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#435186 What’s Behind the International Rush ...
There’s no better way of ensuring you win a race than by setting the rules yourself. That may be behind the recent rush by countries, international organizations, and companies to put forward their visions for how the AI race should be governed.
China became the latest to release a set of “ethical standards” for the development of AI last month, which might raise eyebrows given the country’s well-documented AI-powered state surveillance program and suspect approaches to privacy and human rights.
But given the recent flurry of AI guidelines, it may well have been motivated by a desire not to be left out of the conversation. The previous week the OECD, backed by the US, released its own “guiding principles” for the industry, and in April the EU released “ethical guidelines.”
The language of most of these documents is fairly abstract and noticeably similar, with broad appeals to ideals like accountability, responsibility, and transparency. The OECD’s guidelines are the lightest on detail, while the EU’s offer some more concrete suggestions such as ensuring humans always know if they’re interacting with AI and making algorithms auditable. China’s standards have an interesting focus on promoting openness and collaboration as well as expressly acknowledging AIs potential to disrupt employment.
Overall, though, one might be surprised that there aren’t more disagreements between three blocs with very divergent attitudes to technology, regulation, and economics. Most likely these are just the opening salvos in what will prove to be a long-running debate, and the devil will ultimately be in the details.
The EU seems to have stolen a march on the other two blocs, being first to publish its guidelines and having already implemented the world’s most comprehensive regulation of data—the bedrock of modern AI—with last year’s GDPR. But its lack of industry heavyweights is going to make it hard to hold onto that lead.
One organization that seems to be trying to take on the role of impartial adjudicator is the World Economic Forum, which recently hosted an event designed to find common ground between various stakeholders from across the world. What will come of the effort remains to be seen, but China’s release of guidelines broadly similar to those of its Western counterparts is a promising sign.
Perhaps most telling, though, is the ubiquitous presence of industry leaders in both advisory and leadership positions. China’s guidelines are backed by “an AI industrial league” including Baidu, Alibaba, and Tencent, and the co-chairs of the WEF’s AI Council are Microsoft President Brad Smith and prominent Chinese AI investor Kai-Fu Lee.
Shortly after the EU released its proposals one of the authors, philosopher Thomas Metzinger, said the process had been compromised by the influence of the tech industry, leading to the removal of “red lines” opposing the development of autonomous lethal weapons or social credit score systems like China’s.
For a long time big tech argued for self-regulation, but whether they’ve had an epiphany or have simply sensed the shifting winds, they are now coming out in favor of government intervention.
Both Amazon and Facebook have called for regulation of facial recognition, and in February Google went even further, calling for the government to set down rules governing AI. Facebook chief Mark Zuckerberg has also since called for even broader regulation of the tech industry.
But considering the current concern around the anti-competitive clout of the largest technology companies, it’s worth remembering that tough rules are always easier to deal with for companies with well-developed compliance infrastructure and big legal teams. And these companies are also making sure the regulation is on their terms. Wired details Microsoft’s protracted effort to shape Washington state laws governing facial recognition technology and Google’s enormous lobbying effort.
“Industry has mobilized to shape the science, morality and laws of artificial intelligence,” Harvard law professor Yochai Benkler writes in Nature. He highlights how Amazon’s funding of a National Science Foundation (NSF) program for projects on fairness in artificial intelligence undermines the ability of academia to act as an impartial counterweight to industry.
Excluding industry from the process of setting the rules to govern AI in a fair and equitable way is clearly not practical, writes Benkler, because they are the ones with the expertise. But there also needs to be more concerted public investment in research and policymaking, and efforts to limit the influence of big companies when setting the rules that will govern AI.
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#435174 Revolt on the Horizon? How Young People ...
As digital technologies facilitate the growth of both new and incumbent organizations, we have started to see the darker sides of the digital economy unravel. In recent years, many unethical business practices have been exposed, including the capture and use of consumers’ data, anticompetitive activities, and covert social experiments.
But what do young people who grew up with the internet think about this development? Our research with 400 digital natives—19- to 24-year-olds—shows that this generation, dubbed “GenTech,” may be the one to turn the digital revolution on its head. Our findings point to a frustration and disillusionment with the way organizations have accumulated real-time information about consumers without their knowledge and often without their explicit consent.
Many from GenTech now understand that their online lives are of commercial value to an array of organizations that use this insight for the targeting and personalization of products, services, and experiences.
This era of accumulation and commercialization of user data through real-time monitoring has been coined “surveillance capitalism” and signifies a new economic system.
Artificial Intelligence
A central pillar of the modern digital economy is our interaction with artificial intelligence (AI) and machine learning algorithms. We found that 47 percent of GenTech do not want AI technology to monitor their lifestyle, purchases, and financial situation in order to recommend them particular things to buy.
In fact, only 29 percent see this as a positive intervention. Instead, they wish to maintain a sense of autonomy in their decision making and have the opportunity to freely explore new products, services, and experiences.
As individuals living in the digital age, we constantly negotiate with technology to let go of or retain control. This pendulum-like effect reflects the ongoing battle between humans and technology.
My Life, My Data?
Our research also reveals that 54 percent of GenTech are very concerned about the access organizations have to their data, while only 19 percent were not worried. Despite the EU General Data Protection Regulation being introduced in May 2018, this is still a major concern, grounded in a belief that too much of their data is in the possession of a small group of global companies, including Google, Amazon, and Facebook. Some 70 percent felt this way.
In recent weeks, both Facebook and Google have vowed to make privacy a top priority in the way they interact with users. Both companies have faced public outcry for their lack of openness and transparency when it comes to how they collect and store user data. It wasn’t long ago that a hidden microphone was found in one of Google’s home alarm products.
Google now plans to offer auto-deletion of users’ location history data, browsing, and app activity as well as extend its “incognito mode” to Google Maps and search. This will enable users to turn off tracking.
At Facebook, CEO Mark Zuckerberg is keen to reposition the platform as a “privacy focused communications platform” built on principles such as private interactions, encryption, safety, interoperability (communications across Facebook-owned apps and platforms), and secure data storage. This will be a tough turnaround for the company that is fundamentally dependent on turning user data into opportunities for highly individualized advertising.
Privacy and transparency are critically important themes for organizations today, both for those that have “grown up” online as well as the incumbents. While GenTech want organizations to be more transparent and responsible, 64 percent also believe that they cannot do much to keep their data private. Being tracked and monitored online by organizations is seen as part and parcel of being a digital consumer.
Despite these views, there is a growing revolt simmering under the surface. GenTech want to take ownership of their own data. They see this as a valuable commodity, which they should be given the opportunity to trade with organizations. Some 50 percent would willingly share their data with companies if they got something in return, for example a financial incentive.
Rewiring the Power Shift
GenTech are looking to enter into a transactional relationship with organizations. This reflects a significant change in attitudes from perceiving the free access to digital platforms as the “product” in itself (in exchange for user data), to now wishing to use that data to trade for explicit benefits.
This has created an opportunity for companies that seek to empower consumers and give them back control of their data. Several companies now offer consumers the opportunity to sell the data they are comfortable sharing or take part in research that they get paid for. More and more companies are joining this space, including People.io, Killi, and Ocean Protocol.
Sir Tim Berners Lee, the creator of the world wide web, has also been working on a way to shift the power from organizations and institutions back to citizens and consumers. The platform, Solid, offers users the opportunity to be in charge of where they store their data and who can access it. It is a form of re-decentralization.
The Solid POD (Personal Online Data storage) is a secure place on a hosted server or the individual’s own server. Users can grant apps access to their POD as a person’s data is stored centrally and not by an app developer or on an organization’s server. We see this as potentially being a way to let people take back control from technology and other companies.
GenTech have woken up to a reality where a life lived “plugged in” has significant consequences for their individual privacy and are starting to push back, questioning those organizations that have shown limited concern and continue to exercise exploitative practices.
It’s no wonder that we see these signs of revolt. GenTech is the generation with the most to lose. They face a life ahead intertwined with digital technology as part of their personal and private lives. With continued pressure on organizations to become more transparent, the time is now for young people to make their move.
Dr Mike Cooray, Professor of Practice, Hult International Business School and Dr Rikke Duus, Research Associate and Senior Teaching Fellow, UCL
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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#435127 Teaching AI the Concept of ‘Similar, ...
As a human you instinctively know that a leopard is closer to a cat than a motorbike, but the way we train most AI makes them oblivious to these kinds of relations. Building the concept of similarity into our algorithms could make them far more capable, writes the author of a new paper in Science Robotics.
Convolutional neural networks have revolutionized the field of computer vision to the point that machines are now outperforming humans on some of the most challenging visual tasks. But the way we train them to analyze images is very different from the way humans learn, says Atsuto Maki, an associate professor at KTH Royal Institute of Technology.
“Imagine that you are two years old and being quizzed on what you see in a photo of a leopard,” he writes. “You might answer ‘a cat’ and your parents might say, ‘yeah, not quite but similar’.”
In contrast, the way we train neural networks rarely gives that kind of partial credit. They are typically trained to have very high confidence in the correct label and consider all incorrect labels, whether ”cat” or “motorbike,” equally wrong. That’s a mistake, says Maki, because ignoring the fact that something can be “less wrong” means you’re not exploiting all of the information in the training data.
Even when models are trained this way, there will be small differences in the probabilities assigned to incorrect labels that can tell you a lot about how well the model can generalize what it has learned to unseen data.
If you show a model a picture of a leopard and it gives “cat” a probability of five percent and “motorbike” one percent, that suggests it picked up on the fact that a cat is closer to a leopard than a motorbike. In contrast, if the figures are the other way around it means the model hasn’t learned the broad features that make cats and leopards similar, something that could potentially be helpful when analyzing new data.
If we could boost this ability to identify similarities between classes we should be able to create more flexible models better able to generalize, says Maki. And recent research has demonstrated how variations of an approach called regularization might help us achieve that goal.
Neural networks are prone to a problem called “overfitting,” which refers to a tendency to pay too much attention to tiny details and noise specific to their training set. When that happens, models will perform excellently on their training data but poorly when applied to unseen test data without these particular quirks.
Regularization is used to circumvent this problem, typically by reducing the network’s capacity to learn all this unnecessary information and therefore boost its ability to generalize to new data. Techniques are varied, but generally involve modifying the network’s structure or the strength of the weights between artificial neurons.
More recently, though, researchers have suggested new regularization approaches that work by encouraging a broader spread of probabilities across all classes. This essentially helps them capture more of the class similarities, says Maki, and therefore boosts their ability to generalize.
One such approach was devised in 2017 by Google Brain researchers, led by deep learning pioneer Geoffrey Hinton. They introduced a penalty to their training process that directly punished overconfident predictions in the model’s outputs, and a technique called label smoothing that prevents the largest probability becoming much larger than all others. This meant the probabilities were lower for correct labels and higher for incorrect ones, which was found to boost performance of models on varied tasks from image classification to speech recognition.
Another came from Maki himself in 2017 and achieves the same goal, but by suppressing high values in the model’s feature vector—the mathematical construct that describes all of an object’s important characteristics. This has a knock-on effect on the spread of output probabilities and also helped boost performance on various image classification tasks.
While it’s still early days for the approach, the fact that humans are able to exploit these kinds of similarities to learn more efficiently suggests that models that incorporate them hold promise. Maki points out that it could be particularly useful in applications such as robotic grasping, where distinguishing various similar objects is important.
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#435106 Could Artificial Photosynthesis Help ...
Plants are the planet’s lungs, but they’re struggling to keep up due to rising CO2 emissions and deforestation. Engineers are giving them a helping hand, though, by augmenting their capacity with new technology and creating artificial substitutes to help them clean up our atmosphere.
Imperial College London, one of the UK’s top engineering schools, recently announced that it was teaming up with startup Arborea to build the company’s first outdoor pilot of its BioSolar Leaf cultivation system at the university’s White City campus in West London.
Arborea is developing large solar panel-like structures that house microscopic plants and can be installed on buildings or open land. The plants absorb light and carbon dioxide as they photosynthesize, removing greenhouse gases from the air and producing organic material, which can be processed to extract valuable food additives like omega-3 fatty acids.
The idea of growing algae to produce useful materials isn’t new, but Arborea’s pitch seems to be flexibility and affordability. The more conventional approach is to grow algae in open ponds, which are less efficient and open to contamination, or in photo-bioreactors, which typically require CO2 to be piped in rather than getting it from the air and can be expensive to run.
There’s little detail on how the technology deals with issues like nutrient supply and harvesting or how efficient it is. The company claims it can remove carbon dioxide as fast as 100 trees using the surface area of just a single tree, but there’s no published research to back that up, and it’s hard to compare the surface area of flat panels to that of a complex object like a tree. If you flattened out every inch of a tree’s surface it would cover a surprisingly large area.
Nonetheless, the ability to install these panels directly on buildings could present a promising way to soak up the huge amount of CO2 produced in our cities by transport and industry. And Arborea isn’t the only one trying to give plants a helping hand.
For decades researchers have been working on ways to use light-activated catalysts to split water into oxygen and hydrogen fuel, and more recently there have been efforts to fuse this with additional processes to combine the hydrogen with carbon from CO2 to produce all kinds of useful products.
Most notably, in 2016 Harvard researchers showed that water-splitting catalysts could be augmented with bacteria that combines the resulting hydrogen with CO2 to create oxygen and biomass, fuel, or other useful products. The approach was more efficient than plants at turning CO2 to fuel and was built using cheap materials, but turning it into a commercially viable technology will take time.
Not everyone is looking to mimic or borrow from biology in their efforts to suck CO2 out of the atmosphere. There’s been a recent glut of investment in startups working on direct-air capture (DAC) technology, which had previously been written off for using too much power and space to be practical. The looming climate change crisis appears to be rewriting some of those assumptions, though.
Most approaches aim to use the concentrated CO2 to produce synthetic fuels or other useful products, creating a revenue stream that could help improve their commercial viability. But we look increasingly likely to surpass the safe greenhouse gas limits, so attention is instead turning to carbon-negative technologies.
That means capturing CO2 from the air and then putting it into long-term storage. One way could be to grow lots of biomass and then bury it, mimicking the process that created fossil fuels in the first place. Or DAC plants could pump the CO2 they produce into deep underground wells.
But the former would take up unreasonably large amounts of land to make a significant dent in emissions, while the latter would require huge amounts of already scant and expensive renewable power. According to a recent analysis, artificial photosynthesis could sidestep these issues because it’s up to five times more efficient than its natural counterpart and could be cheaper than DAC.
Whether the technology will develop quickly enough for it to be deployed at scale and in time to mitigate the worst effects of climate change remains to be seen. Emissions reductions certainly present a more sure-fire way to deal with the problem, but nonetheless, cyborg plants could soon be a common sight in our cities.
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