Among the various companies, non-profits and researchers using tech company Google's TensorFlow platform, one application that has caught the attention of developers at the internet giant is PlantMD. Created by high school students Shaza Mehdi and Nile Ravenell, the app can detect diseases in plants. The duo, who showcased the app at Google's I/O annual developer conference this year, built it based on the Internet company's open-source machine learning library for data programming--TensorFlow. "PlantMD's machine learning model was inspired by a dataset from PlantVillage, a research and development unit at Penn State University. PlantVillage created an app called Nuru, Swahili for'light', to assist farmers to grow better cassava, a crop in Africa that provides food for over half a billion people daily," Fred Alcober, a member of Google's TensorFlow team, wrote in a blog post. Cassava plants, wrote Alcober, though very tolerant of harsh weather conditions, is susceptible to pests and diseases.

This article focuses on the political and geopolitical consequences of the feedback relationship linking Artificial Intelligence (AI) in its Deep Learning component and computing power – hardware – or rather high performance computing power (HPC). It builds on a first part where we explained and detailed this connection. There we underlined notably three typical phases where computation is required: creation of the AI program, training, and inference or production (usage). We showed that a quest for improvement across phases, and the overwhelming and determining importance of architecture design – which takes place during the creation phase – generates a crucial need for ever more powerful computing power. Meanwhile, we identified a feedback spiral between AI-DL and computing power, where more computing power allows for advances in terms of AI and where new AI and the need to optimize it demand more computing power.

Working with the news and media industry is always inspiring and dynamic but also sometimes can be quite challenging! In the few previous months I have been working intensively with the media industry in our region between Egypt, Emirates, Oman & Tunisia focusing on two tracks. The first track was through several training courses for digital transformation, consulting our media customers how to build their professional presence online and boost their business through an integrated multi-channel approach utilizing portals, mobile apps, social media & SEO. On second track, we have been working very closely on the technology stack for empowering the digital media industry. Our streaming platform Helixware has been powering several broadcasters, TV & radio stations across the Arab world and also our AI-powered SEO Wordlift has been boosting the business of our online publishing customers.

In a 2016 address to propaganda cadres and state-run media personnel, Chinese President Xi Jinping expressed dreams of instilling a new international media order "wherever the readers are, wherever the viewers are; that is where propaganda reports must extend their tentacles." As Xinhua News, China's largest state-run news agency, equips itself with "Media Brain," an artificial intelligence (AI) newsroom to assist all stages of reporting, these "tentacles" of propaganda may extend faster. Bringing AI to newsrooms can improve accuracy, enhance data analysis, and increase efficiency. According to a video released by Xinhua in January, the AI newsroom will do everything "from finding leads to news gathering, editing, distribution, and, finally, feedback analysis." Last week, Xinhua announced an update to Media Brain called "MAGIC," which will use machine generated content (MGC) for "fast-speed news production" and can automatically generate a news video in as fast as 10 seconds.

Big data, artificial intelligence and machine learning are ruling the tech structure of most industries. We all know how Amazon combines a customer's historical data and other customers' data to power recommendations. Likewise, for Google, it's not difficult to predict our preferences and interests. They make use of big data, analytics and machine learning to be able to process huge amounts of data, identify patterns, analyze them and consequently indulge in predictive analysis. The most complicated disease of the most important organ of the body – the brain, is a clear beneficiary of this AI approach.

Learning of dynamical systems, or state-space models, is central to many machine learning problems, such as reinforcement learning, sequence modeling, and autonomous systems. Furthermore, state-space models are at the core of recent model developments within the machine learning area, such as Gaussian process state-space models (Frigola et al. 2014a; Mattos et al. 2016; etc.), infinite factorial dynamical models (Gael et al., 2009; Valera et al., 2015), and stochastic recurrent neural networks (Fraccaro et al., 2016, for example). A strategy to learn state-space models, independently suggested by Digalakis et al. (1993) and Ghahramani and Hinton (1996), is the use of the Expectation Maximization (EM, Dempster et al. 1977) method. Even though originally proposed only for maximum likelihood estimation of linear models with Gaussian noise, the strategy can be generalized to the more challenging nonlinear and non-Gaussian cases, as well as the empirical Bayes setting. Many contributions have been made during the last decade, and this paper takes another step along the path towards a more computationally efficient method with a solid theoretical ground for learning of nonlinear dynamical systems.

We study the fundamental limits of detecting the presence of an additive rank-one perturbation, or spike, to a Wigner matrix. When the spike comes from a prior that is i.i.d. across coordinates, we prove that the log-likelihood ratio of the spiked model against the non-spiked one is asymptotically normal below a certain reconstruction threshold which is not necessarily of a "spectral" nature, and that it is degenerate above. This establishes the maximal region of contiguity between the planted and null models. It is known that this threshold also marks a phase transition for estimating the spike: the latter task is possible above the threshold and impossible below. Therefore, both estimation and detection undergo the same transition in this random matrix model. We also provide further information about the performance of the optimal test. Our proofs are based on Gaussian interpolation methods and a rigorous incarnation of the cavity method, as devised by Guerra and Talagrand in their study of the Sherrington--Kirkpatrick spin-glass model.

A little over a week after the fervor surrounding Google's involvement in the Department of Defense's Project Maven, an autonomous drone program, showed signs of abating, another machine learning controversy returned to the headlines: local law enforcement deploying Amazon's Rekognition, a computer vision service with facial recognition capabilities. In a letter addressed to Amazon CEO Jeff Bezos, 19 groups of shareholders expressed concerns that Rekognition's facial recognition capabilities will be misused in ways that "violate [the] civil and human rights" of "people of color, immigrants, and civil society organizations." And they said that it set the stage for sales of the software to foreign governments and authoritarian regimes. Amazon, for its part, said in a statement that it will "suspend … customer's right to use … services [like Rekognition]" if it determines those services are being "abused." It has so far declined, however, to define the bright-line rules that would trigger a suspension.

According to James Karuhanga of The New Times, countries in the Common Market for Eastern and Southern Africa are lagging behind with respect to robotics, artificial intelligence and technology infrastructure and skills acquisition. This comes after Timothy Kotin of African Expert Network said that "artificial intelligence could yield a significant developmental dividend in the developing world."

Cylance, a cybersecurity startup that leverages artificial intelligence and machine learning to combat online attacks, has raised $120 million in a series E round of funding led by Blackstone Tactical Opportunities, with participation from other unnamed investors. Founded in 2012 by Stuart McClure, an entrepreneur who sold an Internet security firm to McAfee for $86 million in 2004, Cylance is an endpoint protection platform designed to thwart malware, ransomware, and other forms of advanced threats using AI. Its suite of algorithm-based security protocols essentially inspect networks for weaknesses and shuts them down if any are detected. Cylance claims in excess of 4,000 customers, and said that it has revenues of $130 million for the 2018 fiscal year, representing a year-on-year growth of 90 percent. Prior to now, Cylance had raised around $177 million, including a $100 million tranche two years ago, and with another $120 million in the bank it said that it plans to double down on its global expansion efforts, with a particular focus on Europe, the Middle East, and Asia Pacific, and extend its product range.