Smarter Than Us: The Rise of Machine Intelligence

Curated on Posted on by Stefaan Verhulst

 

Book by Stuart Armstrong at the Machine Intelligence Research Institute: “What happens when machines become smarter than humans? Forget lumbering Terminators. The power of an artificial intelligence (AI) comes from its intelligence, not physical strength and laser guns. Humans steer the future not because we’re the strongest or the fastest but because we’re the smartest. When machines become smarter than humans, we’ll be handing them the steering wheel. What promises—and perils—will these powerful machines present? Stuart Armstrong’s new book navigates these questions with clarity and wit.
Can we instruct AIs to steer the future as we desire? What goals should we program into them? It turns out this question is difficult to answer! Philosophers have tried for thousands of years to define an ideal world, but there remains no consensus. The prospect of goal-driven, smarter-than-human AI gives moral philosophy a new urgency. The future could be filled with joy, art, compassion, and beings living worthwhile and wonderful lives—but only if we’re able to precisely define what a “good” world is, and skilled enough to describe it perfectly to a computer program.
AIs, like computers, will do what we say—which is not necessarily what we mean. Such precision requires encoding the entire system of human values for an AI: explaining them to a mind that is alien to us, defining every ambiguous term, clarifying every edge case. Moreover, our values are fragile: in some cases, if we mis-define a single piece of the puzzle—say, consciousness—we end up with roughly 0% of the value we intended to reap, instead of 99% of the value.
Though an understanding of the problem is only beginning to spread, researchers from fields ranging from philosophy to computer science to economics are working together to conceive and test solutions. Are we up to the challenge?
A mathematician by training, Armstrong is a Research Fellow at the Future of Humanity Institute (FHI) at Oxford University. His research focuses on formal decision theory, the risks and possibilities of AI, the long term potential for intelligent life (and the difficulties of predicting this), and anthropic (self-locating) probability. Armstrong wrote Smarter Than Us at the request of the Machine Intelligence Research Institute, a non-profit organization studying the theoretical underpinnings of artificial superintelligence.”

Code of Conduct: Cyber Crowdsourcing for Good

Curated on Posted on by Stefaan Verhulst

Patrick Meier at iRevolution: “There is currently no unified code of conduct for digital crowdsourcing efforts in the development, humanitarian or human rights space. As such, we propose the following principles (displayed below) as a way to catalyze a conversation on these issues and to improve and/or expand this Code of Conduct as appropriate.
This initial draft was put together by Kate Chapman, Brooke Simons and myself. The link above points to this open, editable Google Doc. So please feel free to contribute your thoughts by inserting comments where appropriate. Thank you.
An organization that launches a digital crowdsourcing project must:

  • Provide clear volunteer guidelines on how to participate in the project so that volunteers are able to contribute meaningfully.
  • Test their crowdsourcing platform prior to any project or pilot to ensure that the system will not crash due to obvious bugs.
  • Disclose the purpose of the project, exactly which entities will be using and/or have access to the resulting data, to what end exactly, over what period of time and what the expected impact of the project is likely to be.
  • Disclose whether volunteer contributions to the project will or may be used as training data in subsequent machine learning research
  • ….

An organization that launches a digital crowdsourcing project should:

  • Share as much of the resulting data with volunteers as possible without violating data privacy or the principle of Do No Harm.
  • Enable volunteers to opt out of having their tasks contribute to subsequent machine learning research. Provide digital volunteers with the option of having their contributions withheld from subsequent machine learning studies
  • … “

When Experts Are a Waste of Money

Curated on Posted on by Stefaan Verhulst

Vivek Wadhwa at the Wall Street Journal: “Corporations have always relied on industry analysts, management consultants and in-house gurus for advice on strategy and competitiveness. Since these experts understand the products, markets and industry trends, they also get paid the big bucks.
But what experts do is analyze historical trends, extrapolate forward on a linear basis and protect the status quo — their field of expertise. And technologies are not progressing linearly anymore; they are advancing exponentially. Technology is advancing so rapidly that listening to people who just have domain knowledge and vested interests will put a company on the fastest path to failure. Experts are no longer the right people to turn to; they are a waste of money.
Just as the processing power of our computers doubles every 18 months, with prices falling and devices becoming smaller, fields such as medicine, robotics, artificial intelligence and synthetic biology are seeing accelerated change. Competition now comes from the places you least expect it to. The health-care industry, for example, is about to be disrupted by advances in sensors and artificial intelligence; lodging and transportation, by mobile apps; communications, by Wi-Fi and the Internet; and manufacturing, by robotics and 3-D printing.
To see the competition coming and develop strategies for survival, companies now need armies of people, not experts. The best knowledge comes from employees, customers and outside observers who aren’t constrained by their expertise or personal agendas. It is they who can best identify the new opportunities. The collective insight of large numbers of individuals is superior because of the diversity of ideas and breadth of knowledge that they bring. Companies need to learn from people with different skills and backgrounds — not from those confined to a department.
When used properly, crowdsourcing can be the most effective, least expensive way of solving problems.
Crowdsourcing can be as simple as asking employees to submit ideas via email or via online discussion boards, or it can assemble cross-disciplinary groups to exchange ideas and brainstorm. Internet platforms such as Zoho Connect, IdeaScale and GroupTie can facilitate group ideation by providing the ability to pose questions to a large number of people and having them discuss responses with each other.
Many of the ideas proposed by the crowd as well as the discussions will seem outlandish — especially if anonymity is allowed on discussion forums. And companies will surely hear things they won’t like. But this is exactly the input and out-of-the-box thinking that they need in order to survive and thrive in this era of exponential technologies….
Another way of harnessing the power of the crowd is to hold incentive competitions. These can solve problems, foster innovation and even create industries — just as the first XPRIZE did. Sponsored by the Ansari family, it offered a prize of $10 million to any team that could build a spacecraft capable of carrying three people to 100 kilometers above the earth’s surface, twice within two weeks. It was won by Burt Rutan in 2004, who launched a spacecraft called SpaceShipOne. Twenty-six teams, from seven countries, spent more than $100 million in competing. Since then, more than $1.5 billion has been invested in private space flight by companies such as Virgin Galactic, Armadillo Aerospace and Blue Origin, according to the XPRIZE Foundation….
Competitions needn’t be so grand. InnoCentive and HeroX, a spinoff from the XPRIZE Foundation, for example, allow prizes as small as a few thousand dollars for solving problems. A company or an individual can specify a problem and offer prizes for whoever comes up with the best idea to solve it. InnoCentive has already run thousands of public and inter-company competitions. The solutions they have crowdsourced have ranged from the development of biomarkers for Amyotrophic lateral sclerosis disease to dual-purpose solar lights for African villages….”

Training Students to Extract Value from Big Data

Curated on Posted on by Stefaan Verhulst
New report by the National Research Council: “As the availability of high-throughput data-collection technologies, such as information-sensing mobile devices, remote sensing, internet log records, and wireless sensor networks has grown, science, engineering, and business have rapidly transitioned from striving to develop information from scant data to a situation in which the challenge is now that the amount of information exceeds a human’s ability to examine, let alone absorb, it. Data sets are increasingly complex, and this potentially increases the problems associated with such concerns as missing information and other quality concerns, data heterogeneity, and differing data formats.
The nation’s ability to make use of data depends heavily on the availability of a workforce that is properly trained and ready to tackle high-need areas. Training students to be capable in exploiting big data requires experience with statistical analysis, machine learning, and computational infrastructure that permits the real problems associated with massive data to be revealed and, ultimately, addressed. Analysis of big data requires cross-disciplinary skills, including the ability to make modeling decisions while balancing trade-offs between optimization and approximation, all while being attentive to useful metrics and system robustness. To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and characteristics of a prospective data-science student; what to teach, that is, the technical and practical content that should be taught to the student; and how to teach, that is, the structure and organization of a data-science program.
Training Students to Extract Value from Big Data summarizes a workshop convened in April 2014 by the National Research Council’s Committee on Applied and Theoretical Statistics to explore how best to train students to use big data. The workshop explored the need for training and curricula and coursework that should be included. One impetus for the workshop was the current fragmented view of what is meant by analysis of big data, data analytics, or data science. New graduate programs are introduced regularly, and they have their own notions of what is meant by those terms and, most important, of what students need to know to be proficient in data-intensive work. This report provides a variety of perspectives about those elements and about their integration into courses and curricula…”

Smarter video games, thanks to crowdsourcing

Curated on Posted on by Stefaan Verhulst

AAAS –Science Magazine: “Despite the stereotypes, any serious gamer knows it’s way more fun to play with real people than against the computer. Video game artificial intelligence, or AI, just isn’t very good; it’s slow, predictable, and generally stupid. All that stands to change, however, if GiantOtter, a Massachusetts-based startup, has its way, New Scientist reports. By crowdsourcing the AI’s learning, GiantOtter hopes to build systems where the computer can learn based on player’s previous behaviors, decision-making, and even voice communication—yes, the computer is listening in as you strategize. The hope is that by abandoning the traditional scripted programming models, AIs can be taught to mimic human behaviors, leading to more dynamic and challenging scenarios even in incredibly complex games like Blizzard Entertainment Inc.’s professionally played StarCraft II.“

Forget GMOs. The Future of Food Is Data—Mountains of It

Curated on Posted on by Stefaan Verhulst

Cade Metz at Wired: “… Led by Dan Zigmond—who previously served as chief data scientist for YouTube, then Google Maps—this ambitious project aims to accelerate the work of all the biochemists, food scientists, and chefs on the first floor, providing a computer-generated shortcut to what Hampton Creek sees as the future of food. “We’re looking at the whole process,” Zigmond says of his data team, “trying to figure out what it all means and make better predictions about what is going to happen next.”

The project highlights a movement, spreading through many industries, that seeks to supercharge research and development using the kind of data analysis and manipulation pioneered in the world of computer science, particularly at places like Google and Facebook. Several projects already are using such techniques to feed the development of new industrial materials and medicines. Others hope the latest data analytics and machine learning techniques can help diagnosis disease. “This kind of approach is going to allow a whole new type of scientific experimentation,” says Jeremy Howard, who as the president of Kaggle once oversaw the leading online community of data scientists and is now applying tricks of the data trade to healthcare as the founder of Enlitic.
Zigmond’s project is the first major effort to apply “big data” to the development of food, and though it’s only just getting started—with some experts questioning how effective it will be—it could spur additional research in the field. The company may license its database to others, and Hampton Creek founder and CEO Josh Tetrick says it may even open source the data, so to speak, freely sharing it with everyone. “We’ll see,” says Tetrick, a former college football linebacker who founded Hampton Creek after working on economic and social campaigns in Liberia and Kenya. “That would be in line with who we are as a company.”…
Initially, Zigmond and his team will model protein interactions on individual machines, using tools like the R programming language (a common means of crunching data) and machine learning algorithms much like those that recommend products on Amazon.com. As the database expands, they plan to arrange for much larger and more complex models that run across enormous clusters of computer servers, using the sort of sweeping data-analysis software systems employed by the likes of Google. “Even as we start to get into the tens and hundreds of thousands and millions of proteins,” Zigmond says, “it starts to be more than you can handle with traditional database techniques.”
In particular, Zigmond is exploring the use of deep learning, a form of artificial intelligence that goes beyond ordinary machine learning. Google is using deep learning to drive the speech recognition system in Android phones. Microsoft is using it to translate Skype calls from one language to another. Zigmond believes it can help model the creation of new foods….”

A Few Useful Things to Know about Machine Learning

Curated on Posted on by Stefaan Verhulst

A new research paper by Pedro Domingos: “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.”
 

Google's fact-checking bots build vast knowledge bank

Curated on Posted on by Stefaan Verhulst

Hal Hodson in the New Scientist: “The search giant is automatically building Knowledge Vault, a massive database that could give us unprecedented access to the world’s facts

GOOGLE is building the largest store of knowledge in human history – and it’s doing so without any human help. Instead, Knowledge Vault autonomously gathers and merges information from across the web into a single base of facts about the world, and the people and objects in it.

The breadth and accuracy of this gathered knowledge is already becoming the foundation of systems that allow robots and smartphones to understand what people ask them. It promises to let Google answer questions like an oracle rather than a search engine, and even to turn a new lens on human history.

Knowledge Vault is a type of “knowledge base” – a system that stores information so that machines as well as people can read it. Where a database deals with numbers, a knowledge base deals with facts. When you type “Where was Madonna born” into Google, for example, the place given is pulled from Google’s existing knowledge base.

This existing base, called Knowledge Graph, relies on crowdsourcing to expand its information. But the firm noticed that growth was stalling; humans could only take it so far. So Google decided it needed to automate the process. It started building the Vault by using an algorithm to automatically pull in information from all over the web, using machine learning to turn the raw data into usable pieces of knowledge.

Knowledge Vault has pulled in 1.6 billion facts to date. Of these, 271 million are rated as “confident facts”, to which Google’s model ascribes a more than 90 per cent chance of being true. It does this by cross-referencing new facts with what it already knows.

“It’s a hugely impressive thing that they are pulling off,” says Fabian Suchanek, a data scientist at Télécom ParisTech in France.

Google’s Knowledge Graph is currently bigger than the Knowledge Vault, but it only includes manually integrated sources such as the CIA Factbook.

Knowledge Vault offers Google fast, automatic expansion of its knowledge – and it’s only going to get bigger. As well as the ability to analyse text on a webpage for facts to feed its knowledge base, Google can also peer under the surface of the web, hunting for hidden sources of data such as the figures that feed Amazon product pages, for example.

Tom Austin, a technology analyst at Gartner in Boston, says that the world’s biggest technology companies are racing to build similar vaults. “Google, Microsoft, Facebook, Amazon and IBM are all building them, and they’re tackling these enormous problems that we would never even have thought of trying 10 years ago,” he says.

The potential of a machine system that has the whole of human knowledge at its fingertips is huge. One of the first applications will be virtual personal assistants that go way beyond what Siri and Google Now are capable of, says Austin…”

Towards Timely Public Health Decisions to Tackle Seasonal Diseases With Open Government Data

Curated on Posted on by Stefaan Verhulst
Paper by Vandana Srivastava and Biplav Srivastava for the Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence : “Improving public health is a major responsibility of any government, and is of major interest to citizens and scientific communities around the world. Here, one sees two extremes. On one hand, tremendous progress has been made in recent years in the understanding of causes, spread and remedies of common and regularly occurring diseases like Dengue, Malaria and Japanese Encephalistis (JE). On the other hand, public agencies treat these diseases in an ad hoc manner without learning from the experiences of previous years. Specifically, they would get alerted once reported cases have already arisen substantially in the known disease season, reactively initiate a few actions and then document the disease impact (cases, deaths) for that period, only to forget this learning in the next season. However, they miss the opportunity to reduce preventable deaths and sickness, and their corresponding economic impact, which scientific progress could have enabled. The gap is universal but very prominent in developing countries like India.
In this paper, we show that if public agencies provide historical disease impact information openly, it can be analyzed with statistical and machine learning techniques, correlated with best emerging practices in disease control, and simulated in a setting to optimize social benefits to provide timely guidance for new disease seasons and regions. We illustrate using open data for mosquito-borne communicable diseases; published results in public health on efficacy of Dengue control methods and apply it on a simulated typical city for maximal benefits with available resources. The exercise helps us further suggest strategies for new regions that may be anywhere in the world, how data could be better recorded by city agencies and what prevention methods should medical community focus on for wider impact.
Full Text: PDF“