5 Artificial Intelligence Developments To Watch Out For In 2019

5 Artificial Intelligence Developments To Watch Out For In 2019

The year 2018 was one significant period that will, for a long time, be etched on the hearts and minds of many techies and ordinary people as well. Of note is that 2018 became a year which ushered the era of driverless cars. (Image source: Unsplash)  

But it did not just end there, or did it? Come in 2019 with a lot more promising technological improvements that have never been seen before! These are the top 5 artificial intelligence developments to watch out for in 2019.  

1. AI meets IoT at the edge computing layer.

The year 2019 is going to witness a major convergence of Internet of Things (IoT) and Artificial Intelligence (AI) at the edge. As a result, a sizeable portion of the models formed at the public cloud will be launched at the edge.  

The IoT in industrial plants is also to get a major shot in the arm with AI capable of performing tasks like routine maintenance of equipment, outlier detection and root-cause analysis of machine failures.  

Similarly, advanced machine learning is set to take over speech synthesis, microphones, cameras, structured & unstructured data, among other sensors like video frames.  

This, they will be able to perform by invoking the use of deep edge-optimized neural networks.   May 2018, Microsoft declared its goal to avail intelligent cloud / Intelligent Edge. This tech improvement will see to it that Azure IoT Edge enables low-power devices to run containers and perform artificial intelligence locally, albeit retaining connections to the cloud for efficient management and modeling.  

According to TechUK, while IoT is about connecting machines and making use of the data generated from those machines, AI is about simulating intelligent behavior in machines of all kinds.  

Examples abound of how IoT and AI will come together to solve every problem. One particular one is Real-time public safety. Through the data obtained from CCTV cameras, Machine Learning will be handy in facial recognition, vehicle identification and a host of other visual patterns.  

Similarly, driverless cars are poised to immensely benefit from Artificial Intelligence. Through the various data the cameras and sensors pick, ML AI is able to create logical patterns to improve efficiency and safety of the car, passengers, and pedestrians.  

Predictive analysis: AI enables IoT devices to predict instances of failure and apply corrective maintenance. This improves efficiency by eliminating guesswork out of the devices.  

2. A flexible and Automated Machine Learning

Business analysts are set to be the primary beneficiaries of automated machine learning. This simplicity will usher in a new era of more time in building strategy rather than analyzing trends.  

Through the use of automated ML, businesses will enjoy more customization never witnessed before. Automated ML borrows from the principles of cognitive Application Program Interface (AIOpS) and ordinary Machine Learning.   

Auto ML, therefore avails the kind of customization that ensures developers’ work is simplified from the workflow and process.   

Benefits of Automated ML

The greatest benefit of Automated ML is that it offers the same standard of flexibility but has additional perks like portability and custom data.   A machine learning model requires domain knowledge, mathematical and computer science skills. Finding all these skills in an individual developer may be difficult. However, with automation, companies get to leverage an array of skills remotely to develop efficient systems.  

As such, areas like Health, banking, marketing, and public sector could easily develop their machine learning technology.   Automated ML also reduces the time to build the ML technology. You could then handle repetitive tasks automatically to focus on the creative and innovative aspects of the build.  

3. Automated DevOps

In 2019, AI programming will help in the development and operations processes of companies through the use of Artificial Intelligence for Operations.    This will be marked by the aggregation and correlation of massive data sets gotten from Operation Systems, servers, hardware and software components of the computer systems.  

This will result in more predictive rather than reactive IT operations. The Ops and AIOps will characterize IT operations in 2019 due to their capacity to perform accurate market analyses.  

Areas for automation 

DevOps automation will see processes such as code generation, configuration and source control automated.   

Once the coding is done, automation will also take over code compilation and storage in version control. This helps to convey the build to other processes as testing and deployment.  

4. More Human involvement

While AI characterizes the use of machine learning to predict trends and make a faster decision based on past experiences, it will not stand alone in 2019.   

Research done by Forrester indicates that about 10% of AI firms will bring in more human intelligence to inform decisions.  

“In 2019, enterprise AI mavericks will rediscover knowledge engineering and digital decisioning platforms to extract and encode inferencing rules and build knowledge graphs from their expert employees and customers. ML’s strength is data. Knowledge engineering’s strength is human wisdom. Used together, enterprises can dramatically accelerate the development of AI applications,” reads the report in part.  

5. Development of AI chips

To complement the processing power of ordinary computers involved in the training of AI, the development of AI chips will be perhaps the best thing in 2019.   

The AI chips are specialized silicon chips that make use of AI technology and are useful in machine learning.  

Apart from just speeding up processes, these chips will perform complex mathematical computations in AI.   

One main setback of this AI chips race is the inadequacy of the right skill-set. Even as major IT companies like Graphcore have secured a $200 million funding to develop Ai chips at large scale, these problems seem to persist.  

As a result, tasks, like testing, bug fixing, and cloud implementation are left to AI chips.   

AI Chip drivers

The other major companies at the forefront of AI chips include AMD (Advanced Micro Devices), Google, Inc., Intel Corporation, NVIDIA, Baidu, Qualcomm, Adapteva, UC-Davis, Mythic, and others.  


While AI in itself is not a new concept, the developments lined this year will be far-reaching. We believe these are the AI trends to watch out for in 2019. 


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