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

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

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

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

Hyperautomation, 2020: A Rule-Based Artificial Intelligence – ResearchAndMarkets.com – Business Wire

DUBLIN--(BUSINESS WIRE)--The "Hyperautomation: A Rule-Based Artificial Intelligence" report has been added to ResearchAndMarkets.com's offering.

The Report Includes:

This key objective of this study is to provide an introductory analysis of the hyperautomation market and its enormous market potential. Hyperautomation, as defined in the report, is the application of technologies such as robotic process automation, artificial intelligence, cognitive process automation, and others to augment workers and automate processes so that they can have a significantly greater impact than traditional automation technologies.

The goal of this report is to provide an up-to-date analysis of the recent developments and current trends in the global hyperautomation market. In this study, the hyperautomation market is extensively defined and mapped out with a strong focus on the current market's competitive situation.

Key Topics Covered:

Chapter 1 Hyperautomation

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/kvet0f

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Hyperautomation, 2020: A Rule-Based Artificial Intelligence - ResearchAndMarkets.com - Business Wire

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

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

Artificial intelligence in the real estate industry – Robotics and Automation News

While it was once considered an advanced technology of the future, artificial intelligence is very much a present-day reality.

Thanks to inventions like self-driving cars, home assistant devices, automatic vacuum cleaners and remote home security solutions, Artificial Intelligence is on everyones lips.

Since AI seems to affect both the public and private sector, we started thinking about all the different ways in which itll shake up the real estate world.

Read on to find out more about the current and future impact of AI in property sales, marketing and operations.

What is Artificial Intelligence?

Artificial Intelligence, or AI for short, refers to smart technological tools whose level of awareness allows them to learn from their environment in order to improve processes and decision-making.

AI tools can learn, plan, comprehend, and self-correct independently, thus saving time and resources. AI is typically separated into three different classes, namely:

Using these AI solutions in the real estate industry can help to fast-track decision making and improve operational efficiency.

Thats because these programs make it easier to identify and examine patterns and make connections between different components of large data sets.

Relevant Application of AI in Real Estate

AI brings innovation wherever its applied. Thats because AI tools are hardwired to self-optimize based on real-time data.

This makes them ideal for improving and streamlining complex processes. AI tools can help to improve efficiency for all the different stakeholders in the real estate industry, from investors, asset managers, brokers and sellers.

Moreover, AI solutions often lead to cost-efficiency as it becomes irrelevant for businesses to hire staff when they can just automate most of their operations. For instance, AI is pegged to automate real estate management, thus eliminating the need for large property management teams.

How AI Affects Real Estate Processes

Currently, information management is the main application of AI in the real estate industry. In this capacity, AI tools can be used to collect data on an entire property portfolio or an individual asset.

This creates a virtual data room that can be used to study documents, translate international real estate transactions in real time, and validate parameters.

However, AI is also useful at analyzing functions related to system control and monitoring, security and fire protection, especially when it comes to managing entire building structure.

Individual homeowners can also use AI to automate security, lighting and cooling systems. In fact, the machine learning processes imbedded in a lot of AI-powered Internet of Things devices allows for loads of energy and cost-saving potential.

AI Benefits for real estate agents and clients

AI can help to speed up real estate transactions by cutting in half the manual input and time required to complete them. AI also holds the promise of increased efficiency when it comes to marketing, due diligence and sales processes.

Commercial property operators stand to benefit the most from AI tools since they can be used to process massive data sets in half the time usually required.

With the right programming, AI can help you spot the potential pitfalls and advantages of a particular transaction without manually sifting through mountains of documents.

Real estate agents can leverage AI tools like chat bots to communicate with clients and analyze the facts from different perspectives, including property values.

Challenges of AI for the Real Estate Sector

One of the main challenges when it comes to AI in real estate is the fact that specialists are required to enable many of the automatic features. Keep in mind that AI tools can only work efficiently when independent learning has been enabled.

Its also important to ensure that you utilize AI tools in compliance with legal regulations while keeping in mind security repercussions.

With all their benefits, AI tools cannot make final decisions on anything. Their job is to improve data collection, organization and presentation methods in order to facilitate better decision making.

To get the most out of AI, real estate operators would have to enable better collaboration between human capability and AI software algorithms.

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Artificial intelligence requires trusted data, and a healthy DataOps ecosystem – ZDNet

Lately, we've seen many "x-Ops" management practices appear on the scene, all derivatives from DevOps, which seeks to coordinate the output of developers and operations teams into a smooth, consistent and rapid flow of software releases. Another emerging practice, DataOps, seeks to achieve a similarly smooth, consistent and rapid flow of data through enterprises. Like many things these days, DataOps is spilling over from the large Internet companies, who process petabytes and exabytes of information on a daily basis.

Such an uninhibited data flow is increasingly vital to enterprises seeking to become more data-driven and scale artificial intelligence and machine learning to the point where these technologies can have strategic impact.

Awareness of DataOps is high. A recent survey of 300 companies by 451 Research finds 72 percent have active DataOps efforts underway, and the remaining 28 percent are planning to do so over the coming year. A majority, 86 percent, are increasing their spend on DataOps projects to over the next 12 months. Most of this spending will go to analytics, self-service data access, data virtualization, and data preparation efforts.

In the report, 451 Research analyst Matt Aslett defines DataOps as "The alignment of people, processes and technology to enable more agile and automated approaches to data management."

The catch is "most enterprises are unprepared, often because of behavioral norms -- like territorial data hoarding -- and because they lag in their technical capabilities -- often stuck with cumbersome extract, transform, and load (ETL) and master data management (MDM) systems," according to Andy Palmer and a team of co-authors in their latest report,Getting DataOps Right, published by O'Reilly. Across most enterprises, data is siloed, disconnected, and generally inaccessible. There is also an abundance of data that is completely undiscovered, of which decision-makers are not even aware.

Here are some of Palmer's recommendations for building and shaping a well-functioning DataOps ecosystem:

Keep it open: The ecosystem in DataOps should resemble DevOps ecosystems in which there are many best-of-breed free and open source software and proprietary tools that are expected to interoperate via APIs." This also includes carefully evaluating and selecting from the raft of tools that have been developed by the large internet companies.

Automate it all:The collection, ingestion, organizing, storage and surfacing of massive amounts of data at as close to a near-real-time pace as possible has become almost impossible for humans to manage. Let the machines do it, Palmer urges. Areas ripe for automaton include "operations, repeatability, automated testing, and release of data." Look to the ways DevOps is facilitating the automation of the software build, test, and release process, he points out.

Process data in both batch and streaming modes. While DataOps is about real-time delivery of data, there's still a place -- and reason -- for batch mode as well. "The success of Kafka and similar design patterns has validated that a healthy next-generation data ecosystem includes the ability to simultaneously process data from source to consumption in both batch and streaming modes," Palmer points out.

Track data lineage: Trust in the data is the single most important element in a data-driven enterprise, and it simply may cease to function without it. That's why well-thought-out data governance and a metadata (data about data) layer is important. "A focus on data lineage and processing tracking across the data ecosystem results in reproducibility going up and confidence in data increasing," says Palmer.

Have layered interfaces. Everyone touches data in different ways. "Some power users need to access data in its raw form, whereas others just want to get responses to inquiries that are well formulated," Palmer says. That's why a layered set of services and design patterns is required for the different personas of users. Palmer says there are three approaches to meeting these multilayered requirements:

Business leaders are increasingly leaning on their technology leaders and teams to transform their organizations into data-driven digital entities that can react to events and opportunities almost instantaneously. The best way to accomplish this -- especially with the meager budgets and limited support that gets thrown out with this mandate -- is to align the way data flows from source to storage.

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Artificial intelligence requires trusted data, and a healthy DataOps ecosystem - ZDNet

MC Hammer Talks Music, Cheetos, and Artificial Intelligence – Cheddar

MC Hammer literally cant touch this. This Sunday, the hip hop legend will appear in a Super Bowl ad spot for Cheetos that plays on the snack foods famous messiness.

The commercial features a man with cheese-dusted fingers avoiding chores like moving a couch or doing paperwork to the tune of Hammers hit single U Cant Touch This.

Hammer himself pops up throughout the 30-second spot to sing his famous hook, including once as a mustachioed, sunglasses-wearing baby that the protagonist refuses to hold.

Cheetos

Given that this is the first Super Bowl spot for Cheetos in 11 years, the Frito-Lay-owned brand appears to have taken the go-big-or-go-home approach to Super Bowl marketing. No mere hired gun, Hammer waxed poetic to Cheddar about the beautiful bond between music and Cheetos.

I think Cheetos and music go hand in hand because all of us as kids ate Cheetos, he said. You take the memory of food, the memory of music, combine them together, and you got something great.

He also commented on the enduring appeal of U Cant Touch This, which marked its 30th anniversary this year.

It lives on because it still stimulates. Kids see it for the first time right now, and they start dancing. Its a special gift thats built inside the song, he said.

When asked if the music industry would continue to produce lasting hits like his own, Hammer expressed confidence that it would. How they produce them is the question, he said.

We dont know the platform. We dont know if its a new AI algorithm that drives engagement and the sharing of music. We dont know if its all going to be by suggestion, Hammer said.

The most likely scenario, according to the rapper, is that artificial intelligence will produce the hit singles of the future.

If machines can learn from machines, they could easily learn how to compose, Hammer said. Theyre already doing it, but its going to get better and better. In the near future, youll have songs that are completely produced by AI.

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MC Hammer Talks Music, Cheetos, and Artificial Intelligence - Cheddar

Why being data-centric is the first step to success with artificial intelligence – Tech Wire Asia

Being successful in deploying AI must start with a data-centric mindset. Source: Shutterstock.

REGARDLESS of industry, artificial intelligence (AI) is a disruptive technology that is greatly sought after.

Many organizations are looking to deploy AI projects at scale, in hopes of boosting performance and ultimately increasing revenues.

However, many fail to see returns on their AI investments. Often, this is because AI projects are not approached in the right manner.

To be AI-first, organizations need to adopt a data-first mindset. Heres how and why:

Using the right methodologies and technologies is crucial for the successful deployment of AI solutions.

It is not enough to just rely on agile methods, as they focus heavily on functionality and application logic delivery. Instead, data-centric methodologies such as the Cross Industry Standard Process for Data Mining (CRISP-DM) should be used, as they concentrate on the steps needed for a successful data project.

Depending on organizational needs, a hybrid methodology can also be deployed by merging the non-agile CRISP-DM with agile methodologies, making it more relevant.

Data-centric methodologies must be followed by the use of data-centric technologies. For any AI projects, organizations must always keep the end in mind, and have clarity on what the desired outcomes are.

Methodology and technology will not be of use without a data-proficient team.

There must be a specialized AI-team in place that can effectively collect, compile, and extract key information from seemingly haphazard data sets.

Ideally, the team should have a good mix of data scientists, engineers, and specialists that possess the skills to put models into operation.

There is no room for guesswork in AI deployment randomly changing data sets wastes unnecessary time and resources and is simply disastrous.

For a successful AI project to materialize, organizations ought to continuously invest for the long term.

Staying complacent is not an option. They must seek to refine the methodologies in place. If the technologies used are no longer relevant, they should be replaced.

AI projects will not work if employees lack the skills and tools needed to deploy them. Thus, employees should be upskilled, and also made to understand the value of AI, and how it can augment the work that they do.

While the technology is still in its infancy for large scale projects, it is only a matter of time before AI is deployed at scale, across organizations and markets.

Ultimately, it all boils down to agility and resilience in the midst of change. Those that adopt the right mindset will succeed, those that resist the change will suffer.

Emily Wong

Emily is a tech writer constantly on the lookout for cool technology and writes about how small and medium enterprises can leverage it. She also reads, runs, and dreams of writing in a mountain cabin.

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Why being data-centric is the first step to success with artificial intelligence - Tech Wire Asia

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

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

Microsoft launches $40 million artificial intelligence initiative to advance global health research – seattlepi.com

Microsoft campus in Redmond.

Microsoft campus in Redmond.

Photo: Xinhua News Agency/Xinhua News Agency Via Getty Ima

Microsoft campus in Redmond.

Microsoft campus in Redmond.

Microsoft launches $40 million artificial intelligence initiative to advance global health research

Microsoft announced Wednesday that its newest $40 million investment in artificial intelligence (AI) will help advance global health initiatives, with two cash grants going to medical research at Seattle-based organizations.

As part of the tech giant's $165 million AI for Good initiative, this new public health branch will focus on three main areas: accelerating medical research around prevention and diagnosis of diseases, generating new insights about morality and global health crises, and improving health equity by increasing access to care for under-served populations.

"As a tech company, it is our responsibility to ensure that organizations working on the most pressing societal issues have access to our latest AI technology and the expertise of our technical talent," wrote John Kahan, Chief Data Analytics Officer at Microsoft in a company blog. "Through AI for Health, we will support specific nonprofits and academic collaboration with Microsofts leading data scientists, access to best-in-class AI tools and cloud computing, and select cash grants."

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One of the grants will go to Seattle Children's Hospital to continue their research on the causes and diagnosis of Sudden Infant Death Syndrome (SIDS). The Centers for Disease Control and Prevention estimated that 3,600 infants died in 2017 alone from SIDS.

Microsoft data scientists have already been working with researchers at Seattle Children's Hospital and discovered a correlation between maternal smoking and the fatal disease, estimating that 22 percent of the deaths from SIDS are attributed to smoking.

This research is personal for Kahan, who lost a son to SIDS.

"I saw firsthand, both personally and professionally, how you can marry artificial intelligence and medical research to advance this field, said Kahan in the program's launch event on Jan. 29. I saw because I lost my first son, and only son, to SIDS and I saw our head of data science partner with leading medical experts at Seattle Childrens and research institutes around the world."

Another grant will go towards Fred Hutchinson Cancer Research Center's Cascadia Data Discovery Initiative, which aims to accelerate cancer research by creating a data-sharing system for instructions and researchers across the Pacific Northwest to share biomedical data.

Other grants will benefit the Novartis Foundation for efforts to eliminate leprosy and Intelligent Retinal Imaging Systems to distribute diabetic retinopathy diagnostic software to prevent blindness.

These grants come as AI's rapidly growing role across industries is being debated by professionals, especially in medicine. Microsoft stated that less than 5% of AI professionals are operating in the health and nonprofit sector, leaving medical researchers with a shortage of talent and knowledge in the field.

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Technological innovations in AI are also moving faster than most doctors can prepare for. A recent study by Stanford Medicine found that only 7% of the 523 U.S. physicians surveyed thought they were "very prepared" to implement AI into their practice. The study called this a "transformation gap," citing that while most medical professionals can perceive the benefits of this technology for their patients, few feel prepared to adequately utilize it.

"Tomorrows clinicians not only need to be prepared to use AI, but they must also be ready to shape the technologys future development," the study states.

Other efforts in Microsoft's AI for Good initiative include AI for Earth, AI for Accessibility, AI for Cultural Heritage and AI for Humanitarian Action.

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Microsoft launches $40 million artificial intelligence initiative to advance global health research - seattlepi.com