It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that iterated learning alone, and communication alone, do not yield the same outcome as clearly.

Although data-driven motion mapping methods are promising to allow intuitive robot control and teleoperation that generate human-like robot movement, they normally require tedious pair-wise training for each specific human and robot pair. This paper proposes a transferability-based mapping scheme to allow new robot and human input systems to leverage the mapping of existing trained pairs to form a mapping transfer chain, which will reduce the number of new pair-specific mappings that need to be generated. The first part of the mapping schematic is the development of a Synergy Mapping via Dual-Autoencoder (SyDa) method. This method uses the latent features from two autoencoders to extract the common synergy of the two agents. Secondly, a transferability metric is created that approximates how well the mapping between a pair of agents will perform compared to another pair before creating the motion mapping models. Thus, it can guide the formation of an optimal mapping chain for the new human-robot pair. Experiments with human subjects and a Pepper robot demonstrated 1) The SyDa method improves the accuracy and generalizability of the pair mappings, 2) the SyDa method allows for bidirectional mapping that does not prioritize the direction of mapping motion, and 3) the transferability metric measures how compatible two agents are for accurate teleoperation. The combination of the SyDa method and transferability metric creates generalizable and accurate mapping need to create the transfer mapping chain.

Differential equations play a pivotal role in modern world ranging from science, engineering, ecology, economics and finance where these can be used to model many physical systems and processes. In this paper, we study two mathematical models of a drug assimilation in the human system using Physics Informed Neural Networks (PINNs). In the first model, we consider the case of single dose of drug in the human system and in the second case, we consider the course of this drug taken at regular intervals. We have used the compartment diagram to model these cases. The resulting differential equations are solved using PINN, where we employ a feed forward multilayer perceptron as function approximator and the network parameters are tuned for minimum error. Further, the network is trained by finding the gradient of the error function with respect to the network parameters. We have employed DeepXDE, a python library for PINNs, to solve the simultaneous first order differential equations describing the two models of drug assimilation. The results show high degree of accuracy between the exact solution and the predicted solution as much as the resulting error reaches10^(-11) for the first model and 10^(-8) for the second model. This validates the use of PINN in solving any dynamical system.

An Artificial General Intelligence is coming, and we have no clue how homo sapiens might be impacted. When we think of preparing for our future, we used to think about going to good college and moving for a good job that would put us on a relatively good career trajectory for a stable life where we will prosper in a free market meritocracy where we compete against fellow humans. However, over the course of the next few decades homo sapiens including generation GenZ and Alpha, may be among the last people to grow up in a pre automation and pre AGI world. Considering the exponential levels of technological progress expected in the next 30 years, that's hard to put into words or even historical context. Namely, because there's no historical precedent and no words to describe what the next-gen AI might become.

Two key characteristics of artificial intelligence (AI) will help us address the challenges we face, says Celine Herweijer, Partner, Innovation and Sustainability, at PwC UK. First, AI helps increase productivity, and second, it speeds up the process of learning and understanding, which enables scientific discovery. Last week's AI for Good Global Summit at ITU headquarters in Geneva, Switzerland gathered top minds across various sectors to discuss the potential of AI to solve some of the most urgent challenges and transform our future. For Ms. Herweijer, the health of our planet is one of the most pressing issues this generation faces. "Our planet has never been under so much strain, whether it's climate change, oceans, air and water quality or the extinction crisis with 1 in 5 species facing extinction." She then posed the question, "How can we transform our human systems, our cities, our transport networks, using technologies so that we don't create the negative impacts that past industrial revolutions have created?"

Can AI make us healthier, happier, better? That's the question on everyone's minds these days. If engineers continue to program AI to take away our jobs and our need to utilize our capacity for deductive reasoning and common sense, then the answer is clearly NO. Human beings thrive on being challenged. It develops will, intelligence, adaptability whether we recognize that fact or not.

Recently, popular career site Glassdoor published its updated list of Best Jobs in America, with data scientist taking top spot for the year. Glassdoor gave the career a job score of 4.8 out of 5, a $110,000 median base salary, and noted 4,000 job openings at the time of writing. The announcement was rather unsurprising, given the fact that it was a repeat of the previous years' findings: According to Glassdoor list of 25 Best Jobs in America, Data Scientist is the No. 1 job! It has 1,736 current openings, median Base Salary $116,840, and good career opportunity and job score. Just a few months ago, job search site CareerCast also published its list of 10 Best IT & Engineering Jobs 2016, which -- surprise!

Organizations play a pivotal role in the dynamics of social, economic, and ecological systems. Current organizational life-cycle models do not adequately consider the impact of propensities (deeply ingrained preferences and patterns of behavior) on organizational culture and evolution. On a global basis, the predominant thinking modes in organizations are driven by senior executives, marketers, financial experts, legal resources, and the engineers and scientists that create our technology-rich world. Each of these groups has, in aggregate, embedded propensities or tendencies that profoundly shape decision-making patterns and overall social dynamics. Dominant propensities can make organizations vulnerable to risks by inhibiting the level of systems thinking and networking necessary to ensure integration within a global socio-ecological context. The spectrum of propensities within an organization shapes the relative resilience of its human and management systems, and ultimately determines organizational effectiveness. This paper proposes a model for organizational evolution that links the role of propensities to adaptability and resilience. Conscious effort to expand the intelligence of organizations through diversification of propensities better equips organizations to achieve adaptability and sustainability.