AI is in a continuous state of evolution like all technologies. The newest AI Monthly digest shows that significant improvements in machine learning and AI, breakthroughs and game-changers are months or even weeks away, not years away. The challenge must, therefore, be summarized and demonstrated as machine learning technology becomes one of the main driving forces in business and society as it is expected to unfold in 2019. Corporations like Amazon, Apple, Facebook, Google, IBM, and Microsoft are investing in AI R&D that will help the ecosystem to get consumers closer to AI. The Adobe figure is projected to increase from 15 percent in 2018 to over 31 percent in 2019, according to Adobe.
In 2019, chip producers like Intel, AMD, NVIDIA, ARM, and Qualcomm will be shipping specialized chips to speed up AI-enabled application execution. These chips are optimized for specific applications, computer vision, language processing, and speech recognition scenarios. The healthcare and automotive industry's next-generation applications will depend on these chips to supply information to end users.
OpenAI experiments with an A.I. They built the GPT-2 text generator. But as with previous projects, they are not sharing it with the public. OpenAI locks it for further research because it is afraid of misuse. The public can not access the code. GPT-2 “studies” is a single text line to learn human language patterns. It can generate full-text paragraphs and imitate the style of writing. It even writes' complete articles. OpenAI soon discovered a major GPT-2 problem. It is impossible to tell that a robot has written it. It started to produce paragraphs of text that were too human.
AI meets IoT on the edge computing layer in 2019. Most models will be deployed on the edge of the public cloud. Industrial IoT is the top-level use case for artificial intelligence that can detect, analyze root causes, and maintain the equipment in a predictive manner. IoT will become the company’s most significant driver of artificial intelligence. Special AI-chips based on FPGAs and ASICs will be provided to edge devices. In order to adopt artificial intelligence quickly, it is necessary to stay updated with the trends and align business strategies and implementations accordingly to stay ahead in the market.