While Apple is reportedly working on AI technology that can detect mental health states and emotion, some are skeptical. It is unclear and still unproven whether AI is reliable for producing clear diagnoses and uncertain how such "emotion AI" would be used in the field, according to Jorge Barraza, assistant professor of the practice of psychology at the University of Southern California and CSO at Immersion, a neuroscience tech vendor. "When we infer things from emotion AI at the macro level -- meaning that we tend to see patterns at the macro level -- at the individual level it starts becoming a little more dubious," Barraza said. Outside of a social context, "it's unclear how much meaning [emotion] has in order for us to understand what people's psychological experiences are," he added. "Different types of expressions or emoting might have different meanings whether it's in a social context or whether it is not."

Organizations that contributed to the report include representatives from arXiv, AI Ethics Lab, Black in AI, Bloomberg Government, Burning Glass Technologies, Computing Research Association, Elsevier, Intento, International Federation of Robotics, Joint Research Center, European Commission, LinkedIn, Liquidnet, McKinsey Global Institute, Microsoft Academic Graph, National Institute of Standards and Technology, Nesta, NetBase Quid, PostEra, Queer in AI, State of AI Report, Women in Machine Learning, and many individual contributors. Supporting partners to the report include McKinsey & Company, Google, OpenAI, Genpact, AI21 labs, and PricewaterhouseCoopers.

Natural language processing (NLP) is a subset of artificial intelligence (AI) that helps computers understand, interpret, and communicate the way humans do. Because of those abilities, industries worldwide have leveraged NLP to upgrade their operations and customer relationships. While figuring out the best data science language for NLP can be quite tricky, considering most people are undecided over the R vs Python debate, companies aren't hindered from pursuing the use of NLP because of its glaring benefits. If you're thinking of using NLP, but you doubt if it's right for your business, check out these six ways you can benefit from the technology. NLP chatbots can boost your customer support and customer service efficiency by giving fast, accurate, and round-the-clock replies to your shoppers' queries and concerns.

In science, macro level descriptions of the causal interactions within complex, dynamical systems are typically deemed convenient, but ultimately reducible to a complete causal account of the underlying micro constituents. Yet, such a reductionist perspective is hard to square with several issues related to autonomy and agency: (1) agents require (causal) borders that separate them from the environment, (2) at least in a biological context, agents are associated with macroscopic systems, and (3) agents are supposed to act upon their environment. Integrated information theory (IIT) (Oizumi et al., 2014) offers a quantitative account of causation based on a set of causal principles, including notions such as causal specificity, composition, and irreducibility, that challenges the reductionist perspective in multiple ways. First, the IIT formalism provides a complete account of a system's causal structure, including irreducible higher-order mechanisms constituted of multiple system elements. Second, a system's amount of integrated information ($\Phi$) measures the causal constraints a system exerts onto itself and can peak at a macro level of description (Hoel et al., 2016; Marshall et al., 2018). Finally, the causal principles of IIT can also be employed to identify and quantify the actual causes of events ("what caused what"), such as an agent's actions (Albantakis et al., 2019). Here, we demonstrate this framework by example of a simulated agent, equipped with a small neural network, that forms a maximum of $\Phi$ at a macro scale.

It has been abundantly clear for quite some time that enterprise technology development has been focused on the digital workplace, but according to a recent book from Tom Seibel, which we discussed last month, what is happening now is a game changer. In Digital Transformation: Survive and Thrive in an Era of Mass Extinction, he argues that technology is at a inflection point and that the principle technology discussion in the digital workplace at the moment is how to manage the convergence of four megatrends: cloud computing, big data, artificial intelligence (AI) and Internet of Things (IoT). While these systems are making work more'intelligent' they are also increasingly difficult to manage. The manufacturing industry, for example, has been working with these trends separately for years in a number of different ways, according to Maryanne Steidinger of Webalo, and they are all starting to dovetail through the use of data. Here's how each technology is working in the enterprise: Software is now being offered as a service (i.e., cloud-based, where you are essentially leasing vs. purchasing it) for the past 10 years.

Irregular Warfare (IW), with its emphasis on social and cognitive phenomena such as population sentiment, is a major new focus of the Department of Defense (DoD). One of the most important classes of IW action is Information Operations (IO), the use of information to influence sentiment. With the DoD’s new focus on IW comes the new need to analyze and forecast the effects of IO actions on population sentiment. Analysts at the DoD traditionally use Modeling and Simulation to analyze and forecast the effects of conventional warfare’s actions on the outcome of wars, but IW and IO in particular are far more complex than conventional physics-based simulations. DoD analysts are in the early stages of looking for scientifically rigorous methods in the Modeling and Simulation of IO’s complex effects. This paper presents the state of IO modeling and simulation in the DoD, using examples from several computer models now being used, in these early stages of IW analysis. It discusses how the ideas of Complex Adaptive Systems (CAS) and threshold events in particular may be incorporated into IO modeling in order to increase its scientific rigor, fidelity, and validity.