In this paper, we propose and demonstrate an adaptive-sliding mode control for trajectory tracking control of robot manipulators subjected to uncertain dynamics, vibration disturbance, and payload variation disturbance. Throughout this work we seek a controller that is, robust to the uncertainty and disturbance, accurate, and implementable. To perform these requirements, we use a nonlinear Lyapunov-based approach for designing the controller and guaranteeing its stability. MATLAB-SIMULINK software is used to validate the approach and demonstrate the performance of the controller. Simulation results show that the derived controller is stable, robust to the disturbance and uncertainties, accurate, and implementable.

Gideon Lichfield: He's also the author of an upcoming book about how AI and other technologies will take over the world and possibly threaten the very structure of the nation-state. Mustafa Suleyman (audio clip): We're now going to have access to highly capable, persuasive teaching AIs that might help us to carry out whatever, you know, sort of dark intention we have. And it is definitely going to accelerate harms--no question about it. And that's what we have to confront. Lauren Goode: So, Gideon, Mustafa is a guest who we both wanted to bring on the podcast, though I think for slightly different reasons.

Transformers have recently been shown to generate high quality images from text input. However, the existing method of pose conditioning using skeleton image tokens is computationally inefficient and generate low quality images. Therefore we propose a new method; Keypoint Pose Encoding (KPE); KPE is 10 times more memory efficient and over 73% faster at generating high quality images from text input conditioned on the pose. The pose constraint improves the image quality and reduces errors on body extremities such as arms and legs. The additional benefits include invariance to changes in the target image domain and image resolution, making it easily scalable to higher resolution images. We demonstrate the versatility of KPE by generating photorealistic multiperson images derived from the DeepFashion dataset. We also introduce a evaluation method People Count Error (PCE) that is effective in detecting error in generated human images.

The award-winning picture of Munzir El Nezzel playfully lifting Mustafa, his son who was born without limbs, has given the family a shot at a new life after becoming a poignant symbol of the plight of war-torn Syria. The shocking yet tender picture taken by Turkish photographer Mehmet Aslan, titled Hardship of Life, captured an intimate moment of affection between a father and son whose bodies have been maimed by the brutal conflict. Six-year-old Mustafa was born with a congenital disorder caused by medications his mother took while pregnant after being sickened by nerve gas. His 35-year-old father had his leg amputated following a bomb attack in Syria. After being declared photo of the year at the Siena International Photo Awards (SIPA) last year and making headlines in Italy, it sparked crowdfunding efforts that raised about 100,000 euros ($114,000) to provide both father and son with prosthetic limbs.

"I'm increasingly coming to terms with the fact that I may never get married," said Mustafa, a 34-year-old Black Muslim man who asked that we not use his real name. He has been on two dates with women he met on dating apps in the past year – and they left him feeling fatigued and doubtful that he would ever find a genuine connection with someone. He had turned to the apps, he said, because, there is no dating scene in his British-Somali community. But, he lamented, "it's really hard to find someone. This is not how Mustafa imagined his life would be in his mid-thirties. When he was younger, he pictured himself as a devoted husband and loving father to a couple of children by now. In this mental image of familial bliss, he was also living in a picturesque cottage in the English countryside complete with "a lake or something". Instead, he recently celebrated his 34th birthday single and living in a flat overlooking the Wembley Stadium arch in North West London. But, he added with a shrug, "I've started learning how to cycle." Discussing his hobbies and interests – cycling, reading, writing – he sounds more optimistic. He has directed his energy away from the fickle and unpredictable pursuit of love and towards those variables of his life he can control, like picking up new pastimes. 'All they see is a Black guy' Although the United Kingdom's Black Muslim community is culturally diverse, including people from a wide range of African and Caribbean backgrounds, it only comprises 10 percent of the UK's Muslim population. This can make dating or finding a marriage partner particularly difficult. A recent survey by Muzmatch, a Muslim-specific dating app that has been heralded for helping 20,000 Muslims meet and marry since its launch in 2015, revealed the challenges faced by Black Muslims dating in the UK. Muzmatch asked 471 of their members from different ethnic groups if they felt that race and ethnicity affected the matches they received and whether they had negative experiences as a result of this. In their answers, Black users pointed to a range of issues – including fetishisation, colourism and discrimination. Most of the Black women surveyed complained about being fetishised and branded "exotic". One West African woman described how dark-skinned women were considered unattractive and how she had been called the n-word by one user. A Sudanese man expressed concern that he was matched with women with similar interests to him who subsequently rejected him because their family wouldn't accept him. "It doesn't matter if you're on your deen and have a successful career.

Two of today's big IT trends, AI/ML and containers, have become part of the same conversation at many organizations. They're increasingly paired together, as teams look for better ways to manage their Artificial Intelligence and Machine Learning workloads – enabled by a growing menu of commercial and open source technologies for doing so. "The best news for IT leaders is that tooling and processes for running machine learning at scale in containers has improved significantly over the past few years," says Blair Hanley Frank, enterprise technology analyst at ISG. "There is no shortage of available open source tooling, commercial products, and tutorials to help data scientists and IT teams get these systems up and running." Before IT leaders and their teams begin to dig into the nitty-gritty technical aspects of containerizing AI/ML workloads, some principles are worth thinking about up front. Here are six essentials to consider.

Based in New York, Grafiti was co-founded by former journalist Farhan Mustafa and venture advisor Akbar Dawood. Dawood is an advisor to Untethered Labs. Mustafa, who also has a background in data analytics, said he came up with the idea for Grafiti.io while working as a consultant and producer for the Al Jazeera Media Network. Frustrated by the drudgery of combing through news stories looking for charts and graphs while reporting for Al Jazeera in the Middle East, Mustafa wanted a tool that would surface visual information onto a single page, instead of having to go into each story to see the visuals. Grafiti has a platform that does that.

Even though you may not realize it, machine learning-powered matchmaking is present everywhere in our daily lives, from the type of content shown on our Facebook news feeds to the suggested TV shows that come up on Netflix, and even to the matches suggested on dating sites/apps like Match.com and Tinder. As machine learning continues to advance, it will start to make its way to the hiring process, driving efficiencies in connecting employers and candidates, especially for technical jobs. Analyzing large amounts of data on candidates will become increasingly important during the hiring process for many companies. Today, matching algorithms use strings and keywords in resumes to filter candidates. This enables companies to get more accurate results, quicker, during the hiring process.

Legal AI company Voltaire has launched an application that will allow lawyers and litigation consultants to rapidly analyse potential jurors by crunching public Big Data, including social media posts. The system is of primary use in America and similar legal systems where lawyers for either side in a trial are allowed to research potential jurors before the case commences and selectively apply preemptory strikes, or make a case for a'for-cause' in order to seek a better outcome for their client. Also known as the voir dire phase of trial, jury analysis and selection is an often complex and time-consuming element to much US litigation. Voltaire hopes to use AI, such as machine learning and natural language processing (NLP) to not just greatly speed up the process, but to provide lawyers with new insights via Big Data analysis that would normally be very hard and expensive to attain using manual methods. Colorado-based founder and former IBM staffer, Basit Mustafa, explains to Artificial Lawyer that Voltaire explores all public data related to the potential juror, correlates the data against known patterns in human behaviour and then produces a detailed profile, with indications of the type of person they are and how their views and biases may be a positive or negative factor as part of a jury.

A new Guardian report shows where AI is headed next, in a joint venture between Google's Deep Mind and the British NHS … The British team behind Google's AI efforts is teaming up with the UK's National Health Service and London's Moorfields Eye Hospital to build a machine learning system capable of recognizing potentially sight-threatening conditions by simply identifying symptoms from a digital scan of the eye. The core of the research will see about a million eye scans (all coming from anonymous patients) being analysed by an AI-fuelled computer, which Deepmind researchers will use to train a special algorithm. The algorithm will then allow the machine to spot early signs of eye conditions, such as wet age-related macular degenerations and diabetic retinopathy; diabetes, in fact, apparently makes it "25 times more likely to go blind", as per Mustafa Suleyman, Deepmind's co-founder. "If we can detect this, and get in there as early as possible, then 98% of the most severe visual loss might be prevented," Mustafa said. And indeed, allowing a computer to do most of the hard work would help immensely in increasing both the speed and the accuracy of a diagnosis, potentially helping the sight of thousands to be saved.