The scale and the surge in attention to artificial intelligence is not a new concept as the ideology behind the human-machine collaboration has been floating around since the 1980s but various factors contributed to the idea being put on hold for a while especially the lack of attention and funding. Billions of dollars are being annually invested in the industry for its research and development. The evolution of hardware and software programs and the innovation of cloud processing and computing power has put an additional advantage to the future of artificial intelligence. Here are four factors that have contributed to the growth of artificial intelligence
Availability of enormous amounts of data
The innovation of cloud storage has enabled easy access to otherwise locked data that wasn’t made available to the public. Before cloud storage became mainstream, accessing data was a costly affair for data scientists in need of data for research, but now governments, research institutes, businesses are unlocking data that were once confined to tape cartridges and magnetic disks. To train machine learning models, data scientists need enough data for precise accuracy and efficiency. With the easy availability of data, research facilities now have the opportunity to train ML models to solve complex problems with data available to them.
Advancement in innovations
The innovation of a new breed of processors like the graphics processing unit(GPU) the training process of ML models is now up to speed. The GPU comes with thousands of cores to aid in ML model training. From consumer devices to virtual machines in the public cloud, GPUs are essential for the future of artificial intelligence. Another innovation that is aiding the growth of artificial intelligence is the Field Programmable Gate Array. The FPGA is programmable processors customized for a specific kind of computing work such as training ML models. Traditional CPUs are designed for general purpose computing but FPGA can be programmed in the field after they are manufactured. Furthermore, the easy availability of bare metal servers in the public cloud is attracting data scientists to run high-performance computing jobs.
Competition drives efficiency and interests
With machine learning and deep learning, AI applications can source for data and analyze new information that can be of advantage to organizations and industries alike. This breeds rivalry between organizations who want efficiency. And these competitive advantages have had an impact accelerating the growth of artificial intelligence as firms would like to have an upper advantage over one another. Financial boosts from the majority of big companies have led to a rapid interest in AI technology and development.
Artificial Intelligence also plays a key role in revolutionizing the Software Quality Assurance testing processes. With the increasing complexity of the applications, the SQA has become a bottleneck to the success of the software projects as yet most of the agile testing processes implement manual testing to test the applications.
This is where Artificial Intelligence can help accelerate the manual testing process. With the help of AI, the QA testers can work on the most malicious functions first after they prioritize the test cases based on the existing test cases and logs.
Advancement in deep learning
Deep learning is a type of artificial intelligence course that allows systems to learn patterns from data and subsequently improve their experience. Deep learning and artificial neural networks are the most essential part of artificial intelligence growth. Artificial neural networks are developed to mimic the human brain and can be trained on thousands of cores to speed up the process of generalizing learning models. Artificial neural networks are replacing traditional machine learning models. Innovative computer technologies such as Single Shot Multibox Detector (SSD) and Generative Adversarial Networks (GAN) are revolutionizing image processing. The ongoing research in computer vision will become important in artificial intelligence healthcare and other domains. The emergence of ML techniques such as Capsule Neural Networks (CapsNet) and Transfer Learning will consequently change the way ML models are trained and deployed. They will be able to accumulate data that are precise in problem-solving and data analysis to give accurate predictions and results.
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