"Data science can be a powerful and transformative arsenal to your business. A culture that supports the experimenting nature of data scientists is not easy for many organizations to embrace and support. Having a longer-term vision, developing an understanding and being purposeful on the strategy can have a significant impact on decision-making velocity and quality and provide significant competitive advantage."
Contributed by: David J Klein, PhD, Chief Scientist, 2predict, Inc.
Being able to distinguish between bot-driven and human behaviors is critical to an organization’s strategy for preventing online fraud. AI-driven techniques and solutions can help organizations do this and are particularly effective for this use case. By using the volume of data and traffic patterns and the way the requests come in, they can spot patterns and determine whether actions are being performed by a bot or a human. Better yet, the machine learning algorithms behind the AI get smarter over time.
Machine learning is finally starting to gain momentum across various industries and use cases. The primary reason is that the past 10-plus years have seen a huge growth in data collection and generation. The more complex the data sets (e.g., number of dimensions), the better ML techniques are suited for the job. So ML-based advanced insights, inference engines and predictive models are how you up the monetization game."
Using Predictive Analytics and Machine Learning to Stay Ahead of the Game
David J Klein, PhD, Chief Scientist, 2predict, Inc.
Data acquisition cost is the number one factor holding life sciences companies back from more fully using predictive analytics and AI for speculative R&D. Moving the needle with predictive analytics often requires large volumes of training data, from thousands to millions of training examples.
“Today AI is gaining traction in a wide range of applications – helping companies streamline processes, improve operations, better serve customers and save money. While traditional Machine Learning still heavily relies on feature engineering, the promise of Deep Learning is to remove human bias from decision making. This dichotomy between big data and expert judgement is a central element of our mission. The reach of AI solutions is still very dependent on industries. Some are ready for advanced deployments while others still struggle with automation and digitization. We help professionals navigate that transition while adding value,” Fraces says.
"Says the recipe for the ideal staffer is someone who possesses deep programming skills, computer science skills, and mathematics skills combined with domain expertise. These smarts are needed for all modes of AI/ML heavy lifting, “including preparing datasets, developing algorithms, developing production-grade models, training models, and debugging models,” says Mandal.