With each passing day, it’s going to be extremely hard to differentiate yourself only on the basis of products. Coming up with new products isn’t just time-consuming, which may delay your market entry. It may fail on the feasibility test, as product creation generally puts severe stress on your economic backbone. This has opened up the possibility where customer experience alone will emerge as the prime factor of differentiation. It’s then wise to predict that the future paradigm shift in business will be more towards customer analysis, data science, customer service and easing up the overall buying process.
That’s precisely where Artificial Intelligence, with its unique ability to automate, optimize and find value where the human eye can’t, will hold the business transformative value. As artificial intelligence is expected to drive nearly $2 trillion worth of business value worldwide in 2019, it’s worth to grab a slice of the AI bounty at the beginning of the year. After closely analyzing the present and carefully peeping in future, we have come up with the 5 artificial intelligence trends which will shape 2019 in never-seen-ways.
Modern applications are performing indexing, searching, and analytics through captured log data. Massive data sets like these, which are obtained through various sources such as hardware and operating systems, can further be aggregated and correlated to find hidden patterns and actionable insights. One way to achieve it is to apply machine learning models to data sets.
It can be fitted in the overall schema of the existing infrastructure by introducing the power of Artificial Intelligence to operations. In order to make operations predictive as opposed to reactive, the future will see the application of ML and AI in IT operations. It will enable ops teams to perform a precise and accurate root cause analysis. Consequently, DevOps will come up with better and valuable intelligence deliveries. There is no doubt that this artificial intelligence trend of AIops will see more adopters in 2019.
Cyber-crime, which was a nuisance a few years ago, has now become a glaring menace. It is predicted that global cyber-crime will cost $6 trillion annually by 2021. The damage may see an exponential rise, as cybercriminals are targeting cloud infrastructure, software-as-a-service (SaaS) platforms and IoT devices. 2019 may see the application of artificial intelligence in weeding out this perpetual threat of cybersecurity.
Companies may use artificial intelligence and machine learning as proactive tools to identify online enemies and cyber threats. It’s very much within the realm of AI, as algorithms can be developed to detect suspicious online activities before they become uncontrollable. Moreover, artificial intelligence will set up standards of regular activity and will identify actions that are different from the established criteria. Through supervised monitoring, it will tell companies whether it’s safe to run a particular code or not.
AutoML is going to change the contours of ML-based solutions. We will see the emergence of machine learning models that can handle complicated issues without adhering to the traditional process of training ML models. It will immensely benefit business analysts and developers, as they can single-mindedly work on solving the issues that are mission-critical rather than meandering in the complexities of processes.
This artificial intelligence trend of AutoML will occupy the rightful place between cognitive APIs and custom ML platforms. That’s because unlike custom ML platforms, it can exhibit the highest order of customization without frequently tampering with the dense process work-flow. And unlike cognitive APIs, AutoML can bring in a better level of flexibility and portability.
Choosing the right framework among multitudes of options is the toughest challenge in building neural network models. It becomes extremely difficult to port a model once it’s been trained and evaluated. And this interoperability is among one of the biggest obstacles in artificial intelligence adoption.
To counter this prevalent issue, giants like AWS, Facebook and Microsoft have collaborated to build Open Neural Network Exchange (ONNX). It enables the reuse of trained neural network models across various frameworks. This ability of ONNX to make frameworks portable is going to make it an absolutely must-to-have technology. Starting from analysts to edge computing experts, everyone will leverage ONNX as the standard runtime for inferencing.
Consumers are increasingly moving away from handing off their data to service providers or third party vendors. Companies will see it as a “service possibility” and come up with offerings that do not require transferring data to the cloud. Though cloud was once the go-to man for performing machine learning computations like facial and speech recognition, the privacy prone mindset of people will ensure that ML and AI will happen on mobile or other smaller edge devices. It will cause a steady decline in sending personal data to centralized servers. With fewer companies like Apple already experimenting with intelligent processing on mobile instead of cloud, this artificial intelligence trend is currently in a nascent stage. In 2019, we will see that more and more IoT and machine learning will move towards edge.
1) Artificial intelligence will find its use in operations teams. Resultantly, DevOps will deliver and act on business insights more closely
2) One of the most significant artificial intelligence trends will be the use of AI in ensuring cybersecurity
3) Neural network model development will become easier due to ONNX, a platform that facilitates interoperability
4) The concerns about privacy will force artificial intelligence development to shift towards mobile and other smaller edge devices
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