You know what's better than one self-driving car on the road? Vehicle-to-vehicle (V2V) communication isn't anything new, of course, but researchers at Switzerland's federal institute of technology, EPFL, are taking things one step further. By wirelessly connecting the LIDAR, sensors and navigation systems of two cars in convoy, both can get a fuller picture of the world around them. Put simply, if two autonomous vehicles are driving down a road, then all they can "see" is the data from their own systems. But if you can connect your car, say, to the one immediately behind you, then both systems get a wider field of view and better situational awareness.

Will artificial intelligence (AI) ring the death knell for humanity, or will it improve our lives immeasurably? It depends who you ask. Tech luminary Elon Musk, for example, believes AI is the biggest threat we face as a civilization, while Facebook's Mark Zuckerberg thinks such viewpoints are irresponsible, at best. The bottom line is, we don't really know how AI will evolve. But we do know that a lot of money is being invested in developing AI technologies, and we are also aware that many people in the know expect that machines will surpass human intellect and abilities at some point in the foreseeable future. It's against this backdrop that Alphabet's U.K.-based AI subsidiary DeepMind has launched a new "ethics and society" research unit tasked with "exploring and understanding" the implications of AI permeating the world.

File photo: Chinese movie patrons wait in front of Hollywood star Keanu Reeves poster in the movie Matrix Reloaded showing at Paradise Warner Bros Cinema City in Shanghai July 18, 2003. Some people fear we humans are nothing more than pickled brains floating in a glass bowl as we're fed a false version of reality through a bundle of wires. Now a team of scientists at Oxford University has demolished the theory that we are all living in a computer simulation that's been masterminded by alien overlords. Science fiction fans and modern philosophers have long debated whether the world is actually the same as we percieve it to be. Following the popularity of 90s classic The Matrix, many have questioned whether the philosophical "Brain in a Vat" scenario may actually be our reality.

Google will launch the Pixel 2 and Pixel XL 2 tonight, but they're arguably not the most important new products we'll see from the company. Google is working on something much bigger than smartphones, and it's planning to bring it to more people than ever before. The Google Home Mini may just prove to be most important new product we'll see tonight. The successor to Google Home, which came to the UK earlier this year, the Mini is a small and affordable smart speaker. It will feature Google Assistant, a virtual assistant that both understands and responds to your voice commands. It's a clever piece of technology, which also lives on a number of Android phones, and is growing more and more capable by the month.

According to the CIO 100, 38% of CIOs expect AI to have an impact on their sector, with organisations including AstraZeneca, DEFRA and Hargreaves Lansdown currently using the technology. This interest in AI has seen CIOs and organisations investigate and implement AI to help with a variety of tasks, from monitoring customers to improving the overall efficiency of their business model. CIO UK looks at how some leading CIOs are using AI within their organisations to increase staff productivity, reduce costs and increase customer engagement. "Machine learning and AI is something we've just started to track as it will have an impact at some point on the operation of the railway, but it's early days from a railways systems perspective and there is always a bit of reticence around bleeding-edge technology where safety is critical." "AI is coming on in leaps and bounds. Things that were ropey not that long ago are getting good very quickly. Speech-to-text recognition is really phenomenal now; there's really good text-to-speech that we are experimenting with at the FT. "All of this with AI is not aimed at replacing journalism but augmenting it, how can we use those things to make sure our journalists are concentrating on the high-value content that's going to really drive engagement while removing some of the repetitive steps they may have concentrated on in the past." "I've seen some early prototypes in our North American labs of virtual agents – be that chatbots, be that the recently announced integration into Amazon's Alexa product – and I think we'll see a lot more of virtual agents in the financial services industry and other industries; I think it's a good example of helping customers interact with financial services companies with a lot less friction." "Things like using Tensor Flow [Google's open source AI framework], AI is really starting to get interesting and seeing how we can use that to help UK Households to save more money will be fun!" Tim Jones, Moneysupermarket.com "For me, the coming year is all about the big data side of it.

In a recent cautioning about Artificial Intelligence, a UN research institute has said that machine intelligence and robots could destabilize the world, according to reports in the Guardian. The unnerving as-hellfire warning from the United Nations Interregional Crime and Justice Research Institute (UNICRI) comes in prior to the opening of Center for Artificial Intelligence and Robotics in The Hague – UN's first center focused on Artificial Intelligence and the conceivable dangers that could emerge from such advances. Featuring the requirement for the workplace in a meeting with Dutch daily paper de Telegraaf, Irakli Beridze, senior key counsel at UNICRI, stated, Artificial Intelligence advancements were related with awesome dangers that need tending to. "In the event that social orders don't adjust rapidly enough, this can cause instability". Be that as it may, a similar innovation has likewise raised plenty of legitimate, moral and societal concerns, some of which may even prove perilous for the prosperity and wellbeing of people – for example, mass unemployment or the ascent of autonomous'killer robots'.

Software solution firm Finantix launched a collection of engines, tools and data structures to inject Artificial Intelligence into wealth management platforms. The technology firm claims its new service will bring tangible benefits by automating tasks, distilling intelligence, supporting decisions and enforcing compliance for wealth managers, private banks and even robo-advisers. The main areas initially addressed by Finantix AI Gears for Wealth (Finantix AI) are: lead generation, automated client profiling and know your client (KYC), portfolio and financial planning optimisation, suitability and cross border rule enforcement, next best action determination, news tagging and content personalisation. Finantix AI has been specifically designed to slot easily into any legacy or digital wealth management platform thanks to an application programming interface (API) oriented and configurable architecture. To support its roll out Finantix is organising a series of events in Singapore, Zurich and Tokyo to discuss how AI will affect wealth management and the benefits it can bring.

As we hand over more of our lives to artificial intelligence systems, keeping a firm grip on their ethical and societal impact is crucial. For DeepMind, whose stated mission is to "solve intelligence", that task will be the work of a new initiative tackling one of the most fundamental challenges of the digital age: technology is not neutral. DeepMind Ethics & Society (DMES), a unit comprised of both full-time DeepMind employees and external fellows, is the company's latest attempt to scrutinise the societal impacts of the technologies it creates. In development for the past 18 months, the unit is currently made up of around eight DeepMind staffers and six external, unpaid fellows. The full-time team within DeepMind will swell to around 25 people within the next 12 months.

One of the most fundamental problems in Markov decision processes is analysis and control synthesis for safety and reachability specifications. We consider the stochastic reach-avoid problem, in which the objective is to synthesize a control policy to maximize the probability of reaching a target set at a given time, while staying in a safe set at all prior times. We characterize the solution to this problem through an infinite dimensional linear program. We then develop a tractable approximation to the infinite dimensional linear program through finite dimensional approximations of the decision space and constraints. For a large class of Markov decision processes modeled by Gaussian mixtures kernels we show that through a proper selection of the finite dimensional space, one can further reduce the computational complexity of the resulting linear program. We validate the proposed method and analyze its potential with numerical case studies.

We introduce a new approach to functional causal modeling from observational data. The approach, called Causal Generative Neural Networks (CGNN), leverages the power of neural networks to learn a generative model of the joint distribution of the observed variables, by minimizing the Maximum Mean Discrepancy between generated and observed data. An approximate learning criterion is proposed to scale the computational cost of the approach to linear complexity in the number of observations. The performance of CGNN is studied throughout three experiments. First, we apply CGNN to the problem of cause-effect inference, where two CGNNs model $P(Y|X,\textrm{noise})$ and $P(X|Y,\textrm{noise})$ identify the best causal hypothesis out of $X\rightarrow Y$ and $Y\rightarrow X$. Second, CGNN is applied to the problem of identifying v-structures and conditional independences. Third, we apply CGNN to problem of multivariate functional causal modeling: given a skeleton describing the dependences in a set of random variables $\{X_1, \ldots, X_d\}$, CGNN orients the edges in the skeleton to uncover the directed acyclic causal graph describing the causal structure of the random variables. On all three tasks, CGNN is extensively assessed on both artificial and real-world data, comparing favorably to the state-of-the-art. Finally, we extend CGNN to handle the case of confounders, where latent variables are involved in the overall causal model.