A new generation of global inequalities fuelled by climate change and technology could trigger violence and political instability if left unchecked, the United Nations warned on Monday. Climate change and technology rather than wealth and income are the modern-day wedges that are increasingly dividing the haves from the have-nots, said the United Nations Development Programme (UNDP) in its 2019 Human Development Report. These forms of inequality are rising as progress has been made in more traditional measures of inequality such as extreme poverty and disease, it said. "Under the shadow of the climate crisis and sweeping technological change, inequalities in human development are taking new forms," the report said. "The climate crisis is already hitting the poorest hardest, while technological advances such as machine learning and artificial intelligence can leave behind entire groups of people, even countries."

Trapezoids, triangles and many other geometric shapes -- that's what one would see if they flew a drone over the high desert in Peru, South America. These giant geometric figures resemble birds, insects and other living beings. These are the famous Nazca lines which were discovered in the 1920s. In total, there are over 800 straight lines and 300 geometric figures. Archaeologists have been studying these lies ever since their discovery and still continue to do so till date.

Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve based models such as Renewables.ninja (RN) have been used for deriving synthetic time series for wind power generation despite their need for accurate location information as well as for bias correction, and their insufficient replication of extreme events and short-term power ramps. We assess how time series generated by machine learning models (MLM) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we apply neural networks to one MERRA2 reanalysis wind speed input dataset with no location information and one with basic location information. The resulting time series and the RN time series are compared with actual generation. Both MLM time series feature equal or even better time series quality than RN depending on the characteristics considered. We conclude that MLM models can, even when reducing information on turbine locations and turbine types, produce time series of at least equal quality to RN.

What AI tools are needed for SE? Ideally, we should have simple rules that peek at data, then say "use this tool" or "use that tool". To find such a rule, we explored 120 different data sets addressing numerous problems, including bad smell detection, predicting Github issue close time, bug report analysis, defect prediction and dozens of other non-SE problems. To this data, we apply a SE-based tool that (a)~out-performs the state-of-the-art for these SE problems yet (b)~fails very badly on standard AI problems. In those results, we can find a simple rule for when to use/avoid the SE-based tool. SE data is often about infrequent issues, like the occasional defect, or the rarely exploited security violation, or the requirement that holds for one special case. But as we show, standard AI tools work best when the target is relatively more frequent. Also, we can exploit these special properties of SE, to great effect (to rapidly find better optimizations for SE tasks via a tactic called "dodging", explained in this paper). More generally, this result says we need a new kind of SE research for developing new AI tools that are more suited to SE problems.

In multi-agent domains (MADs), an agent's action may not just change the world and the agent's knowledge and beliefs about the world, but also may change other agents' knowledge and beliefs about the world and their knowledge and beliefs about other agents' knowledge and beliefs about the world. The goals of an agent in a multi-agent world may involve manipulating the knowledge and beliefs of other agents' and again, not just their knowledge/belief about the world, but also their knowledge about other agents' knowledge about the world. Our goal is to present an action language (mA+) that has the necessary features to address the above aspects in representing and RAC in MADs. mA+ allows the representation of and reasoning about different types of actions that an agent can perform in a domain where many other agents might be present -- such as world-altering actions, sensing actions, and announcement/communication actions. It also allows the specification of agents' dynamic awareness of action occurrences which has future implications on what agents' know about the world and other agents' knowledge about the world. mA+ considers three different types of awareness: full-, partial- awareness, and complete oblivion of an action occurrence and its effects. This keeps the language simple, yet powerful enough to address a large variety of knowledge manipulation scenarios in MADs. The semantics of mA+ relies on the notion of state, which is described by a pointed Kripke model and is used to encode the agent's knowledge and the real state of the world. It is defined by a transition function that maps pairs of actions and states into sets of states. We illustrate properties of the action theories, including properties that guarantee finiteness of the set of initial states and their practical implementability. Finally, we relate mA+ to other related formalisms that contribute to RAC in MADs.

What will the report include?

We are living in an era of information overload, where there is an overabundance of information, yet at the same time, it's hard to ascertain what's credible and what's not. Technology changes rapidly, and innovation is on the rise. How can we educate ourselves to know: (1) What products are available on the market? INTERVIEW HIGHLIGHTS: This episode of #AskTheCEO features a presentation Avrohom Gottheil gave in New Delhi, India for India's First Annual Deep Tech Summit, titled Deep Tech for All. "Time is the new currency, and that is what's driving the mass adoption of voice-based technology in the marketplace", said Avrohom [13:30] J. Dianne Dotson, Science Fiction Writer and Research Scientist, shares how in the future we will be able to leverage AI to search global DNA databases, such as 23 and me, and analyze people's genomes for disease-causing proteins so that we can disable them and stop diseases from spreading, right from the source.

To write this script we enter the first sentence into GPT-2 artificial intelligence. Every single word from then on was written by AI. This is how GPT-2's website describes it: While GPT-2 was only trained to predict the next word in a text, it surprisingly learned basic competence in some tasks like translating between languages and answering questions. That's without ever being told that it would be evaluated on those tasks. How do you say "hello" in spanish?

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