Artificial Intelligence Tech Will Arrive in Three Waves. DARPA: A Track Record of Innovation I’ve done a lot of writing and research recently about the bright future of AI: that it’ll be able to analyze human emotions, understand social nuances, conduct medical treatments and diagnoses that overshadow the best human physicians, and in general make many human workers redundant and unnecessary.
I still stand behind all of these forecasts, but they are meant for the long term – twenty or thirty years into the future. And so, the question that many people want answered is about the situation at the present. Right here, right now. Luckily, DARPA has decided to provide an answer to that question. DARPA is one of the most interesting US agencies. We Need a Plan for When AI Becomes Smarter Than Us. In Brief There will come a time when artificial intelligence systems are smarter than humans.
When this time comes we will need to build more AI systems to monitor and improve current systems. This will lead to a cycle of AI creating better AI, with little to no human involvement. When Apple released its software application, Siri, in 2011, iPhone users had high expectations for their intelligent personal assistants. Yet despite its impressive and growing capabilities, Siri often makes mistakes. However, as artificial intelligence advances, experts believe that intelligent machines will eventually – and probably soon – understand the world better than humans. If humans cannot understand and evaluate these machines, how will they control them? Paul Christiano, a Ph.D. student in computer science at UC Berkeley, has been working on addressing this problem. There's An Invention That Will be the End of All Other Human Inventions. In Brief Artificial intelligence is already transforming the way people live their lives.
2015 in Review: The Year Artificial Intelligence Went Mainstream. The top AI breakthroughs of 2015. (credit: iStock) By Richard Mallah Courtesy of Future of Life Institute Progress in artificial intelligence and machine learning has been impressive this year.
Those in the field acknowledge progress is accelerating year by year, though it is still a manageable pace for us. The vast majority of work in the field these days actually builds on previous work done by other teams earlier the same year, in contrast to most other fields where references span decades. These Robots Learn New Tasks by Watching YouTube. Video Learning Cornell researchers are using instructional videos off the Internet to teach robots the step-by-step instructions required to perform certain tasks.
This ability may become necessary in a future where menial laborer robots – the ones responsible for mundane tasks such as cooking, cleaning, and other household chores – can readily carry out such tasks. Robots such as these will definitely be beneficial in assisting the elderly and the disabled, though it remains to be seen when (and if) they will truly become available for use. Hopefully, these early tests will help us make such determinations. Jeff Hawkins Makes A Bold AI Claim - Thinking and Reasoning Computers in Five Years. RoboBrain: First Knowledge Engine for Robots. Memcomputers: Faster, More Energy-Efficient Devices That Work Like a Human Brain. Glass Fibers May Open Door to Neuromorphic Computing.
In Brief Researchers have demonstrated how neural networks and synapses in the brain can be reproduced using special glass fibers made that are sensitive to light, known as chalcogenides.
Using conventional fiber drawing techniques, microfibers can be produced from chalcogenide glass based on sulfur that possess a variety of broadband photo-induced effects, which allow the fibers to be switched on and off.This optical switching or light switching light, can be exploited for a variety of next generation computing applications capable of processing vast amounts of data in a much more energy-efficient manner. In the proposed optical version of this brain function, the changing properties of the glass act as the varying electrical activity in a nerve cell, and light provides the stimulus to change these properties.
Quantum Computing Could Advance Artificial Intelligence by Orders of Magnitude. In Brief Combining the vast processing power of quantum computers with cognitive computing systems like IBM's Watson will lead to huge advances in artificial intelligence.
IBM’s Watson supercomputer first rose to prominence in 2011 when it became the first computer to beat human contestants at the US gameshow Jeopardy! In the years since, IBM and other companies have put Watson’s immense computing power to a variety of uses, from working with doctors to develop treatment plans for cancer patients, to assisting the world’s media in crunching tennis statistics at Wimbledon.IBM is yet to announce plans to integrate a quantum computer system with Watson but recently unveiled a new superconducting chip that demonstrates a technique crucial to the development of quantum computers.
The chip was the first to integrate quantum bits –or qubits– into a two-dimensional grid. IBM sticks Watson's brain into a friendly virtual assistant. In Brief Meet Amelia.
Peering into the Future: AI and Robot brains. In Singularity or Transhumanism: What Word Should We Use to Discuss the Future?
On Slate, Zoltan Istvan writes: "The singularity people (many at Singularity University) don't like the term transhumanism. Transhumanists don't like posthumanism. Posthumanists don’t like cyborgism. And cyborgism advocates don't like the life extension tag. If you arrange the groups in any order, the same enmity occurs. " Artificial intelligence: two common misconceptions. Recent comments by Elon Musk and Stephen Hawking, as well as a new book on machine superintelligence by Oxford professor Nick Bostrom, have the media buzzing with concerns that artificial intelligence (AI) might one day pose an existential threat to humanity.
Should we be worried? Let’s start with expert opinion. Watson. Stanford to Research the effects of Artificial Intelligence. What will intelligent machines mean for society and the economy in 30, 50 or even 100 years from now? That’s the question that Stanford University scientists are hoping to take on with a new project, the One Hundred Year Study on Artificial Intelligence (AI100). “If your goal is to create a process that looks ahead 30 to 50 to 70 years, it’s not altogether clear what artificial intelligence will mean, or how you would study it,” said Russ Altman, a professor of bioengineering and computer science at Stanford.
“But it’s a pretty good bet that Stanford will be around, and that whatever is important at the time, the university will be involved in it.” FLI - Future of Life Institute. (If you have questions about this letter, please contact email@example.com) Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents - systems that perceive and act in some environment.
In this context, "intelligence" is related to statistical and economic notions of rationality - colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Artificial intelligence. AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers.
AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence is still among the field's long-term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI.
History Research Goals Planning Artifical intelligence diagram. Artificial Intelligence @ MIRI. Applications of artificial intelligence. Artificial intelligence has been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, remote sensing, scientific discovery and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore," Nick Bostrom reports. "Many thousands of AI applications are deeply embedded in the infrastructure of every industry. " In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes.
Computer science AI researchers have created many tools to solve the most difficult problems in computer science. A.I. Artificial Intelligence. A.I. Outline of artificial intelligence. Artificial Intelligence. Introduction › cross-links to AI Context. Category:Artificial intelligence. From Wikipedia, the free encyclopedia Subcategories This category has the following 32 subcategories, out of 32 total.