Artificial Intelligence could sideline and "destroy" its human creators if engineers cannot get a grip on the ethics behind it, Stephen Hawking has warned. Speaking at the Web Summit in Lisbon, the theoretical physicist said AI has the potential to be the best or worst thing humanity has ever seen and the scary reality is we just don't know which yet. "We cannot know if we will be infinitely helped by AI or ignored by it and sidelined, or conceivably destroyed by it," he said. The Cambridge professor said that while AI could be hugely beneficial for reducing poverty, disease and restoring the natural environment, it's impossible to predict "what we might achieve when our own minds are amplified by AI." "AI could be the worst invention of the history of our civilization, that brings dangers like powerful autonomous weapons or new ways for the few to oppress the many." "AI could develop a will of its own, a will that is in conflict with ours and which could destroy us. In short, the rise of powerful AI will be either the best or the worst thing ever to happen to humanity."

Artificial Intelligence could sideline and "destroy" its human creators if engineers cannot get a grip on the ethics behind it, Stephen Hawking has warned. Speaking at the Web Summit in Lisbon, the theoretical physicist said AI has the potential to be the best or worst thing humanity has ever seen and the scary reality is we just don't know which yet. "We cannot know if we will be infinitely helped by AI or ignored by it and sidelined, or conceivably destroyed by it," he said. The Cambridge professor said that while AI could be hugely beneficial for reducing poverty, disease and restoring the natural environment, it's impossible to predict "what we might achieve when our own minds are amplified by AI." "AI could be the worst invention of the history of our civilization, that brings dangers like powerful autonomous weapons or new ways for the few to oppress the many." "AI could develop a will of its own, a will that is in conflict with ours and which could destroy us. In short, the rise of powerful AI will be either the best or the worst thing ever to happen to humanity."

In Leones, Argentina, a drone with a special camera flies low over 150 acres of wheat. It's able to check each stalk, one-by-one, spotting the beginnings of a fungal infection that could potentially threaten this year's crop. Many food producers are struggling to manage threats to their crop like disease and pests, made worse by climate change, monocropping, and widespread pesticide use. Catching things early is key. Taranis, a company that works with farms on four continents, flies high-definition cameras above fields to provides "the eyes."

The Asian Development Bank (ADB) was formed with the aim of fostering economic growth in some of the poorest regions in the world. With that as its charter, ADB's CIO from the organisation's office of information systems and technology, Shirin Hamid, said it's difficult to compare ADB to the likes of other banks when talking about digital transformation. "DBS for example in Singapore has basically positioned itself as a digital bank, but as a development bank, where our partners and what we deliver is on the ground with countries that are from different landscapes -- we have middle income and the least developing countries -- are they ready for a bank like us to be a digital bank?," she said, speaking at Gartner IT Symposium/Xpo on the Gold Coast last week. You can download all of the articles in this special report in one PDF (free registration required). Hamid said she recently had a conversation with ADB's vice president of finance and risk management centred on determining if ADB was ready to be a digital bank.

The world is currently discussing if artificial systems are good or bad, will help us or destroy us, and if they will ever function or not, and by doing that people make the mistake of actually trying to answer the wrong question. As of today, the biggest question about artificial intelligence is not the system itself, but the biggest challenge is the interface consequences between the human and the machine, or to be more precise the system existent out of two elements -- a carbon and a silicon body. We all have learned in our life how difficult, dangerous, or even fatal the coordination, cooperation, and operation between these two objects -- the human and a machine -- can be, and some of us may have been hurt by it or even worse. At least I can say I have been many times, and if you read the news today you will certainly find many other examples. This has been true since humans invented the first machines moved by any energy form, be it animals, steam, or oil, and it is true for the new era we are entering -- a time when software-driven artificial intelligence performs better in defined areas than a human will ever be.

A startup will develop a proof-of-concept for an artificial intelligence (AI)-based object recognition capability for the US Transportation Security Administration (TSA), that could greatly enhance security checkpoint operations. The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) awarded $199,961.29 to Synthetik Applied Technologies, for a solution that "proposes an advancement to three-dimensional (3D) object recognition" said Melissa Oh, S&T's Silicon Valley Innovation Program (SVIP) Managing Director. "The solution has many applications to DHS use-cases that we have not yet fully explored." The focus of this development is a capability using real-time voxel-wise instance segmentation to detect objects during property screening in airports. A voxel is a unit of measurement in a 3D image--a 3D pixel--and object segmentation is the problem of delineating each object of interest in an image.

Google has launched the second iteration of its no-code Teachable Machine so that inexperienced users can take their bespoke machine learning (ML) models and apply them to projects such as classroom activities. Teachable Machine 2.0 carries over the features from the original, allowing users to record images and video from a webcam and use them to train ML models for tasks like pattern recognition. Now, these same models can be taken and exported to websites, apps and physical machines. Open source curriculums are making use of the tool to give children their first taste of ML, without the intimidating aspect of learning to code. One such example is a programme run out of MIT's Media Lab by education researcher Blakeley H. Payne for six to 10-year olds.

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.