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Traditionally, we have designed machines to handle well-defined, high-volume or high-speed tasks, freeing humans to focus on problems of ever-increasing complexity. In the 1950s and 1960s, early computers were automating tedious or laborious tasks. It was during this era that scientists realized it was possible to simulate human intelligence and the field of artificial intelligence (AI) was born. AI would be the means for enabling computers to solve problems and perform functions that would ordinarily require a human intellect.
DARPA breaks down AI technology development into three distinct waves. The first wave, “describe,” focused on developing platforms that employed a rules-based system. These AI platforms are the basis of commercial products such as TurboTax.
Early work in AI emphasized handcrafted knowledge, and computer scientists constructed so-called expert systems that captured the specialized knowledge of experts in rules that the system could then apply to situations of interest. Such “first wave” AI technologies were quite successful – tax preparation software is a good example of an expert system – but the need to handcraft rules is costly and time-consuming and therefore limits the applicability of rules-based AI.
The second wave is “recognize,” and is made up of the machine learning systems that are prevalent today. The past few years have seen an explosion of interest in a sub-field of AI dubbed machine learning that applies statistical and probabilistic methods to large data sets to create generalized representations that can be applied to future samples. Foremost among these approaches are deep learning (artificial) neural networks that can be trained to perform a variety of classification and prediction tasks when adequate historical data is available.
Such systems can classify objects of interest and take the burden off of human analysts who often must pore through mounds of data and turn it into actionable information. However, while the theory behind these second wave technologies was established in the 1970s, much more work still needs to be done to mature them, he added. Additionally , the task of collecting, labelling, and vetting data on which to train such “second wave” AI techniques is prohibitively costly and time-consuming.
The third wave, “explain,” is where the future of AI is headed and focuses on adding context and trust to artificial intelligence platforms, Peter Highnam, the agency’s deputy director said. DARPA’s AI Next program has three thrusts: to increase the robustness of second wave AI technologies, to aggressively apply second wave systems to new applications and to further examine third wave technologies, he said.
The advance of technology has evolved the roles of humans and machines in conflict from direct confrontations between humans to engagements mediated by machines. Originally, humans engaged in primitive forms of combat. With the advent of the industrial era, however, humans recognized that machines could greatly enhance their warfighting capabilities. Networks then enabled teleoperation, which eventually proved vulnerable to electronic attack and subject to constraint due to long signal propagation distances and times. The next stage in warfare will involve more capable autonomous systems, but before we can allow such machines to supplement human warfighters, they must achieve far greater levels of intelligence.
DARPA announced in Sep 2018 a multi-year investment of more than $2 billion in new and existing programs called the “AI Next” campaign. Common sense reasoning is defined as “the basic ability to perceive, understand, and judge things that are shared by nearly all people and can be reasonably expected of nearly all people without need for debate.” AI experts note the gap between AI inference and the ability to design systems that can draw directly on the rules of inference to achieve common sense reasoning. “Articulating and encoding this obscure-but-pervasive capability is no easy feat,” DARPA program managers note.
“With AI Next, we are making multiple research investments aimed at transforming computers from specialized tools to partners in problem-solving,” said Dr. Walker. “Today, machines lack contextual reasoning capabilities, and their training must cover every eventuality, which is not only costly, but ultimately impossible. We want to explore how machines can acquire human-like communication and reasoning capabilities, with the ability to recognize new situations and environments and adapt to them.”
DARPA envisions a future in which machines are more than just tools that execute human-programmed rules or generalize from human-curated data sets. Rather, the machines DARPA envisions will function more as colleagues than as tools. Towards this end, DARPA research and development in human-machine symbiosis sets a goal to partner with machines. Enabling computing systems in this manner is of critical importance because sensor, information, and communication systems generate data at rates beyond which humans can assimilate, understand, and act. Incorporating these technologies in military systems that collaborate with warfighters will facilitate better decisions in complex, time-critical, battlefield environments; enable a shared understanding of massive, incomplete, and contradictory information; and empower unmanned systems to perform critical missions safely and with high degrees of autonomy. DARPA is focusing its investments on a third wave of AI that brings forth machines that understand and reason in context.
AI Next” campaign
DARPA announced in September 2018 a multi-year investment of more than $2 billion in new and existing programs called the “AI Next” campaign. Key areas of the campaign include automating critical DoD business processes, such as security clearance vetting or accrediting software systems for operational deployment; improving the robustness and reliability of AI systems; enhancing the security and resiliency of machine learning and AI technologies; reducing power, data, and performance inefficiencies; and pioneering the next generation of AI algorithms and applications, such as “explainability” and common sense reasoning.
DARPA will create powerful capabilities for the DoD by attending specifically to the following areas:
- New Capabilities: AI technologies are applied routinely to enable DARPA R&D projects, including more than 60 exisiting programs, such as the Electronic Resurgence Initiative, and other programs related to real-time analysis of sophisticated cyber attacks, detection of fraudulent imagery, construction of dynamic kill-chains for all-domain warfare, human language technologies, multi-modality automatic target recognition, biomedical advances, and control of prosthetic limbs. DARPA will advance AI technologies to enable automation of critical Department business processes. One such process is the lengthy accreditation of software systems prior to operational deployment. Automating this accreditation process with known AI and other technologies now appears possible.
- Robust AI: AI technologies have demonstrated great value to missions as diverse as space-based imagery analysis, cyberattack warning, supply chain logistics and analysis of microbiologic systems. At the same time, the failure modes of AI technologies are poorly understood. DARPA is working to address this shortfall, with focused R&D, both analytic and empirical. DARPA’s success is essential for the Department to deploy AI technologies, particularly to the tactical edge, where reliable performance is required.
- Adversarial AI: The most powerful AI tool today is machine learning (ML). ML systems can be easily duped by changes to inputs that would never fool a human. The data used to train such systems can be corrupted. And, the software itself is vulnerable to cyber attack. These areas, and more, must be addressed at scale as more AI-enabled systems are operationally deployed.
- High Performance AI: Computer performance increases over the last decade have enabled the success of machine learning, in combination with large data sets, and software libraries. More performance at lower electrical power is essential to allow both data center and tactical deployments. DARPA has demonstrated analog processing of AI algorithms with 1000x speedup and 1000x power efficiency over state-of-the-art digital processors, and is researching AI-specific hardware designs. DARPA is also attacking the current inefficiency of machine learning, by researching methods to drastically reduce requirements for labeled training data.
- Next Generation AI: The machine learning algorithms that enable face recognition and self-driving vehicles were invented over 20 years ago. DARPA has taken the lead in pioneering research to develop the next generation of AI algorithms, which will transform computers from tools into problem-solving partners. DARPA research aims to enable AI systems to explain their actions, and to acquire and reason with common sense knowledge. DARPA R&D produced the first AI successes, such as expert systems and search, and more recently has advanced machine learning tools and hardware. DARPA is now creating the next wave of AI technologies that will enable the United States to maintain its technological edge in this critical area.
“Today, DARPA is pursuing more than 20 programs actively exploring ways to advance the state of the art in AI, pushing beyond second wave machine learning towards the third wave of contextual reasoning capabilities,” said Dr. Peter Highnam, DARPA’s deputy director. “In addition, we are actively working on over 50 programs that are leveraging AI in some capacity.
DARPA Seeks Proposals to Drive CPS Design With AI
The Defense Advanced Research Projects Agency (DARPA) announced in an Aug. 2019 news release it is seeking proposals for a new program in the agency’s multi-year, $2-billion AI Next Campaign. The Symbiotic Design for Cyber Physical Systems (CPS) program looks to introduce artificial intelligence (AI) and automation into the CPS design process, which DARPA said currently requires a significant amount of manual, skilled engineering work and creates long design cycles.
“Current approaches to designing cyber-physical systems are largely manual, costly, and inefficient, sometimes taking decades to complete,” said Dr. Sandeep Neema, a DARPA Information Innovation Office program manager. “To support the rapidly evolving defense landscape, we need a way of accelerating and streamlining CPS design – one that could take advantage of new machine learning and automation capabilities,” he said. Cyber-Physical Systems can be anything from automated vehicles to drones to missiles.
Symbiotic Design looks to fuse model-based design with machine learning to create a core of AI-enabled tools that designers can deploy to accelerate the CPS process from a design concept to developed system. “The tools will support the search, composition, evaluation, and exploration of knowledge bases and design corpora, and will come together to form an AI-enabled co-designers that provides its human counterparts with a true designed partner,” DARPA added.
The ultimate goal of the program is to exchange ideas between human and AI designers to allow the CPS to “‘autocomplete’ the design based on past learnings or experience.” Once a human designer has selected a potential design, the CPS AI will automate the evaluation of the design’s points using domain-specific analysis and simulation tools. DARPA also wants the AI solution to have a user-friendly interface to facilitate the human-machine collaboration so that the design process is accessible to more individuals and reduces the need for skilled experts to utilize the tool.
Machine Common Sense (MCS) equivalent to General knowledge of an average American
The Defense Advanced Research Projects Agency (DARPA), which announced a multi-year $2 billion “AI Next” “campaign , is tightening its focus on teaching machine “common sense” reasoning. Common sense reasoning is defined as “the basic ability to perceive, understand, and judge things that are shared by nearly all people and can be reasonably expected of nearly all people without need for debate.”
MCS will create the computing foundations needed to develop machine commonsense services to enable AI applications to understand new situations, monitor the reasonableness of their actions, transfer learning to new domains, and communicate more effectively with people. Machine common sense has long been a critical—but missing—component of Artificial Intelligence (AI). Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized. Developers must carefully train or program systems for every situation. General commonsense reasoning remains elusive. Common sense is the basic ability to perceive and understand the world that is shared by (“common to”) nearly all people.
Typical AI systems lack a general understanding of how the physical world works (i.e., intuitive physics), a basic understanding of human motives and behavior (i.e., intuitive psychology), and knowledge of the common facts that are obvious to an average adult.
The absence of common sense prevents an intelligent system from understanding its world, communicating naturally with people, behaving reasonably in unforeseen situations, and learning from new experiences. This absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general AI applications we would like to create in the future. However, there has been significant progress in AI along a number of dimensions that make it possible to address this difficult problem now. There continues to be rapid progress in all aspects of machine learning, especially deep learning, that is producing new representations and new techniques for semisupervised, self-supervised and unsupervised learning, as well as techniques for integrating learning and reasoning.
The MCS program proposes to take on the common sense problem by pursuing two diverse strategies to develop two different commonsense services, each with its own evaluation method:
• Foundations of Human Common Sense (Technical Area 1): to learn from experience, like a child, to construct computational models that mimic the foundational core knowledge systems of human cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation). To evaluate the computational models against cognitive development milestones, as evidenced in developmental psychology research studies and literature a Foundations of Human Common Sense Test Environment (Technical Area 2) will be used.
• Broad Common Sense (Technical Area 3): to learn from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language and image-based queries about commonsense phenomena. This service will mimic the general knowledge of an average American adult in 2018, as measured by the Allen Institute for Artificial Intelligence (AI2) Common Sense benchmark tests.
References and Resources also include:
https://www.darpa.mil/work-with-us/ai-next-campaign
https://www.meritalk.com/articles/darpa-seeks-proposals-to-drive-cps-design-with-ai/
