The U.S. government will take steps next week to limit the export of artificial intelligence (AI) software. The decision by the Trump administration comes at a time when powerful rival nations, such as China, are becoming increasingly dominant in the field. The move is meant to keep certain sensitive technologies from falling into the hands of those nations.
The new rule goes into effect on January 6, 2020, and it will be aimed at certain companies that export geospatial imagery software from the United States. Those companies will be required to apply for a license to export it. The only exception is that a license will not be required to export to Canada.
The new measure was the first of its kind to be finalized by the Commerce Department under a mandate from a 2018 law passed by Congress. That law updated arms controls to include emerging technology.
The new rules will likely have an effect on a growing part of the tech industry. These algorithms are currently being used in order to analyze satellite images of crops, trade patterns and other changes within the economy and environment.
Chinese companies are responsible for having exported artificial intelligence surveillance technology to over 60 countries. Some of those countries have dismal human rights records and include Iran, Myanmar, Venezuela, and Zimbabwe.
Within the nation of China itself, the Communist Party is using facial recognition technology systems to target Uighurs and other Muslim minorities located in China’s far western Xinjang region. According to a report released by a U.S. think tank, Beijing has been involved in “authoritarian tech.”
The think tank that released the report was the Carnegie Endowment for International Peace, and they did so after rising concerns of authoritarian regimes using the technology as a way to gain power.
“Technology linked to Chinese companies — particularly Huawei, Hikvision, Dahua and ZTE — supply AI surveillance technology in 63 countries, 36 of which have signed onto China’s Belt and Road Initiative,” the report said.
One of China’s leading technology companies, Huawei Technologies Co., alone provides AI surveillance technology to at least 50 countries.
“Chinese product pitches are often accompanied by soft loans to encourage governments to purchase their equipment,” according to the report. “This raises troubling questions about the extent to which the Chinese government is subsidizing the purchase of advanced repressive technology.”
China has faced increased scrutiny after an investigative report by the International Consortium of Investigative Journalists was released detailing the nation’s surveillance and policing systems, which are being used to oppress Uighurs and send them to internment camps.
The new rules implemented by the U.S. government will at first only go into effect within the country. However, U.S. authorities have said that they could be submitted to international bodies at a later time.
There has been recent bi-partisan frustration over the long amount of time it is taking to roll-out new export controls for the technology.
“While the government believes that it is in the national security interests of the United States to immediately implement these controls, it also wants to provide the interested public with an opportunity to comment on the control of new items,” according to Senate Minority Leader Chuck Schumer.
Google’s CEO Calls For Increased Regulation To Avoid “Negative Consequences of AI”
Last year saw an increasing amount of attention drawn to the regulation of the AI industry, and this year seems to be continuing the trend. Just recently, Sundar Pichai, the CEO of Google and Alphabet Inc., supported the regulation of AI at an economic think tank taking place in Brugel.
Pichai’s comments were likely made in anticipation of new EU plans to regulate AI, which will be revealed in a few weeks. It’s possible that the EU regulations could contain policies legally enforcing certain standards for AI used in transportation, healthcare, and other high-risk sectors. The new EU regulations may also require increased transparency regarding AI systems and platforms.
According to Bloomberg, Google has previously tried to challenge antitrust fines and copyright enforcement in the EU. Despite previous attempts to push back against certain regulatory frameworks in Europe, Pichai stated that regulation is welcome as long as it takes “a proportionate approach, balancing potential harms with social opportunities.”
Pichai recently wrote an opinion piece in Financial Times, where he acknowledged that along with many opportunities to improve society, AI also has the potential to be misused. Pichai stated that regulations should help avoid the “negative consequences of AI”, citing abusive use of facial recognition and deepfakes as negative applications of AI. Pichai stated that international alignment is necessary for regulatory principles to work, and as such, there needs to be agreement on core values. Beyond that, Pichai said that it is the responsibility of AI companies like Google to give consideration to how AI can be used in an ethical manner and that this is why Google adopted its own standards for ethical AI use in 2018.
Pichai stated that government regulatory bodies and policies will play an important role in ensuring AI is used ethically, but that these bodies need not start from scratch. Pichai suggests that regulatory entities can look to previously established regulations for inspiration, such as Europe’s General Data Protection Regulation. Pichai also wrote that ethical AI regulation can potentially be both broad and flexible, with regulation providing general guidance that can be tailored for specific implementations in specific AI sectors. Newer technologies like self-driving vehicles will require new rules and policies that weigh benefits and costs against one another, while for more well-tread ground like medical devices, existing frameworks can be a good starting point.
Finally, Pichai stated that Google wants to partner with regulators to develop policies and find solutions that will balance trade-offs, Pichai wrote in Financial Times:
“We want to be a helpful and engaged partner to regulators as they grapple with the inevitable tensions and trade-offs. We offer our expertise, experience and tools as we navigate these issues together.”
While some have applauded Google for taking a stance on the need for regulation to ensure ethical AI usage, the debate continues over the extent to which it’s appropriate that AI companies should be involved with the creation of regulatory frameworks.
As for the upcoming EU regulations themselves, it’s possible that the EU is pursuing a risk-based rules system, which would put tighter restrictions on high-risk applications of AI. This includes restrictions that could be much tighter than Google hopes for, including a potential multi-year ban on facial recognition technology (with exceptions for research and security). In contrast to the EU’s more restrictive approaches, the US has pushed for relatively light regulations. It remains to be seen how the different regulation strategies will impact AI development, and society at large, in the two different regions of the globe.
AI Now Institute Warns About Misuse Of Emotion Detection Software And Other Ethical Issues
The AI Now Institute has released a report that urges lawmakers and other regulatory bodies to set hard limits on the use of emotion-detecting technology, banning it in cases where it may be used to make important decisions like employee hiring or student acceptance. In addition, the report contained a number of other suggestions regarding a range of topics in the AI field.
The AI Now Institute is a research institute based at NYU, possessing the mission of studying AI’s impact on society. AI Now releases a yearly report demonstrating their findings regarding the state of AI research and the ethical implications of how AI is currently being used. As the BBC reported, this year’s report addressed topics like algorithmic discrimination, lack of diversity in AI research, and labor issues.
Affect recognition, the technical term for emotion-detection algorithms, is a rapidly growing area of AI research. Those who employ the technology to make decisions often claim that the systems can draw reliable information about people’s emotional states by analyzing microexpressions, along with other cues like tone of voice and body language. The AI Now institute notes that the technology is being employed across a wide range of applications, like determining who to hire, setting insurance prices, and monitoring if students are paying attention in class.
Prof. Kate Crawford, co-founder of AI Now explained that its often believed that human emotions can accurately be predicted with relatively simple models. Crawford said that some firms are basing the development of their software on Paul Ekman’s work, a psychologist who hypothesized there are only six basic types of emotions that register on the face. However, Crawford notes that since Ekman’s theory was introduced studies have found that is far greater variability in facial expressions and that expressions can change across situations and cultures very easily.
“At the same time as these technologies are being rolled out, large numbers of studies are showing that there is… no substantial evidence that people have this consistent relationship between the emotion that you are feeling and the way that your face looks,” said Crawford to the BBC.
For this reason, the AI Now institute argues that much of affect recognition is based on unreliable theories and questionable science. Hence, emotion detection systems shouldn’t be deployed until more research has been done and that “governments should specifically prohibit the use of affect recognition in high-stakes decision-making processes”. AI Now argued that we should especially stop using the technology in “sensitive social and political contexts”, contexts that include employment, education, and policing.
At least one AI-development firm specializing in affect recognition, Emteq, agreed that there should be regulation that prevents misuse of the tech. The founder of Emteq, Charles Nduka, explained to the BBC that while AI systems can accurately recognize different facial expressions, there is not a simple map from expression to emotion. Nduka did express worry about regulation being taken too far and stifling research, noting that if “things are going to be banned, it’s very important that people don’t throw out the baby with the bathwater”.
As NextWeb reports, AI Now also recommended a number of other policies and norms that should guide the AI industry moving forward.
AI Now highlighted the need for the AI industry to make workplaces more diverse and stated that workers should be guaranteed a right to voice their concerns about invasive and exploitative AI. Tech workers should also have the right to know if their efforts are being used to construct harmful or unethical work.
AI Now also suggested that lawmakers take steps to require informed consent for the use of any data derived from health-related AI. Beyond this, it was advised that data privacy be taken more seriously and that the states should work to design privacy laws for biometric data covering both private and public entities.
Finally, the institute advised that the AI industry begin thinking and acting more globally, trying to address the larger political, societal, and ecological consequences of AI. It was recommended that there be a substantial effort to account for AI’s impact regarding geographical displacement and climate and that governments should make the climate impact of the AI industry publically available.
DeepMind Reports New Method Of Training Reinforcement Learning AI Safely
Reinforcement learning is a promising avenue of AI development, producing AI that can handle extremely complex tasks. Reinforcement AI algorithms are used in the creation of mobile robotics systems and self-driving cars among other applications. However, due to the way that reinforcement AI is trained, they can occasionally manifest bizarre and unexpected behaviors. These behaviors can be dangerous, and AI researchers refer to this problem as the “safe exploration” problem, which is where the AI becomes stuck in the exploration of unsafe states.
Recently, Google’s AI research lab DeepMind released a paper that proposed new methods for dealing with the safe exploration problem and training reinforcement learning AI in a safer fashion. The method suggested by DeepMind also corrects for reward hacking or loopholes in the reward criteria.
DeepMind’s new method has two different systems intended to guide the behavior of the AI in situations where unsafe behavior could arise. The two systems used by DeepMind’s training technique are a generative model and a forward dynamics model. Both of these models are trained on a variety of data, such as demonstrations by safety experts and completely random vehicle trajectories. The data is labeled by a supervisor with specific reward values, and the AI agent will pick up on patterns of behavior that will enable it to collect the greatest reward. The unsafe states have also been labeled, and once the model has managed to successfully predict rewards and unsafe states it is deployed to carry out the targeted actions.
The research team explains in the paper that the idea is to create possible behaviors from scratch, to suggest the desired behaviors, and to have these hypothetical scenarios be as informative as possible while simultaneously avoiding direct interference with the learning environment. The DeepMind team refers to this approach as ReQueST, or reward query synthesis via trajectory optimization.
ReQueST is capable of leading to four different types of behavior. The first type of behavior tries to maximize uncertainty regarding ensemble reward models. Meanwhile, behavior two and three attempts to both minimize and maximize predicted rewards. Predicted rewards are minimized in order to lead to the discovery of behaviors that the model may be incorrectly predicting. On the other hand, predicted reward is maximized in order to lead to behavior labels possessing the highest information value. Finally, the fourth type of behavior tries to maximize the novelty of trajectories, in order that the model continue to explore regardless of the rewards projected.
Once the model has reached the desired level of reward collection, a planning agent is used to make decisions based on the learned rewards. This model-predictive control scheme lets agents learn to avoid unsafe states by using the dynamic model and predicting possible consequences, in contrast to the behaviors of algorithms that learn through pure trial and error.
As reported by VentureBeat, the DeepMind researchers believe that their project is the first reinforcement learning system that is capable of learning in a controlled, safe fashion:
“To our knowledge, ReQueST is the first reward modeling algorithm that safely learns about unsafe states and scales to training neural network reward models in environments with high-dimensional, continuous states. So far, we have only demonstrated the effectiveness of ReQueST in simulated domains with relatively simple dynamics. One direction for future work is to test ReQueST in 3D domains with more realistic physics and other agents acting in the environment.”