Those fretfully debating artificial intelligence (AI) might best start by appraising the half dozen general pathways under exploration in laboratories around the world. While these general approaches overlap, they offer distinct implications for what characteristics emerging, synthetic minds might display, including (for example) whether it will be easy or hard to instill human-style ethical values. Most problematic may be those efforts taking place in secret. The "Moore's Law crossing" argument is appraised, in light of discoveries that brain computation may involve much more than just synapses. Will efforts to develop Sympathetic Robotics tweak compassion from humans long before automatons are truly self-aware? It is argued that most foreseeable problems might be dealt with the same way that human versions of oppression and error are best addressed -- via reciprocal accountability. For this to happen, there should be diversity of types, designs and minds, interacting under fair competition in a generally open environment. As varied concepts from science fiction are reified by rapidly advancing technology, some trends are viewed worriedly by our smartest peers. Portions of the intelligencia -- typified by Google's Ray Kurzweil [1] -- foresee AI, or Artificial General Intelligence (AGI) as likely to bring good news, perhaps even transcendence for members of the Olde Race of bio-organic humanity 1.0. Others, such as Stephen Hawking and Francis Fukuyama, warn that the arrival of sapient, or supersapient machinery may bring an end to our species -- or at least its relevance on the cosmic stage -- a potentiality evoked in many a lurid Hollywood film. Swedish philosopher Nicholas Bostrom, in Superintelligence [2], suggests that even advanced AIs who obey their initial, human defined goals will likely generate "instrumental subgoals" such as self-preservation, cognitive enhancement, and resource acquisition. In one nightmare scenario, Bostrom posits an AI that -- ordered to "make paperclips" -- proceeds to overcome all obstacles and transform the solar system into paper clips. A variant on this theme makes up the grand arc in the famed "three laws" robotic series by science fiction author Isaac Asimov [3]. Taking middle ground, SpaceX/Tesla entrepreneur Elon Musk has joined with YCombinator founder Sam Altman to establish OpenAI [4], an endeavor that aims to keep artificial intelligence research -- and its products -- accountable by maximizing transparency and accountability. As one who has promoted those two key words for a quarter of a century, I wholly approve [5].

Topological data analysis is an emerging mathematical concept for characterizing shapes in multi-scale data. In this field, persistence diagrams are widely used as a descriptor of the input data, and can distinguish robust and noisy topological properties. Nowadays, it is highly desired to develop a statistical framework on persistence diagrams to deal with practical data. This paper proposes a kernel method on persistence diagrams. A theoretical contribution of our method is that the proposed kernel allows one to control the effect of persistence, and, if necessary, noisy topological properties can be discounted in data analysis. Furthermore, the method provides a fast approximation technique. The method is applied into several problems including practical data in physics, and the results show the advantage compared to the existing kernel method on persistence diagrams.

Artificial intelligence is changing the world and doing it at breakneck speed. The promise is that intelligent machines will be able to do every task better and more cheaply than humans. Rightly or wrongly, one industry after another is falling under its spell, even though few have benefited significantly so far. And that raises an interesting question: when will artificial intelligence exceed human performance? More specifically, when will a machine do your job better than you? Today, we have an answer of sorts thanks to the work of Katja Grace at the Future of Humanity Institute at the University of Oxford and a few pals.

Beijing, June 11: China the largest makers of Drones has launched a record-breaking swarm of 119 fixed wings unmanned aerial vehicles. The previous record was a swarm of 67 drones, said the China Electronics Technology Group Corporation. The 119 drones performed catapult-assisted take-offs and formation in the sky. According to the CETC, "swarm intelligence" is regarded as the core of artificial intelligence of unmanned systems and the future of intelligent unmanned systems.

Here, she focuses on how AI can provide real value for end users. In the first article in this three-part series, I outlined reasons why designing artificial intelligence (AI) for humans is a strategic imperative. In this second article, we'll discuss how to create AI experiences that go beyond technology buzz and provide real value for end users. I've identified eight strategic pillars to serve as a consumer focused foundation for any ideation, design and development process. We'll begin with the first four of these pillars: The more we emphasize the end user of a potential AI experience, the more likely we are to create something that truly adds value to both our businesses and our lives.

South Korean tech powerhouse LG Electronics Inc. said Sunday it has set up two research centers to develop technologies related to artificial intelligence, Yonhap News Agency reported. One center will focus on developing AI and the other robotics, it said. The AI center will focus on developing technologies applicable to LG Electronics' home appliance lineup, smartphones and automobile parts, while the robotics center will focus on developing "core technologies of smart robotics," Yonhap reported. A division within the AI center "will be devoted to R&D for deep learning, a new area of AI research where a computer emulates the way the human brain creates patterns from data and processes them," the report said.

Imagine your favorite go-to recipe mutated to conform to the traditional methods and ingredients of any number of diverse regional food cultures. Consider, say, lasagne, but a sort of lasagne that's instead a naturally occurring part of Japanese or Ethiopian cuisine. Not "fusion," but something deeper--a whole rewriting of what a lasagne even is according to the culinary traditions of some other place. It's not necessarily an easy or natural thing to do, but a new machine learning algorithm developed by a team of French, American, and Japanese researchers offers an automated solution based on neural networks and large amounts of food data. The result, which is described in a paper published this month to the arXiv preprint server (via I Programmer), is a system that can take a given recipe and shift it into an alternative dietary style--sushi lasagne, say--as well as parse a recipe for its underlying style components.

An AI machine has completed the maths section of the annual Chinese university exams in record time. The system, called AI-Maths, completed the test 12 times faster than it normally takes to complete. Despite the speedy result, its scores were well below average compared to students. A machine learning system called AI-Maths has completed the maths section of the annual Chinese university exams in blistering time. An AI system developed in 2014 using big data, artificial intelligence and natural language recognition technologies, had mixed results after attempting China's annual university entrance exam.

The AI Economy is coming faster then most people realize. AI will be a game-changer for business, government and society. I have published this in my last book Future Smart. This AI Economy has already started and is in full swing. There are many that would dismiss AI as science fiction.

The country is hoping to use the technology to transform a variety of industries, including manufacturing, health and transport. Hundreds of gadgets and must-have electronic devices were on display at Shanghai's Consumer Electronic Show, or CES Asia. Among them was a driverless car powered by Baidu's Project Apollo self-driving car platform. The tech giant is using machine-learning, a new step in artificial intelligence, to develop the technology. Machine-learning allows computers to learn without being explicitly programmed.