Ray Kurzweil keynote presentation at a Council on Foreign Relations meeting…(More)”.
The Human Strategy
A Video Conversation with Sandy Pentland at Edge: “People have lots of capabilities; they know lots of things about the world; they can perceive things in a human way. What would happen if you had a network of people where you could reinforce the ones that were helping and maybe discourage the ones that weren’t?
That begins to sound like a society or a company. We all live in a human social network. We’re reinforced for things that seem to help everybody and discouraged from things that are not appreciated. Culture is something that comes from a sort of human AI, the function of reinforcing the good and penalizing the bad, but applied to humans and human problems. Once you realize that you can take this general framework of AI and create a human AI, the question becomes, what’s the right way to do that? Is it a safe idea? Is it completely crazy?…(More)”.
Growing the artificial intelligence industry in the UK
Summary from an independent review, carried out by Professor Dame Wendy Hall and Jérôme Pesenti: “Increased use of Artificial Intelligence (AI) can bring major social and economic benefits to the UK. With AI, computers can analyse and learn from information at higher accuracy and speed than humans can. AI offers massive gains in efficiency and performance to most or all industry sectors, from drug discovery to logistics. AI is software that can be integrated into existing processes, improving them, scaling them, and reducing their costs, by making or suggesting more accurate decisions through better use of information.
It has been estimated that AI could add an additional USD $814 billion (£630bn) to the UK economy by 2035, increasing the annual growth rate of GVA from 2.5 to 3.9%.
Our vision is for the UK to become the best place in the world for businesses developing and deploying AI to start, grow and thrive, to realise all the benefits the technology offers….
Key factors have combined to increase the capability of AI in recent years, in particular:
- New and larger volumes of data
- Supply of experts with the specific high level skills
- Availability of increasingly powerful computing capacity. The barriers to achieving performance have fallen significantly, and continue to fall.
To continue developing and applying AI, the UK will need to increase ease of access to data in a wider range of sectors. This Review recommends:
- Development of data trusts, to improve trust and ease around sharing data
- Making more research data machine readable
- Supporting text and data mining as a standard and essential tool for research.
Skilled experts are needed to develop AI, and they are in short supply. To develop more AI, the UK will need a larger workforce with deep AI expertise, and more development of lower level skills to work with AI. …
Increasing uptake of AI means increasing demand as well as supply through a better understanding of what AI can do and where it could be applied. This review recommends:
- An AI Council to promote growth and coordination in the sector
- Guidance on how to explain decisions and processes enabled by AI
- Support for export and inward investment
- Guidance on successfully applying AI to drive improvements in industry
- A programme to support public sector use of AI
- Funded challenges around data held by public organisations.
Our work has indicated that action in these areas could deliver a step-change improvement in growth of UK AI. This report makes the 18 recommendations listed in full below, which describe how Government, industry and academia should work together to keep the UK among the world leaders in AI…(More)”
Linux Foundation Debuts Community Data License Agreement
Press Release: “The Linux Foundation, the nonprofit advancing professional open source management for mass collaboration, today announced the Community Data License Agreement(CDLA) family of open data agreements. In an era of expansive and often underused data, the CDLA licenses are an effort to define a licensing framework to support collaborative communities built around curating and sharing “open” data.
Inspired by the collaborative software development models of open source software, the CDLA licenses are designed to enable individuals and organizations of all types to share data as easily as they currently share open source software code. Soundly drafted licensing models can help people form communities to assemble, curate and maintain vast amounts of data, measured in petabytes and exabytes, to bring new value to communities of all types, to build new business opportunities and to power new applications that promise to enhance safety and services.
The growth of big data analytics, machine learning and artificial intelligence (AI) technologies has allowed people to extract unprecedented levels of insight from data. Now the challenge is to assemble the critical mass of data for those tools to analyze. The CDLA licenses are designed to help governments, academic institutions, businesses and other organizations open up and share data, with the goal of creating communities that curate and share data openly.
For instance, if automakers, suppliers and civil infrastructure services can share data, they may be able to improve safety, decrease energy consumption and improve predictive maintenance. Self-driving cars are heavily dependent on AI systems for navigation, and need massive volumes of data to function properly. Once on the road, they can generate nearly a gigabyte of data every second. For the average car, that means two petabytes of sensor, audio, video and other data each year.
Similarly, climate modeling can integrate measurements captured by government agencies with simulation data from other organizations and then use machine learning systems to look for patterns in the information. It’s estimated that a single model can yield a petabyte of data, a volume that challenges standard computer algorithms, but is useful for machine learning systems. This knowledge may help improve agriculture or aid in studying extreme weather patterns.
And if government agencies share aggregated data on building permits, school enrollment figures, sewer and water usage, their citizens benefit from the ability of commercial entities to anticipate their future needs and respond with infrastructure and facilities that arrive in anticipation of citizens’ demands.
“An open data license is essential for the frictionless sharing of the data that powers both critical technologies and societal benefits,” said Jim Zemlin, Executive Director of The Linux Foundation. “The success of open source software provides a powerful example of what can be accomplished when people come together around a resource and advance it for the common good. The CDLA licenses are a key step in that direction and will encourage the continued growth of applications and infrastructure.”…(More)”.
Reboot for the AI revolution
Yuval Noah Harari in Nature: “The ongoing artificial-intelligence revolution will change almost every line of work, creating enormous social and economic opportunities — and challenges. Some believe that intelligent computers will push humans out of the job market and create a new ‘useless class’; others maintain that automation will generate a wide range of new human jobs and greater prosperity for all. Almost everybody agrees that we should take action to prevent the worst-case scenarios….
Governments might decide to deliberately slow down the pace of automation, to lessen the resulting shocks and allow time for readjustments. But it will probably be both impossible and undesirable to prevent automation and job loss completely. That would mean giving up the immense positive potential of AI and robotics. If self-driving vehicles drive more safely and cheaply than humans, it would be counterproductive to ban them just to protect the jobs of taxi and lorry drivers.
A more sensible strategy is to create new jobs. In particular, as routine jobs are automated, opportunities for new non-routine jobs will mushroom. For example, general physicians who focus on diagnosing known diseases and administering familiar treatments will probably be replaced by AI doctors. Precisely because of that, there will be more money to pay human experts to do groundbreaking medical research, develop new medications and pioneer innovative surgical techniques.
This calls for economic entrepreneurship and legal dexterity. Above all, it necessitates a revolution in education…Creating new jobs might prove easier than retraining people to fill them. A huge useless class might appear, owing to both an absolute lack of jobs and a lack of relevant education and mental flexibility….
With insights gleaned from early warning signs and test cases, scholars should strive to develop new socio-economic models. The old ones no longer hold. For example, twentieth-century socialism assumed that the working class was crucial to the economy, and socialist thinkers tried to teach the proletariat how to translate its immense economic power into political clout. In the twenty-first century, if the masses lose their economic value they might have to struggle against irrelevance rather than exploitation….The challenges posed in the twenty-first century by the merger of infotech and biotech are arguably bigger than those thrown up by steam engines, railways, electricity and fossil fuels. Given the immense destructive power of our modern civilization, we cannot afford more failed models, world wars and bloody revolutions. We have to do better this time….(More)”
The Supreme Court Is Allergic To Math
Oliver Roeder at FiveThirtyEight: “The Supreme Court does not compute. Or at least some of its members would rather not. The justices, the most powerful jurists in the land, seem to have a reluctance — even an allergy — to taking math and statistics seriously.
For decades, the court has struggled with quantitative evidence of all kinds in a wide variety of cases. Sometimes justices ignore this evidence. Sometimes they misinterpret it. And sometimes they cast it aside in order to hold on to more traditional legal arguments. (And, yes, sometimes they also listen to the numbers.) Yet the world itself is becoming more computationally driven, and some of those computations will need to be adjudicated before long. Some major artificial intelligence case will likely come across the court’s desk in the next decade, for example. By voicing an unwillingness to engage with data-driven empiricism, justices — and thus the court — are at risk of making decisions without fully grappling with the evidence.
This problem was on full display earlier this month, when the Supreme Court heard arguments in Gill v. Whitford, a case that will determine the future of partisan gerrymandering — and the contours of American democracy along with it. As my colleague Galen Druke has reported, the case hinges on math: Is there a way to measure a map’s partisan bias and to create a standard for when a gerrymandered map infringes on voters’ rights?…(More)”.
Crowdsourced Morality Could Determine the Ethics of Artificial Intelligence
Dom Galeon in Futurism: “As artificial intelligence (AI) development progresses, experts have begun considering how best to give an AI system an ethical or moral backbone. A popular idea is to teach AI to behave ethically by learning from decisions made by the average person.
To test this assumption, researchers from MIT created the Moral Machine. Visitors to the website were asked to make choices regarding what an autonomous vehicle should do when faced with rather gruesome scenarios. For example, if a driverless car was being forced toward pedestrians, should it run over three adults to spare two children? Save a pregnant woman at the expense of an elderly man?
The Moral Machine was able to collect a huge swath of this data from random people, so Ariel Procaccia from Carnegie Mellon University’s computer science department decided to put that data to work.
In a new study published online, he and Iyad Rahwan — one of the researchers behind the Moral Machine — taught an AI using the Moral Machine’s dataset. Then, they asked the system to predict how humans would want a self-driving car to react in similar but previously untested scenarios….
This idea of having to choose between two morally problematic outcomes isn’t new. Ethicists even have a name for it: the double-effect. However, having to apply the concept to an artificially intelligent system is something humankind has never had to do before, and numerous experts have shared their opinions on how best to go about it.
OpenAI co-chairman Elon Musk believes that creating an ethical AI is a matter of coming up with clear guidelines or policies to govern development, and governments and institutions are slowly heeding Musk’s call. Germany, for example, crafted the world’s first ethical guidelines for self-driving cars. Meanwhile, Google parent company Alphabet’s AI DeepMind now has an ethics and society unit.
Other experts, including a team of researchers from Duke University, think that the best way to move forward is to create a “general framework” that describes how AI will make ethical decisions….(More)”.
How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem
Paper by Amanda Levendowski: “As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s technological gains and potential benefits. While legal and computer science scholars have analyzed many sources of bias, including the unexamined assumptions of its often-homogenous creators, flawed algorithms, and incomplete datasets, the role of the law itself has been largely ignored. Yet just as code and culture play significant roles in how AI agents learn about and act in the world, so too do the laws that govern them. This Article is the first to examine perhaps the most powerful law impacting AI bias: copyright.
Artificial intelligence often learns to “think” by reading, viewing, and listening to copies of human works. This Article first explores the problem of bias through the lens of copyright doctrine, looking at how the law’s exclusion of access to certain copyrighted source materials may create or promote biased AI systems. Copyright law limits bias mitigation techniques, such as testing AI through reverse engineering, algorithmic accountability processes, and competing to convert customers. The rules of copyright law also privilege access to certain works over others, encouraging AI creators to use easily available, legally low-risk sources of data for teaching AI, even when those data are demonstrably biased. Second, it examines how a different part of copyright law — the fair use doctrine — has traditionally been used to address similar concerns in other technological fields, and asks whether it is equally capable of addressing them in the field of AI bias. The Article ultimately concludes that it is, in large part because the normative values embedded within traditional fair use ultimately align with the goals of mitigating AI bias and, quite literally, creating fairer AI systems….(More)”.
How We Can Stop Earthquakes From Killing People Before They Even Hit
Justin Worland in Time Magazine: “…Out of that realization came a plan to reshape disaster management using big data. Just a few months later, Wani worked with two fellow Stanford students to create a platform to predict the toll of natural disasters. The concept is simple but also revolutionary. The One Concern software pulls geological and structural data from a variety of public and private sources and uses machine learning to predict the impact of an earthquake down to individual city blocks and buildings. Real-time information input during an earthquake improves how the system responds. And earthquakes represent just the start for the company, which plans to launch a similar program for floods and eventually other natural disasters….
Previous software might identify a general area where responders could expect damage, but it would appear as a “big red blob” that wasn’t helpful when deciding exactly where to send resources, Dayton says. The technology also integrates information from many sources and makes it easy to parse in an emergency situation when every moment matters. The instant damage evaluations mean fast and actionable information, so first responders can prioritize search and rescue in areas most likely to be worst-hit, rather than responding to 911 calls in the order they are received.
One Concern is not the only company that sees an opportunity to use data to rethink disaster response. The mapping company Esri has built rapid-response software that shows expected damage from disasters like earthquakes, wildfires and hurricanes. And the U.S. government has invested in programs to use data to shape disaster response at agencies like the National Oceanic and Atmospheric Administration (NOAA)….(More)”.
Let’s create a nation of social scientists
Geoff Mulgan in Times Higher Education: “How might social science become more influential, more relevant and more useful in the years to come?
Recent debates about impact have largely assumed a model of social science in which a cadre of specialists, based in universities, analyse and interpret the world and then feed conclusions into an essentially passive society. But a very different view sees specialists in the academy working much more in partnership with a society that is itself skilled in social science, able to generate hypotheses, gather data, experiment and draw conclusions that might help to answer the big questions of our time, from the sources of inequality to social trust, identity to violence.
There are some powerful trends to suggest that this second view is gaining traction. The first of these is the extraordinary explosion of new ways to observe social phenomena. Every day each of us leaves behind a data trail of who we talk to, what we eat and where we go. It’s easier than ever to survey people, to spot patterns, to scrape the web or to pick up data from sensors. It’s easier than ever to gather perceptions and emotions as well as material facts and easier than ever for organisations to practice social science – whether investment organisations analysing market patterns, human resources departments using behavioural science, or local authorities using ethnography.
That deluge of data is a big enough shift on its own. However, it is also now being used to feed interpretive and predictive tools using artificial intelligence to predict who is most likely to go to hospital, to end up in prison, which relationships are most likely to end in divorce.
Governments are developing their own predictive tools, and have also become much more interested in systematic experimentation, with Finland and Canada in the lead, moving us closer to Karl Popper’s vision of “methods of trial and error, of inventing hypotheses which can be practically tested…”…
The second revolution is less visible but could be no less profound. This is the hunger of many people to be creators of knowledge, not just users; to be part of a truly collective intelligence. At the moment this shift towards mass engagement in knowledge is most visible in neighbouring fields. Digital humanities mobilise many volunteers to input data and interpret texts – for example making ancient Arabic texts machine-readable. Even more striking is the growth of citizen science – eBird had 1.5 million reports last January; some 1.5 million people in the US monitor river streams and lakes, and SETI@home has 5 million volunteers. Thousands of patients also take part in funding and shaping research on their own conditions….
We’re all familiar with the old idea that it’s better to teach a man to fish than just to give him fish. In essence these trends ask us a simple question: why not apply the same logic to social science, and why not reorient social sciences to enhance the capacity of society itself to observe, analyse and interpret?…(More)”.