AI plays a significant role in monitoring, production development, industrial applications, and analytics, among others in several business environments. Further, with the rising number of IoT-based devices, the need to effectively process the enormous real-time data generated from connected devices to reduce downtime and maintain costs drive demand in the AI in IoT market. The two technologies together are projected to offer futuristic opportunities in several industry verticals such as retail, transport, and healthcare. Moreover, IoT-based sensor data is gaining prominent attention among researchers in healthcare, information science, and bioinformatics domains, government policy, and decision-makers, and enterprises as players seek to tap the potential of the colossal data stored by sensors. Implementation of Machine Learning and Deep Learning Technologies to Develop Digital Ecosystems: The pervasiveness of IoT, owing to the integration of functions with AI, offers significant opportunities for the development of digital ecosystems.

The advent of self-driving cars revived the decades-old philosophical conundrum known as the "trolley problem." The basic setup is this: A vehicle is hurtling toward a group of five pedestrians, and the only way to save them is to swerve and run over a single pedestrian instead. For philosophers and psychologists, it's pure thought experiment -- a tool to tease out and scrutinize our moral intuitions. Most people will never face such a stark choice, and even if they did, studies suggest their reaction in the moment would have little to do with their views on utilitarianism or moral agency. Self-driving cars have given the problem a foothold in the real world.

The fact that AI can pass on bias and prejudice is now widely recognized, probably because recent incidents of apparently racist or sexist algorithms involved big companies like Google and Amazon. A better understanding of how bad data gets encoded might make it easier to prevent. The large-scale machine learning AI that undergirds most recent advances relies on immense quantities of data. As the system feeds on the data provided, thousands of small adjustments are made to internal parameters to tweak how the data will be categorized. So, if the original training data is biased, the training is biased and the results will be biased.

The international nuclear arms control regime is approaching a critical juncture. If new nuclear weapons treaties are to be negotiated, ratified and enforced, they will need to be underpinned by strong technical monitoring capabilities. The Department of Energy's National Nuclear Security Administration is leveraging its expertise and technology to meet this challenge, understanding that in nuclear nonproliferation, you can't verify what you can't see. The United States is placing renewed urgency on developing the science and technology required to monitor our adversaries' nuclear activity -- specifically by harnessing the power of artificial intelligence and the unmatched, high-performance computing capabilities found at DOE's national laboratories. DOE houses four of the world's top 10 fastest supercomputers, including the top two, and we are already at work on developing three next-generation, exascale machines, able to conduct a billion billion calculations per second.

He shared his vision of how AI can become handy for the government at a conference in Moscow called'Artificial Intelligence Journey', which brought together 5,000 people, including representatives of over 1,000 companies working in the AI sphere. "AI technologies make it possible to get rid of the inertia and sluggishness of the bureaucratic machine to radically increase the transparency and efficiency of the administrative procedures," he said, adding that it could allow people to see "what the authorities are doing and what they are motivated by when taking certain decisions." Russia's red tape – and vast army of state and regional bureaucrats – have been quite an issue for the country for decades. The situation seems to have improved recently, as citizen-state interactions are done through computers more often. For AI to be more efficient, Putin said, it has to have wider access to various data – but there's a fine line between that and privacy rights, which must be protected.

Earlier this year, videos of a robot being kicked, hit with a chair, and shot at by its human owners spread online. Created by an LA-based production company, Corridor Digital, the videos were a parody of those released by Boston Dynamics, a company that has been making robots since 1992. You've almost certainly seen their videos. A robotic dog takes on a human in a tug of war. Sometimes the robots are cute, like the Sand Flea, which flicks itself effortlessly over 30ft walls.

Newcrest Mining has deployed Microsoft cloud, AI and IoT technologies at Australia's largest underground, block cave mine to monitor and manage crushed ore bin levels. The soft sensor delivered a return in investment within the first three months of operation. The technology, developed by Newcrest in association with Microsoft and its partner Insight Enterprises, has been rolled out at Newcrest's Cadia Valley gold mine in NSW. The challenge facing Newcrest at Cadia was managing the levels in the underground crushed ore bins. If the bins overfill, they have to be manually emptied introducing lengthy and expensive production delays.

The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternative for public health monitoring to complement the traditional survey-based approaches. The data was extracted from Twitter based on pre-defined keywords (i.e. The following deep learning architectures were adopted in the experiments: CNN, RNN, LSTM and GRU. Both default (GloVe) and domain-specific (HF) word embeddings were used in training the classifiers.

The new European Union General Data Protection Regulation (GPDR, General Data Protection Regulation) includes regulations on how to use Machine Learning. These regulations aim to give control of personal data to the user introducing the Right to explanation. Right to explanation is a demand from the European Union to make Artificial Intelligence more transparent and ethical. This regulation promotes to build Algorithms that ensures an explanation for every Machine Learning decision. Explainability is still without a consensus on how an explanation needs to look like.

Scientists have used AI to design a foldable material that could allow you to put a bicycle in your pocket, all using only a simulation. In short: you can now use AI to design a material that can fit your exact stiffness and compression specifications. Bessa, is a man on a mission, a mission to make a bicycle that can fold into your pocket. Maybe he had his bike stolen 1 too many times or maybe he's just an avid commuter. Regardless of his motivation, he's successfully used AI to streamline the material testing and design process.