Although every country should look for ways to respond to the effects of automation, it's especially critical for developing nations, which will be hit hardest and have the fewest resources to cushion the blows. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Much has been written about the rise of automation in developed countries. Economists have been busily creating models seeking to quantify the likely impact of automation on employment.1 However, far less has been written about the potential effects on work in developing nations.

In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal. In the natural world, intelligent organisms learn from internal drives, bypassing the need for external signals, which is beneficial for a wide range of tasks. Motivated by this observation, we propose to formulate an intrinsic objective as the mutual information between the goal states and the controllable states. This objective encourages the agent to take control of its environment. Subsequently, we derive a surrogate objective of the proposed reward function, which can be optimized efficiently. Lastly, we evaluate the developed framework in different robotic manipulation and navigation tasks and demonstrate the efficacy of our approach. A video showing experimental results is available at \url{https://youtu.be/CT4CKMWBYz0}.

ABOARD, A U.S. MILITARY AIRCRAFT – The top U.S. commander for the Middle East slipped quietly into Iraq Tuesday, as the Trump administration works to salvage relations with Iraqi leaders and shut down the government's push for an American troop withdrawal. Marine Gen. Frank McKenzie became the most senior U.S. military official to visit since an American drone strike in Baghdad last month killed a top Iranian general, enraging the Iraqis. McKenzie met with Iraq leaders in Baghdad and then went to see American troops at al-Asad Air base, which was bombed by Iran last month in retaliation for the drone attack. Later, he said he was "heartened" by the meetings, adding, "I think we're going to be able to find a way forward." His visit comes amid heightened anti-American sentiment that has fueled violent protests, rocket attacks on the embassy and a vote by the Iraqi parliament pushing for withdrawal of U.S. troops from the country.

The need for diagnostic images is rapidly exceeding the capacity of available specialists worldwide, and more acutely so in developing countries where access to healthcare remains challenging. AI is showing promise in TB diagnosis, but it could do much more, provided incentives went in the right direction, recent initiatives in Uganda have shown. Shortage of radiologists is a worldwide phenomenon, but the lack of specialists is much more pronounced in developing countries. In Uganda, only 20 doctors signed up for radiology residency in 2018 and the radiologist-to-population ratio was approximately 1:1,600,000; and it was even lower in Malawi – 1:8,000,000. With AI, possibilities are emerging to fill in this vertiginous gap and tend to these populations' medical imaging needs.

The world's tech powers are sending giant sums of money spinning into Canada, but while many see this as a sign of success, others are worried about researchers and intellectual property being swallowed wholesale. The country is in the midst of an artificial intelligence (AI) boom, with Google, Microsoft, Facebook, Huawei and other global heavyweights spending millions or even hundreds of millions of dollars on research hubs in Quebec, Ontario and Alberta. Canadian doors are open – some fear too open. Jim Hinton, an IP lawyer and founder of the Own Innovation consultancy, reckons that more than half of all AI patents in Canada end up being owned by foreign companies. What we need to be doing is getting money out of our ideas ourselves, instead of seeing foreign talent scoop it all up," said Hinton. "Otherwise we'll never have a Canadian champion." The country is home to hundreds of fledgling AI companies, including much-talked-about start-ups like Element AI and Deep Genomics, but they remain relatively small. "They don't have a strong market position yet," Hinton says. Deep learning pioneers such as Yoshua Bengio and Geoffrey Hinton (no relation to Jim) have nurtured top-notch talent in AI in Canada for years, back when AI was an emerging field. But despite Canadian inheriting this brilliant AI lead from the country's AI "godfathers", big foreign players have an unassailable advantage over homegrown efforts, Hinton said. "It's not an easy go for the average company to make a business out of AI.

In the last 10 years or so, global financial institutions and regulatory bodies have come together to unleash a battery of regulations for Banking, Insurance, and Micro-economies. The advancement of new technologies such as AI, Machine Learning Engineering, Big Data, Cloud and Edge Computing, Blockchain and Crypto, and low-code DevOps, have heavily disrupted the RegTech industry. This primer dives deep into the world of Regulatory Tech, or RegTech which is disrupted by the new emerging technologies. But, first, let's learn some basic definitions and industry trends. In a recent blog, Brian Clark, CEO of Ascent had said that RegTech is slated to become mainstream, even as early adopters begin "to see the actual, tangible benefit" these RegTech tools can provide.

State-of-the-art performance and low system complexity has made deep-learning an increasingly attractive solution for big data analytics. However, limiting assumptions of end-to-end learning regimes hinder the use of neural networks on large application-grade datasets. This work addresses the assumption that output class-labels are defined for all classes in the domain. The amount of data collected by modern-day sensors span over an incomprehensible range of potential classes. Therefore, we propose a new learning regime where only some, but not all, classes of the training data are of interest to the classification system. The semi-supervised learning scenario in big data requires the assumption of a partial class mismatch between labelled and unlabelled training data. With classification systems required to classify source classes indicated by labelled samples while separating novel classes indicated by unlabelled samples, we find ourselves in an open-set case (vs closed set with only source classes). However, introducing samples from novel classes into the training set indicates a more relaxed open-set case. As such, our proposed regime of \textit{quasi-open set semi-supervised learning} is introduced. We propose a suitable method to train under quasi-open set semi-supervised learning that makes use of Wasserstein generative adversarial networks (WGANs). A trained classification certainty estimation within the discriminator (or critic) network is used to enable a reject option for the classifier. By placing a threshold on this certainty estimation, the reject option accepts classifications of source classes and rejects novel classes. Big data end-to-end training is promoted by developing models that recognize input samples do not necessarily belong to output labels. We believe this essential for big data analytics, and urge more work under quasi-open set semi-supervised learning.

Lightning strokes create powerful electromagnetic pulses that routinely cause very low frequency (VLF) waves to propagate across hemispheres along geomagnetic field lines. VLF antenna receivers can be used to detect these whistler waves generated by these lightning strokes. The particular time/frequency dependence of the received whistler wave enables the estimation of electron density in the plasmasphere region of the magnetosphere. Therefore the identification and characterisation of whistlers are important tasks to monitor the plasmasphere in real time and to build large databases of events to be used for statistical studies. The current state of the art in detecting whistler is the Automatic Whistler Detection (A WD) method developed by Lichtenberger (2009) [1]. This method is based on image correlation in 2 dimensions and requires significant computing hardware situated at the VLF receiver antennas (e.g. in Antarctica). The aim of this work is to develop a machine learning based model capable of automatically detecting whistlers in the data provided by the VLF receivers. The approach is to use a combination of image classification and localisation on the spectrogram data generated by the VLF receivers to identify and localise each whistler. The data at hand has around 2300 events identified by A WD at SANAE and Marion and will be used as training, validation, and testing data. Three detector designs have been proposed. The first one using a similar method to A WD, the second using image classification on regions of interest extracted from a spectrogram, and the last one using YOLO, the current state of the art in object detection. It has been shown that these detectors can achieve a misdetection and false alarm of less than 15% on Marion's dataset. 1 Introduction Lightning strokes create powerful electromagnetic pulses that result in Very Low Frequency (VLF) waves propagating along the magnetic field lines of the earth. Due to the dipole shape of the geomagnetic field, these waves travel upward from the stroke location out through portions of the plasmasphere and back to the Earth's surface at the field line foot point in the opposite hemisphere. VLF antenna receivers set up at various high and middle latitude locations can detect whistler waves generated by these lightning strokes. The propagation time delay of these waves is dependent on the plasma density along the propagation path. This enables the use of whistler wave observations for characterising the plasmasphere in terms of particle number and energy density. The dynamics of energetic particle populations in the plasmasphere are an important factor in characterising the risk to spacecraft in orbit around Earth. Annual global lightning flash rates are on the order of 45 flash/s [2].

Today's Daily AI Roundup covers the latest Artificial Intelligence announcements on AI capabilities, AI mobility products, Robotic Service, Technology from Blue Prism, HCL Technologies, Noble.AI, Tata Consultancy Services and 4Cite. To equip young talent with the digital skills and experience needed for the future job market, Blue Prism, a global leader in Robotic Process Automation, has collaborated with the EY Foundation, a UK charity helping young people access employment opportunities, to provide paid work experience and mentors through the EY Foundation ten month Smart Futures programme. HCL Technologies (HCL), a leading global technology company, announced that it has been named a Top Employer 2020 in the United Kingdom, Sweden, Germany, the Netherlands, Poland, France and South Africa. Dentsu Aegis Network has acquired 4Cite Marketing, a leading people-based identification and data services technology company.

As more and more complex AI systems are introduced into our day-to-day lives, it becomes important that everyday users can work and interact with such systems with relative ease. Orchestrating such interactions require the system to be capable of providing explanations and rationale for its decisions and be able to field queries about alternative decisions. A significant hurdle to allowing for such explanatory dialogue could be the mismatch between the complex representations that the systems use to reason about the task and the terms in which the user may be viewing the task. This paper introduces methods that can be leveraged to provide contrastive explanations in terms of user-specified concepts for deterministic sequential decision-making settings where the system dynamics may be best represented in terms of black box simulators. We do this by assuming that system dynamics can at least be partly captured in terms of symbolic planning models, and we provide explanations in terms of these models. We implement this method using a simulator for a popular Atari game (Montezuma's Revenge) and perform user studies to verify whether people would find explanations generated in this form useful.