WASHINGTON – The leader of the Pentagon's artificial intelligence hub said Tuesday that he wants the office to become the "world's best software company." Nand Mulchandani, acting director of the Joint Artificial Intelligence Center, said he wants the DoD to be in a place where service members can write lines of code quickly to do a specific task, receive an authorization to operate quickly and perform the function and throw the code away. If that's the case "we will have won the next war," he said, "because that level of agility and insight and reconfigurability is where we want to be. "We'll be the world's best software company; we happen to be in the military business," Mulchandani said, said on a webinar hosted by the Institute for Security and Technology. The cornerstone of the JAIC's work is the Joint Common Foundation, a recently awarded platform that's designed to provide common artificial intelligence tools and datasets across the Defense Department in an effort to knock down silos.

The report begins with a brief introduction and market overview of the Artificial Intelligence (AI) in Cybersecurity industry followed by its market scope and …

Enterprises around the world are rapidly incorporating artificial intelligence (AI) into existing and new products and processes. This effort is not just to improve such offerings and services, but to achieve a qualitatively higher level of capability not possible before. It is clear that AI carries the potential for many new opportunities, across all industries, but it is also already recognized that it brings numerous risks as well. As with any technology, senior management and board directors need to be aware of both the opportunity and the risk in order to successfully and responsibly manage the enterprise. The opportunities are great--AI can assist in robotic process automation (RPA), machine learning, natural language processing, finding new drugs and therapies, and will be essential for driverless transportation--but if the risks are downplayed or overlooked, there can be serious reputational and/or legal consequences.

The first exoplanet was discovered in 1992 and nearly 30 years later, the feat has been accomplished by artificial intelligence (AI). Scientists developed a new machine-learning algorithm that uncovered 50 potential Martian worlds beyond our solar system. The technology is capable of separating real planets from fake ones in samples of thousands of candidates spotted by NASA telescope missions, such as TESS and Kepler. The team trained the AI to recognize exoplanets using a database of confirmed cosmic orbs and false positives shown in data. The first exoplanet was discovered in 1992 (artist's impression) and nearly 30 years later, the feat has been accomplished by artificial intelligence (AI).

The Defense Innovation Unit is bringing together the best of commercially available artificial intelligence technology and the Defense Department's vast cache of archived medical data to teach computers how to identify cancers and other medical irregularities. The result will be new tools medical professionals can use to more accurately and more quickly identify medical issues in patients. The new DIU project, called "Predictive Health," also involves the Defense Health Agency, three private-sector businesses and the Joint Artificial Intelligence Center. The new capability directly supports the development of the JAIC's warfighter health initiative, which is working with the Defense Health Agency and the military services to field AI solutions that are aimed at transforming military health care. The JAIC is also providing the funding and adding technical expertise for the broader initiative.

In this paper, we propose an unsupervised neural model for learning a discrete embedding of words. While being discrete, our embedding supports vector arithmetic operations similar to continuous embeddings by interpreting each word as a set of propositional statements describing a rule. The formulation of our vector arithmetic closely reflects the logical structure originating from the symbolic sequential decision making formalism (classical/STRIPS planning). Contrary to the conventional wisdom that discrete representation cannot perform well due to the lack of ability to capture the uncertainty, our representation is competitive against the continuous representations in several downstream tasks. We demonstrate that our embedding is directly compatible with the symbolic, classical planning solvers by performing a "paraphrasing" task. Due to the discrete/logical decision making in classical algorithms with deterministic (non-probabilistic) completeness, and also because it does not require additional training on the paraphrasing dataset, our system can negatively answer a paraphrasing query (inexistence of solutions), and can answer that only some approximate solutions exist -- A feature that is missing in the recent, huge, purely neural language models such as GPT-3.

Efforts furthering the advancement of Artificial Intelligence (AI) will increasingly encompass AI Legal Reasoning (AILR) as a crucial element in the practice of law. It is argued in this research paper that the infusion of AI into existing and future legal activities and the judicial structure needs to be undertaken by mindfully observing an alignment with the core principles of justice. As such, the adoption of AI has a profound twofold possibility of either usurping the principles of justice, doing so in a Dystopian manner, and yet also capable to bolster the principles of justice, doing so in a Utopian way. By examining the principles of justice across the Levels of Autonomy (LoA) of AI Legal Reasoning, the case is made that there is an ongoing tension underlying the efforts to develop and deploy AI that can demonstrably determine the impacts and sway upon each core principle of justice and the collective set.

Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of individuals correlate in practice with different modes of human-computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia.

The first exoplanet was discovered in 1992 and nearly 30 years later, the feat has been accomplished by artificial intelligence (AI). Scientists developed a new machine learning algorithm that uncovered 50 potential Martian worlds beyond our solar system. The technology is capable of separating real planets from fake ones in samples of thousands of candidates spotted by NASA telescope missions, such as TESS and Kepler. The team trained the AI to recognize exoplanets using a database of confirmed cosmic orbs and false positives shown in data. The first exoplanet was discovered in 1992 (artist's impression) and nearly 30 years later, the feat has been accomplished by artificial intelligence (AI).

To help keep first responders safe, University of Central Florida researchers have developed an artificial intelligence method that not only rapidly and remotely detects the powerful drug fentanyl, but also teaches itself to detect any previously unknown derivatives made in clandestine batches. The method, published recently in the journal Scientific Reports, uses infrared light spectroscopy and can be used in a portable, tabletop device. "Fentanyl is a leading cause of drug overdose death in the U.S.," said Mengyu Xu, an assistant professor in UCF's Department of Statistics and Data Science and the study's lead author. "It and its derivatives have a low lethal dose and may lead to death of the user, could pose hazards for first responders and even be weaponized in an aerosol." Fentanyl, which is 50 to 100 times more potent than morphine according to the U.S. Centers for Disease Control and Prevention, can be prescribed legally to treat patients who have severe pain, but it also is sometimes made and used illegally.