The Trump administration has reportedly awarded a contract to a California-based tech startup to set up hundreds of "autonomous surveillance towers" along the U.S.-Mexico border to aid its immigration enforcement efforts. U.S. Customs and Border Protection (CBP) announced on Thursday that the towers, which use artificial intelligence and imagery to identify people and vehicles, were now a "program of record" for the agency and that 200 would be deployed along the southern border by 2022. CBP did not mention the contract in its announcement, though the Washington Post reported that the effort includes a five-year agreement with Anduril Industries, a tech startup backed by investors such as Peter Thiel. Anduril executives told the Post that the deal is worth hundreds of millions of dollars. The company, which specializes in AI and other technologies, is valued at $1.9 billion, according to Bloomberg News.
It's no secret that Palmer Luckey's Anduril Industries has been developing a "virtual wall" to heighten national security -- he's been at it for the better part of three years. That work (for better or worse) has finally paid off. According to a new report from the Washington Post, the Trump administration awarded Anduril a lucrative five-year contract to erect hundreds of AI-powered surveillance towers along the U.S.-Mexico border by 2022. "These towers give agents in the field a significant leg up against the criminal networks that facilitate illegal cross-border activity," said Border Patrol Chief Rodney Scott in a statement released by U.S. Customs and Border Protection. Anduril's hardware almost looks like it belongs in orbit, rather than sitting amid desert scrub.
Even before president Trump's executive order on June 22, the US was already bucking global tech immigration trends. Over the past five years, as other countries have opened up their borders to highly skilled technical people, the US has maintained--and even restricted--its immigration policies, creating a bottleneck for meeting domestic demand for tech talent. Now Trump's decision to suspend a variety of work visas has left many policy analysts worried about what it could mean for long-term US innovation. In particular, the suspension of the H-1B, a three-year work visa granted to foreign workers in specialty fields and one of the primary channels for highly skilled tech workers to join the US workforce, could impact US dominance in critical technologies such as AI. "America's key competitors are going in a different direction," says Tina Huang, a research analyst at Georgetown's Center for Security and Emerging Technology (CSET).
IBM will no longer develop technology for facial recognition following protests against racial inequality in the US and UK. In a letter to congress, IBM CEO Arvind Krishna said that the company "no longer offers general purpose IBM facial recognition or analysis software." "IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency." "We believe now is the time to begin a national dialogue on whether and how facial recognition technology should be employed by domestic law enforcement agencies," the letter states. Amazon workers'refuse' to build tech for US immigration Amazon workers'refuse' to build tech for US immigration Facial recognition software has been often criticised on both privacy grounds – in that it increases the UK's already large surveillance state – but also because the software disproportionately misidentifies people of colour.
The rapid development of artificial intelligence has the potential to remake how the federal government delivers a broad range of core services to citizens in a profound way. When implemented correctly, these technologies can help the government to render decisions faster, using better data, at a far lower cost in vital areas ranging from awarding disability benefits to granting patents to adjudicating immigration applications and healthcare insurance benefits. Pursuing AI transformation will also position the government to better respond to sudden demand surges for relief as a result of pandemics or other unforeseen future emergencies. Right now, AI strategies are at an early adoption stage in most administrative agencies, with just 45 percent of surveyed agencies having an AI use case according to a recent report, Government by Algorithm, issued by Stanford University and NYU to the Administrative Conference of the United States. Out of those agencies that have implemented AI, only 12 percent were considered to be highly sophisticated applications, according to Stanford's computer scientists.
The Immigration Game (invented by Don Woods in 1971) extends the solitaire Game of Life (invented by John Conway in 1970) to enable two-player competition. The Immigration Game can be used in a model of evolution by natural selection, where fitness is measured with competitions. The rules for the Game of Life belong to the family of semitotalistic rules, a family with 262,144 members. Woods' method for converting the Game of Life into a two-player game generalizes to 8,192 members of the family of semitotalistic rules. In this paper, we call the original Immigration Game the Life Immigration Game and we call the 8,192 generalizations Immigration Games (including the Life Immigration Game). The question we examine here is, what are the conditions for one of the 8,192 Immigration Games to be suitable for modeling open-ended evolution? Our focus here is specifically on conditions for the rules, as opposed to conditions for other aspects of the model of evolution. In previous work, it was conjectured that Turing-completeness of the rules for the Game of Life may have been necessary for the success of evolution using the Life Immigration Game. Here we present evidence that Turing-completeness is a sufficient condition on the rules of Immigration Games, but not a necessary condition. The evidence suggests that a necessary and sufficient condition on the rules of Immigration Games, for open-ended evolution, is that the rules should allow growth.
AI is adding power to these chatbots and helping bridge the gap between humans and machines by employing natural language capabilities. These more intelligent chatbots are more capable and are being used in a variety of different contexts such as online customer support, phone interactions, information retrieval, assisting with online commerce or tech support, and the increasing popularity of voice assistants. Because chatbots are easy to deploy, companies find them a great first use case for AI within their organization. Because bots can provide consistent results without the need to sleep or take breaks, companies are able to keep them deployed to engage and interact with customers. Organizations including banking, finance, retail, and others have AI-enabled chatbots to help enhance customer engagement, collect basic customer information and answer general company questions.
The use of facial recognition technology has been spreading rapidly. Before Clearview AI became the target of public scrutiny earlier this year, the facial recognition app was used freely by the company's investors, clients and friends, according to a report Thursday from The New York Times. The app was reportedly demonstrated at events like parties, business gatherings and even on dates. Clearview identifies people by comparing photos to a database of images scraped from social media and other sites. It came under fire after a New York Times investigation in January.
An algorithm deployed across the United States is now known to underestimate the health needs of black patients1. The algorithm uses health-care costs as a proxy for health needs. But black patients' health-care costs have historically been lower because systemic racism has impeded their access to treatment -- not because they are healthier. This example illustrates how machine learning and artificial intelligence can maintain and amplify inequity. Most algorithms exploit crude correlations in data.
Self-exciting Hawkes processes are used to model events which cluster in time and space, and have been widely studied in seismology under the name of the Epidemic Type Aftershock Sequence (ETAS) model. In the ETAS framework, the occurrence of the mainshock earthquakes in a geographical region is assumed to follow an inhomogeneous spatial point process, and aftershock events are then modelled via a separate triggering kernel. Most previous studies of the ETAS model have relied on point estimates of the model parameters due to the complexity of the likelihood function, and the difficulty in estimating an appropriate mainshock distribution. In order to take estimation uncertainty into account, we instead propose a fully Bayesian formulation of the ETAS model which uses a nonparametric Dirichlet process mixture prior to capture the spatial mainshock process. Direct inference for the resulting model is problematic due to the strong correlation of the parameters for the mainshock and triggering processes, so we instead use an auxiliary latent variable routine to perform efficient inference.