Artificial intelligence in the healthcare industry is predicted to save $150 billion annually for the US. As such, AI is being rapidly deployed in many areas of the healthcare landscape. This event will primarily focus on the Providers, attracting CIOs, CDOs, CTOs, VPs of IT and Informatics along with senior Physicians and Clinicians from leading US hospitals who will assess the business value outcomes of AI and share experiences of implementation in clinical care and hospital operations. Demand for basic and advanced analytics is growing exponentially in healthcare. Building a robust data ecosystem to support the spectrum of analytics, from descriptive analytics to support efficient operations to advanced ML / AI analytics to drive differentiated outcomes is a critical enabler for provider systems.
At Bossa Nova we create service robots for the global retail industry. Our robots' mission is to make stores run efficiently by automating the collection and analysis of on-shelves inventory data in large scale stores. Navigating smoothly along the aisles, we circulate autonomously among busy customers and employees. If we were a self- driving car we'd be operating at level 5 autonomy. Yep, it is possible to move, scan and analyze all at the same time.
The vulnerabilities of machine learning models open the door for deceit, giving malicious operators the opportunity to interfere with the calculations or decision making of machine learning systems. Scientists at the Army Research Laboratory, specializing in adversarial machine learning, are working to strengthen defenses and advance this aspect of artificial intelligence. Often, in a data set, corrupted inputs or an adversarial attack enters a machine learning model undetected. Adversaries also impact a model whether or not they know the machine learning algorithm in use, training a substitute machine learning model for use on a "victim" model. Corruption can even occur on sophisticated machine learning models trained with an abundance of data to perform critical tasks.
One of the byproducts of our digitally transformed world is the accumulation of large quantities of data. Online transactions, medical records, social media posts, emails, instant messages, and connected sensors are just a few examples of the kinds of data being captured and stored on a daily basis. Scientists and research organizations have been exploring how to leverage big data for artificially intelligent applications since the 1970s. Nonetheless, until fairly recently, the big data issues for enterprises remained how to store it cost effectively, how to retrieve it efficiently when needed, and how to protect it from unauthorized access. The growth of the cloud opened up a whole new realm of cost-effective data storage and retrieval solutions, but big data was still largely perceived by enterprises as a passive asset that did not contribute significantly to their bottom lines.