AI robot lawyers are here--and they aren't going away. When you hear the phrase "robot lawyer," what comes to mind? My brain conjures up an image of C-3PO in a three-piece suit, shuffling around a courtroom, while throwing out cross-examination quips such as: "Don't call me a mindless philosopher, you overweight glob of prosecuting witness grease!" Artificial intelligence (AI) is, in fact, becoming a mainstay component of the legal profession. In some circumstances, this analytics-crunching technology is using algorithms and machine learning to do work that was previously done by entry-level lawyers.

AI robot lawyers are here--and they aren't going away. When you hear the phrase "robot lawyer," what comes to mind? My brain conjures up an image of C-3PO in a three-piece suit, shuffling around a courtroom, while throwing out cross-examination quips such as: "Don't call me a mindless philosopher, you overweight glob of prosecuting witness grease!" Artificial intelligence (AI) is, in fact, becoming a mainstay component of the legal profession. In some circumstances, this analytics-crunching technology is using algorithms and machine learning to do work that was previously done by entry-level lawyers.

The U.S. Patent and Trademark Office recently launched a recruitment effort to hire its first-ever senior-level artificial intelligence expert to advance the agency's applications of the emerging technology and provide technical expertise to keep employees on the leading edge. In a conversation with Nextgov, USPTO's chief information officer provided a look inside the search to fill the new role and explained how it all fits into the agency's broader vision around modernization. "We need to figure out how we can use those algorithms to the best of our abilities," CIO Henry "Jamie" Holcombe said Friday. "We've seen an explosion in AI submissions and so AI is now maturing to a point to where it actually can be used--we don't want it to be a buzzword." USPTO's mission is to award patents to inventors and businesses and register trademarks for products and intellectual property. When Holcombe took on the agency's top information technology management role earlier this year, the office already had many AI-related efforts underway.

Diveplane, the company keeping the humanity in artificial intelligence (AI), today announced the availability of GEMINAI, the industry's first verifiable synthetic'twin' dataset. GEMINAI empowers businesses and government organizations to easily and safely sell, share and analyze sensitive datasets without the fear of mishandling, loss or theft. The'twin' dataset looks, acts, and feels realistic for the purposes of data modeling and analysis, but does not contain any personally identifiable information, which is critical for businesses that need to adhere to national and international privacy laws and compliance requirements, like GDPR, PHI and HIPAA. "We love seeing AI increasingly adopted by many industries, but we're finding that not all AI is created and trained equally," said Dr. Michael Capps, CEO of Diveplane. "Many businesses are forced to use inaccurate or incomplete data to train their AI due to privacy requirements, which can lead to the AI making poor or misleading decisions. With GEMINAI, we're eliminating that risk by creating a verifiable synthetic'twin' of the dataset, so that businesses don't need to sacrifice the quality of their AI for the sake of privacy. GEMINAI offers the best of both worlds and we're excited to introduce this first-of-its-kind technology to the market."

AI is poised to have an increasing influence on the way companies create new content, paving the way for new forms of human-machine collaboration. AI is maturing at varying rates around the world, with some organizations using these technologies--including machine learning, deep learning, natural language processing, and computer vision--to support external and internal organizational capabilities. For media and entertainment companies and other content producers in particular, AI may also offer a startling range of possibilities for the creative process, enabling individuals and businesses to generate new content with minimized human input. In a global analysis based on Deloitte's most recent State of AI in the Enterprise survey, early adopters were asked to identify the primary benefits of implementing AI in their organizations.¹ Respondents say using AI to enhance existing products and services is their most sought-after externally focused benefit, with 43% ranking it in their top three, while 31% prioritize using it to optimize external processes. The top internally focused benefit, meanwhile, is optimizing business operations, with 41% placing it among their top three choices, followed by using AI to make better decisions, cited by 34% of respondents.

The process of patent application examination by USPTO examiners or by patent attorneys and registered patent agents in preparing applications is a significant intellectual activity that at present must be undertaken by individuals based on their knowledge of [among other things] the state of relevant technology and existence of prior art, the evolution of that technology, its scientific basis, its present and future use, and existing published work on the subject. Patent application preparation and examination to include the preparation of rejections by PTO examiners and responses to these rejections from applicants are driven by a "sacred" text called the Manual of Patent Examining Procedures (MPEP). The MPEP, a document of more than 3700 pages, is known to disturb the sleep of even the most brilliant and seasoned patent practitioners. While a detailed discussion of patent examination and prosecution procedures is far beyond the scope of this essay and the professional competence of its author, it is well worth noting some of the frequent activities included in patent prosecution and examination for reasons that will soon become apparent. For example, in determining if an invention is patentable, inventors and patent practitioners must identify links to prior art references.

On a recent afternoon at the NVIDIA robotics research lab in Seattle's University District, researchers use a simulated kitchen to test robots' ability to perform simple tasks such as grabbing objects. A 5-feet 7-inch tall white robot, basically a spindly arm affixed with a claw of the sort customarily found in an arcade vending machine, glided around the kitchen on its two Segway wheels. Following the command of a research scientist sitting at a nearby computer, the robot grabbed a Cheez-It box on the counter and extended its limb to gently place the snacks inside a cabinet. "What's deceptive is that what's simple to us in the kitchen is challenging for a robot," said University of Washington Computer Science and Engineering Professor Dieter Fox, who also serves as the lab's senior director of robotics research. The Silicon Valley-based technology company opened the robotics lab last fall to harness the UW's talent in a sector where Seattle plays a central role. Still, paranoia around the capabilities of AI technology persist.

Do large datasets provide value to psychologists? Without a systematic methodology for working with such datasets, there is a valid concern that analyses will produce noise artifacts rather than true effects. In this paper, we offer a way to enable researchers to systematically build models and identify novel phenomena in large datasets. One traditional approach is to analyze the residuals of models---the biggest errors they make in predicting the data---to discover what might be missing from those models. However, once a dataset is sufficiently large, machine learning algorithms approximate the true underlying function better than the data, suggesting instead that the predictions of these data-driven models should be used to guide model-building. We call this approach "Scientific Regret Minimization" (SRM) as it focuses on minimizing errors for cases that we know should have been predictable. We demonstrate this methodology on a subset of the Moral Machine dataset, a public collection of roughly forty million moral decisions. Using SRM, we found that incorporating a set of deontological principles that capture dimensions along which groups of agents can vary (e.g. sex and age) improves a computational model of human moral judgment. Furthermore, we were able to identify and independently validate three interesting moral phenomena: criminal dehumanization, age of responsibility, and asymmetric notions of responsibility.

We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. In settings that have historically had discrimination, we are interested in defining fairness with respect to a protected group, the group which has historically been disadvantaged. Among many recent attempts to achieve algorithmic fairness (Dwork et al., 2012; Hardt et al., 2016; Zemel et al., 2013; Zafar et al., 2015), learning fair representations has attracted increasing attention However, it has long been empirically observed (Calders et al., 2009) and recently been proved (Zhao Part of this work was done when Han Zhao was visiting the V ector Institute, Toronto. In this work, we provide an affirmative answer to the above question by proposing an algorithm to align the conditional distributions (on the target variable) of representations across different demographic subgroups.

If you remember the days of cassette tapes, floppy disks and flip phones, then we don't need to tell you how quickly technology is moving lately. Now it seems we're running headlong into the era of artificial intelligence. Yes, smart computers capable of learning and adapting to solve complex problems. We're not quite to HAL yet, but it seems technology is getting there. And with these new technologies comes a new vocabulary.