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#435436 Undeclared Wars in Cyberspace Are ...
The US is at war. That’s probably not exactly news, as the country has been engaged in one type of conflict or another for most of its history. The last time we officially declared war was after Japan bombed Pearl Harbor in December 1941.
Our biggest undeclared war today is not being fought by drones in the mountains of Afghanistan or even through the less-lethal barrage of threats over the nuclear programs in North Korea and Iran. In this particular war, it is the US that is under attack and on the defensive.
This is cyberwarfare.
The definition of what constitutes a cyber attack is a broad one, according to Greg White, executive director of the Center for Infrastructure Assurance and Security (CIAS) at The University of Texas at San Antonio (UTSA).
At the level of nation-state attacks, cyberwarfare could involve “attacking systems during peacetime—such as our power grid or election systems—or it could be during war time in which case the attacks may be designed to cause destruction, damage, deception, or death,” he told Singularity Hub.
For the US, the Pearl Harbor of cyberwarfare occurred during 2016 with the Russian interference in the presidential election. However, according to White, an Air Force veteran who has been involved in computer and network security since 1986, the history of cyber war can be traced back much further, to at least the first Gulf War of the early 1990s.
“We started experimenting with cyber attacks during the first Gulf War, so this has been going on a long time,” he said. “Espionage was the prime reason before that. After the war, the possibility of expanding the types of targets utilized expanded somewhat. What is really interesting is the use of social media and things like websites for [psychological operation] purposes during a conflict.”
The 2008 conflict between Russia and the Republic of Georgia is often cited as a cyberwarfare case study due to the large scale and overt nature of the cyber attacks. Russian hackers managed to bring down more than 50 news, government, and financial websites through denial-of-service attacks. In addition, about 35 percent of Georgia’s internet networks suffered decreased functionality during the attacks, coinciding with the Russian invasion of South Ossetia.
The cyberwar also offers lessons for today on Russia’s approach to cyberspace as a tool for “holistic psychological manipulation and information warfare,” according to a 2018 report called Understanding Cyberwarfare from the Modern War Institute at West Point.
US Fights Back
News in recent years has highlighted how Russian hackers have attacked various US government entities and critical infrastructure such as energy and manufacturing. In particular, a shadowy group known as Unit 26165 within the country’s military intelligence directorate is believed to be behind the 2016 US election interference campaign.
However, the US hasn’t been standing idly by. Since at least 2012, the US has put reconnaissance probes into the control systems of the Russian electric grid, The New York Times reported. More recently, we learned that the US military has gone on the offensive, putting “crippling malware” inside the Russian power grid as the U.S. Cyber Command flexes its online muscles thanks to new authority granted to it last year.
“Access to the power grid that is obtained now could be used to shut something important down in the future when we are in a war,” White noted. “Espionage is part of the whole program. It is important to remember that cyber has just provided a new domain in which to conduct the types of activities we have been doing in the real world for years.”
The US is also beginning to pour more money into cybersecurity. The 2020 fiscal budget calls for spending $17.4 billion throughout the government on cyber-related activities, with the Department of Defense (DoD) alone earmarked for $9.6 billion.
Despite the growing emphasis on cybersecurity in the US and around the world, the demand for skilled security professionals is well outpacing the supply, with a projected shortfall of nearly three million open or unfilled positions according to the non-profit IT security organization (ISC)².
UTSA is rare among US educational institutions in that security courses and research are being conducted across three different colleges, according to White. About 10 percent of the school’s 30,000-plus students are enrolled in a cyber-related program, he added, and UTSA is one of only 21 schools that has received the Cyber Operations Center of Excellence designation from the National Security Agency.
“This track in the computer science program is specifically designed to prepare students for the type of jobs they might be involved in if they went to work for the DoD,” White said.
However, White is extremely doubtful there will ever be enough cyber security professionals to meet demand. “I’ve been preaching that we’ve got to worry about cybersecurity in the workforce, not just the cybersecurity workforce, not just cybersecurity professionals. Everybody has a responsibility for cybersecurity.”
Artificial Intelligence in Cybersecurity
Indeed, humans are often seen as the weak link in cybersecurity. That point was driven home at a cybersecurity roundtable discussion during this year’s Brainstorm Tech conference in Aspen, Colorado.
Participant Dorian Daley, general counsel at Oracle, said insider threats are at the top of the list when it comes to cybersecurity. “Sadly, I think some of the biggest challenges are people, and I mean that in a number of ways. A lot of the breaches really come from insiders. So the more that you can automate things and you can eliminate human malicious conduct, the better.”
White noted that automation is already the norm in cybersecurity. “Humans can’t react as fast as systems can launch attacks, so we need to rely on automated defenses as well,” he said. “This doesn’t mean that humans are not in the loop, but much of what is done these days is ‘scripted’.”
The use of artificial intelligence, machine learning, and other advanced automation techniques have been part of the cybersecurity conversation for quite some time, according to White, such as pattern analysis to look for specific behaviors that might indicate an attack is underway.
“What we are seeing quite a bit of today falls under the heading of big data and data analytics,” he explained.
But there are signs that AI is going off-script when it comes to cyber attacks. In the hands of threat groups, AI applications could lead to an increase in the number of cyberattacks, wrote Michelle Cantos, a strategic intelligence analyst at cybersecurity firm FireEye.
“Current AI technology used by businesses to analyze consumer behavior and find new customer bases can be appropriated to help attackers find better targets,” she said. “Adversaries can use AI to analyze datasets and generate recommendations for high-value targets they think the adversary should hit.”
In fact, security researchers have already demonstrated how a machine learning system could be used for malicious purposes. The Social Network Automated Phishing with Reconnaissance system, or SNAP_R, generated more than four times as many spear-phishing tweets on Twitter than a human—and was just as successful at targeting victims in order to steal sensitive information.
Cyber war is upon us. And like the current war on terrorism, there are many battlefields from which the enemy can attack and then disappear. While total victory is highly unlikely in the traditional sense, innovations through AI and other technologies can help keep the lights on against the next cyber attack.
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#435161 Less Like Us: An Alternate Theory of ...
The question of whether an artificial general intelligence will be developed in the future—and, if so, when it might arrive—is controversial. One (very uncertain) estimate suggests 2070 might be the earliest we could expect to see such technology.
Some futurists point to Moore’s Law and the increasing capacity of machine learning algorithms to suggest that a more general breakthrough is just around the corner. Others suggest that extrapolating exponential improvements in hardware is unwise, and that creating narrow algorithms that can beat humans at specialized tasks brings us no closer to a “general intelligence.”
But evolution has produced minds like the human mind at least once. Surely we could create artificial intelligence simply by copying nature, either by guided evolution of simple algorithms or wholesale emulation of the human brain.
Both of these ideas are far easier to conceive of than they are to achieve. The 302 neurons of the nematode worm’s brain are still an extremely difficult engineering challenge, let alone the 86 billion in a human brain.
Leaving aside these caveats, though, many people are worried about artificial general intelligence. Nick Bostrom’s influential book on superintelligence imagines it will be an agent—an intelligence with a specific goal. Once such an agent reaches a human level of intelligence, it will improve itself—increasingly rapidly as it gets smarter—in pursuit of whatever goal it has, and this “recursive self-improvement” will lead it to become superintelligent.
This “intelligence explosion” could catch humans off guard. If the initial goal is poorly specified or malicious, or if improper safety features are in place, or if the AI decides it would prefer to do something else instead, humans may be unable to control our own creation. Bostrom gives examples of how a seemingly innocuous goal, such as “Make everyone happy,” could be misinterpreted; perhaps the AI decides to drug humanity into a happy stupor, or convert most of the world into computing infrastructure to pursue its goal.
Drexler and Comprehensive AI Services
These are increasingly familiar concerns for an AI that behaves like an agent, seeking to achieve its goal. There are dissenters to this picture of how artificial general intelligence might arise. One notable alternative point of view comes from Eric Drexler, famous for his work on molecular nanotechnology and Engines of Creation, the book that popularized it.
With respect to AI, Drexler believes our view of an artificial intelligence as a single “agent” that acts to maximize a specific goal is too narrow, almost anthropomorphizing AI, or modeling it as a more realistic route towards general intelligence. Instead, he proposes “Comprehensive AI Services” (CAIS) as an alternative route to artificial general intelligence.
What does this mean? Drexler’s argument is that we should look more closely at how machine learning and AI algorithms are actually being developed in the real world. The optimization effort is going into producing algorithms that can provide services and perform tasks like translation, music recommendations, classification, medical diagnoses, and so forth.
AI-driven improvements in technology, argues Drexler, will lead to a proliferation of different algorithms: technology and software improvement, which can automate increasingly more complicated tasks. Recursive improvement in this regime is already occurring—take the newer versions of AlphaGo, which can learn to improve themselves by playing against previous versions.
Many Smart Arms, No Smart Brain
Instead of relying on some unforeseen breakthrough, the CAIS model of AI just assumes that specialized, narrow AI will continue to improve at performing each of its tasks, and the range of tasks that machine learning algorithms will be able to perform will become wider. Ultimately, once a sufficient number of tasks have been automated, the services that an AI will provide will be so comprehensive that they will resemble a general intelligence.
One could then imagine a “general” intelligence as simply an algorithm that is extremely good at matching the task you ask it to perform to the specialized service algorithm that can perform that task. Rather than acting like a single brain that strives to achieve a particular goal, the central AI would be more like a search engine, looking through the tasks it can perform to find the closest match and calling upon a series of subroutines to achieve the goal.
For Drexler, this is inherently a safety feature. Rather than Bostrom’s single, impenetrable, conscious and superintelligent brain (which we must try to psychoanalyze in advance without really knowing what it will look like), we have a network of capabilities. If you don’t want your system to perform certain tasks, you can simply cut it off from access to those services. There is no superintelligent consciousness to outwit or “trap”: more like an extremely high-level programming language that can respond to complicated commands by calling upon one of the myriad specialized algorithms that have been developed by different groups.
This skirts the complex problem of consciousness and all of the sticky moral quandaries that arise in making minds that might be like ours. After all, if you could simulate a human mind, you could simulate it experiencing unimaginable pain. Black Mirror-esque dystopias where emulated minds have no rights and are regularly “erased” or forced to labor in dull and repetitive tasks, hove into view.
Drexler argues that, in this world, there is no need to ever build a conscious algorithm. Yet it seems likely that, at some point, humans will attempt to simulate our own brains, if only in the vain attempt to pursue immortality. This model cannot hold forever. Yet its proponents argue that any world in which we could develop general AI would probably also have developed superintelligent capabilities in a huge range of different tasks, such as computer programming, natural language understanding, and so on. In other words, CAIS arrives first.
The Future In Our Hands?
Drexler argues that his model already incorporates many of the ideas from general AI development. In the marketplace, algorithms compete all the time to perform these services: they undergo the same evolutionary pressures that lead to “higher intelligence,” but the behavior that’s considered superior is chosen by humans, and the nature of the “general intelligence” is far more shaped by human decision-making and human programmers. Development in AI services could still be rapid and disruptive.
But in Drexler’s case, the research and development capacity comes from humans and organizations driven by the desire to improve algorithms that are performing individualized and useful tasks, rather than from a conscious AI recursively reprogramming and improving itself.
In other words, this vision does not absolve us of the responsibility of making our AI safe; if anything, it gives us a greater degree of responsibility. As more and more complex “services” are automated, performing what used to be human jobs at superhuman speed, the economic disruption will be severe.
Equally, as machine learning is trusted to carry out more complex decisions, avoiding algorithmic bias becomes crucial. Shaping each of these individual decision-makers—and trying to predict the complex ways they might interact with each other—is no less daunting a task than specifying the goal for a hypothetical, superintelligent, God-like AI. Arguably, the consequences of the “misalignment” of these services algorithms are already multiplying around us.
The CAIS model bridges the gap between real-world AI, machine learning developments, and real-world safety considerations, as well as the speculative world of superintelligent agents and the safety considerations involved with controlling their behavior. We should keep our minds open as to what form AI and machine learning will take, and how it will influence our societies—and we must take care to ensure that the systems we create don’t end up forcing us all to live in a world of unintended consequences.
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