2018 was the year in which we saw specific applied artificial intelligence and machine learning solutions flourish, attain maturity and reach new heights of functionality
What lies ahead for the industry in 2019? Machine Learning is likely to further consolidate its position in many sectors and reach into areas that haven’t yet been significantly influenced, and developers are likely to make further efforts to counter the inherent challenges in the technology. Here are my five key predictions for 2019.
“Explainable” tech on the rise
As members of the public become more aware of the capabilities and limitations of AI, they will demand more transparency and accountability in AI decision making, which will drive funding and research into such “explainable” tech. As a result, we will see more responsible use of AI, rather than just using AI for AI’s sake. The European Union’s General Data Protection Regulation will also promote a cautionary approach to machine learning rollouts within the EU, particularly where use cases involve direct or indirect processing of personal data.
Underpinning this is a massive technological research drive to try and unpick the inherent “black box” nature of deep neural networks, either by re-architecting or developing complementary explicatory systems. Data Scientists are for example working on solutions to “slice” up complex ML decisions into more manageable (and defined) steps, each of which will hopefully make the task of auditing decisions easier.
Anthropomorphisation – making robots more “human”
As the adoption of AI enabled devices becomes more widespread, there will be an increased trend to make these more palatable and speed up adoption through the use of simulated human characteristics (ie. anthropomorphisation).
We are inherently creatures of emotion and feeling so expect AI and robotics developers to exploit this emotional side in the forthcoming year – after all, if you’re emotionally connected to something, you’re less likely to get rid of it in favour of something else! Anki Robotics Vector is a very clever manifestation of this – rolling an Amazon Echo style voice assistant into a cute pet-like robot.
Increased personalisation and the struggle for data
AI will increasingly be used to serve up more and more predictive and dynamic content which will be highly personalised to you as an individual. This will assist online providers who strive to predict what you would like to purchase next, or suggest where you should take your next holiday. The Data gold rush drives personalisation and content, so expect battles to break out between the major providers who will become increasingly aggressive in their attempts to control and defend data sources. Watch out for flashpoints between the major consumer content platform providers such as Apple and Google, with large social media platforms.
Commodity AI service providers
As with traditional IT services, we are likely to see companies stepping away from single enterprise funded development (due to its inherently high cost) and towards standardised commodity AI cloud offerings from the likes of AWS and Microsoft Azure – defining an increased level of maturity in the delivery model. This will of course mean the “siloing” of more complex AI offerings with the big providers. Expect to see more and more marketing efforts to drive these commodity offerings which will be structured in a diverse PAYG spectrum all the way from renting access to data sets for defined applications (such as facial recognition) at one end to “out of the box” AI as a service solutions at the other.
Brick and mortar retailers
Brick and mortar retailers will begin to adopt the technology in a big way and will automate as a way to conserve costs and increase profit in an increasingly challenged environment, as well as using it to take on online operators. Expect to see more robots in the aisles – businesses such as Bossa Nova Robotics are providing a compelling business case to enable enhanced data analytics (by for example mapping products and SKUs to store footprints to enhance sales) and stock management (by pinpointing in real time where items are under or oversold).
About the Author
John C. Buyers is Partner – Head of Machine Learning & AI at Osborne Clarke LLP. John leads Osborne Clarke’s international AI and machine learning client team, and has just written a book on the legal implications of these new and innovative technologies. He is currently advising a number of private sector and public sector clients (including a large UK healthcare trust) on the use and implementation of machine learning systems, and recently advised a global software services company on the liability issues of deploying AI within Europe. He recently completed a large outsourcing on behalf of a big four accountancy practice to implement an AI powered solution for an international bank to automate its client due diligence processes
