The blog post below is adapted from a longer article in VA Research Currents. When Facebook suggests a new friend for you, or Gmail shows you ads based on your email content, or Alexa or Siri understands your verbal command to do some chore in the house, that's artificial intelligence at work. Or, for a more dramatic example, think of driverless cars that read traffic and make lightning-fast decisions to stay on course and avoid accidents. Basically, artificial intelligence (AI) means using computers to simulate human thinking. Computers will never be able to fully replicate the human mind in all its amazing nuance, speed, and complexity--at least most people hope not!--but scientists have made remarkable strides in teaching computers to handle tasks such as finding patterns in data, analyzing and weighing risk factors, choosing the best option from among many choices, predicting future events based on past ones, and solving problems.
As the Veterans Affairs Department's inaugural Director of Artificial Intelligence, Gil Alterovitz aims to leverage the emerging technology and the agency's cornucopia of data to proactively anticipate and tackle problems afflicting veterans like never before. In a conversation with Nextgov, Alterovitz detailed his present efforts and future-facing vision to support VA in executing that mission. "Nowhere in the country is there such potential for research to be developed and translated into clinical care so quickly. In this case, it's to help our special population of veterans … and those patients have actually asked us to deal with their needs," Alterovitz said. "We really want to be the go-to place for veterans through AI research and development--so instead of reacting, we can really anticipate their needs."
When we began our 14-week tech health sprint in October 2018, we did not realize the profound lessons we would learn in just a few months. Together with federal agencies and private sector organizations, we demonstrated the power of applying artificial intelligence (AI) to open federal data. Through this collaborative process, we showed that federal data can be turned into products for real-world health applications with the potential to help millions of Americans have a better life. Joshua Di Frances, the executive director of the Presidential Innovation Fellows (PIF) program, says that this collaboration across agencies and private companies represents a new way of approaching AI and federal open data. "Through incentivizing links between government and industry via a bidirectional AI ecosystem, we can help promote usable, actionable data that benefits the American people," Di Frances said.
"We are thrilled to have Dr. Halamka aboard," stated Tory Cenaj, founder and publisher of the journal. "We are poised at the foot of a precipice. This is a Renaissance - an awakening. The question is whether the ideals of those that forge an egalitarian system succumb to recreating a similar rewards system, on a different platform. I believe John understands this, and brings the virtuoso and impartiality of science and humanitarianism to Blockchain in Healthcare Today. This is a turning point for both healthcare, and civilization."
The most critical moves that determine the success of a manipulation task are performed within the close vicinities of the object prior to grasping, and the goal prior to the final placement. Memorizing these state-action sequences and reusing them can dramatically improve the task efficiency, whereas even the state-of-the-art planning algorithms may require significant amount of time and computational resources to generate a solution from scratch depending on the complexity and the constraints of the task. In this paper, we propose a hybrid approach that combines the reliability of the past experiences gained through demonstration and the flexibility of a generative motion planning algorithm, namely RRT*, to improve task execution efficiency. As a side benefit of reusing these final moves, we can dramatically reduce the number of nodes used by the generative planner, hence the planning time, by either early-terminating the planner when the generated plan reaches a "recalled state", or deliberately biasing it towards the memorized state-action sequences that are feasible at the moment. This complementary combination of the already available partial plans and the generated ones yield to fast, reliable, and repeatable solutions.