2018: AI gets smarter and more practical

We all know AI is coming to the forefront of technology in the form of machine learning (ML) and deep neural networks, where AI can understand the surroundings and apply that information to the current context, whether it is about data pattern analysis or driving a vehicle. Coupled with increasing capabilities of AI hardware, in 2018 we can expect:

  • AI research will reach new heights in the fields of neural networks and generative adversarial networks.
  • Cooperative learning among networks will lead to hybrid intelligence.
  • Continuous evolution of AI will empower enterprise-scale business processes with intelligence and agility.
  • AI will redefine the IT landscape with smart machines and borg systems.

In the last couple of years, we have seen advanced ML models being invented in fancy research labs like Google Brain or DeepMind, which have been exciting the tech community. Some of these models moved AI from reactive and limited memory space into theory of mind space, exhibiting the characteristic of understanding the environment, unique behaviour and learned experiences. These sophisticated AI models have been enabled by massive data sets available for model training, CPU and GPU power, and large memory pools, among other factors.

AI research will reach new heights

Continuing this, in 2018 we will see the Convolutional Neural Networks (CNNs), a class of feedforward artificial neural networks, evolving to Very Deep CNNs and pushing the limits of image recognition, video analysis and natural language processing (NLP). Generative adversarial networks (GANs) for cooperative, unsupervised learning will be on the rise as we reach a saturation point in supervised learning. Long Short-Term Memory (LSTM), a class of Recurrent Neural Networks (RNNs) capable of learning from long-term dependencies in sequential data, will help build better enterprise use cases in areas like automated content generation, image captioning and question answering. This list continues.

Cooperative learning among networks will lead to hybrid intelligence

Neutral networks will evolve from monolithic to distributed co-operative/competitive models. Coupled with evolving neural fuzzy and generic fuzzy algorithms — reinforced learning models that are more adaptive to the environment and context (i.e., to noisy and time varying systems) — these neural networks will evolve into hybrid intelligent systems that integrate different learning and adaptation techniques to overcome individual limitations. This will lead to an explosion of opportunities in 2018:

  • Very Deep CNNs will push computer vision and NLP closer to achieving emotional intelligence with end-to-end conversation capabilities.
  • CNNs will open new opportunities in fields like system-driven drug synthesis models, leading to cost-effective drug discovery.
  • Improvements in NLP will lead the way to automated content generation. Gartner predicted back in 2015 that by 2018, 20 percent of all business content would be written by AI systems.
  • Community projects like Oxford’s Visual Geometry Group (an RNN that achieved top results on object recognition) and ImageNet (an image database organized by the WordNet hierarchy) will lead the way to many such initiatives and will keep encouraging researchers and enthusiasts to research and experiment in AI algorithms and models.



Continuous evolution of AI will add intelligence and agility to enterprise business processes

These advancements in AI models will help design patterns evolve for enterprise-class business use cases. These models will focus on applied AI. Some patterns will become more common, such as:

  • traditional UI being replaced by voice as more applications get integrated with voice bots
  • data analytics reaching new dimensions of context and event sensitivity, alerting businesses to decision opportunities that are completely unanticipated

AI will redefine the IT landscape

AI is also going to redefine the IT landscape, which will one day be dominated by smart machines that are intelligent enough to spin and run themselves, understand their operational parameters, and constantly monitor, automatically tune, scale up and down and send distress signals. Going a step further, machines will run as swarms, spinning up borg machines — systems that take instructions from a central entity and work on them without any prior intelligence — to ensure that a collection can always respond to business demands without human intervention.

AI will prevail

Most of the AI that we will see in 2018 will redefine what is sci-fi and what is real or possible. But nothing is going to put AI close to self-awareness. AI will prevail, but the debate will continue as to whether current AI models can be classified as real thinking.

Sankar Rao Vema is a technology enthusiast and researcher in the fields of artificial intelligence and deep learning. Based in Hyderabad, India, he is passionate about next-generation digital systems in mixed reality space integrated with IoT devices and drones. Sankar believes that smarter and intelligent systems will make our lives simpler. @sansvema



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  1. Clay DiGiorgio says:

    Great blog! There’s a lot of stuff in here that I’ve never heard of before. It’s very well written and sparks my excitement for the future! Later in this post, you wrote that these machines will be able to scale themselves up. What would prevent them from scaling themselves up as far as they can? Possibly even to the point where they overtake the company that runs them?

    Also, you wrote something that I found particularly interesting:
    “Very Deep CNNs will push computer vision and NLP closer to achieving emotional intelligence with end-to-end conversation capabilities.”
    I was hoping you cold tell me more about emotional intelligence as it relates to AI – how AI can obtain it, and how Very Deep CNNs will help accomplish this.

    Thank you!

    • Lisa Braun says:

      Clay, thanks for your comments. I work at DXC Technology and edited this blog post. You ask some very good questions and we plan to follow up with some additional blog posts. This post was fairly high level as a commentary on trends, so we’ll do another post or two that dives deeper.


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