Draw a few lines on top of a photo of a face – the shape of an eyebrow, say –and an AI can turn your sketch into a realistic edit, with no artistic skills required. Created by Youngjoo Jo and Jongyoul Park at the Electronics and Telecommunications Research Institute in South Korea, the face-editing program is more advanced than simple retouching apps – it lets you change hairstyles, add smiles, and even insert earrings. It can also convincingly complete a picture of a face that is partially obscured, editing out objects like sunglasses.

Effective network slicing requires an infrastructure/network provider to deal with the uncertain demand and real-time dynamics of network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This article develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demand from tenants. Specifically, we first propose a novel system model which enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state/action spaces. This makes Q-learning practically inapplicable to our case in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with real-time resource requests and the dynamic demands of users. Extensive simulations show that the proposed framework yields up to 40% higher long-term average return while being few thousand times faster, compared with state of the art network slicing approaches.

At $3,500 apiece, Microsoft's HoloLens 2 may not be the transformational consumer device we were all hoping to buy. But the company addressed many of the shortcomings of the original HoloLens at the Mobile World Congress launch of the second generation, holding out hope that we may one day see a more consumer-oriented product. As Microsoft has signaled for several years now, HoloLens 2 is designed to work with its Azure cloud and business customers, complete with an intriguing new Remote Rendering technology that implies Microsoft's using the power of its Azure cloud to boost the HoloLens headset's image processing capabilities. Epic chief Tim Sweeney appeared on stage to endorse HoloLens and bring the Unreal engine to HoloLens beginning in May. He did not announce a HoloLens-specific game, though.

Chinese phone company Huawei has unveiled its own foldable phone, the latest bendy phone to hit the market. Its Mate X will take on the Samsung Galaxy Fold as well as a whole host of other foldable handsets. Manufacturers claim that the new phones – which include screens that can bend out, fold over and become twice as big, like a tablet – will allow people to combine a traditional phone and an iPad-sized screen all in one. The release comes at a controversial time for Huawei, which has been accused by countries around the world of being too close to the Chinese government. The phones are difficult to buy in some counties like the US, which has repeatedly said the phones are unsafe.

With the impending confluence of 5G, AI, and AR/VR, text, voice, photos, images, and video streaming will act as seemingly magical channels that connect the physical and the digital worlds. But, the barriers to entry for AI are high – as is cost. In Making Up the Mind, the neuroscientist Chris Frith describes how our perception of the world is not direct, but instead relies on "unconscious reasoning". Before we can perceive an object, the brain must infer what the object is based on the information that reaches our senses. And this constitutes humans' most important ability – the ability to predict and handle unexpected events.

Samsung chose to unveil not just one but five new phones at its Unpacked event this week, complementing its range of Galaxy S10 devices with the innovative Galaxy Fold. The Galaxy S10, S10, S10e, S10 5G and Fold all offer something unique for users, be it size, specs, or the ability to unfold and transform into a tablet. Combined with the vast range of non-Samsung alternatives – the iPhone XS, the Google Pixel 3, the Huawei P30 Pro, to name just three – it may seem more difficult than ever to decide which device is the right one to buy. As American psychologist Barry Schwartz wrote in Paradox of Choices, having too many options "no longer liberates but debilitates" consumers. With prices ranging from around £700 to close to £2,000 for the new Samsung phones, making the wrong choice could be costly.

After many rumours and leaks, Samsung has finally revealed its groundbreaking folding phone. It's certainly an exciting innovation but, really, what is the benefit and what will it be used for – Oh, and how can Samsung get away with charging $1,980 or €2,000 for it? The Galaxy Fold was the opening gambit at Samsung's San Francisco keynote event, where it also revealed three versions, no, four, of the Samsung Galaxy S10, a new fitness tracker and a smartwatch with a sports angle. But it was the Fold which caught the attention. In the current vogue for maximum screen size but minimum phone size, while noting that our hands aren't getting any bigger, a screen that can double in size was bound to come along sooner or later. And it looks like other companies like Xiaomi, Huawei, Motorola and even Apple are exploring the technology.

Artificial intelligence and machine learning have emerged in the marketing industry as a pathway to competitive advantage. The best marketers are identifying, evaluating and testing AI-driven applications to make better sense of their data, create personalized customer experiences and accelerate revenue growth. In fact, 84% of marketing organizations either implemented or expanded AI and machine learning experiments and implementations in 2018. While it's no doubt that artificial intelligence has helped marketing teams improve their productivity, with most brands spending between 25 and 43% of their marketing budget on content, it's important to understand how AI can impact this specific department. The truth is, artificial intelligence has actually had an active presence in the content marketing industry for years.

It's been a big week for 5G, the next generation of wireless networks. Samsung announced its first 5G capable phone, the S10, on Wednesday. Qualcomm announced a new 5G modem on Tuesday. But President Trump is aiming higher. "I want 5G, and even 6G, technology in the United States as soon as possible," Trump wrote in a tweet urging carriers to pick up their pace.

Training complex machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, reduces the volume of exchanged data by aggregating the model updates from multiple workers in the network. We co-design the switch processing with the end-host protocols and ML frameworks to provide a robust, efficient solution that speeds up training by up to 300%, and at least by 20% for a number of real-world benchmark models.