AI and robotics are leading to significant healthcare advances in South Africa.

The way Timnit Gebru sees it, the foundations of the future are being built now. In Silicon Valley, home to the world's biggest tech companies, the artificial intelligence (AI) revolution is already well under way. Software is being written and algorithms are being trained that will determine the shape of our lives for decades or even centuries to come. If the tech billionaires get their way, the world will run on artificial intelligence. Cars will drive themselves and computers will diagnose and cure diseases. Art, music and movies will be automatically generated.

Specifically, Huerta and his then graduate student Daniel George pioneered the use of so-called convolutional neural networks (CNNs), which are a type of deep-learning algorithm, to detect and decipher gravitational-wave signals in real time. Roughly speaking, training or teaching a deep-learning system involves feeding it data that are already categorized--say, images of galaxies obscured by lots of noise--and getting the network to identify the patterns in the data correctly. After their initial success with CNNs, Huerta and George, along with Huerta's graduate student Hongyu Shen, scaled up this effort, designing deep-learning algorithms that were trained on supercomputers using millions of simulated signatures of gravitational waves mixed in with noise derived from previous observing runs of Advanced LIGO--an upgrade to LIGO completed in 2015. For instance, Adam Rebei, a high school student in Huerta's group, showed in a recent study that deep learning can identify the complex gravitational-wave signals produced by the merger of black holes in eccentric orbits--something LIGO's traditional algorithms cannot do in real time. In a preprint paper last September, Nicholas Choma of New York University and his colleagues reported the development of a special type of deep-learning algorithm called a graph neural network, whose connections and architecture take advantage of the spatial geometry of the sensors in the ice and the fact that only a few sensors see the light from any given muon track.

The terms Artificial Intelligence (AI) and Machine Learning (mL) to a tech neophyte is synonymous with robots wearing human emotions.

More than half of all players at the finals of last year's European Championship and the Africa Cup of Nations (AFCON) in February were subjected to discriminatory abuse online, a report published by global football governing body FIFA has revealed. The independent report used artificial intelligence to track more than 400,000 posts on social media platforms during the semi-final and final stages of the two football competitions and found the majority of abuse to be homophobic, 40 percent, and racist, 38 percent. The report found that much of the abuse came from players' home nations and took place before, during and after games. England's Marcus Rashford, Jadon Sancho and Bukayo Saka, who are Black, were bombarded with online abuse after missing their penalty shots in a shoot-out against Italy which settled the July 11 European Championship final after the game finished in a draw. A substitute player from Egypt was the most abused player at the AFCON finals this year, the report found.

While Artificial intelligence (AI) has been developing for decades, recent years have seen increasing attention to its various societal impacts. These impacts range from positive and helpful to harmful and even life-threatening in some cases. Parliaments have responded to such developments by undertaking various programmes of work. What have they done, and what can Scotland learn from these approaches? This short review provides a snapshot of the work that various Parliaments around the world have undertaken on AI. It outlines the various approaches adopted by Parliaments and highlights common themes. In noting the key points for Scotland, it is designed to inform and guide the Scottish Parliament and others, as Scotland considers its own approach to the many opportunities and challenges AI presents. The report was written by Robbie Scarff on an internship supported by the Scottish Graduate School of Social Science. From this work, here are some key areas and questions for the Scottish Parliament to consider.

Black Swan Data is a fast-growing technology and data science business, with offices in the UK, South Africa, Hungary. We build high quality SaaS solutions which automate data science using advanced machine learning and deep learning techniques. We use some of the coolest technology on the planet so you will never get bored of doing the same thing. You'll be part of a dynamic and growing global team As we continue to grow across the world, you'll find every day brings with it fresh challenges and opportunities to try new things. You'll be part of a diverse, smart, agile but laid-back team passionate about technology, who are also open-minded and always open to new ideas.

Tensor decompositions have proven to be effective in analyzing the structure of multidimensional data. However, most of these methods require a key parameter: the number of desired components. In the case of the CANDECOMP/PARAFAC decomposition (CPD), this value is known as the canonical rank and greatly affects the quality of the results. Existing methods use heuristics or Bayesian methods to estimate this value by repeatedly calculating the CPD, making them extremely computationally expensive. In this work, we propose FRAPPE and Self-FRAPPE: a cheaply supervised and a self-supervised method to estimate the canonical rank of a tensor without ever having to compute the CPD. We call FRAPPE cheaply supervised because it uses a fully synthetic training set without requiring real-world examples. We evaluate these methods on synthetic tensors, real tensors of known rank, and the weight tensor of a convolutional neural network. We show that FRAPPE and Self-FRAPPE offer large improvements in both effectiveness and speed, with a respective $15\%$ and $10\%$ improvement in MAPE and an $4000\times$ and $13\times$ improvement in evaluation speed over the best-performing baseline.

This week Aidoc announced that they have raised $110 million in their Series D expansion round. This round of funding was co-led by TCV and Alpha Intelligence Capital with participation from CDIB Capital. Funding raised in this round will go toward expansion of Aidoc's first of its kind AI Care Platform. The platform offers health systems a singular platform solution designed to help doctors manage the entire patient lifecycle--from diagnostic aid, to consultation, to suggested treatment paths, to patient follow-up tools. In clinical studies, this platform has proven to reduce turnaround time, shorten patient length of stay and improve patient outcomes.

Figure 1: In real-world applications, we think there exists a human-machine loop where humans and machines are mutually augmenting each other. We call it Artificial Augmented Intelligence. How do we build and evaluate an AI system for real-world applications? In most AI research, the evaluation of AI methods involves a training-validation-testing process. The experiments usually stop when the models have good testing performance on the reported datasets because real-world data distribution is assumed to be modeled by the validation and testing data.