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Graham Allison alerts us to artificial intelligence being the epicenter of todays superpower arms race.

Drawing heavily on Kai-Fu Lees basic thesis, Allison draws the battlelines: the United States vs. China, across the domains of human talent, big data, and government commitment.

Allison further points to the absence of controls, or even dialogue, on what AI means for strategic stability. With implied resignation, his article acknowledges the smashing of Pandoras Box, noting many AI advancements occur in the private sector beyond government scrutiny or control.

However, unlike the chilling and destructive promise of nuclear weapons, the threat posed by AI in popular imagination is amorphous, restricted to economic dislocation or sci-fi depictions of robotic apocalypse.

Absent from Allisons call to action is explaining the so what?why does the future hinge on AI dominance? After all, the few examples (mass surveillance, pilot HUDs, autonomous weapons) Allison does provide reference continued enhancements to the status quoincremental change, not paradigm shift.

As Allison notes, President Xi Jinping awoke to the power of AI after AlphaGo defeated the worlds number one Go human player, Lee Sedol. But why? What did Xi see in this computation that persuaded him to make AI the centerpiece of Chinese national endeavor?

The answer: AIs superhuman capacity to think.

To explain, lets begin with what I am not talking about. I do not mean so-called general AIthe broad-spectrum intelligence with self-directed goals acting independent of, or in spite of, preferences of human creators.

Eminent figures such as Elon Musk and Sam Harris warn of the coming of general AI. In particular, the so-called singularity, wherein AI evolves the ability to rewrite its own code. According to Musk and Harris, this will precipitate an exponential explosion in that AIs capability, realizing 10,000 IQ and beyond in a matter of mere hours. At such time, they argue, AI will become to us what we are to ants, with similar levels of regard.

I concur with Sam and Elon that the advent of artificial general superintelligence is highly probable, but this still requires transformative technological breakthroughs the circumstances for which are hard to predict. Accordingly, whether general AI is realized 30 or 200 years from now remains unknown, as is the nature of the intelligence created; such as if it is conscious or instinctual, innocent or a weapon.

When I discuss the AI arms race I mean the continued refinement of existing technology. Artificial intelligence that, while being a true intelligence in the sense of having the ability to self-learn, it has a single programmed goal constrained within a narrow set of rules and parameters (such as a game).

To demonstrate what President Xi saw in AI winning a strategy game, and why the global balance of power hinges on it, we need to talk briefly about games.

Artificial Intelligence and Games

There are two types of strategy games: games of complete information and games of incomplete information. A game of complete information is one in which every player can see all of the parameters and options of every other player.

Tic-Tac-Toe is a game of complete information. An average adult can solve this game with less than thirty minutes of practice. That is, adopt a strategy that no matter what your opponent does, you can correctly counter it to obtain a draw. If your opponent deviates from that same strategy, you can exploit them and win.

Conversely, a basic game of uncertainty is Rock, Scissors, Paper. Upon learning the rules, all players immediately know the optimal strategy. If your opponent throws Rock, you want to throw Paper. If they throw Paper, you want to throw Scissors, and so on.

Unfortunately, you do not know ahead of time what your opponent is going to do. Being aware of this, what is the correct strategy?

The unexploitable strategy is to throw Rock 33 percent of the time, Scissors 33 percent of the time, and Paper 33 percent of the time, each option being chosen randomly to avoid observable patterns or bias.

This unexploitable strategy means that, no matter what approach your opponent adopts, they won't be able to gain an edge against you.

But lets imagine your opponent throws Rock 100 percent of the time. How does your randomized strategy stack up? 33 percent of the time you'll tie (Rock), 33 percent of the time you'll win (Paper), and 33 percent of the time you'll lose (Scissors)the total expected value of your strategy against theirs is 0.

Is this your optimal strategy? No. If your opponent is throwing Rock 100 percent of the time, you should be exploiting your opponent by throwing Paper.

Naturally, if your opponent is paying attention they, in turn, will adjust to start throwing Scissors. You and your opponent then go through a series of exploits and counter-exploits until you both gradually drift toward an unexploitable equilibrium.

With me so far? Good. Let's talk about computing and games.

As stated, nearly any human can solve Tic-Tac-Toe, and computers solved checkers many years ago. However more complex games such as Chess, Go, and No-limit Texas Holdem poker have not been solved.

Despite all being mind-bogglingly complex, of the three chess is simplest. In 1997, reigning world champion Garry Kasparov was soundly beaten by the supercomputer Deep Blue. Today, anyone reading this has access to a chess computer on their phone that could trounce any human player.

Meanwhile, the eastern game of Go eluded programmers. Go has many orders of magnitude more combinations than chess. Until recently, humans beat computers by being far more efficient in selecting moveswe don't spend our time trying to calculate every possible option twenty-five moves deep. Instead, we intuitively narrow our decisionmaking to a few good choices and assess those.

Moreover, unlike traditional computers, people are able to think in non-linear abstraction. Humans can, for example, imagine a future state during the late stages of the game beyond which a computer could possibly calculate. We are not constrained by a forward-looking linear progression. Humans can wonderfully imagine a future endpoint, and work backwards from there to formulate a plan.

Many previously believed that this combination of factorsnear-infinite combinations and the human ability to think abstractlymeant that go would forever remain beyond the reach of the computer.

Then in 2016 something unprecedented happened. The AI system, AlphaGo, defeated the reigning world champion go player Lee Sedol 4-1.

But that was nothing: two years later, a new AI system, AlphaZero, was pitched against AlphaGo.

Unlike its predecessor which contained significant databases of go theory, all AlphaZero knew was the rules, from which it played itself continuously over forty days.

After this period of self-learning, AlphaZero annihilated AlphaGo, not 4-1, but 100-0.

In forty days AlphaZero had superseded 2,500 years of total human accumulated knowledge and even invented a range of strategies that had never been discovered before in history.

Meanwhile, chess computers are now a whole new frontier of competition, with programmers pitting their systems against one another to win digital titles. At the time of writing the world's best chess engine is a program known as Stockfish, able to smash any human Grandmaster easily. In December 2017 Stockfish was pitted against AlphaZero.

Again, AlphaZero only knew the rules. AlphaZero taught itself to play chess over a period of nine hours. The result over 100 games? AlphaZero twenty-eight wins, zero losses, seventy-two draws.

Not only can artificial intelligence crush human players, it also obliterates the best computer programs that humans can design.

Artificial Intelligence and Abstraction

Most chess computers play a purely mathematical strategy in a game yet to be solved. They are raw calculators and look like it too. AlphaZero, at least in style, appears to play every bit like a human. It makes long-term positional plays as if it can visualize the board; spectacular piece sacrifices that no computer could ever possibly pull off, and exploitative exchanges that would make a computer, if it were able, cringe with complexity. In short, AlphaZero is a genuine intelligence. Not self-aware, and constrained by a sandboxed reality, but real.

Despite differences in complexity there is one limitation that chess and go both share they're games of complete information.

Enter No-limit Texas Holdem (hereon, Poker). This is the ultimate game of uncertainty and incomplete information. In poker, you know what your hole cards are, the stack sizes for each player, and the community cards that have so far come out on the board. However, you don't know your opponent's cards, whether they will bet or raise or how much, or what cards are coming out on later streets of betting.

Poker is arguably the most complex game in the world, combining mathematics, strategy, timing, psychology, and luck. Unlike Chess or Go, Pokers possibilities are truly infinite and across multiple players simultaneously. The idea that a computer could beat top Poker professionals seems risible.

Except that it has already happened. In 2017, the AI system Libratus comprehensively beat the best Head's-up (two-player) poker players in the world.

And now, just months ago, another AI system Pluribus achieved the unthinkableit crushed super high stakes poker games against multiple top professionals simultaneously, doing so at a win-rate of five big blinds per hour. For perspective, the difference in skill level between the best English Premier League soccer team and the worst would not be that much.

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Explained: The Artificial Intelligence Race is an Arms Race - The National Interest Online

NASA/JPL-Caltech/UCLA/MPS/DLR/IDA

Ever since the Dawn spacecraft picked up images of what look to be a vast network of bright spots in the Occator crater on Ceresa dwarf planet in the asteroid beltthere's been conjecture over whether the whiteish spots are made up of ice, or some kind of volcanic salt deposits. Meanwhile, another controversy has been brewing over them: What exactly are those shapes seen in the bright spots, called Vinalia Faculae? Are they squares or triangles? Did extraterrestrials create them?

Because the strange patterns are so strikingly geometric, researchers from the University of Cadiz in Spain have taken a closer look at the bright spots to figure out whether humans and machines look at planetary images differently. The overall goal was to figure out if artificial intelligence can help us discover and make sense of technosignatures, or potentially detectable signals from distant, advanced civilizations, according to NASA.

"One of the potential applications of artificial intelligence is not only to assist in big data analysis but to help to discern possible artificiality or oddities in patterns of either radio signals, megastructures or techno-signatures in general," the authors wrote in a new paper published in the scientific journal Acta Astronautica.

To figure out what people thought they saw in the images of Occator, study author Gabriel G. De la Torre, a neuropsychologist from the University of Cadiz in Spain, brought together 163 volunteers who had no prior astronomy training. Overwhelmingly, these people identified a square shape in the crater's bright spots.

Then, the same was done with an artificial vision system trained with convolutional neural networks, which are mostly used in image recognition. Training data for the neural net included thousands of images of both squares and triangles so the system could identify those shapes.

NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI

Strangely, the neural net saw the same square the people noticed, but also identified a triangle, as shown in the image above. It appears the square is inside a larger triangle. After the people in the study were faced with this new triangular option, the percentage of them who claimed to have seen a triangle skyrocketed.

It's just one example of how our minds can be easily tricked when faced with a false positive. If we're told a system identified a given blip, we're more likely to blindly believe and truly think we saw the same blip due to our own tendency toward confirmation bias, or interpreting information in a particular way to fit a pre-existing belief.

That makes AI potentially dangerous in the search for far-away extraterrestrial life. False positives can confuse researchers, compromising its own usefulness in detecting technosignatures. De la Torre points out in the paper that what this all adds up to is actually a commonality between humans and AI systems: We both struggle with implicit bias.

So, if not artificial structures, what exactly are those funny shapes in the Ceres bright spot images? The quick answer is "we don't know." But De la Torre has an idea: It's "probably just a play of light and shadow."

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AI Will Probably Trick Us Into Thinking We Found Aliens - Popular Mechanics

One click, your mortgage process begins. Another click, that mortgage loan is pre-approved.Five minutes pass and you are ready to buy a home.

The digital application process for single-family mortgages has flourished with new technology, new companies entering the space and new capabilities that, even just 10 years ago, we wouldnt have thought possible.

Borrowers can sign closing papers on a new home remotely so that they dont have to miss hours of work. Travelers can close on their home from the other side of the world. The credit invisible, or those with no credit score, can learn more about their financial situation and what they can do to prepare to buy a home after a quick and painless application process.

But if you fast forward just a few weeks, to when a borrower is getting settled into their new home, the experience changes dramatically. Their mortgage loan is sold and their servicer steps in to introduce themselves. The smooth digital process is suddenly transformed into a rough, paper-heavy and confusing process.

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Coming soon: The promise of artificial intelligence in servicing - HousingWire

In 2018, Google unveiled Duplex, an artificial intelligence-powered assistant that sounds eerily human-like, complete with umms and ahs that are designed to make the conversation more natural. The demo had Duplex call a salon to schedule a haircut and then call a restaurant to make a reservation.

As Googles CEO Sundar Pichai demonstrated, the system at Googles I/O (input/output) developer conference, the crowd cheered, hailing the technological achievement. Indeed, this represented a big leap toward developing AI voice assistants that can pass the Turing Test, which requires machines to be able to hold conversations while being completely indistinguishable from humans.

But not everyone was so enthusiastic. Some technology commentators saw it as a form of deception by design. In a 2018 tweet, prominent University of North Carolina techno-sociologist Zeynep Tufekici described the system as horrifying, and wrote: Silicon Valley is ethically lost, rudderless, and has not learned a thing.

Responding to public pressure, a Google spokeswoman said in a statement, We are designing this feature with disclosure built-in, and well make sure the system is appropriately identified.

But what if knowing that we are interacting with a bot made for a worse human experience? Suppose you are interacting with a customer service agent that you know is just a computer program. Might you give yourself a little more license to use abusive language or to lob insults? After all, you are not going to hurt any real human beings. As satisfying as this might be, could this shift in your behavior lead to longer and less efficient customer service calls and a worse overall experience for you?

To explore these questions, we ran studies in which participants played a cooperation game with either a human associate or a bot that used AI to adapt its behavior to maximize its payoffs. This game was designed to capture situations in which each of the interacting parties can either act selfishly in an attempt to exploit the other, or act cooperatively in an attempt to attain a mutually beneficial outcome.

In some instances, participants were told who they were interacting with: a human or a bot. In others, we gave false information about the associates identity. Some were told they were interacting with a bot when they were actually interacting with a human, and others were told they were interacting with a human, when in fact it was a bot.

The results showed that bots posing as humans were very efficient at persuading the partner to cooperate in the game. In fact, these bots were better at eliciting cooperation with humans than other humans were. When the bots true nature was revealed, however, cooperation rates dropped significantly, and the bots superiority was negated.

In fact, among all conditions we studied, the best outcome was achieved when people interacted with bots but were told they were interacting with humans. This is precisely the situation that outraged people over the Google Duplex demo and that caused Google to back off and indicate that they will disclose the nonhuman nature of the system.

As AI systems continue to approach or exceed human-level performance in various tasks, bots will be increasingly capable of passing as humans. In the near future, we will interact with bots on the phone, social media, or even video, in a variety of contexts, from business to government to entertainment and they will be indistinguishable from their human counterparts.

Our research reveals that while the much-touted algorithmic transparency is important, it may sometimes come at a cost. So now we must ask ourselves: Should we allow companies to deceive us into thinking bots are human if this makes us happier customers or more polite, cooperative people? Or does interacting with a machine we believe is a human violate something sacred, like human dignity? What is important to us: transparency or efficiency? And in what context might we prefer one or the other? Although there is broad consensus that machines should be transparent about how they make decisions, it is less clear whether they should be transparent about who they are.

Science, including our own experiment, cannot answer this question, since it is a question about what we value most transparency or efficiency. Maybe we can have both and as humans learn to work cooperatively with machines. But until we do, society needs to recognize and grapple with the ethics and trade-offs.

Talal Rahwan is an associate professor at New York University Abu Dhabi. Jacob Crandall is an associate professor at Brigham Young University. Fatimah Ishowo-Oloko is a PhD graduate from Khalifa University. Iyad Rahwan is an associate professor at MIT and director of the Max Planck Institute for Human Development.

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Ethics, efficiency, and artificial intelligence - The Boston Globe

By Robbin Laird

During my visit to Australia last October, I had a chance to talk to a number of people about the evolving approach in Australia to maritime remotes and their evolving role within the fifth generation warfare approach or what I refer to as building a distributed integratable force or an integrated distributed force.

Towards the end of my stay, I had a chance to discuss with the key presenter on this topic at the Seapower Conference held in Sydney in early October, Commander Paul Hornsby, the Royal Australian Navy lead on maritime remotes.

We discussed a number of issues, but I am going to focus on where maritime remotes fit within the evolving strategic thinking of the Royal Australian Navy and its contribution to the ADF.

The broad point is that Australia is focusing on robotics and artificial intelligence more generally in its economy, with clear opportunities for innovation to flow between the civil and military sectors. Australia is a large island continent with a relatively small population. For both economic and defense reasons, Australia needs to extend the capabilities of its skilled manpower with robotic and AI capabilities. For the Navy, this means shaping a much large fleet in terms of a significant web of maritime remotes working interactively with the various manned assets operating in an area of interest.

Commander Hornsby highlighted the 2018 Australian Robotics Roadmap as an indicator of the Australian approach to cross-leveraging robotic systems and AI. As the report noted:

Robotics can be the force multiplier needed to augment Australias highly valued humanworkforce and to enable persistent, wide-area operations in air, land, sea, subsurface, spaceand cyber domains.

A second broad point is that Australia is working closely with core allies to forge a common R and D pool and to cross-learn from one another with regard to the operation of maritime remotes and their ability to deliver capabilities to the operational forces.

An example of the cross-learning and collaborative approach was Autonomous Warrior 2018. The exercise was a milestone in allied cooperation, according to Lt. Andrew Herring, in an article published on November 24, 2018.

When more than 50 autonomous technologies and over 500 scientists, technicians and support staff came together for AUTONOMOUS WARRIOR 2018 (AW18) in Jervis Bay, ACT, it marked the culmination of four years collaboration between the militaries, defence scientists and defence industries of five nations.

Today, Navys Deputy Director Mine Warfare Diving and Special Ops Capability, Commander Paul Hornsby, and Defence Science and Technologys (DST) Trusted Autonomous Systems Program Leader, Professor Jason Scholz, are exploring autonomous technologies with US Air Force Research Labs Senior Engineering Research Manager, Dr Mark Draper and Dr Philip Smith from the UKs Defence Science and Technology Laboratory.

The four, with their respective organisations, are collaborating under the Five Eyes Technical Cooperation Program (TTCP), which shares information and ideas among defence scientists from Australia, UK, USA, Canada and New Zealand, pursuing strategic challenges in priority areas.

Among them is TTCPs Autonomy Strategic Challenge, which aims to integrate autonomous technologies to operate together in different environments.

AUTONOMOUS WARRIOR2018 includes the Strategic Challenges fifth and final scientific trial Wizard of Aus a software co-development program aimed at managing autonomous vehicles from a shared command and control system that integrates with combat systems used by Five Eyes nations.

US Air Force Research Labs Dr Mark Draper summarises AW18s ambitious objective. What we are trying to achieve here is force multiplication and interoperability, where multiple unmanned systems from different countriesin the air, on the ground and on the surface of the water or even underwaterwould all be controlled and managed by one person sitting at one control station.

Two systems together

To achieve this, two systems have come together: AIM and MAPLE.

Allied IMPACT, known as AIM, combines best of breed technologies from Australia, United Kingdom, United States and Canada.

Weve brought these technologies together and integrated them into one control station and we are testing its effectiveness in reasonable and realistic military scenarios, Dr Draper said.

Australia has led development of three of AIMs eight modules: the Recommender, which uses artificial intelligence to analyse information and recommend actions to commanders; the Narrative, which automatically generates multimedia briefings about emerging operational situations; and DARRT, which enables real time test and evaluation of autonomous systems.

The Maritime Autonomous Platform Exploitation (MAPLE) system is a UK-led project providing the information architecture required to integrate a diverse mix of live unmanned systems into a common operating picture that is fed into the AIM Command and Control Station.

The sort of software co-development we are doing here is not usually done, UK Defence Scientist Dr Philip Smith said.

The evaluation team is using real time data logging to evaluate system performance, apply lessons learned and improve the software.

This is also giving us detailed diagnostics to determine where to focus effort for future development, he said.

Revolutionary potential

DSTs Professor Jason Scholz is optimistic about the potential for these technologies beyond AW18.

This activity has demonstrated what can be achieved when a spirit of cooperation, understanding and support exists between military personnel, scientists, engineers and industry.

Systems became more reliable as the exercise progressed with improvements made daily.

These highly disruptive technologies can potentially revolutionise how armed forces operate. The sort of cooperation weve seen at AW18 is vital for bringing these technologies into service.

It would be interesting to run a similar activity with these rapidly evolving technologies in two or three years, Professor Scholz said.

Lasting impact

Commander Hornsby, who has been the ADF lead for AW18 and is developing Navys autonomous systems strategy, says the activity has raised awareness among Australias Defence Force and defence industry.

The nearly 1000 visitors to AW18 gained fresh insights into the technologys current state of development and its potential to enhance capability.

As a huge continent occupied by a relatively small population with a mid-sized defence force by world standards, the force multiplier effect of autonomous systems is vital, which is why Australia is a leading developer.

The evaluations done at AW18 are also important internationally.

The world is watching AW18 closely because Australia offers the most challenging operating conditions for unmanned technologies. If they can make it here, they can make it anywhere, Commander Hornsby said.

Autonomous Warrior 2018 was a major demonstration and evaluation of the potential of robotic, autonomous and uninhabited systems, in support of Defence operations in coastal environments. It combined a dynamic exhibition, trials and exercising of in-service systems.

Australian industry contributed semi-autonomous vehicles for use in AW18 and developed data interfaces to enable control by Five Eyes systems. Contributing companies included Bluezone Group, Ocius, Defendtex, Australian Centre for Field Robotics, Silverton and Northrop Grumman. Vehicles were also contributed by Australian, NZ, US and UK government agencies.

In our discussion, Commander Hornsby noted that collaborative R and D and shared experiences was a key element of the Australian approach, but that Australia had unique operating conditions in the waters off of Australia, and systems that might work in other waters would not necessarily be successful in the much more challenging waters to be found in Northern and Western Australia, areas where the deployment of maritime remotes is a priority.

But one must remember that the maritime remote effort is a question of payloads and platforms. Not simply building platforms. Rear Admiral Mark Darrah, US Navy, made a comment about unmanned air systems which is equally applicable to maritime remotes: Many view UAS as a capability when in fact it should be viewed as a means of employing payloads to achieve particular capabilities.

His approach to maritime remotes is very much in the character of looking at different platforms, in terms of speed, range, endurance, and other performance parameters, measured up against the kind of payload these various platforms might be able to carry.

Calculations, of the payload/platform pairing and their potential impacts then needed to be measured up against the kind of mission which they are capable of performing. And in this sense, the matching of the payload/platform dyad to the mission or task, suggests prioritization for the Navy and the ADF in terms of putting in to operation the particular capability.

This also means that different allied navies might well have different views of their priority requirements, which could lead to very different timelines with regard to deployment of particular maritime remotes.

And if the sharing approach prevails, this could well provide the allied nations to provide cross-cutting capabilities when deployed together or provide acquisition and export opportunities for those allies with one another.

Commander Hornsby breaks out the missions for AUV and UUV employment in the following manner:

Home & Away operations

Pending combination, provides: Deterrence, Sea Control, Sea Denial, Power Projection or Force Protection

What this means is that different payload/platform combinations can work these different missions more or less effectively. And quite obviously, in working the concepts of operations for each mission or task which will include maritime remotes needs to shape an approach where their capabilities are properly included in that approach.

And in a 2016 briefing by Hornsby., he highlighted this point as follows:

But importantly, maritime remotes should not be looked at in isolation of the operation of the distributed force and how integratable data can be accumulated and communicated to allow for C2 which can shape effective concepts of operations.

This means that how maritime remotes are worked as an interactive grid is a key part of shaping an effective way ahead. And this allows for creative mix and matching of remotes with manned assets and the shaping of decision making at the tactical edge. Remotes and AI capabilities are not ends in of themselves; but are key parts of the reshaping of the C2/ISR capabilities which are reshaping the concepts of operations of the combat force.

In that 2016 briefing, Commander Hornsby provided an example of the kind of grid which maritime remotes enable:

To use an example in the European context, as the fourth battle of the Atlantic shapes up, if the allies can work cross-cutting maritime remote payload/platform capabilities and can operate those in the waters which the Russians intend to use to conduct their operations against NATO, then a new grid could be created which would have significant ISR data which could be communicated through UUV and USV grids to various parts of the 21st century integrated distributed combat force.

Such an approach is clearly crucial for Australia as it pushes out its defense perimeter but needs to enhance maritime security and defense of its ports and adjacent waters. And that defense will highlight a growing role for maritime remotes.

As Robert Slaven of L3Harris Technologies, a former member of the Royal Australian Navy, has put it:

The remotes can be distributed throughout the area of interest and be there significantly in advance of when we have to create a kinetic effect. In fact, they could be operating months or years in advance of shaping the decision of what kind of kinetic effect we would need in a crisis situation.

We need to learn how to work the machines to shape our understanding of the battlespace and to shape the kind of C2 which could direct the kind of kinetic or non-kinetic effect we are trying to achieve.

The featured photo showsHead of Royal Australian Navy Capability, Rear Admiral Peter Quinn, AM, CSC, RAN (right), Australian Defence Force personnel and industry partners watch the Defendtex Tempest Unmanned Aerial Vehicle display during AUTONOMOUS WARRIOR 2018 at HMAS Creswell.

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Manned-Unmanned Teaming: Shaping Future Capabilities

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Shaping an Australian Navy Approach to Maritime Remotes, Artificial Intelligence and Combat Grids - Second Line of Defense