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Showing 15 results for Adaptability + Artificial Intelligence RSS
Self-driving taxis. Cell phones that react appropriately to spoken requests. Computers that outcompete world-class chess and Go players. Artificial Intelligence (AI) is becoming part and parcel of the technological landscape—not only in the civilian and commercial worlds but also within the Defense Department, where AI is finding application in such arenas as cybersecurity and dynamic logistics planning.
Machine learning (ML) and artificial intelligence (AI) systems have significantly advanced in recent years. However, they are currently limited to executing only those tasks they are specifically designed to perform and are unable to adapt when encountering situations outside their programming or training. DARPA’s Lifelong Learning Machines (L2M) program, drawing inspiration from biological systems, seeks to develop fundamentally new ML approaches that allow systems to adapt continually to new circumstances without forgetting previous learning.
A key ingredient in effective teams – whether athletic, business, or military – is trust, which is based in part on mutual understanding of team members’ competence to fulfill assigned roles. When it comes to forming effective teams of humans and autonomous systems, humans need timely and accurate insights about their machine partners’ skills, experience, and reliability to trust them in dynamic environments. At present, autonomous systems cannot provide real-time feedback when changing conditions such as weather or lighting cause their competency to fluctuate. The machines’ lack of awareness of their own competence and their inability to communicate it to their human partners reduces trust and undermines team effectiveness.
Current AI systems excel at tasks defined by rigid rules – such as mastering the board games Go and chess with proficiency surpassing world-class human players. However, AI systems aren’t very good at adapting to constantly changing conditions commonly faced by troops in the real world – from reacting to an adversary’s surprise actions, to fluctuating weather, to operating in unfamiliar terrain. For AI systems to effectively partner with humans across a spectrum of military applications, intelligent machines need to graduate from closed-world problem solving within confined boundaries to open-world challenges characterized by fluid and novel situations.
Today’s machine learning systems are restricted by their inability to continuously learn or adapt as they encounter new situations; their programs are fixed after training, leaving them unable to react to new, unforeseen circumstances once they are fielded. Adding new information to cover programming deficits overwrites the existing training set. With current technology, this requires taking the system offline and retraining it on a dataset that incorporates the new information. It is a long and arduous process that DARPA’s Lifelong Learning Machines (L2M) program is working to overcome.