After carrying out a successful pilot at its Bagdad copper operation, Freeport McMoRan says it is rolling out a program across its North America and South America mines involving the use of data science, machine learning and integrated functional teams. The program, aimed at addressing bottlenecks, providing cost benefits and driving improved overall performance, was announced in its December quarter results this week. It said: "During 2019, FCX (Freeport) advanced initiatives in its North America and South America mining operations to enhance productivity, expand margins and reduce the capital intensity of the business through the utilisation of new technology applications in combination with a more interactive operating structure." It said the Bagdad mine (Arizona, USA) pilot program, initiated in late 2018, was "highly successful" in utilising these innovative technologies and it would build on this for the implementation across its other mines in North and South America. According to a report in the Financial Times, the system at Bagdad found that the mine was producing seven distinct types of ore and that the processing method, which involves flotation, could be adjusted to recover more copper by adjusting the PH level.

This week, Alphabet CEO Sundar Pichai and IBM CEO Ginni Rometty called for AI to get its own regulation system. Alphabet CEO Pichai stated that it was "too important not to", going on to expand by explaining that sectors within AI technology, such as autonomous cars and healthtech, needed their own sets of rules. IBM CEO Rometty joined the discussion with the idea of'precision regulation', stating that it is not the technology itself, but how it is used that should be regulated, using facial recognition as an example of technology that can harm people's privacy as well as having its benefits, such as catching criminals. Asheesh Mehra, co-founder and CEO at AntWorks, explains why regulating AI is important. Without it, the technology won't take the world by storm These announcements have come in spite of recent setbacks in the sphere; just last week it was revealed that the European Commission were considering a five year ban on facial recognition, and Google's last attempt to assemble an AI ethics board lasted under two weeks due to controversy over who was appointed.

We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes and apply these to three game environments.

The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.

"We are already at the point where you can't tell the difference between deepfakes and the real thing," Professor Hao Li of the University of Southern California tells the BBC. We are at the computer scientist's deepfake installation at the World Economic Forum in Davos which gives a hint of what he means. Like other deepfake tools, his software creates computer-manipulated videos of people - often politicians or celebrities - that are designed to look real. Most often this involves "face swapping", whereby the face of a celebrity is overlaid onto the likeness of someone else. As I sit, a camera films my face and projects it onto a screen in front of me; my features are then digitally mapped.

If Sunspring is anything to go by, artificial intelligence in film-making has some way to go. This short film, made as an entry to Sci-Fi London's 48-hour film-making competition in 2016, was written entirely by an AI. The director, Oscar Sharp, fed a few hundred sci-fi screenplays into a long short-term memory recurrent neural network (the type of software behind predictive text in a smartphone), then told it to write its own. The result was almost, but not quite, incoherent nonsense, riddled with cryptic nonsequiturs, bizarre turns of phrase and unfathomable stage directions such as "he is standing in the stars and sitting on the floor". All of which Sharp and his actors filmed with sincere commitment.

The value of building data-driven businesses with AI at their core is well known today, and business executives are rushing to implement the technology into their operations and gain a competitive advantage, but it's not as simple as creating a data lake and crafting AI models. A large number of AI companies attempting to implement more AI models or build AI-first businesses have experienced challenges. A December 2018 PwC survey found that only 4% of businesses have successfully implemented AI. That's why today the World Economic Forum released the AI toolkit for Boards of Directors. The AI toolkit for Boards of Directors is being released ahead of the annual WEF meeting in Davos, Switzerland where the toolkit will be formally debuted next week.

São Paulo, January 2020 - Virtual assistants are increasingly present in people's routine, whether to help, answer questions and facilitate daily life. What they all have in common are women's names and the standard female voice, such as Lu, Siri, Alexa, Nat, Bia, etc. According to a study entitled "I'd Blush If I Could" published by UNESCO in May 2019, virtual assistants via Artificial Intelligence suffer from high levels of gender prejudice, although they usually answer with tolerant, subservient and passive phrases. Based on this context, the "Hey Update My Voice" movement was launched in partnership with UNESCO with the objective of drawing attention to cyber education and respect for virtual assistants, and ask companies to update their assistants' responses. If even virtual assistants are harassed, can you imagine how many women are victims of this kind of violence?

Will the proliferation of AI and machine learning reinforce the worldwide digital divide? It's one of the questions the Global Talent Competitiveness Index (GTCI) and Global Cities Talent Competitiveness Index (GCTCI) seek to answer by benchmarking the ability of countries and cities to compete for talent. An answer has historically proven elusive, but the 7th annual reports published by Insead, Adecco Group, and Google suggest it might instead provide "significant" opportunities despite the fact that AI skills are "scarce" and "unequally distributed" across nations. "AI is changing many facets of business and society and, if properly used and governed, has potential to foster sustainable development," said Katell Le Goulven, executive director of the Insead Hoffmann Global Institute for Business and Society. "The GTCI report argues that with multi-stakeholder cooperation the technology could help achieve some of the SDGs [the United Nations' Sustainable Development Goals] such as those related to health (via personalized remote diagnosis and big data analysis to track and reduce endemic disease). But it also points to the imperative of closing the global digital skills gap to harness the potential of AI for good."

Clustering analysis has become a ubiquitous information retrieval tool in a wide range of domains, but a more automatic framework is still lacking. Though internal metrics are the key players towards a successful retrieval of clusters, their effectiveness on real-world datasets remains not fully understood, mainly because of their unrealistic assumptions underlying datasets. We hypothesized that capturing {\it traces of information gain} between increasingly complex clustering retrievals---{\it InfoGuide}---enables an automatic clustering analysis with improved clustering retrievals. We validated the {\it InfoGuide} hypothesis by capturing the traces of information gain using the Kolmogorov-Smirnov statistic and comparing the clusters retrieved by {\it InfoGuide} against those retrieved by other commonly used internal metrics in artificially-generated, benchmarks, and real-world datasets. Our results suggested that {\it InfoGuide} can enable a more automatic clustering analysis and may be more suitable for retrieving clusters in real-world datasets displaying nontrivial statistical properties.