AI News, It's Time to Strategize for the Next Fight
This fall, for instance, the president who swore he was going to give us an infrastructure plan that would blow our minds discovered that, after a tax cut for billionaires, a ballooning national debt, and a staggering $716 billion Pentagon budget, there were few dollars left over for much of anything else.
On Tuesday, the newly nominated head of U.S. Central Command, Lieutenant General Kenneth McKenzie, appeared before the Senate Armed Services Committee and insisted that any future Pentagon budget below $733 billion would “increase risk and that risk would be manifested across the force.”
(And here’s a little footnote to that change in numbers: Senator Inhofe walked out of that lunch and within the week had purchased “tens of thousands of dollars of stock in one of the nation’s top defense contractors.”
claimed to know nothing about it, and cancelled the order.) And then, of course, there’s always the purely secondary question: What is the U.S. military -- its budget already bigger than of that those of god-knows-how-many-other countries combined -- going to spend all that money on?
Kennedy faced just such a moment during the Cuban Missile Crisis of 1962 and, after envisioning the catastrophic outcome of a U.S.-Soviet nuclear exchange, he came to the conclusion that the atomic powers should impose tough barriers on the precipitous use of such weaponry.
With artificial intelligence, or AI, soon to play an ever-increasing role in military affairs, as in virtually everything else in our lives, the role of humans, even in nuclear decision-making, is likely to be progressively diminished.
Rather than focusing mainly on weaponry and tactics aimed at combating poorly armed insurgents in never-ending small-scale conflicts, the American military is now being redesigned to fight increasingly well-equipped Chinese and Russian forces in multi-dimensional (air, sea, land, space, cyberspace) engagements involving multiple attack systems (tanks, planes, missiles, rockets) operating with minimal human oversight.
“The major effect/result of all these capabilities coming together will be an innovation warfare has never seen before: the minimization of human decision-making in the vast majority of processes traditionally required to wage war,”
“In this coming age of hyperwar, we will see humans providing broad, high-level inputs while machines do the planning, executing, and adapting to the reality of the mission and take on the burden of thousands of individual decisions with no additional input.”
Ordinarily, national leaders seek to control the pace and direction of battle to ensure the best possible outcome, even if that means halting the fighting to avoid greater losses or prevent humanitarian disaster.
Yes, remotely piloted aircraft (RPA), or drones, have been widely used in Africa and the Greater Middle East to hunt down enemy combatants, but those are largely ancillary (and sometimes CIA) operations, intended to relieve pressure on U.S. commandos and allied forces facing scattered bands of violent extremists.
To ensure continued military supremacy, he added, the Pentagon would have to focus more “investment in technological innovation to increase lethality, including research into advanced autonomous systems, artificial intelligence, and hypersonics.”
Self-driving cars, for instance, rely on specialized algorithms to process data from an array of sensors monitoring traffic conditions and so decide which routes to take, when to change lanes, and so on.
Similarly, someday drone aircraft -- without human operators in distant locales -- will be capable of scouring a battlefield for designated targets (tanks, radar systems, combatants), determining that something it “sees”
As General Paul Selva, vice chairman of the Joint Chiefs of Staff, told Congress in 2017, “It is very compelling when one looks at the capabilities that artificial intelligence can bring to the speed and accuracy of command and control and the capabilities that advanced robotics might bring to a complex battlespace, particularly machine-to-machine interaction in space and cyberspace, where speed is of the essence.”
Aside from aiming to exploit AI in the development of its own weaponry, U.S. military officials are intensely aware that their principal adversaries are also pushing ahead in the weaponization of AI and robotics, seeking novel ways to overcome America’s advantages in conventional weaponry.
As the fighting intensifies, however, communications between headquarters and the front lines may well be lost and such systems will, according to military scenarios already being written, be on their own, empowered to take lethal action without further human intervention.
Advocates of the new technology claim that machines will indeed become smart enough to sort out such distinctions for themselves, while opponents insist that they will never prove capable of making critical distinctions of that sort in the heat of battle and would be unable to show compassion when appropriate.
However, strategists worry that, in a future hyperwar environment, such systems could be jammed or degraded just as the speed of the fighting begins to exceed the ability of commanders to receive battlefield reports, process the data, and dispatch timely orders.
As a report from the Congressional Research Service puts it, in the future “AI algorithms may provide commanders with viable courses of action based on real-time analysis of the battle-space, which would enable faster adaptation to unfolding events.”
Incoming data from battlefield information systems would instead be channeled to AI processors focused on assessing imminent threats and, given the time constraints involved, executing what they deemed the best options without human instructions.
Keep in mind, then, that the very nature of such a future AI-driven hyperwar will only increase the risk that conventional conflicts could cross a threshold that’s never been crossed before: an actual nuclear war between two nuclear states.
Such a danger arises from the convergence of multiple advances in technology: not just AI and robotics, but the development of conventional strike capabilities like hypersonic missiles capable of flying at five or more times the speed of sound, electromagnetic rail guns, and high-energy lasers.
Such weaponry, though non-nuclear, when combined with AI surveillance and target-identification systems, could even attack an enemy’s mobile retaliatory weapons and so threaten to eliminate its ability to launch a response to any nuclear attack.
scenario, any power might be inclined not to wait but to launch its nukes at the first sign of possible attack, or even, fearing loss of control in an uncertain, fast-paced engagement, delegate launch authority to its machines.
They certainly are capable of processing vast amounts of information over brief periods of time and weighing the pros and cons of alternative actions in a thoroughly unemotional manner.
It’s Time to Strategize for the Next Fight
While we continue to fight our post-9/11 wars, our military leaders are doing all they can to make sure we prepare for the next war.
Four items come to mind: America’s unpreparedness with respect to foreign influence operations, war-waging capacity, the velocity of change and understanding the dual use of military force.
Both forms of national will are important, though, for they either expand or contract strategic and grand strategic decision space in which our senior civil and military leaders must operate.
Called “influence operations,” actions taken in the information sphere—now accelerated by social media and AI-enhanced software—can have a direct effect on the coherence of national political leadership as well as popular support.
Popular support can be fragmented as well, leaving political leaders having neither sufficient bipartisan political support nor sufficient popular support for the national security decisions the country faces.
First, they must achieve and sustain coherency—that is, aligning war aims with military and nonmilitary strategies, policies and campaigns necessary to increase the probability of achieving the aims set.
Second, they must generate and sustain sufficient organizational capacity—across the government, not just within military departments—to translate plans into actions, adapt as the war unfolds and bring the war to a successful end.
Contrary to the previous three industrial revolutions, the author of The Fourth Industrial Revolution, Klaus Schwab, reminds readers, “This one is evolving at an exponential rather than linear pace.” He goes on to explain, “The scale and scope of change explain why disruption and innovation feels so acute today.” Two reasons Schwab discusses the velocity of change are these: to point out that such rapidity is already creating inequality, unfairness and widening gaps within populations and among nations that have security implications;
The national departments responsible for carrying out the nonmilitary campaigns necessary to achieve political war aims, and DoD itself—to say nothing of the legislative branch—are huge bureaucracies, none of which are designed to deal well with the pace of linear change, let alone the pace of geometric change that we face.
Whatever kinds of wars pop up, therefore, will have to be viewed through two lenses—one lens to determine how to resolve the situation, and a second to figure out how to use that resolution to influence the direction of the international order.
Improving the nation’s capacity to construct a sufficiently bipartisan vision of the kind of strategic environment conducive to American interests and America’s role in creating that environment is difficult, but that difficulty does not mitigate its importance.
But for the other issues, the answer may well be “too many, therefore no one.” Yet for the nation to be better prepared for the next war—which, given our history of being unable to predict when and against whom we may have to go to war—these kinds of issues need to be studied.
One role may be to take up these kinds of “strategic lessons learned” issues as courses of study in senior service colleges, thus preparing the next generation of general and flag officers who will participate in future civil-military discussions.
Another might be stimulating a discussion by funding studies of these kinds of issues through the major American schools of international and security studies, thus educating a future generation of civilian security professionals.
third could be to sponsor, in conjunction with national think tanks, a multiyear set of conferences that might stimulate a discussion of how the U.S. prepares for and wages war that is broader than just military preparations, stimulating a civil-military process of learning.
Major technology companies are investing heavily in AI assistants to act as intermediaries between companies and customers, creating a shopping environment where humans can offload vast swaths of the customer experience to machine-learning algorithms.
As AI matures, organizations that can’t compete with the tech giants to hire data scientists—which is to say, most of them—will face a life-or-death struggle to develop strategies for defending their market share, maintaining customer relationships, and retaining relevance.
Customers will be able to hand over any purchasing decisions they don’t find particularly interesting or engaging (though not necessarily unimportant) to an AI that has learned to calculate their preferred combination of features, price, and ethical considerations.
So it’s not a huge leap to imagine that many customers will welcome the opportunity to tell an AI assistant that they need socks, knowing they can trust the algorithm to parse that as “buy me six pairs of black organic cotton crew socks for less than $15 a pair from the vendor offering the best deal.” But it creates a challenge for the hosiery manufacturer: how do you make sure your socks are the ones the AI picks?
For background shopping, the AI will begin to winnow the available options based on parameters the customer sets, such as preferred brands, product features, or price points, as well as emerging values-based considerations, such as transparency, ethical sourcing, fair pay and benefits, and gender and minority equality.
Over time, the AI will learn and adjust both individual and group preferences by combining the makeup, habits, and buying patterns of a household (or a company, in the case of B2B purchases) with changing dynamics in the market, such as price changes and new product introductions.
“Basically, brand managers have to determine what makes certain consumers or market segments think about shopping for their products or services as a meaningful experience instead of a chore and then figure out how to make more consumers think the same way,” says Niraj Dawar, professor of marketing at the Ivey Business School at Western University, London, Ontario, Canada, and author of Tilt: Shifting your Strategy from Products to Customers.
These companies push the most intimidating aspects of the home cooking experience to the background, while allowing families and friends to connect by cooking together, enjoying the resulting meal, and creating an experience worth talking about.
Companies can turn that uncertainty into meaningful shopping by helping customers understand how their products work and teaching them how to get the most from their purchases, whether they’re housewares, power tools, or luxury automobiles.
A mobile phone manufacturer competing on the quality of its cameras can go beyond offering in-store and online photography classes to allowing students to submit their photos to a digital art gallery where the public votes on the best images.
For example, a golf club could include Internet of Things sensors that monitor the user’s swing and provide feedback to a smartwatch in real time, while a winemaker could offer a wine club experience that pairs a monthly wine selection with recorded or live webinars with wine experts.
Companies can also tap into that desire by offering, sponsoring, or partnering in tools and communities where customers can share goals and monitor progress for both personal aims, such as weight loss, and societal needs, such as charitable fundraising.
Dozens of companies are already hiring celebrities to post pictures of themselves enjoying a product on social media, resulting in increased sales to people who aspire to a similar lifestyle.
Once they pick up their new wheels, they can embark on a European road trip that includes recommendations for hotels (Mercedes even throws in a free night’s stay) and preplanned routes that show off how enjoyable the luxury vehicles are to drive.
Even the humble roll of toilet paper can stand apart from the pack in the right context: made from recycled materials or with eco-friendly processes for resolutely green shoppers, unscented for shoppers with sensitive skin, or in industrial-sized packs for those buying for a big crowd.
While this collected data will help companies understand their markets and identify opportunities to address a particular individual, group, or segment more precisely, it will also require them to optimize continually for each of those individuals, groups, or segments.
By interfacing with multiple retailers, these third-party AIs could generate reverse auctions to ensure an optimal combination of products, services, and price—as, for example, a smart refrigerator’s AI requesting bids from several supermarkets’ own AIs for a total shopping list or even individual items.
However, one critical fact remains: brand managers lack the skills to pry open the black box of AI shopping algorithms to see how they make decisions, and while the number of data scientists worldwide may be growing, it’s still small.
AI Permeation: How Far Does Artificial Intelligence Go?
Molly has features like speech recognition, dynamically generated speech, a text chat mode, data and device integration, and it can receive images and video.
The program screens between 10 and 20 million molecules a day and intelligently builds new compounds to create medications to help fight serious conditions like Ebola and multiple sclerosis.
Large banks like Bank of America, American Express and Capital One are also adopting artificial intelligence and creating their own branded chatbots that offer the same or similar capabilities as independently operated programs.
Then something incredible happens — the intelligence is able to strategize live trades that are most likely to be successful based on intelligent algorithmic reading.
Artificial intelligence in marketing is not a new concept, but it's growing and evolving to create targeted ads that are more accurately in tune with what customers are looking for than ever before.
AI can gather data from the web pages a user visits, their activities online, what they watch, what they read and more to ensure that they receive only the most applicable ads for them.
Teslaannounced in the fall of 2016 that all of its new vehicles would come equipped with hardware necessary for automated driving, while popular ride-sharing company Uberhas said it will have a fleet of self-driving SUVs on the streets by 2021.
- On Thursday, June 4, 2020
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