A new security system is changing the way people protect themselves from viruses, and it could be coming to a smartphone or tablet in a matter of weeks.

In a series of experiments, researchers at the University of California at Berkeley have developed a new method for using artificial intelligence (AI) to automatically scan and classify images of infected cells in a laboratory setting.

The new technology has been developed by researchers from the University and the California Institute of Technology, and was presented at the 2016 International Conference on Machine Learning in San Jose.

“We have a really interesting problem in our field where we want to be able to detect infections,” said Dr. Peter M. Clements, a research scientist in the biomedical engineering department of UC Berkeley.

“It’s really important for our research to be accurate, but also to have a degree of control.”

The method was created to detect viruses by using artificial neural networks to create a set of images of cells in the lab, which the researchers then could then analyze and classify.

In the past, this kind of system was limited to detecting viruses by analyzing individual cells in specific samples.

But in this new method, the system also analyzes the entire virus’s genome.

The researchers found that this is a much more efficient method of detecting viruses, especially since the AI has the ability to recognize the viral sequence of a cell and classify it accordingly.

“The algorithm itself is very simple,” Clements said.

“We could write this algorithm on a chip and it would be able recognize a virus and classify the virus into different types, which would be very useful for our purposes.”

It’s not going to be as fast as it was before, but it’s going to still be very accurate.

“In the future, the researchers hope that this technology could be used to help identify viruses before they spread.

The researchers used an algorithm called ImageNet to identify viruses by looking for viral proteins and looking for patterns in these proteins that could be interpreted as patterns in the image of the virus.

The algorithm also found that it was relatively easy to distinguish between different types of viruses.”

You could have a virus that’s just a virus with a few viral proteins, and a virus like the common cold virus that has thousands of viral proteins,” Cbonsons said.”

And then you could have one that’s very simple and a lot more complex and has thousands and thousands of different viral proteins.””

If we can get these images to look at the virus in the right way, we can figure out what is the virus that is responsible for a certain disease or virus that may be present in a certain population or a specific population and identify it in a reasonable amount of time.

“The researchers believe that they have developed an algorithm that can identify viruses at a much faster rate than other methods that rely on human analysts.”

What we’re trying to do is develop a method for looking at viral proteins that is much faster than other techniques,” C. J. Condon, one of the authors of the paper, said.

While these new methods are very different from previous methods, they have similarities in that they use AI to automatically detect the presence of viruses, which is not possible with previous methods.”

This is an important step in the evolution of detection, as we now have AI capabilities that can be used by any kind of systems,” Condon said.

For more information on this study, please contact Dr. Christopher B. Cray of the UC Berkeley School of Engineering.

For a video of the presentation, please click here