There are huge strides coming in the world of artificial intelligence. And the first big changes we are going to see, are going to actually come in the form of software. This is the biggest contender, as it can largely determine how scalable, affordable and economically feasible it is to create AI systems. Unfortunately, there was a slow innovation period in the business of enterprise software until more recent years. Those years could have set us back farther than we would have likes. In 2015, many companies (which are also involved in something called the billion dollar startup club) have begun providing financial support and funding for artificial intelligence development.
This money will help researchers develops services like predictive analystics, and begin employing a short range of AI and machine learning. Of course, with a jump in investment, we are sure to see a good amount of hype surrounding the research, without much of the experience. Large quantities of money will be spent as well on configuring database systems to allow these projects to store and retrieve information the way the human brain does. This is critical to the development of AI. This is also referred to as semantic standards, which was originally incorporated by one leading vendor and is now closely followed by a second in the market.
With multiple choices available in the market, competition is going to become very fierce. This emergence has resulting in the first real competition in database systems, many of which are steadily fighting for critical systems and high performance levels… but at a much lower price.
These programs are hard to use and manipulate, usually, with each company having their own way of issuing commands in the system and it’s responses. Interoperability is another critical component to developing AI further, but with this competition, it’s going to slow the process down. The artificial intelligence programs down show much adaptability, which essentially makes the governing of its creation and use all but impossible. Sure, early users of semantic standard may include intelligence agencies and computational science, but now we are seeing banks beginning to incorporate those standards into their own systems as well. This data is sensitive, which also makes it high quality. High quality data (which is built on incredibly descriptive standards and advanced analytics) can certainly provide an increased defense against risks.
Standards are necessary, and these companies need to begin working along the same lines if they want to begin making bigger waves in the development of artificial intelligence. That high quality data that protects against risks can also make operations more efficient, speed up research and development, and even improve a company’s performance in their finances. Without standards and compatibility, we may see research stagnate. Many think that the only thing that will change their outlook may be a catastrophic event involving data and information. We’ve been on the brink of it for so long, would it even be surprising to see digital disruption? It may have already happened, and this clarity may only be achieved via hindsight.
