"I will not sell the future for instant profit!" Werner von Siemens, 1884 In Atlas Shrugged (1957), by Ayn Rand, the system falls apart to the point that the remaining producers choose to simply withdraw rather than proliferate and disrupt the society from within. "In 1995, Fukuyama argued that only those societies with a high degree of social trust would be able to create the kind of flexible, large-scale business organizations that are needed for successful competition in the global economy." Carrying proudly the responsibility of its 170 years history and legacy, a Tech Giant, an Atlas of the modern era of turbulent markets and exponentially growing challenges, the largest industrial manufacturing company in Europe with its footprint in 180 countries around the globe, the German conglomerate company Siemens AG (German pronunciation: [ˈziːmɛns]) is shaping the future – the digital future. "With its Vision 2020, Siemens has recently once again clearly answered these questions: a company faces up to its responsibilities, furnishes lasting benefit and generates added value from a position of strength – for its shareholders, employees, customers, business partners and societies all over the world. Joe Kaeser, President and Chief Executive Officer of Siemens AG, puts it like this: "Only the strong can help the weak, take responsibility and then fulfill it.

If you're a tiny bit freaked out by the enormous potential of AI and blockchain, you're not the only one. When Dolly the sheep was cloned in the 90s, a pertinent question arose. Just because we can, does it mean we should? Just because AI and blockchain technologies combined may stop crimes before they happen, replace human jobs with robots, and assign every "thing" in the stratosphere an identity–does it mean they should? Are AI and blockchain combined super cool or a little creepy?

In The Optical Society's journal for high-impact research, Optica, the researchers report teaching a type of machine learning algorithm known as a deep neural network to recognize images of numbers from the pattern of speckles they create when transmitted to the far end of a fiber. The work could improve endoscopic imaging for medical diagnosis, boost the amount of information carried over fiber-optic telecommunication networks, or increase the optical power delivered by fibers. "We use modern deep neural network architectures to retrieve the input images from the scrambled output of the fiber," said Demetri Psaltis, Swiss Federal Institute of Technology, Lausanne, who led the research in collaboration with colleague Christophe Moser. "We demonstrate that this is possible for fibers up to 1 kilometer long" he added, calling the work "an important milestone." Optical fibers transmit information with light.

For many years, the main goal of companies is to collect as much user data as possible. Dealing with all these and new incoming data quickly and effectively is impossible without intelligent systems. This is why companies desperately need to harness AI technologies to come to the top place among competitors – and the sooner the better. However, the challenge is that modern AI systems are "idiot savants" as Gurdeep Singh Pall of Microsoft put it in one of his talks. "They are great at what they do, but if you don't use them correctly, it's a disaster."

Analysis America's largest manufacturer of body cameras – and the biggest supplier to police forces across the United States – says today's facial recognition technology is not safe for making serious decisions. Speaking during its second-quarter earnings call with investors this week, the CEO of Axon, Rick Smith, answered a question about whether the company would be adding facial-recognition systems to its suite of products and, if so, whether that would come with an additional cost. Smith responded in clear terms that current facial recognition is simply not accurate enough to "make operational decisions," ie: for police to use it to recognize individuals and use positive responses as justification for automatically and unquestioningly apprehending people. Well, the computer says you're wanted, so here come the cuffs, we can imagine a conversation with officers going. "We don't have a timeline to launch facial recognition," Smith said on the conference call (listen in at around the 40-minute mark), noting that Axon doesn't have a team "actively developing it" either.

Companies are using AI to prevent and detect everything from routine employee theft to insider trading. Many banks and large corporations employ artificial intelligence to detect and prevent fraud and money laundering. Social media companies use machine learning to block illicit content such as child pornography. Businesses are constantly experimenting with new ways to use artificial intelligence for better risk management and faster, more responsive fraud detection -- and even to predict and prevent crimes. While today's basic technology is not necessarily revolutionary, the algorithms it uses and the results they can produce are.

If policymaking is never easy, and military policymaking is very difficult, it stands to reason that space military policymaking is basically impossible. Yet today, in a speech at the Pentagon, Vice President Mike Pence announced the formation of a sixth branch of the US armed services: a SPACE FORCE! But can that really happen? Well, let's proceed with the go/no-go. "The time has come to establish the United States Space Force," Pence said in his speech, asking for $8 billion to build out the idea.

Humans have the fascinating capacity of processing non-verbal visual cues to understand and anticipate the actions of other humans. This "intention reading" ability is underpinned by shared motor-repertoires and action-models, which we use to interpret the intentions of others as if they were our own. We investigate how the different cues contribute to the legibility of human actions during interpersonal interactions. Our first contribution is a publicly available dataset with recordings of human body-motion and eye-gaze, acquired in an experimental scenario with an actor interacting with three subjects. From these data, we conducted a human study to analyse the importance of the different non-verbal cues for action perception. As our second contribution, we used the motion/gaze recordings to build a computational model describing the interaction between two persons. As a third contribution, we embedded this model in the controller of an iCub humanoid robot and conducted a second human study, in the same scenario with the robot as an actor, to validate the model's "intention reading" capability. Our results show that it is possible to model (non-verbal) signals exchanged by humans during interaction, and how to incorporate such a mechanism in robotic systems with the twin goal of : (i) being able to "read" human action intentions, and (ii) acting in a way that is legible by humans.

Finding the diameter of a dataset in multidimensional Euclidean space is a well-established problem, with well-known algorithms. However, most of the algorithms found in the literature do not scale well with large values of data dimension, so the time complexity grows exponentially in most cases, which makes these algorithms impractical. Therefore, we implemented 4 simple greedy algorithms to be used for approximating the diameter of a multidimensional dataset; these are based on minimum/maximum l2 norms, hill climbing search, Tabu search and Beam search approaches, respectively. The time complexity of the implemented algorithms is near-linear, as they scale near-linearly with data size and its dimensions. The results of the experiments (conducted on different machine learning data sets) prove the efficiency of the implemented algorithms and can therefore be recommended for finding the diameter to be used by different machine learning applications when needed.

Deep neural networks are powerful machine learning approaches that have exhibited excellent results on many classification tasks. However, they are considered as black boxes and some of their properties remain to be formalized. In the context of image recognition, it is still an arduous task to understand why an image is recognized or not. In this study, we formalize some properties shared by eight state-of-the-art deep neural networks in order to grasp the principles allowing a given deep neural network to classify an image. Our results, tested on these eight networks, show that an image can be sub-divided into several regions (patches) responding at different degrees of probability (local property). With the same patch, some locations in the image can answer two (or three) orders of magnitude higher than other locations (spatial property). Some locations are activators and others inhibitors (activation-inhibition property). The repetition of the same patch can increase (or decrease) the probability of recognition of an object (cumulative property). Furthermore, we propose a new approach called Deepception that exploits these properties to deceive a deep neural network. We obtain for the VGG-VDD-19 neural network a fooling ratio of 88\%. Thanks to our "Psychophysics" approach, no prior knowledge on the networks architectures is required.