The rapid pace of artificial intelligence (AI) has raised fears about whether robots could act unethically or soon choose to harm humans. Some are calling for bans on robotics research; others are calling for more research to understand how AI might be constrained. But how can robots learn ethical behavior if there is no “user manual” for being human?

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by JOSHUA OGAWA, Nikkei staff writer

The game of GO is considered more complex than either chess or shogi.

Google has developed a computer program capable of beating professional go players, opening the door to new applications — and new ethical concerns — as artificial intelligence draws closer to matching human thinking.

The U.S. tech giant presented its results in the Jan. 27 issue of Nature. Though a computer managed to topple the world chess champion in 1997 and defeat the top women’s shogi (Japanese chess) player in 2010, Google’s AlphaGo program (official blog site) is the first to triumph over professional go players under official rules.

TOUGHER THAN CHESS The go board is larger than that used in chess or shogi, with total possible gameplay scenarios numbering 10 to the 360th power. Anticipating and solving all possible board combinations is impossible even for today’s most advanced computers, and many researchers had predicted that a program capable of besting pro players was at least 10 years away.

Google’s go AI bypassed that problem with deep learning, a technology that mimics human neural pathways and learning processes. Rather than working through the possible scenarios by brute force, the program considers the board as a whole and draws on accumulated experience to choose its next move. The company has used such technology before. Last year, it presented its deep Q-network algorithm, which let computers master electronic games by analyzing pixel and score data over repeated plays.

For its go project, Google collaborated with pro players to teach the computer 30 million plays, eventually enabling it to predict humans’ moves with 57% accuracy. The AI then was put through several million matches against itself, forcing it to work out winning strategies by experience. Its ability to select the best move by analyzing the state of the board is now nearly equal to a human’s ability to do the same based on skill and intuition.

The program can beat existing go software 99.8% of the time, and it won all five games against reigning European champion Fan Hui in October. The AI will face another five-game challenge in March, when it goes up against Lee Se-dol, one of the world’s top players.

WAY FORWARD The question now is where Google will direct its AI efforts next. Games are an excellent arena in which to develop and test AI, but the goal is to turn such technology toward solving real-world problems, said Demis Hassabis, Google’s AI chief. The priority, he indicated, is on developing robust multipurpose AI tech.

Deep learning is particularly promising, given its ability to process visual and audio information in a manner resembling human perception byGoogle Deep Mind LOGO finding patterns in large data sets. Recent research has sought to apply the technology in diverse fields, such as using it to control robots’ movements or to analyze medical image data to help diagnose patients. Simple forms are already at work in today’s tech, such as voice recognition software on smartphones. The dawn of AI capable of replicating the intuition of human professionals would drastically expand the current slate of applications.

Yet researchers also are growing more cautious as AI advances. Though Google is pleased to have overcome a major challenge for AI, Hassabis said, the company is aware of the ethical issues surrounding the technology. Academics and other public figures have warned that unchecked development could lead to AI programs that are hostile to society as a whole.

As with “any powerful new technology,” developers of AI must “take seriously our responsibilities” and “have ethical concerns at the top of our minds,” Hassabis told the BBC last year. Google has established an AI ethics board to address those concerns. Now that technology has won out against humanity in one of the ultimate game-based challenges, the focus of research should turn to cooperation between man and machine.

MIT-deep learning chip Eyeriss
Chip could bring deep learning to mobile devices

via MASSACHUSETTS INSTITUTE OF TECHNOLOGY

In recent years, some of the most exciting advances in artificial intelligence have come courtesy of convolutional neural networks, large virtual networks of simple information-processing units, which are loosely modeled on the anatomy of the human brain.

Neural networks are typically implemented using graphics processing units (GPUs), special-purpose graphics chips found in all computing devices with screens. A mobile GPU, of the type found in a cell phone, might have almost 200 cores, or processing units, making it well suited to simulating a network of distributed processors.

At the International Solid State Circuits Conference in San Francisco this week, MIT researchers presented a new chip designed specifically to implement neural networks. It is 10 times as efficient as a mobile GPU, so it could enable mobile devices to run powerful artificial-intelligence algorithms locally, rather than uploading data to the Internet for processing.

Neural nets were widely studied in the early days of artificial-intelligence research, but by the 1970s, they’d fallen out of favor. In the past decade, however, they’ve enjoyed a revival, under the name “deep learning.”

“Deep learning is useful for many applications, such as object recognition, speech, face detection,” says Vivienne Sze, an assistant professor of electrical engineering at MIT whose group developed the new chip. “Right now, the networks are pretty complex and are mostly run on high-power GPUs. You can imagine that if you can bring that functionality to your cell phone or embedded devices, you could still operate even if you don’t have a Wi-Fi connection. You might also want to process locally for privacy reasons. Processing it on your phone also avoids any transmission latency, so that you can react much faster for certain applications.”

The new chip, which the researchers dubbed “Eyeriss,” could also help usher in the “Internet of things” — the idea that vehicles, appliances, civil-engineering structures, manufacturing equipment, and even livestock would have sensors that report information directly to networked servers, aiding with maintenance and task coordination. With powerful artificial-intelligence algorithms on board, networked devices could make important decisions locally, entrusting only their conclusions, rather than raw personal data, to the Internet. And, of course, onboard neural networks would be useful to battery-powered autonomous robots.

Division of labor

A neural network is typically organized into layers, and each layer contains a large number of processing nodes. Data come in and are divided up among the nodes in the bottom layer. Each node manipulates the data it receives and passes the results on to nodes in the next layer, which manipulate the data they receive and pass on the results, and so on. The output of the final layer yields the solution to some computational problem.

In a convolutional neural net, many nodes in each layer process the same data in different ways. The networks can thus swell to enormous proportions. Although they outperform more conventional algorithms on many visual-processing tasks, they require much greater computational resources.

The particular manipulations performed by each node in a neural net are the result of a training process, in which the network tries to find correlations between raw data and labels applied to it by human annotators. With a chip like the one developed by the MIT researchers, a trained network could simply be exported to a mobile device.

This application imposes design constraints on the researchers. On one hand, the way to lower the chip’s power consumption and increase its efficiency is to make each processing unit as simple as possible; on the other hand, the chip has to be flexible enough to implement different types of networks tailored to different tasks.

Sze and her colleagues — Yu-Hsin Chen, a graduate student in electrical engineering and computer science and first author on the conference paper; Joel Emer, a professor of the practice in MIT’s Department of Electrical Engineering and Computer Science, and a senior distinguished research scientist at the chip manufacturer NVidia, and, with Sze, one of the project’s two principal investigators; and Tushar Krishna, who was a postdoc with the Singapore-MIT Alliance for Research and Technology when the work was done and is now an assistant professor of computer and electrical engineering at Georgia Tech — settled on a chip with 168 cores, roughly as many as a mobile GPU has.

Act locally

The key to Eyeriss’s efficiency is to minimize the frequency with which cores need to exchange data with distant memory banks, an operation that consumes a good deal of time and energy. Whereas many of the cores in a GPU share a single, large memory bank, each of the Eyeriss cores has its own memory. Moreover, the chip has a circuit that compresses data before sending it to individual cores.

Each core is also able to communicate directly with its immediate neighbors, so that if they need to share data, they don’t have to route it through main memory. This is essential in a convolutional neural network, in which so many nodes are processing the same data.

The final key to the chip’s efficiency is special-purpose circuitry that allocates tasks across cores. In its local memory, a core needs to store not only the data manipulated by the nodes it’s simulating but data describing the nodes themselves. The allocation circuit can be reconfigured for different types of networks, automatically distributing both types of data across cores in a way that maximizes the amount of work that each of them can do before fetching more data from main memory.

At the conference, the MIT researchers used Eyeriss to implement a neural network that performs an image-recognition task, the first time that a state-of-the-art neural network has been demonstrated on a custom chip.

READ MORE: http://news.mit.edu/2016/neural-chip-artificial-intelligence-mobile-devices-0203

ADDITIONAL: http://web.mit.edu/

The Trivium and the Quadrivium

The Liberal Arts

The liberal arts consists of seven branches of knowledge that prepare students for lifelong learning.

The concept is Classical, but the term Liberal Arts and their division into the TRIVIUM and the QUADRIVIUM date back to the Middle Ages.

The Trivium and the Quadriviumtrivium-wikipedia

The trivium includes those aspects of the liberal arts that pertain to mind.  The quadrivium, those aspects of the liberal arts that pertain to matter.

TRIVIUM= Logic, Grammar, and Rhetoric.

QUADRIVIUM= Arithmetic, Music, Geometry, and Astronomy.

Logic is the art of thinking; grammar, the art of inventing symbols and combining them to express thought; and rhetoric, the art of communicating thought from one mind to another, the adaptation of language to circumstance. Arithmetic, the theory of number, and music, an application of the theory of number (the measurement of discrete quantities in motion), are the arts of discrete quantity or number. Geometry, the theory of space, and astronomy, an application of the theory of space, are the arts of continuous quantity or extension.

liberal-arts-fig1

 

These arts of reading, writing, and reckoning have formed the traditional basis of liberal education, each constituting both a field of knowledge and the technique to acquire that knowledge. The degree bachelor of arts is awarded to those who demonstrate the requisite proficiency in these arts, and the degree master of arts, to those who have demonstrated a greater proficiency.

Today, as in centuries past, a mastery of the liberal arts is widely recognized as the best preparation for work in professional schools, such as those of medicine, law, engineering, or theology.

Those who first perfect their own faculties through liberal education are thereby better prepared to serve others in a professional or other capacity.

 

 

Source: The Trivium: The Liberal Arts of Logic, Grammar, and Rhetoric
by Sister Miriam Joseph Rauh, C.S.C., (1898-1982) earned her doctorate from Columbia University.
A member of the Sisters of the Holy Cross, Sister Miriam was professor of English at Saint Mary’s College from 1931 to 1960.

 

thinker_artist-rodin

During the era of classical antiquity (when ancient Greece and ancient Rome intertwined creating the Greco-Roman world), liberal arts was considered essential education for a free individual active in civic life. At the time, this would have entailed being able to participate in public debate, defend oneself and serve in court and on juries, and perform military service. At this time, liberal arts covered only three subjects: grammar, rhetoric and logic, collectively known as the trivium. This was extended in medieval times to include four further subjects: arithmetic, geometry, music and astronomy, named the quadrivium – so there were seven liberal arts subjects in the medieval liberal arts curriculum.

The trivium was considered preparatory work for the considerably more difficult quadrivium, with the quadrivium in turn being considered preparatory work for the more serious study of philosophy and theology. The aim of a liberal arts education was to produce a person who was virtuous and ethical, knowledgeable in many fields and highly articulate.

Although modern liberal arts curriculums have an updated choice of a larger range of subjects, it still retains the core aims of the liberal arts curricula maintained by the medieval universities: to develop well-rounded individuals with general knowledge of a wide range of subjects and with mastery of a range of transferable skills.

They will become ‘global citizens’, with the capacity to pursue lifelong learning and become valuable members of their communities.

Original Source: http://www.topuniversities.com/blog/what-liberal-arts-education

The Great Lesson in Liberal Arts

The Thinker- Liberal Arts Education

How does a liberal arts education help develop an individual’s character? As a working class individual with degrees in the humanities, I have argued the notion that my area of study helped me become a better person rather than a person with hard skills.

I have skills in writing, analysis, logical thinking, and the big one: empathy. I have come to terms that a liberal arts education is not a stepping-stone in a fully ordered career plan. However, I do have imagination and imagination is what I found to be the common trait in students that have studied Liberal Arts.

1) What makes a Liberal Arts Student Different

These individuals­ appeal to a certain type of imagination: empathy. For what is empathy if not imagination?

You imagine yourself in someone else’s shoes, you imagine “what if that starving child was my own child?” you imagine yourself closer to the atrocities being done even when your fifteen million miles away. Empathy is a form of imagination that demands you to stand outside of yourself and connect with another being.

2) The Lesson in Liberal Arts

Studying the humanities communicates the same message. Are we responsible to individuals we don’t even know, that are half way across the world?

The answer is yes. My duty as a moral individual is to help anyone in need, to be my brother’s keeper, to empathize with those who are suffering. My background in history and philosophy taught me not only that history repeats itself, but that you can learn from it too.

3) The Benefits of Liberal Arts

History may be tales of the victors, but future generations can learn from past mistakes. I began to realize that violence or self-shame are not choices that will aid me in my life journey.

I learned from my studies that life is unfair, yet there is hope because there are people who can empathize, imagine, and act. I have learned that value must be based on its moral implications and its ability to develop an individual into a better citizen of the world.

4) Why learning about the Liberal Arts is important

Why is learning of and about the Liberal Arts important? Because in this time and age, we need to teach our generation and the next one that a person’s value is not measured by utility. Teaching and learning the Liberal Arts will not fix everything, it will not cure or nullify the tragedies in the world.

However, it can provide a demarcation towards understanding. An understanding that will ultimately lead to empathy and action.

Given our current state in the world, from terrorism to environmental challenges, the ability to recognize “the other”- the “other” less fortunate, the “other struggling”- catapults an individual towards passionate change.

Knowing this, I highly recommend that anyone that reads this article share it on social media. Not just to learn about Liberal Art studies, but to discover the benefits of learning about the depths of human history.

Source: http://www.saycampuslife.com/2016/02/03/the-great-lesson-in-liberal-arts-what-we-can-all-takeaway/