As the field of artificial intelligence continues to grow, companies across the country have found that techniques emerged from the research lab and were applied to benefit their operations.
Recently, the Boston Medical Center implemented predictive analytics in its system to determine staffing during the busiest hours of the hospital. With this information, the center is able to staff various areas of the hospital to ensure rapid treatment of patients. This technology not only prevents the hospital from becoming understaffed, it significantly improves the efficiency and response time of each patient.
Netflix and other entertainment sites generally take advantage of this technology by suggesting that users watch programs based on a variety of behavioral factors.
With AI, such companies have the power to measure and collect data, to recognize patterns and to make inferences. This greatly improves the cloud computing experience for businesses and their customers.
The artificial intelligence grows from year to year and companies can take advantage of countless possibilities. It is crucial for you to identify the areas in which you most need to be targeted and then look for the AI-based tools and skills that will ensure success. AI can help you in three ways if you want to develop the capabilities of your company:
1. The AI allows perception.
Humans can look at images and clearly understand who is involved and what happens in milliseconds. With the help of AI, machines can now do the same thing. Implementing this type of tool gives businesses the unique ability to perceive what is happening. This has many useful applications, from reading radiological scans to automatic inspection of equipment in factories to automatic detection of buildings on satellite imagery.
In a recent example, a Japanese cucumber farm has adopted TensorFlow technology to facilitate the tedious task of ranking cucumbers by quality – a task that can take several hours to complete. The farm took pictures of her products and formed an in-depth learning technology to see what she could find. Eventually, the system could identify some of the most important features and characteristics of each cucumber and sort them with a good degree of accuracy.
Several tools are available for companies that want to add similar features to their own workflows. For example, Amazon's recognition technology adds image and video analysis to applications. Users can download files to the Rekognition application programming interface. The service examines the attributes of the content. It then provides an accurate analysis to the user. This could help companies to verify the identity of users, count the number of people attending the events and ensure the safety of the areas.
Computer vision is one of the newest and most exciting areas of applied AI. Once fully familiar with complete datasets, the machines will greatly increase human capabilities: they will be able to handle a greater amount of image and sound data with a higher level of accuracy.
2. AI improves pattern recognition.
Computers have always been a tool for determining meaningful models from large datasets. As organizations continue to expand their customer data resources, it's critical for them to be able to recognize more complex customer models by using more advanced techniques to stay as focused on the customer as possible. Constant improvements in computing power and storage availability mean that machines can now process large amounts of data, far more than any individual or human team can measure.
For example, pattern recognition gives companies the ability to make service or article suggestions to new customers based on their activity and profile. Companies such as Babylist use predictive analytics to identify items that consumers may want to save or buy. Google and Facebook use a similar approach to show ads that people can click.
Another more common example of how companies can use this type of artificial intelligence is the recognition of customers likely to turn. By reviewing user data to determine if they will stop using a product or service, companies can step in by offering special offers or other attempts to build customer loyalty. The loss of customers can have a particularly important impact in sectors where consumers have many choices. For example, many software companies as services, like telecommunications companies, are looking into this issue closely.
In addition to business, the medical system benefits from this type of pattern recognition and inference. Companies like Better Therapeutics provide members with personalized data-based care recommendations.
All of these data sets are so large that no human can really look at all the available information and understand its meaning, unlike computer algorithms.
3. Has refined the forecasts for the future.
Unlike perception or recognition, when an objective truth can be verified by humans during the algorithmic training process, future predictions deal with intrinsic unknowns.
The classic example is the stock market: if you can predict where it will go, you can earn a lot of money. Unfortunately, the stock markets are not predictable. But the considerable potential benefits spur users to introduce all kinds of data sets to try to gain an advantage. An extreme example is the Numerai Market Information Platform, a multi-source hedge fund created by data scientists around the world.
The prediction of the future has long been a sacred grail of data science and artificial intelligence, because the potential benefits are enormous, but many challenges remain.
Just as there is a difference between these three areas in terms of magnitude of impact, there is also a difference in terms of data engineering and in-depth learning. The AI for perception is a new superpower to apply. The AI to find patterns in the data improves performance over previous techniques. The forecast of the future remains difficult, with some possibilities for improvement.
You will not hire a statistician who does the time series prediction and then expects him to put into production a model for deep learning of computer vision. Ask yourself which of these areas your business needs most, and then focus on the tools and skills you need to implement it.