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What's in store for AI

Artificial Intelligence: Today and tomorrow

Kim Palko and Matthew Farrellee
artificial intelligence
© Shutterstock / JoeZ

AI is coming. Adoption of artificial intelligence grew globally in 2017 in a variety of industries. But where is it heading? In this article, Kim Palko and Matthew Farrellee explain where they think this emerging tech is going in 2018.

2017 saw steady growth in AI adoption globally. Industries like financial services, telecoms and high tech are leading the way using AI to better serve customers, innovate faster and operate more efficiently, while others including automotive, healthcare, energy and retail are also embracing it.  Here we take a look at key milestones for AI in 2017 and the road that lies ahead.

Working with data

In recent years, many organizations have sat on treasure troves of data gathered thanks to maturing storage, connectivity and compute technologies, yet waiting to be fully exploited, because handling data is highly complex. However, over the last year the world has made good progress in understanding the people skills needed to work with data and how to bring these into organizations.

We are steadily learning how data scientists and AI developers work, the tools they need, and how to integrate them to existing app dev teams. If we are to address the shortage of data workers in 2018, we need a concerted effort to engage and educate the next generation. Vendors should also do their bit to make AI more accessible to developers by building out machine learning (ML) frameworks. Together, we can continue the momentum in 2018 and bring AI a step closer to fulfilling its potential ‘superpower’ status for developers as an innovation enabler.

SEE MORE: Python’s growth comes from the enormous expansion of data science and machine learning

Machine learning frameworks

While AI and ML libraries and projects have existed for decades, the past 5-7 years have seen a lot more investment in building deep learning libraries and artificial neural networks, and in the last twelve months the consolidation of machine learning frameworks has begun in earnest.

Some of the big players have started to open up their frameworks in recent months: we’ve seen Onyx from Facebook and Microsoft, and Gluon from AWS and Microsoft. This is a positive move for the industry: when we build together we can innovate faster, we can provide greater freedom of choice for developers, and improve interoperability for the AI ecosystem.

 SEE MORE: Top 5 machine learning frameworks for Java and Python

The intelligent application

Many, if not most, mobile applications already make use of AI today. For example, your voice assistant searches the web and compiles results for you; your keyboard will improve itself based on how you and others use it; ride-sharing apps calculate the best route for multiple users. With almost 8.5 billion mobile connections globally and counting (see GSMA data), intelligent applications are likely to emerge in even greater numbers to delight us in 2018.

Chatbots

Chatbots have become an increasingly popular use of AI in the last year. Businesses across industries are looking to give users the same experience online as they would get in-store, as well as to reduce their contact center costs by enabling people to self-service. Chatbots can respond rapidly and in a personalized way to customers, categorizing them by crunching data and deploying algorithms. Twitter is one such business advancing its chatbot strategy, recently announcing a new enterprise API to power chatbots and help enable “more natural conversational experiences”. Look out for more of this in 2018.

SEE MORE: Atlassian’s Stride gives access of its API to all developers

Like human, like algorithm

In 2017, the issue of bias in AI gained headlines. Algorithms are only as good as the training data they are fed, and the cases of humans passing on their own biases and prejudices to algorithms are mounting, from crime prediction to risk assessment to language translation. This phenomenon is due a lot more attention, and we hope to see real action to mitigate this in 2018.

The industry needs to put resources into tools and training to help data scientists, developers and businesses at large gain a better understanding of data and the human impact of AI. We need to make a real effort to foresee and respect the implications of how we create AI, and not try to run before we can walk, or else we risk losing control over the outcomes of this powerful technology.

Watch this space…

In 2018, expect to see problems solved, new services hit the market and processes revamped thanks to the application of artificial intelligence and machine learning. AI can help more organizations drive business value and competitive advantage from their data. Exciting times ahead!

Author

Kim Palko and Matthew Farrellee

As Product Manager at Red Hat, Kim Palko has responsibility for the Red Hat JBoss Big Data initiative for JBoss Middleware and is also the Product Manager for the JBoss Data Virtualization product.

 

Matthew Farrellee is a Senior Principal Software Engineer for Emerging Technology & Strategy at Red Hat. Follow him on Twitter @spinningmatt


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