In medicine, diseases can be detected at a much earlier stage, and we can support the elderly to live a more independent life, simply by identifying deviations from their usual behaviour and body movements. The UK Government recently announced that AI could help the National Health Service predict those in an early stage of cancer, to ultimately prevent thousands of cancer-related deaths by 2033. The algorithms will examine medical records, habits and genetic information pooled from health charities, the NHS and AI. Virtual nurses could transform patient care, being available round the clock to answer questions, monitor patients and provide quick answers. Beyond healthcare, AI could inform a better allocation of resources in energy, logistics and transport, as well as support the digital advertising industry with more efficient marketing.
AI is, at heart, about making machines smarter, so that they can think and act like humans (or even better). We need only look at the popularity of devices like smart phones, smart fitness trackers and smart thermostats to see how consumers wholeheartedly embrace products and services that can make their life easier, smarter, more streamlined, more connected.
Artificial intelligence technology is continually evolving and finding its way into more industries and applications. Many businesses, especially smaller ones, struggle to decide whether they should invest in an AI plan. Doing so can be both time-consuming and costly, but it might pay off in the long run. The members of Forbes Technology Council generally agree that artificial intelligence, even on a small scale, can benefit most modern businesses. Below, 11 of them recommend some first steps for businesses to take when deciding on an AI plan.
DeepMind's Research Platform Team has open-sourced TF-Replicator, a framework that enables researchers without previous experience with the distributed system to deploy their TensorFlow models on GPUs and Cloud TPUs. The move aims to strengthen AI research and development. Synced invited Yuan Tang, a senior software engineer at Ant Financial, to share his thoughts on TF-Replicator. How would you describe TF-Replicator? TF-Replicator is a framework to simplify the writing of distributed TensorFlow code for training machine learning models, so that they can be effortlessly deployed to different cluster architectures.
StellarGraph is a Python 3 library. The StellarGraph library implements several state-of-the-art algorithms for applying machine learning methods to discover patterns and answer questions using graph-structured data. Added GraphConvolution layer, GCN class for a stack of GraphConvolution layers, and FullBatchNodeGenerator class for feeding data into GCN (from version 0.5.0) We provide examples of using StellarGraph to solve such tasks using several real-world datasets.
Find more about us on www.virtualeforce.com Virtual eForce LLC is a Seattle-based tech company with mission to prevent mass shooting and save lives. Using the most innovative artificial intelligence (AI) technology, we developed a gun detection, lockdown, active shooter(s) tracking and response system – Gun Lockers & SafeSchool . Guns can be detected using existing surveillance IP cameras and our machine learning algorithms. Security or dispatch will be notified in no time through our messaging, emails and mobile app services, and they can immediately activate building or campus lockdown after confirmation.
Within the corporate world AI chatbots are becoming commonplace: most businesses are increasingly integrating them into their enterprise systems. A recent Forrester survey says that within 5 years, 85% of customer interaction will occur through the use of AI chatbots. Introduction of AI, coupled with natural language processing (NLP) and machine learning (ML) is likely to be the mainstay of enterprise digitalization strategies of future. This is expected to improve labor efficiency, enhance customer and employee experiences and provide cost-effective solutions. According to Raleyware CEO George Elfond, with the need to realign corporate training in the light of changing demographics, wide prevalence of mobile technology and distribution of workforce, incorporating AI is a must for efficient training.
The field of machine ethics is concerned with the question of how to embed ethical behaviors, or a means to determine ethical behaviors, into artificial intelligence (AI) systems. The goal is to produce artificial moral agents (AMAs) that are either implicitly ethical (designed to avoid unethical consequences) or explicitly ethical (designed to behave ethically). Van Wynsberghe and Robbins' (2018) paper Critiquing the Reasons for Making Artificial Moral Agents critically addresses the reasons offered by machine ethicists for pursuing AMA research; this paper, co-authored by machine ethicists and commentators, aims to contribute to the machine ethics conversation by responding to that critique. The reasons for developing AMAs discussed in van Wynsberghe and Robbins (2018) are: it is inevitable that they will be developed; the prevention of harm; the necessity for public trust; the prevention of immoral use; such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. In this paper, each co-author addresses those reasons in turn. In so doing, this paper demonstrates that the reasons critiqued are not shared by all co-authors; each machine ethicist has their own reasons for researching AMAs. But while we express a diverse range of views on each of the six reasons in van Wynsberghe and Robbins' critique, we nevertheless share the opinion that the scientific study of AMAs has considerable value.
With billions of people and things connected by sensors and devices, and unprecedented processing power, storage capacity, and access to knowledge, the possibilities for innovation are endless. Advances in artificial intelligence are visible everywhere - from the Roombas that clean our homes, to the algorithms that suggest the movies we watch, to self-driving cars and drones delivering packages. Artificial intelligence is just one aspect of the explosion in technological breakthroughs including robotics, the Internet of Things, 3-D printing, nanotechnology, biotechnology, materials science, energy storage, and quantum computing.