AI Businesses and Data Science Algorithms

A couple of weeks ago, we released our new annual AI Business Report focusing on the impact of Artificial Intelligent software in the business domain. Today, we release our second annual AI B-Conference bringing together business leaders from around the globe to discuss the future of AI and Artificial Intelligent technology. This year’s theme is “combating threats – protecting America’s future.” It makes sense to remind everyone once again that AI and Artificial Intelligent systems can be the key to preventing national security issues. Consider this, if we have AI system that determine whether or not someone is a risk, then it could decide whether or not to allow them to fly, shoot missiles at us, sneak into our bases, et cetera.

AI Businesses

There are many applications of AI in business. Some examples include self-driving cars, self-piloted planes, automated warehousing, computer-assisted cruise ships, weather prediction services, stock trading, automated warehousing, automated data processing, robotic warehousing, and more. These software companies, like Carnegie Mellon University in Pittsburgh, have a long history of developing cutting edge technologies. As we discussed in our previous AI report, AI software companies are developing tools to assist human decision making process. They are doing research to develop diagnostic and predictive skills for AI vehicles.

As discussed in Part I of this series, artificial intelligence will continue to dramatically impact every sector of the business realm with its ability to recognize patterns and make inferences. This ability will extend well beyond traditional network security measures, covering everything from controlling your warehouse to protecting your network. In fact, one of the biggest challenges we face as a society relates to the security of our electrical grids, water treatment facilities, natural and artificial intelligence computer networks, transportation systems, large scale manufacturing, and large public information systems such as the internet.

As mentioned in Part I of this series, many companies have taken an approach of building artificially intelligent teams of experts in different areas with the aim of improving efficiency. These artificial intelligence teams are then used to build, test, and maintain applications. In many cases, these applications are proprietary and protect the company’s intellectual property rights. This can be a very important asset for businesses because it allows them to be more creative and able to solve problems better and faster.

One of the most interesting applications is in the area of enterprise software. Enterprise software is typically used by larger organizations that require a plethora of functions to be able to run their business. Because of this, many companies that own and operate their own network effects require high levels of automation. Automation helps to achieve this by removing mundane tasks that consume valuable time that should instead be focused on core business. AI software companies have developed applications that can detect network problems, send alerts, and fix issues as they arise. For example, some programs can scale up and down as network sizes change, detect bottlenecks, and adjust network management techniques in order to achieve the highest levels of productivity.

AI process automation has even made itself available for smaller startups, as well. This is because the technology can greatly reduce the cost per transaction by automating business processes that are not immediately related to sales or marketing. In other words, a fast and accurate analysis of sales leads or customer interactions can be done before these activities produce results, reducing the need for investment in human capital and salesperson training.

AI businesses are also making inroads into the area of finance. Several top-tier software companies are applying AI to financial services. They use the data to identify trends and anomalies in order to improve their overall efficiency. The result is reduced costs and improved gross margins for all parties. For example, financial service providers can reduce the number of people they need to staff to handle customer inquiries. Alternatively, they can improve their ability to collect data from underutilized sources such as third-party vendors and reduce the need for hiring additional staff to manage third-party logistics.

Finally, AI has made inroads into areas once considered too difficult to implement by humans, such as weather forecasting. Software developers have created state-of-the-art weather forecasting platforms that are now available to the general public. These platforms, which are primarily composed of mathematical algorithms and machine learning, work together to forecast upcoming weather conditions. Because the number of people who have access to the forecast is so low, it is likely that this form of weather forecasting will become widely available to the general population in the near future.