As the field of artificial intelligence continues to grow, companies across the country have found that techniques are coming out of the research lab and being applied to benefit their operations.
Recently, the Boston Medical Center implemented predictive analysis in its system to determine staffing during the busy 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 being understaffed, it significantly improves the efficiency and response time of each patient.
Netflix and other entertainment sites generally take advantage of this technology to offer users watching shows based on various 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 determine 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 understand who is involved and what is happening in milliseconds . With the help of AI, the 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, ranging from reading radiological scans to automatic equipment inspection in factories, to automatic detection of buildings in satellite imagery.
In a recent example, a Japanese cucumber farm adopted TensorFlow technology to facilitate the tedious task of ranking cucumbers by quality – a job that can take several hours to complete manually. The farm took pictures of her products and formed an in-depth learning technology to see what she could find. Ultimately, the system could identify some of the most important features and characteristics of each cucumber and sort them with a good degree of accuracy.
There are several tools available to companies that want to add similar features to their own workflows. For example, Amazon's Rekognition 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 verify the identity of users, count the number of people attending events and ensure the safety of the areas.
Computer vision is one of the most recent most exciting fields of applied AI. Once fully familiar with complete data sets, machines will greatly increase human capabilities: they will be able to process more image and sound data with a higher level of accuracy.
2. Artificial intelligence 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 handle large amounts of data, far more than any individual or human team can measure.
For example, pattern recognition gives companies the ability to offer services or article suggestions to new customers based on their activity and profile. Companies like Babylist use predictive analytics of customers to identify items that consumers might want to save or buy. Google and Facebook use a similar approach to show ads that people might click.
Another more common example of how companies can use this type of artificial intelligence is to recognize customers likely to disengage. 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 a service pay attention to it, as do the telecommunications companies .
In addition to business, the medical system benefits from this type of pattern recognition and inference. Companies like Better Therapeutics provide members with personalized care recommendations based on data.
All of these data sets are so large that no human can actually look at all the available information and understand its meaning, but computer algorithms can do it.
3. AI refines its forecasts for the future.
Contrary to 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 in this area is the stock market: you can make a lot of money if you can predict where it is going. 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 of this is the Market Information Platform Numerai a multicultural hedge fund created by data specialists from 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 domains in terms of order of magnitude of impact, there is also a difference in terms of data engineering and deep learning. The AI for perception is a new superpower to apply. The AI to find patterns in the data improves performance over previous techniques. Predicting the future remains difficult, with some opportunities for improvement.
You will not hire a statistician who performs time series forecasting and then expect that she put into production a model for deep learning computer vision. These are two different things, although, at present, they are both under the umbrella of AI. Ask yourself which of these areas your business needs most, and then focus on the tools and skills you need to implement it.