They promised us the robots wouldn’t attack…
In what seems like dialogue lifted straight from the pages of a post-apocalyptic science fiction novel, researchers from the University of Massachusetts Amherst and Stanford claim they’ve developed an algorithmic framework that guarantees AI won’t misbehave. Read More
Monthly Archives: November 2019
How To Leverage Deep Learning For Automation Of Mobile Applications
Mobile applications have already made a mark on the digital front. With a large number of applications already on the Google Play Store and Apple Store. There are applications for almost everything today. But, as the markets of mobile apps expand, they face new challenges and obstacles to be overcome.
Deep Learning is a subsidiary technology for Artificial Intelligence. It uses algorithms to parse the data and provide deep insights into the applications and their issues. Often, time constraints and deadline pressures get the better of developers and do not allow the developers or higher management to test the app properly before the grand launch and here, deep learning can help automate the mobile application testing and deployment.
The interactions between the user and system are facilitated through the GUI(Graphic User Interface). Especially, an interaction may include clicking, scrolling, or inputting text into a GUI element, such as a button, an image, or a text block. An input generator can produce interactions for several tests, Read More
Top 7 Data Science Use Cases in Trust and Security
What are trust and safety? What is the role of trust and security in the modern world?
We often come across this word combination ‘Trust & Safety’ on numerous web sites and platforms. It is called upon to regulate the interaction between the visitors and specialists so that it would be fair and peaceful.
Everyone, starting from e-commerce websites to social networks need to prevent fraud and provide a high level of security for the visitors. The platforms do their best to get the trust of their visitors. Safe and trusted platforms are expected to be actively visited by a broad spectrum of people eager to communicate, buy, learn, etc. There is nothing strange that the words trust and safety are used together so often. These terms are closely interrelated by their nature. Trust is a multidimensional concept. Trusting to a brand, source, etc. strengthens confidence and feeling of safety to the potential users. Read More
Increasing transparency with Google Cloud Explainable AI
June marked the first anniversary of Google’s AI Principles, which formally outline our pledge to explore the potential of AI in a respectful, ethical and socially beneficial way. For Google Cloud, they also serve as an ongoing commitment to our customers—the tens of thousands of businesses worldwide who rely on Google Cloud AI every day—to deliver the transformative capabilities they need to thrive while aiming to help improve privacy, security, fairness, and the trust of their users.
We strive to build AI aligned with our AI Principles and we’re excited to introduce Explainable AI, which helps humans understand how a machine learning model reaches its conclusions. Read More
Robot debates humans about the dangers of artificial intelligence
An artificial intelligence has debated with humans about the the dangers of AI – narrowly convincing audience members that AI will do more good than harm.
Project Debater, a robot developed by IBM, debated on both sides of the argument, with two human team mates for each side helping it out. Speaking in a female American voice to a crowd at the University of Cambridge Union on Thursday evening, the AI gave each side’s opening statements, using arguments drawn from more than 1100 human submissions ahead of time. Read More
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build,especially in the post-Moore’s Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. Read More
#nvidiaArtificial Intelligence and National Security — Updated November 21, 2019
Artificial intelligence (AI) is a rapidly growing field of technology with potentially significant implications for national security. As such, the U.S. Department of Defense (DOD) and other nations are developing AI applications for a range of military functions. AI research is underway in the fields of intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and in a variety of semiautonomous and autonomous vehicles. Already, AI has been incorporated into military operations in Iraq and Syria. Congressional action has the potential to shape the technology’s development further, with budgetary and legislative decisions influencing the growth of military applications as well as the pace of their adoption.
AI technologies present unique challenges for military integration, particularly because the bulk of AI development is happening in the commercial sector. Although AI is not unique in this regard, the defense acquisition process may need to be adapted for acquiring emerging technologies like AI. In addition, many commercial AI applications must undergo significant modification prior to being functional for the military. A number of cultural issues also challenge AI acquisition, as some commercial AI companies are averse to partnering with DOD due to ethical concerns, and even within the department, there can be resistance to incorporating AI technology into existing weapons systems and processes. Read More
Russia bans sale of gadgets without Russian-made software
Russia has passed a law banning the sale of certain devices that are not pre-installed with Russian software.
The law will come into force in July 2020 and cover smartphones, computers and smart televisions.
Proponents of the legislation say it is aimed at promoting Russian technology and making it easier for people in the country to use the gadgets they buy. Read More
How Artificial Intelligence Is Getting Us Closer to Star Trek’s Universal Translators
Universal translators make everything possible in the Star Trek series: First Contacts, interspecies relationships, human characters crying to Guinan over their synthale. In fact, they work so seamlessly that the viewer tends not to notice they exist until they encounter the occasional problem, as they do in DS9’s “Sanctuary” or Voyager’s “Nothing Human.”
By comparison, machine translation as we know it in the early 21st century is messy and incomplete. Everyone who’s used Google Translate or seen automatically translated text on social media knows that it’s not yet at Starfleet’s level. Read More
New powers, new responsibilities A global survey of journalism and artificial intelligence
No, the robots are not going to take over journalism. Yes, the machines might soon be able to do much routine journalism labour. But the reality and the potential of artificial intelligence (AI), machine learning, and data processing is to give journalists new powers of discovery, creation and connection.
The hope is that journalists will be algorithmically turbo-charged, capable of using their human skills in new and more effective ways. AI could also transform newsrooms from linear production lines into networked information and engagement hubs that give journalists the structures to take the news industry forward into the data-driven age.
Algorithms will power the systems. But the human touch – the insight and judgement of the journalist – will be at a premium. Can the news industry seize this opportunity? Read More
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