Artificial Intelligence (AI) is often talked about as a wave of the future. However, there's no question these emerging technologies already play a significant part in our everyday lives—from personalized movie and book recommendations, to the seamless conversations we're able to have with virtual assistants, such as Amazon's Alexa.
One area AI is impacting that may not be so obvious, however, is the lab—it's transforming research and development (R&D) by accelerating how innovators discover, access, and apply data to develop the new products and processes that benefit our lives and our world. In research labs around the world, AI is driving an innovation transformation by enabling innovators to gain insight faster.
Finding the most relevant data
It's estimated that 1.8 million scientific papers are published every year—and that's just journal publications. Consider the multitude of other assets that innovators reference across the R&D spectrum, including patents, historical research archives, chemical substance and reaction collections, regulations, and more. The sheer volume of information can be overwhelming, making it increasingly challenging to efficiently find the key data and connections necessary to drive true innovation without the help of technology. AI is able to transform the innovation process by enabling innovators to answer mission-critical questions with a single search and extract patterns and insights that the human mind alone is unlikely to find.
For example, CAS manages the largest hub of curated scientific information for researchers worldwide, and we're applying AI to make the user experience even better. Through machine learning, our platforms recognize search patterns, and using those patterns optimize the accuracy and relevance of search results. Most importantly, our technologies learn from every new piece of human-curated information added to our collection, which means they're constantly improving and getting smarter. This empowers users with highly applicable insights that allow them to keep the innovation process moving forward.
Accessing the latest advances, faster
Efficiency gains are a critical outcome of AI's impact on the R&D process and the information management landscape. By employing machines to support the basic steps of data extraction, organization and curation, the process not only goes faster, but hours upon hours of expert human capacity are made available to spend on more intellectually demanding, value creating tasks.
Expediting this process minimizes the gap between the publication of information and its availability in searches and analysis, which becomes increasingly important as data production volumes continue to grow. As a result, CAS solutions, like SciFindern, put the absolute latest scientific data and findings at scientists' fingertips throughout the innovation process. It also allows for additional insights and value to be added to the content, and for new data types to be curated.
CAS has maintained industry-leading currency of our content collection while the overall volume of published scientific information that we receive has nearly doubled over the last 15 years. This rapid increase in efficiency is in part thanks to our application of key new technologies. These technologies and advanced engineering, in concert with our expert scientific content analysts, now enable analysis of more than 1,000 pages of data every day—a 550 percent increase from the average 150 pages of content human-only processes can analyze in a day.
Gaining unmatched insights from scientific big data
One of the most powerful ways AI helps innovators is by finding patterns within complex data. While current AI technology is not capable of making complicated decisions on its own, it can help inform those decisions by uncovering insights that would otherwise be missed. It does this by analyzing large volumes of data at a rate and depth far beyond what is humanly possible. Insights based on the learned patterns uncover unseen connections and can be used to help innovators predict the likelihood of their hypotheses, and ultimately, the success or failure of their developments.
While increased throughput and the ability to process more information than ever before are key benefits of machine learning, it is perhaps its ability to literally learn and apply this acquired knowledge to new sources of data that makes it so tremendously valuable. For example, predictive models can learn from CAS's expansive repository of information and apply that learning to deliver unmatched insight from a client's proprietary internal data collections. Leveraging machine learning in that way creates a powerful synergy between CAS content and client's assets, turning their collaboration with CAS into a unique competitive advantage.
Don't let your organization fall behind the curve when it comes to AI and related digital technologies. Learn how CAS can help optimize your data and provide solutions to accelerate your R&D pipeline.