about the industries impacted, but about rethinking who the innovation is for. “I used to say we used to have 3 clients: our everyday external clients, our Internal client - ‘Accenture’ – and also the world,” Carrel-Billiard says. “There are a lot of things the lab is developing in AI that we apply to Accenture such as our huge delivery centre where we deliver things to our clients. Believe it or not, we don’t recruit enough software developers, so the lab has developed software bots that we use 24 hours a day for example for testing services to check software. Another area where we are using AI is to develop a personalised training plan for our employees, which is really useful because when you need to run a company which is close to 500,000 people then everyone needs to be looked at in terms of how we can drive their training and learning.”

 

The idea of ‘the world’ being an Accenture client is a compelling one. When companies can put into perspective their impact on broader society, surely outcomes are better for all. Carrel-Billiard goes on: “There are a lot of programs that the lab is doing to change the world which we are doing through our ‘tech for good program’ such as how do we address world hunger, how do we address poverty, how do we give disabled people the capability to engage, to get work, to do all these things. I really believe in applying digital transformations for under-developed communities because this is where the future of the world is, and the future of the business. We have a lab in Bangalore which is where we initiated our tech for good program, which then became viral which is now in every lab and all around the world.”

 

Carrel-Billiard, in his thinking about the future of the technology, also considers when AI is a ‘must have’ versus a ‘nice to have’: “Sometimes there are mundane tasks where if you eventually forgot how to complete them if they were taken over by AI then you wouldn’t be missing out on much, you can relearn it fast, it’s not a big problem and can give you more time to be more creative. However those tasks which bring us creativity, help us learn, those are the tasks that we should really not automate because that’s where we can draw a line between machine and humans. Here’s an example: I recently got a new car, and it has an automatic parking assistant. I could use this capability all the time, every day. My question is: should I? Because at some point I may have to drive a car which is not fully automated, and I will have completely forgotten how to park a car manually. I think it’s important to reflect on what is important to automate and what is AI going to bring as a progress (such as in healthcare) and what are the other ways where we need to keep these for ourselves.”

 

Overall though, Carrel-Billiard has an optimistic view on the AI-powered future ahead of us: “I’m a positive person, otherwise I would probably not be leading our R&D labs: if you're pessimistic you probably shouldn’t work in technology! So I really have a positive view of where technology is going.” 

 

It’s not just the technology that Carrel-Billiard is optimistic and excited about, though: “I really believe, and I trust in, human beings…that we’ll be able to leverage AI in a good way.”

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Topics: AI, Artificial Intelligence