AI’s adoption of machine learning (ML) allows it to solve complex problems through its own systematic testing. Instead of coding what it needs to know, developers create AI with the ability to learn and analyze data – resulting in innovative solutions virtually unreachable through human ability alone. Unprecedented data sets have become available the past decade that can be added to and accessed by professionals worldwide. The global amalgamation of data is too vast for even the most experienced analysts, but AI programs armed with ML capabilities can interpret the data into usable solutions.
The biotech field has been held back by the technological limitations, but ML and AI programs have broken through the barriers into new possibilities. Security and healthcare trends suggest future generations will utilize biotech on a daily basis, either to thwart identity theft or to cure cancer. We may still ask the big questions, but AI programs are finding the solutions.
Biotech and Public Opinion
Public opinion is mixed about biotech. 79% of the public believe it has positively affected healthcare. On the other hand, 44% of the public are opposed to stem cell research. These numbers immediately show a distortion in public knowledge regarding biotechnology, or at least believe it should be used in some healthcare aspects and not others. Regardless, the manipulation of organic materials and the microscopic level has already produced new drugs and treatments while ethics committees discern what is acceptable.
Meanwhile, biometrics – cousin to biotechnology focused on the storage and identification of individuals via features and behavioral patterns – has also seen benefits from our increased use of data centers and ML. There will be 30 billion devices connected to the internet by 2020, and their information will be stored remotely in data centers. The growth of the IoT and data center utilization corelates with our use of Big Data to reveal patterns and solutions, increasing biometric options and accuracy.
AI’s Impact on Biotech and Healthcare
Naturally, AI advanced biotech research and development which, in turn, positively impacted healthcare. People are still against concepts like cloning or stem cell research, but drug and disease research have benefitted greatly thanks to ML. Electronic Health Records (EHR) systems are slowly integrating so that health records can be transferred and accessed globally, opening the door to a holistic perspective. Tech companies like IBM are taking advantage by working with clients to create AI systems and supercomputers capable of sifting through the almost infinite amounts of health data to provide complex insights. Doctors, researchers, and other professionals that work with cancer or diseases like diabetes have been able to successfully leverage these globally-begotten insights within their own research.
Yet as with any time an individual or organization stores data online, there is a chance of that private information falling victim to a security breaches. These security breaches are not only bad PR, but they are also a financial burden with average costs being $2.2 million for a single data breach. If you’re storing data online, it will be essential to take intense measures to protect that data in the short and long-term.
If anything is going to benefit from mass amounts of information stored in data centers that is accessible via AI, it’s going to be security. Biometrics, like the thumbprint, have been used as identification and security even before the rise of the computer. Now, thanks to the cloud and digital storage, physical and behavioral biometrics can be shared, collected, and accessed around the world. Public and private entities have seen the possibilities, using biometrics to protect company data or to access personal information.
Multi-factor authentication has been the solution to single-factor security like just using a thumbprint. Making that possible was the collection and storage of people’s biometrics – from hair and eyes, to fingerprints and facial features. Behavioral biometrics has even become possible thanks to ML. A user’s behavior and patterns are recognized by a program, so that it can identify a stranger accessing a device. Multi-factor authentication is an improvement, but continuous authentication is near perfect.
ML, AI, and biotech are a ménage à trois made in heaven. The seemingly insurmountable, complex obstacles have been overcome by accessing equally complex amounts of data for answers. We are still in the beginning stages of leveraging AI and SaaS, suggesting there will be more breakthroughs sooner than later.