Here’s one example. Let’s say you’re in charge of a grocery store, and you’ve been tasked with increasing profits by 3%. In order to do that, you need to know which products to promote, what prices to charge, and how to adjust your inventory accordingly. Getting all those details right is crucial to your success, but without a time machine, it’s really just a guessing game. However, using the power of predictive analytics, you can feed historical data into a framework and train it to spit out precise figures that lead you to fiscal victory. And it’s much cheaper than a time machine.

Incorporating AI into the manufacturing sector may be a less obvious choice to some, but its implementation is no less impressive. It can accomplish a plethora of tasks that saves your business money: predictive maintenance, shortened design times, reduced waste, and even quicker visual inspections. Once trained properly, these systems can streamline any manufacturing environment. While simple robots already do most of the repetitive tasks involved in large assemblies, programming those robots is now much easier, as they require significantly fewer lines of code to operate effectively, making them more autonomous.

Some manufacturing companies have already started building “smart facilities” to bring this future to life. For example, Siemens, a longtime customer of BOXX, has equipped gas turbine systems with hundreds of sensors that generate large amounts of data that can then be used to increase efficiency.

While many businesses are starting to catch on, one industry has been using this technology for years—banking. If you’re a financial institution, the challenges are different, but no less conquerable with AI. Although there are additional risks involved with respect to regulatory requirements, there are multiple use cases already in effect. Digital personal assistants (e.g., chatbots), voice recognition, customized investment plans, fraud prevention, hedge fund management, card authentication—all enhanced by AI.

By 2020, there will be around 40 trillion gigabytes of data,1 and with the ubiquity of Internet of Things (IoT) devices producing absurd amounts on a daily basis, there is no shortage of data to use that will continue to improve the accuracy of deep learning models. The main hurdle at this point is figuring out how to use all that data effectively. That said, to address the question posed in the title: the answer is a resounding yes. Eventually, it seems as if not taking advantage of using things like predictive analytics in your business will become a liability rather than a fun future prospect.

As you might expect, there are also exciting plans for AI in the near future. A recent development with the nonprofit organization WattTime seems particularly promising. They will soon be using satellite data and machine learning to track pollution coming from every power plant in the world—in real time. AI can also potentially increase crop yields for farmers, again with help from satellites. And one pest control company, Rentokil, is already using AI combined with IoT devices to kill bugs for the food and beverage industry. Though it will need to be guided smartly toward maximizing social benefits, the future of AI looks almost radioactively bright.

However, you don’t need predictive analytics to know that BOXX is uniquely suited to fit nicely into all of those use cases, and more. If you’re just entering the world of AI, or are looking for a way to iterate faster, our brand-new NVIDIA® powered Data Science Workstations are loaded with machine learning libraries so you can quickly reach new insights and lower the cost of your projects. Combined with an Intel® Xeon® processor and NVIDIA Quadro® RTX GPUs, they can accelerate any workload that relies on the radical parallel processing power of professional GPUs, like deep learning.

If you only need temporary access to that sort of power, or simply prefer to use machines off-site that don’t require maintenance, our friends at Cirrascale provide full access2 to a private cloud of mutli-GPU supercomputers—like the NVIDIA® DGX-1™—at a flat rate, with no virtualization, no ingress/egress data fees, and no surprises.

1 Source

2 Includes the full resources of the CPU and GPU, as well as data storage.