AI News, The promise and peril of military applications of artificial intelligence ... artificial intelligence

The Promise and the Peril of Artificial Intelligence and Robotics

Oregon State’s Open Source Lab was instrumental in launching and hosting the Mozilla Firefox 1.0 internet browser.

Oregon State was the second GRAMMY Museum affiliate university in the nation, giving students and faculty access to its extensive archives and music industry resources.

In 1946, Oregon State engineering professor Fred Merryfield and three of his former students founded CH2M, which today one of the world’s leading engineering firms.

Oregon State organized a women’s basketball team in 1898 — three years before a¬¬ men’s team was established.

The 1933 Oregon State football team, known as The Ironmen, ended the 26-game winning streak of reigning champion University of Southern California with a 0-0 tie — and used no substitutes.

new strain of a succulent red marine algae called dulse developed at Oregon State’s Hatfield Marine Science Center in Newport has an unusual trait when cooked — it tastes like bacon.

Lady MacDuff, a white leghorn chicken in Oregon State’s poultry program, made international news by laying a record-breaking 303 eggs in 1913.

The Extension Service was established in 1911 to extend knowledge from Oregon’s land grant university to the rest of the state, three years before the nationwide program began.

Research by Oregon State entomologist George Poinar inspired Jurassic Park author Michael Crichton by providing a plausible scientific basis for a fictional story about obtaining dinosaur DNA preserved in amber.

Extension Home Economics programs date back to 1914, and that tradition continues today with OSU Extension Service resources like Food Hero — a go-to site for quick, tasty, healthy recipes.

Glenn Odekirk, a 1927 engineering alumnus, served as Howard Hughes’ right-hand man for many years, contributing to the design of the innovative Hughes H-1 Racer and the Spruce Goose, which is now displayed the Evergreen Aviation & Space Museum in McMinnville, Oregon.

Bruce Mate, director of the Oregon State Marine Mammal Institute, pioneered the use of satellite-monitored tags to track threatened and endangered whales, allowing discoveries about their migration routes, habitats and behaviors.

For 40 years, he and his students and colleagues proved the formulas, which contributed to real-world applications in nuclear reaction and heat conduction research.

Founded in 1908, the OSU Folk Club promotes friendship and service to the community, providing more than $100,000 in grants to non-profits and scholarships to OSU students each year.

Oregon State researchers turned a fashion accessory into a pollution detector — using silicon wristbands that absorb molecules of organic chemicals people are exposed to in the environment.

Milton Harris, a 1926 chemistry alumnus, earned 35 patents over his career leading research teams that developed coated razor blades, flame- and radiation resistant textiles, wrinkle-free cotton, moth-proof wool, and the forerunner of a hairstyling staple, the perm.

Oregon State oceanographers have helped the West Coast oyster industry adapt to increasing ocean acidification, implementing a seawater monitoring and treatment system to stabilize the pH level in a hatchery’s tanks.

A-dec, a company created by 1953 engineering graduate Ken Austin, is considered the largest dental equipment maker in the U.S. and second largest in the world, known for multiple advances in the work environment for dentists, including giving them the ability to sit down while working through the development of oral suction.

His credits include “Monsters, Inc.,” “Ratatouille,” “Cars2,” “Up,” “Toy Story 2” and “The Hunt for Red October.” OSU scientists were the first to see a community of living creatures around deep-sea hydrothermal vents more than 40 years ago, expanding our vision of life on Earth.

Jensen Huang, a 1984 electrical engineering alumnus, is the founder, president and CEO of NVIDIA, a driving force in the gaming market and a world leader in developing chips for self-driving cars.

Linus Pauling, who earned a degree in chemical engineering in 1922, is the only person to have received two unshared Nobel Prizes — for chemistry in 1954 and peace in 1962.

In the 1920s, food scientist Ernest Wiegand perfected the maraschino cherry — one of many Oregon State contributions to the state’s $74 million sweet cherry industry.

Successful spinoff companies include faculty-led Agility Robotics, which is developing walking robots, and student-led Seiji’s Bridge, which makes therapy products for people with autism.

John Blankenbaker, a 1952 mathematics alumnus, is widely credited with developing the first commercially available personal computer in 1971, predating the Apple 1 by five years.

In 2017, veterinary specialists at the Lois Bates Acheson Veterinary Teaching Hospital treated more than 12,000 dogs, cats, horses, goats, pigs, llamas, cows, sheep and alpacas.

In 2007, OSU created a groundbreaking partnership with the Oregon Humane Society – the first of its kind in the nation – where fourth-year veterinary students spend four weeks helping to treat sick shelter animals and assisting with hundreds of spay and neuter surgeries.

When hazelnut orchards were threatened by a fatal disease, a team of OSU researchers developed new, resistant varieties that saved this regional industry.

It’s the first public university campus to open in Oregon in 50 years and one of only five universities in the U.S. to offer a fully-accredited program that combines mechanical, industrial, and electrical engineering into one multidisciplinary degree.

Project CROOS (Collaborative Research on Oregon Ocean Salmon) researchers team with local fishers to understand migration patterns for salmon species so the fishers can target healthy stock and avoid endangered ones.

The compound is in clinical trials to evaluate its safety, and patients and their families are cautiously optimistic that this might help extend the lives of people with Lou Gehrig’s disease.

Through testing enamel on a tooth, they determined it lived 12,000 years ago, meaning mammoths lived alongside humans in Oregon longer than previously thought.

As the dirty water flows through the cells, tiny microbes break down organic matter, cleaning the water and releasing energy.

OSU food scientists have identified a sixth flavor, the flavor of starch, that helps us seek out foods that release energy slowly.

OSU assistant professor Sam Logan is a leader in the national Go Baby Go program, which developed modifications to relatively inexpensive toy cars that allow young children with mobility issues to move, play and socialize.

In 1889, Dr. Margaret Snell established a new program in household economics and hygiene that included lectures on preventative medicine, science-based nutrition and food safety.

OSU 1936 nutrition and food management graduate Mercedes Alison Bates was the first woman officer at General Mills, serving as vice president of the Betty Crocker division until she retired in 1983.

Oregon State chemist David (Xiulei) Ji is working with “coronene”, a pure hydrocarbon, to see if this unwanted pollutant could provide energy storage in addition to scavenging for pollution.

Enrollment at OSU for the 2017-2018 academic year is 31,904 – this includes 24,760 students at the main campus in Corvallis, 6,087 in Ecampus (online), and 1,204 at OSU-Cascades.

The Linus Pauling Science Center is home to the OSU Electron Microscopy Facility which provides faculty, staff, students and international collaborators access to electron microscopes so they can visualize the structure, composition and arrangement of atoms.

OSU helped develop a way to process low-value fish into surimi products that can be processed to imitate expensive seafood, such as imitation crabmeat.

OSU professor “Deepsea Dawn” Wright has traveled two miles below the sea using an ALVIN submersible where she studies volcanic mountain ranges and hydrothermal vents.

Two of OSU’s early African American graduates have residence halls named after them – 1926 commerce graduate Carrie Halsell and 1948 engineering graduate William Tebeau.

Oregon State University has been ranked among the top three universities in the world in forestry and oceanography on the basis of the number of research articles published in top-tier scientific journals.

Starker, a graduate from the first forestry class in 1910, became a professor of forestry and founder of Starker Forests and helped establish OSU as one of the world’s elite forestry schools.

Professor Kaichang Li developed a new adhesive inspired by the rock-holding power of mussels that was adopted by about 60 percent of the plywood and veneer industry.

Wargaming with Athena: How to Make Militaries Smarter, Faster, and More Efficient with Artificial Intelligence

At a recent AI conference, former U.S. Deputy Secretary of Defense Robert Work stated, “I am starting to believe very, very deeply that it is also going to change the nature of war.” In a September 2017 televised speech, Russian President Vladimir Putin predicted that the first nation to develop true AI will rule the world.

The potential of AI lies less in smarter missiles than in augmented battle networks and organizations combining human creativity with AI applications to produce new concepts of operation, tactics, and command relationships.

Our wargame, Athena, offers a way to build up a repository of data for future testing, enhance understanding of how AI can assist with training, red-teaming, and simulation, and highlight the limits of these capabilities as they interact with humans in uncertain environments.

There is a virtual dimension, as illustrated by the way AI helps monitor financial transactions from fraudulent activity, as well as a physical dimension, such as how Amazon predicts customer demands and intends to use this information in the future to ship goods before you order them.

Advances in hardware and software – including the revival of the mid-20th century concept of neural networks, techniques like reinforcement learning, and big data – create new possibilities.

As the planners develop courses of action, software agents could run simulations on whether these options are logistically feasible given theater supply levels, historical ammunition expenditure rates, and estimated losses.

Applied to military education, military personnel could one day have their own tailored AI application that understands their analytical blind spots and risk profile, and even adjusts the background colors and language used in exams and wargames to account for the student’s strengths and weaknesses.

The Defense Department can use unclassified gaming suites to let military personnel fight formations from the squad to the coalition joint task force in contemporary scenarios as a means of teaching doctrine, tactics, and enemy order of battle.

If war truly is a “dynamic process of human competition requiring both the knowledge of science and the creativity of art,” the U.S. military needs to identify those individuals and teams best able to apply operational judgement.

Athena: Building a Test Bed for AI and Decision-Making “Where’s our Ender’s Game battle lab…where we cannot just give our leadership reps, but we can actually find out who the really good leaders are?” -General Robert Neller, USMC Imagine logging onto a wargame named Athena to practice planning an air assault mission to seize blocking positions in support of an amphibious landing.

As you play the game, an AI application captures the data and compares your use of cover and intersecting fields of fire, among other factors, to rate your performance while contributing to a larger database of how U.S. military professionals fight.

For example, as part of the wargaming tournament the team put together, Sea Dragon 3.0, Jensen and Colonel Timothy Barrick introduced scenarios reflecting the Marine Corps Operating Concept and gave players a chance to fight against a contemporary Russian order of battle.

As more players plan and execute missions while interacting with Alexa-like interfaces – think Tony Stark’s J.A.R.V.I.S for war –  we will build a corpus of data that illustrates our biases and risk tolerances.

An obvious recent example of these shortcomings was Google’s image recognition system debacle, in which photos of African-American individuals were misidentified and placed into an album titled “Gorillas.” Here, machine learning was applied in such a way that the output reflected underlying societal pathologies rather than the intended smart process.

The short answer is that militaries across history have cultivated depth and flexibility through training and education, often grounded in wargaming, that ensures key functions are routed based on expertise.

Even after decades of subsequent scientific development, Turing’s insights still effectively describe the modern AI field: Algorithms can be trained to mimic and predict human behaviors based on an underlying deconstruction of variables and knowledge of real-world conditions, but they occasionally make serious mistakes and have a ceiling on their performance.

AI systems can effectively reproduce basic human skills, operate given a particular understanding of complex rules, and help military personnel develop knowledge toolkits that empower their judgment.

His research interests include a range of international security topics related to the use of information technology in war and peace, political communication and cybersecurity doctrine/policy. The views expressed belong singularly to the authors and do not reflect government policy or the will of Skynet.

The Promise and Peril of AI – Hash it Out with Tech Entrepreneur Lars Perkins

The past episode we talked about machine learning specifically and we spoke to our fellow producer Cameron Hickey who together with Miles O’Brien put together this series for the PBS NewsHour about junk news and how it proliferates on social media, on the internet in general, and the issues around that.

And when we previously asked for topics to cover, we had Steven Gammon on Twitter who has since disappeared so we can’t read his tweet, but who asked us to look into AI algorithms and try to explain what is going on and why they’re important Brian Truglio: It would be funny if Steven turned out to be an AI algorithm.

And if you want to hear about machine learning, which is how we divided we did in machine learning first and now we’re going to do AI generally and the issues and interests applications surrounding AI, but if you listen to that and listen to the previous episode.

Fedor Kossakovski: Basically machine learning and AI right now often you hear them interchangeably as terms when people write about it or you hear them on the radio or in the news or whatever but really machine learning is a subset of artificial intelligence or AI.

Machine learning is just one of several flavors of approaching how computer programs can rewrite themselves and learn from past experience with usually the direction of humans which I think is going to be a crucial part we’re going to get into.

This is hard to understand in the abstract but when you start thinking about it specifically one of the biggest machine learning algorithms that we all interact with on a daily basis is the Facebook machine learning programs that figure out what to show you–the News Feed–which is what we covered in the previous episode.

And I think there’s a real question to be asked about is this because of a bias that is encoded by the mostly I would assume liberal coders that are working for Facebook by that training data that we discussed last time of, you know, you give it something and you tell it what it is at the end–is it coming from that or is it from the algorithm itself?

It sets a dangerous precedent if we demand tech companies to be able to explain how their technologies work as it is entirely possible that the models underlying their algorithm are essentially indescribable in any meaningful way,”

So he said “Considering a machine learning model such as a neural net with 10 million parameters doesn’t lend itself to an easy explanation especially it’s not even clear what a satisfactory explanation would be.

I mean that’s one of the beautiful things about machine learning is it gives you the answer when you can’t necessarily always tell how it got there but it gives you something that seems to be working and works better than people can do or classify things at times, so it brings up an interesting correlation that weirdly we’re kind of encoding these biases sometimes into the systems.

However like in an auditing situation, people who work with machine learning who understand it at a deep level who understand the different techniques–we didn’t mention it last time but you know there’s basically five different schools of machine learning theory–somebody who understands this stuff at a higher level…

But also there’s just so much AI being used that it’s really hard for one entity or a group of people or whatever to track everything so I think it’s important for people the laypeople to understand how this kind of stuff works and there’s so many misconceptions.

And we thought that was super cool and whatnot but then you know came back a few days later with a Snopes article kind of debunking or putting that into perspective and I think you know kind of shows how misconstrued this AI application stuff is.

I’ll just give you a few details so in a report that was published the day before Musk gave his speech delivered a fascinating account of the it was called the FAIR team, which stands for Facebook’s Artificial Intelligence Research team, it gave an account of their experiment and basically it gives a little snippet of the language that they discovered between these two bots were called Bob and Alice.

And then more recently I’ve been involved with the venture fund here in Los Angeles and it’s just seeing an enormous number of businesses that are being enabled because of the advances in AI and then other businesses and even industries that are being transformed because of the breakthroughs really that have occurred over the last decade.

And so when we talk about machine learning and artificial intelligence I guess the first thing is we have to agree on our terms and artificial intelligence has two words and the more controversial of the two is intelligence in terms of trying to come up with an accepted definition.

So it’s hard to define but it manifests itself in sort of goal oriented behavior that’s influenced by stimulus from many different directions you know sort of achieving objectives that are more complicated than just flying towards a light but ranging from flying towards a light to flying towards that big white disk in the sky which we call the Moon.

So that’s intelligence and then the artificial part I think is less controversial and we could think of it as basically for purposes of this conversation as being manmade and I think mostly we’re concerned with kind of silicon based contraptions here even though there have been attempts at artificially intelligent things over the years that are purely mechanical and some speculation is how we might use substrates other than silicon going forward to create the types of intelligence.

So if we step back from machine learning as a technique prior to machine learning techniques that have burst onto the scene as being practically possible over the past ten years we have sort of more techniques that are based more algorithmically more on algorithms and probably the best example that I could think of would be chess games.

Computers playing chess have been around for decades but before machine learning they were based largely on trying to codify the knowledge that human chess players have into a set of rules that a computer could execute.

So we sort of say let’s create a way of sort of evaluating what the relative advantage of the two players is based on the position on the boards and then OK if I make this move do I have a superior position and okay if I make this move maybe the other person will probably make this move.

Machine learning actually means creating sort of a meta algorithm that allows the computer to learn from data and create its own set of rules for how to behave based on the interpretation of the data and the understanding of the the objective, the goal that we’ve set for the system.

And it seems like one of the big recent booms of technology that is hardware wise allowed this to accelerate is GPUs versus CPUs right, using graphics cards to handle large amounts of data and you can really run machine learning algorithms on graphics cards.

And that kind of processing that kind of learning where you’re extracting features from the images and then correlating those features with a particular object requires an enormous amount of processing power.

It has fur and it has whiskers and trying to create an ever more detailed description of what a cat is, we show the machine learning system–and in this case I’m describing a deep learning system which is a subset of machine learning–lots and lots of pictures of cats and we let it figure it out.

Now in more recent years GPUs as you talked about, the graphic processing units that have been developed in order to do parallel processing to enable virtual reality and ever more realistic videogames which require a similar type of simultaneous or parallel computing, have evolved and those have become a way that we can now have that kind of massively parallel computing capability with 2000 cores on my 1080i on my computer that’s underneath my desk.

So when I say deep learning were largely talking about what’s called CNNs or convolutional neural networks which take information that consists of lots of data points and synthesises rules for understanding that data sort of on its own.

Another type of machine learning that’s not deep learning necessarily would be it’s called a support vector machine where we might take a dataset that has a finite number of characteristics.

So essentially we’re let’s say hypothetically we’re creating oh I don’t know 20 or to 50 to 100 different pieces of information about the heartbeat that we’re looking at or listening to.

Where is the image processing image recognition which needs the more advanced deep learning techniques, you never say, well you know what if the thirty seventh pixel on the fourth thousandth line is white, that’s probably a good indicator that it’s a cat.

You know with every aspect of a patient being recorded, you look at a vast population of patients, you look at their blood chemistries, and their EKGs, and diagnoses made by physicians using conventional techniques and treatment modalities and outcomes.

We think about self driving cars the ability to recognize an environment and respond intelligently to the environment is also enabled by these deep learning techniques in a way that just was not possible 50 or 60 years ago when we tried to use algorithmic non statistical techniques to guide behavior of autonomous vehicles.

I’m seeing kind of this at the periphery so I’m not involved in the you know the big initiatives that are out there I’m seeing some startups where we can deploy a relatively modest amount of capital for a particular vertical application like a heartbeat analysis or there’s a dozen other ones that are similar but I think there are larger initiatives but they’ve been mostly guided rounds of augmenting the skills of the physician rather than a complete sort of autonomous doctor who’s going to make and implement treatment decisions.

And over time you can imagine that would progress you know go upmarket so as to enable people with less training in less developed parts of the world to make better treatment decisions because theses treatment decisions are going to be augmented by these AIs.

I’m just like imagining you know this is just you know me just imagining now for example some system where you’re trying to determine how to allocate certain medicine better and based on the information that’s coming in maybe minority groups or underprivileged people have a less you know because of other circumstances have worse outcomes.

I’m just you know this is just an extreme example but I’m wondering if we do allow these computers to learn and set up their own parameters and then draw conclusions from it’s like a black box that we’ve, Brian and I’ve discussed before, is like a black box they put stuff into, you’re not quite sure exactly what is going on there, and then you get a result on the other side.

There’s a funny there’s another example that’s a lot funnier which is I think they looked at the decisions that were made by judges in Israel and determined, for comparable crimes, offenders sentence much more leniently if they were sentenced just after lunch.

And this is Nick Bostrom’s book Superintelligence you know talks about the hypothetical paperclip machine that if you were a paperclip business and you want to create a machine that is absolutely focused on creating paperclips at the lowest possible cost, you know don’t be surprised when it starts eating your atoms in order to make paperclips.

So I haven’t–there are people out there that have been far far deeper involved with the philosophical implications of this and I’m sort of a very you know layperson bystander and don’t sort of have intimate knowledge as to how these issues are being dealt with at the societal level.

Brian Truglio: Is there a way to kind of audit these systems or create test facilities or or something some way that we could create an independent way to put AI under extreme conditions and test results?

I would say we’re going to have you know, there’ll be a few more self driving cars running over people before society really takes a hard look at how to keep this genie in an appropriately sized box.

But I do think you know autonomous vehicles are likely to become on of the first test cases for how closely this type of technology needs to be regulated quality control tested et cetera.

And the one point that I’d like to make which I believe I first heard from Tristan Harris on a podcast, so it’s not original, but for decades we have science fiction novels about the robot uprisings where robots become sentient and the alignment goes wrong and they take control whether that’s Terminator or the Matrix you know or the Asimov “I, Robot”

So Facebook unleashed a machine learning algorithm working at, according to the exact principles that I talked about and set the goal to be attention and engagement with only the most minor rules like no pornography or graphic violence otherwise it would have optimized for those things.

And so we ought to be worried about this a whole lot more than we are and we ought to be worrying about it not in the context necessarily of what happens when we create a machine that’s sentient or conscious or has the ability to prevent us from unplugging it but rather what about these artificially intelligent algorithms that are kind of sneaking their way into our lives in a way that appears innocuous but who may have goals that are not purely aligned with our goals as a species.

And I think you know the whole Facebook fake news uproar kind of brings that into the public consciousness at some level which is good and also getting a high profile essentially celebrities like Elon talking about the existential threat that this may represent is also a good thing, but it’s got to work its way into curricula.

I think that’s something that people, they’re so self centered and think humans are so unique in that they have intelligence but other animals are intelligent also in different ways and in certain ways machines and you know artificial intelligence at some level is already here and it’s just going to be growing and growing and growing and we are and need to be having those conversations now instead of when suddenly we have that conscious quote unquote conscious machine suddenly when it’s there.

You know so much of what we do in terms of the way that we practice medicine now is well we’ll look back on it in maybe not ten years but in 50 years or 100 years, we’ll think we were basically you know the barbers you know.

And when you can get into treatment plans and medication plans that are a function or influenced by your genetic makeup or what other other characteristics it is that we can discover from looking at the data, I think we have the chance to transform the way that we deliver health care, hopefully lower costs and improve outcomes for everyone.

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