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[AI] is going to change the world more than anything in the history of mankind. More than electricity. AI oracle and venture capitalist Dr. Kai-Fu Lee, 2018

In a nondescript building close to downtown Chicago, Marc Gyongyosi and the small but growing crew of IFM/Onetrack.AI have one rule that rules them all: think simple. The words are written in simple font on a simple sheet of paper thats stuck to a rear upstairs wall of their industrial two-story workspace. What theyre doing here with artificial intelligence, however, isnt simple at all.

Sitting at his cluttered desk, located near an oft-used ping-pong table and prototypes of drones from his college days suspended overhead, Gyongyosi punches some keys on a laptop to pull up grainy video footage of a forklift driver operating his vehicle in a warehouse. It was captured from overhead courtesy of a Onetrack.AI forklift vision system.

Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future.

Employing machine learning and computer vision for detection and classification of various safety events, the shoebox-sized device doesnt see all, but it sees plenty. Like which way the driver is looking as he operates the vehicle, how fast hes driving, where hes driving, locations of the people around him and how other forklift operators are maneuvering their vehicles. IFMs software automatically detects safety violations (for example, cell phone use) and notifies warehouse managers so they can take immediate action. The main goals are to prevent accidents and increase efficiency. The mere knowledge that one of IFMs devices is watching, Gyongyosi claims, has had a huge effect.

If you think about a camera, it really is the richest sensor available to us today at a very interesting price point, he says. Because of smartphones, camera and image sensors have become incredibly inexpensive, yet we capture a lot of information. From an image, we might be able to infer 25 signals today, but six months from now well be able to infer 100 or 150 signals from that same image. The only difference is the software thats looking at the image. And thats why this is so compelling, because we can offer a very important core feature set today, but then over time all our systems are learning from each other. Every customer is able to benefit from every other customer that we bring on board because our systems start to see and learn more processes and detect more things that are important and relevant.

IFM is just one of countless AI innovators in a field thats hotter than ever and getting more so all the time. Heres a good indicator: Of the 9,100 patents received by IBM inventors in 2018, 1,600 (or nearly 18 percent) were AI-related. Heres another: Tesla founder and tech titan Elon Musk recently donated $10 million to fund ongoing research at the non-profit research company OpenAI a mere drop in the proverbial bucket if his $1 billion co-pledge in 2015 is any indication. And in 2017, Russian president Vladimir Putin told school children that Whoever becomes the leader in this sphere [AI] will become the ruler of the world. He then tossed his head back and laughed maniacally.

OK, that last thing is false. This, however, is not: After more than seven decades marked by hoopla and sporadic dormancy during a multi-wave evolutionary period that began with so-called knowledge engineering, progressed to model- and algorithm-based machine learning and is increasingly focused on perception, reasoning and generalization, AI has re-taken center stage as never before. And it wont cede the spotlight anytime soon.

Theres virtually no major industry modern AI more specifically, narrow AI, which performs objective functions using data-trained models and often falls into the categories of deep learning or machine learning hasnt already affected. Thats especially true in the past few years, as data collection and analysis has ramped up considerably thanks to robust IoT connectivity, the proliferation of connected devices and ever-speedier computer processing.

Some sectors are at the start of their AI journey, others are veteran travelers. Both have a long way to go. Regardless, the impact artificial intelligence is having on our present day lives is hard to ignore:

But those advances (and numerous others, including this crop of new ones) are only the beginning; theres much more to come more than anyone, even the most prescient prognosticators, can fathom.

I think anybody making assumptions about the capabilities of intelligent software capping out at some point are mistaken, says David Vandegrift, CTO and co-founder of the customer relationship management firm 4Degrees.

With companies spending nearly $20 billion collective dollars on AI products and services annually, tech giants like Google, Apple, Microsoft and Amazon spending billions to create those products and services, universities making AI a more prominent part of their respective curricula (MIT alone is dropping $1 billion on a new college devoted solely to computing, with an AI focus), and the U.S. Department of Defense upping its AI game, big things are bound to happen. Some of those developments are well on their way to being fully realized; some are merely theoretical and might remain so. All are disruptive, for better and potentially worse, and theres no downturn in sight.

Lots of industries go through this pattern of winter, winter, and then an eternal spring, former Google Brain leader and Baidu chief scientist Andrew Ng told ZDNet late last year. We may be in the eternal spring of AI.

During a lecture last fall at Northwestern University, AI guru Kai-Fu Lee championed AI technology and its forthcoming impact while also noting its side effects and limitations. Of the former, he warned:

The bottom 90 percent, especially the bottom 50 percent of the world in terms of income or education, will be badly hurt with job displacementThe simple question to ask is, How routine is a job? And that is how likely [it is] a job will be replaced by AI, because AI can, within the routine task, learn to optimize itself. And the more quantitative, the more objective the job isseparating things into bins, washing dishes, picking fruits and answering customer service callsthose are very much scripted tasks that are repetitive and routine in nature. In the matter of five, 10 or 15 years, they will be displaced by AI.

In the warehouses of online giant and AI powerhouse Amazon, which buzz with more than 100,000 robots, picking and packing functions are still performed by humans but that will change.

Lees opinion was recently echoed by Infosys president Mohit Joshi, who at this years Davos gathering told the New York Times, People are looking to achieve very big numbers. Earlier they had incremental, 5 to 10 percent goals in reducing their workforce. Now theyre saying, Why cant we do it with 1 percent of the people we have?

On a more upbeat note, Lee stressed that todays AI is useless in two significant ways: it has no creativity and no capacity for compassion or love. Rather, its a tool to amplify human creativity. His solution? Those with jobs that involve repetitive or routine tasks must learn new skills so as not to be left by the wayside. Amazon even offers its employees money to train for jobs at other companies.

One of the absolute prerequisites for AI to be successful in many [areas] is that we invest tremendously in education to retrain people for new jobs, says Klara Nahrstedt, a computer science professor at the University of Illinois at UrbanaChampaign and director of the schools Coordinated Science Laboratory.

Shes concerned thats not happening widely or often enough. IFMs Gyongyosi is even more specific.

People need to learn about programming like they learn a new language, he says, and they need to do that as early as possible because it really is the future. In the future, if you dont know coding, you dont know programming, its only going to get more difficult.

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And while many of those who are forced out of jobs by technology will find new ones, Vandegrift says, that wont happen overnight. As with Americas transition from an agricultural to an industrial economy during the Industrial Revolution, which played a big role in causing the Great Depression, people eventually got back on their feet. The short-term impact, however, was massive.

The transition between jobs going away and new ones [emerging], Vandegrift says, is not necessarily as painless as people like to think.

"In the future, if you dont know coding, you dont know programming, its only going to get more difficult.

Mike Mendelson, a learner experience designer for NVIDIA, is a different kind of educator than Nahrstedt. He works with developers who want to learn more about AI and apply that knowledge to their businesses.

If they understand what the technology is capable of and they understand the domain very well, they start to make connections and say, Maybe this is an AI problem, maybe thats an AI problem, he says. Thats more often the case than I have a specific problem I want to solve.

In Mendelsons view, some of the most intriguing AI research and experimentation that will have near-future ramifications is happening in two areas: reinforcement learning, which deals in rewards and punishment rather than labeled data; and generative adversarial networks (GAN for short) that allow computer algorithms to create rather than merely assess by pitting two nets against each other. The former is exemplified by the Go-playing prowess of Google DeepMinds Alpha Go Zero, the latter by original image or audio generation thats based on learning about a certain subject like celebrities or a particular type of music.

On a far grander scale, AI is poised to have a major effect on sustainability, climate change and environmental issues. Ideally and partly through the use of sophisticated sensors, cities will become less congested, less polluted and generally more livable. Inroads are already being made.

Once you predict something, you can prescribe certain policies and rules, Nahrstedt says. Such as sensors on cars that send data about traffic conditions could predict potential problems and optimize the flow of cars. This is not yet perfected by any means, she says. Its just in its infancy. But years down the road, it will play a really big role.

Of course, much has been made of the fact that AIs reliance on big data is already impacting privacy in a major way. Look no further than Cambridge Analyticas Facebook shenanigans or Amazons Alexa eavesdropping, two among many examples of tech gone wild. Without proper regulations and self-imposed limitations, critics argue, the situation will get even worse. In 2015, Apple CEO Tim Cook derided competitors Google and Facebook (surprise!) for greed-driven data mining.

Theyre gobbling up everything they can learn about you and trying to monetize it, he said in a 2015 speech. We think thats wrong.

Last fall, during a talk in Brussels, Belgium, Cook expounded on his concern.

Advancing AI by collecting huge personal profiles is laziness, not efficiency," he said. For artificial intelligence to be truly smart, it must respect human values, including privacy. If we get this wrong, the dangers are profound."

If implemented responsibly, AI can benefit society. However, as is the case with most emerging technology, there is a real risk that commercial and state use has a detrimental impact on human rights."

Plenty of others agree. In a paper published recently by UK-based human rights and privacy groups Article 19 and Privacy International, anxiety about AI is reserved for its everyday functions rather than a cataclysmic shift like the advent of robot overlords.

If implemented responsibly, AI can benefit society, the authors write. However, as is the case with most emerging technology, there is a real risk that commercial and state use has a detrimental impact on human rights. In particular, applications of these technologies frequently rely on the generation, collection, processing, and sharing of large amounts of data, both about individual and collective behavior. This data can be used to profile individuals and predict future behavior. While some of these uses, like spam filters or suggested items for online shopping, may seem benign, others can have more serious repercussions and may even pose unprecedented threats to the right to privacy and the right to freedom of expression and information (freedom of expression). The use of AI can also impact the exercise of a number of other rights, including the right to an effective remedy, the right to a fair trial, and the right to freedom from discrimination.

Speaking at Londons Westminster Abbey in late November of 2018, internationally renowned AI expert Stuart Russell joked (or not) about his formal agreement with journalists that I wont talk to them unless they agree not to put a Terminator robot in the article. His quip revealed an obvious contempt for Hollywood representations of far-future AI, which tend toward the overwrought and apocalyptic. What Russell referred to as human-level AI, also known as artificial general intelligence, has long been fodder for fantasy. But the chances of its being realized anytime soon, or at all, are pretty slim. The machines almost certainly wont rise (sorry, Dr. Russell) during the lifetime of anyone reading this story.

There are still major breakthroughs that have to happen before we reach anything that resembles human-level AI, Russell explained. One example is the ability to really understand the content of language so we can translate between languages using machines When humans do machine translation, they understand the content and then express it. And right now machines are not very good at understanding the content of language. If that goal is reached, we would have systems that could then read and understand everything the human race has ever written, and this is something that a human being can't do... Once we have that capability, you could then query all of human knowledge and it would be able to synthesize and integrate and answer questions that no human being has ever been able to answer because they haven't read and been able to put together and join the dots between things that have remained separate throughout history.

Thats a mouthful. And a mind full. On the subject of which, emulating the human brain is exceedingly difficult and yet another reason for AGIs still-hypothetical future. Longtime University of Michigan engineering and computer science professor John Laird has conducted research in the field for several decades.

The goal has always been to try to build what we call the cognitive architecture, what we think is innate to an intelligence system, he says of work thats largely inspired by human psychology. One of the things we know, for example, is the human brain is not really just a homogenous set of neurons. Theres a real structure in terms of different components, some of which are associated with knowledge about how to do things in the world.

Thats called procedural memory. Then theres knowledge based on general facts, a.k.a. semantic memory, as well as knowledge about previous experiences (or personal facts) thats called episodic memory. One of the projects at Lairds lab involves using natural language instructions to teach a robot simple games like Tic-Tac-Toe and puzzles. Those instructions typically involve a description of the goal, a rundown of legal moves and failure situations. The robot internalizes those directives and uses them to plan its actions. As ever, though, breakthroughs are slow to come slower, anyway, than Laird and his fellow researchers would like.

Every time we make progress, he says, we also get a new appreciation for how hard it is.

More than a few leading AI figures subscribe (some more hyperbolically than others) to a nightmare scenario that involves whats known as singularity, whereby superintelligent machines take over and permanently alter human existence through enslavement or eradication.

The late theoretical physicist Stephen Hawking famously postulated that if AI itself begins designing better AI than human programmers, the result could be machines whose intelligence exceeds ours by more than ours exceeds that of snails. Elon Musk believes and has for years warned that AGI is humanitys biggest existential threat. Efforts to bring it about, he has said, are like summoning the demon. He has even expressed concern that his pal, Google co-founder and Alphabet CEO Larry Page, could accidentally shepherd something evil into existence despite his best intentions. Say, for example, a fleet of artificial intelligence-enhanced robots capable of destroying mankind. (Musk, you might know, has a flair for the dramatic.) Even IFMs Gyongyosi, no alarmist when it comes to AI predictions, rules nothing out. At some point, he says, humans will no longer need to train systems; theyll learn and evolve on their own.

I dont think the methods we use currently in these areas will lead to machines that decide to kill us, he says. I think that maybe five or ten years from now, Ill have to reevaluate that statement because well have different methods available and different ways to go about these things.

While murderous machines may well remain fodder for fiction, many believe theyll supplant humans in various ways.

Last spring, Oxford Universitys Future of Humanity Institute published the results of an AI survey. Titled When Will AI Exceed Human Performance? Evidence from AI Experts, it contains estimates from 352 machine learning researchers about AIs evolution in years to come. There were lots of optimists in this group. By 2026, a median number of respondents said, machines will be capable of writing school essays; by 2027 self-driving trucks will render drivers unnecessary; by 2031 AI will outperform humans in the retail sector; by 2049 AI could be the next Stephen King and by 2053 the next Charlie Teo. The slightly jarring capper: by 2137, all human jobs will be automated. But what of humans themselves? Sipping umbrella drinks served by droids, no doubt.

Diego Klabjan, a professor at Northwestern University and founding director of the schools Master of Science in Analytics program, counts himself an AGI skeptic.

Currently, computers can handle a little more than 10,000 words, he explains. So, a few million neurons. But human brains have billions of neurons that are connected in a very intriguing and complex way, and the current state-of-the-art [technology] is just straightforward connections following very easy patterns. So going from a few million neurons to billions of neurons with current hardware and software technologies I don't see that happening.

Klabjan also puts little stockin extreme scenarios the type involving, say, murderous cyborgs that turn the earth into asmoldering hellscape. Hes much more concerned with machines war robots, for instance being fed faulty incentives by nefarious humans. As MIT physics professors and leading AI researcher Max Tegmark put it in a 2018 TED Talk, The real threat from AI isnt malice, like in silly Hollywood movies, but competence AI accomplishing goals that just arent aligned with ours. Thats Lairds take, too.

I definitely dont see the scenario where something wakes up and decides it wants to take over the world, he says. I think thats science fiction and not the way its going to play out.

What Laird worries most about isnt evil AI, per se, but evil humans using AI as a sort of false force multiplier for things like bank robbery and credit card fraud, among many other crimes. And so, while hes often frustrated with the pace of progress, AIs slow burn may actually be a blessing.

Time to understand what were creating and how were going to incorporate it into society, Laird says, might be exactly what we need.

But no one knows for sure.

There are several major breakthroughs that have to occur, and those could come very quickly, Russell said during his Westminster talk. Referencing the rapid transformational effect of nuclear fission (atom splitting) by British physicist Ernest Rutherford in 1917, he added, Its very, very hard to predict when these conceptual breakthroughs are going to happen.

But whenever they do, if they do, he emphasized the importance of preparation. That means starting or continuing discussions about the ethical use of A.G.I. and whether it should be regulated. That means working to eliminate data bias, which has a corrupting effect on algorithms and is currently a fat fly in the AI ointment. That means working to invent and augment security measures capable of keeping the technology in check. And it means having the humility to realize that just because we can doesnt mean we should.

Our situation with technology is complicated, but the big picture is rather simple, Tegmark said during his TED Talk. Most AGI researchers expect AGI within decades, and if we just bumble into this unprepared, it will probably be the biggest mistake in human history. It could enable brutal global dictatorship with unprecedented inequality, surveillance, suffering and maybe even human extinction. But if we steer carefully, we could end up in a fantastic future where everybodys better offthe poor are richer, the rich are richer, everybodys healthy and free to live out their dreams.

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7 Ways An Artificial Intelligence Future Will Change The ...

Vendors of artificial intelligence technology should not be shielded by intellectual property claims and will have to disclose elements of their designs and be able to explain how their offering works in order to establish accountability, according to a leading official from the Cybersecurity and Infrastructure Security Agency.

I dont know how you can have a black-box algorithm thats proprietary and then be able to deploy it and be able to go off and explain whats going on, said Martin Stanley, a senior technical advisor who leads the development of CISAs artificial intelligence strategy. I think those things are going to have to be made available through some kind of scrutiny and certification around them so that those integrating them into other systems are going to be able to account for whats happening.

Stanley was among the speakers on a recent Nextgov and Defense One panel where government officials, including a member of the National Security Commission on Artificial Intelligence, shared some of the ways they are trying to balance reaping the benefits of artificial intelligence with risks the technology poses.

Experts often discuss the rewards of programming machines to do tasks humans would otherwise have to labor onfor both offensive and defensive cybersecurity maneuversbut the algorithms behind such systems and the data used to train them into taking such actions are also vulnerable to attack. And the question of accountability applies to users and developers of the technology.

Artificial intelligence systems are code that humans write, but they exercise their abilities and become stronger and more efficient using data that is fed to them. If the data is manipulated, or poisoned, the outcomes can be disastrous.

Changes to the data could be things that humans wouldnt necessarily recognize, but that computers do.

Weve seen ... trivial alterations that can throw off some of those results, just by changing a few pixels in an image in a way that a person might not even be able to tell, said Josephine Wolff, a Tufts University cybersecurity professor who was also on the panel.

And while its true that behind every AI algorithm is a human coder, the designs are becoming so complex, that youre looking at automated decision-making where the people who have designed the system are not actually fully in control of what the decisions will be, Wolff says.

This makes for a threat vector where vulnerabilities are harder to detect until its too late.

With AI, theres much more potential for vulnerabilities to stay covert than with other threat vectors, Wolff said. As models become increasingly complex it can take longer to realize that something is wrong before theres a dramatic outcome.

For this reason, Stanley said an overarching factor CISA uses to help determine what use cases AI gets applied to within the agency, is to assess the extent to which they offer high benefits and low regrets.

We pick ones that are understandable and have low complexity, he said.

Among other things federal personnel need to be mindful of is who has access to the training data.

You can imagine you get an award done, and everyone knows how hard that is from the beginning, and then the first thing that the vendor says is OK, send us all your data, hows that going to work so we can train the algorithm? he said. Those are the kinds of concerns that we have to be able to address.

Were going to have to continuously demonstrate that we are using the data for the purpose that it was intended, he said, adding, Theres some basic science that speaks to how you interact with algorithms and what kind of access you can have to the training data. Those kinds of things really need to be understood by the people who are deploying them.

A crucial but very difficult element to establish is liability. Wolff said ideally, liability wouldbe connected to a potential certification program where an entity audits artificial intelligence systems for factors like transparency and explainability.

Thats important, she said, for answering the question of how can we incentivize companies developing these algorithms to feel really heavily the weight of getting them right and be sure to do their own due diligence knowing that there are serious penalties for failing to secure them effectively.

But this is hard, even in the world of software development more broadly.

Making the connection is still very unresolved. Were still in the very early stages of determining what would a certification process look like, who would be in charge of issuing it, what kind of legal protection or immunity might you get if you went through it, she said. Software developers and companies have been working for a very long time, especially in the U.S., under the assumption that they cant be held legally liable for vulnerabilities in their code, and when we start talking about liability in the machine learning and AI context, we have to recognize that thats part of what were grappling with, an industry that for a very long time has had very strong protections from any liability.

View from the Commission

Responding to this, Katharina McFarland, a member of the National Security Commission on Artificial Intelligence, referenced the Pentagons Cybersecurity Maturity Model Certification program.

The point of the CMMC is to establish liability for Defense contractors, Defense Acquisitions Chief Information Security Officer Katie Arrington has said. But McFarland highlighted difficulties facing CMMC that program officials themselves have acknowledged.

Im sure youve heard of the [CMMC], theres a lot of thought going on, the question is the policing of it, she said. When you consider the proliferation of the code thats out there, and the global nature of it, you really will have a challenge trying to take a full thread and to pull it through a knothole to try to figure out where that responsibility is. Our borders are very porous and machines that we buy from another nation may not be built with the same biases that we have.

McFarland, a former head of Defense acquisitions, stressed that AI is more often than not viewed with fear and said she wanted to see more of a balance in procurement considerations for the technology.

I found that we had a perverse incentive built into our system and that was that we took, sometimes, I think extraordinary measures to try to creep into the one percent area for failure, she said, In other words, we would want to 110% test a system and in doing so, we might miss the venue of where its applicability in a theater to protect soldiers, sailors, airmen and Marines is needed.

She highlighted upfront a need for testing a verification but said it shouldnt be done at the expense of adoption. To that end, she asks that industry help by sharing the testing tools they use.

I would encourage industry to think about this from the standpoint of what tools would we needbecause theyre using themin the department, in the federal space, in the community, to give us transparency and verification, she said, so that we have a high confidence in the utility, in the data that were using and the AI algorithms that were building.

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Artificial Intelligence Systems Will Need to Have Certification, CISA Official Says - Nextgov

On 1st of July 2020, the Committee of Ministers decided, in line with paragraph 8 of the Resolution CM/Res(2011)24, to give Israel the observer status totheAd hoc Committee on Artificial Intelligence (CAHAI).

Israel will, as from now, fully contribute to the work of the CAHAI. Its participation expands the reach of the CAHAI,which already includes Canada, the Holy See, Japan, Mexico and the United States of America among its observers.

The CAHAI is currently examining the feasibility of a legal framework for the development, design and application of artificial intelligence, based on the Council of Europe standards on human rights, democracy and the rule of law.

The CAHAI's work will be the result of a unique and close co-operation between numerous stakeholders from various sectors ranging from member and non-member States, but also representatives of civil society, research and academia, and the private sector.

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Israel obtains the observer status to the Ad hoc Committee on Artificial Intelligence (CAHAI) - Council of Europe

In 2018, Congress established the National Security Commission on Artificial Intelligence (NSCAI)a temporary, independent body tasked with reviewing the national security implications of artificial intelligence (AI). But two years later, the commissions activities remain little known to the public. Critics have charged that the commission has conducted activities of interest to the public outside of the public eye, only acknowledging that meetings occurred after the fact and offering few details on evolving commission decision-making. As one commentator remarked, Companies or members of the public interested in learning how the Commission is studying AI are left only with the knowledge that appointed people met to discuss these very topics, did so, and are not yet releasing any information about their recommendations.

That perceived lack of transparency may soon change. In June, the U.S. District Court for the District of Columbia handed down its decision in Electronic Privacy Information Center v. National Security Commission on Artificial Intelligence, holding that Congress compelled the NSCAI to comply with the Federal Advisory Committee Act (FACA). Under FACA, the commission must hold open meetings and proactively provide records and other materials to the public. This decision follows a ruling from December 2019, holding that the NSCAI must also provide historical documents upon request under the Freedom of Information Act (FOIA). As a result of these decisions, the public is likely to gain increased access to and insight into the once-opaque operations of the commission.

Lawmakers established the NSCAI in the John S. McCain National Defense Authorization Act (NDAA) for fiscal 2019 1051, which tasked the commission with consider[ing] the methods and means necessary to advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the national security and defense needs of the United States. The commissions purview includes an array of issues related to the implications and uses of artificial intelligence and machine learning for national security and defense, including U.S. competitiveness and leadership, research and development, ethics, and data standards.

The NSCAI is currently chaired by Eric Schmidt, the former executive chairman of Googles parent company, Alphabet. The commissions 15 membersappointed by a combination of Congress, the secretary of defense and the secretary of commercereceive classified and unclassified briefings, meet in working groups and engage with industry. They report their findings and recommendations to the president and Congress, including in an annual report.

The Electronic Privacy Information Center (EPIC), a research center focused on privacy and civil liberties issues in the digital age, submitted a request to the NSCAI in September 2019, seeking access to upcoming meetings and records prepared by the commission under FACA and FOIA. In the six-month period prior to the request, the NSCAI held more than a dozen meetings and received more than 100 briefings, according to EPIC. At the time it filed the lawsuit, EPIC noted that the commissions first major report was also one month overdue for release. When the commission did not comply with the requests under FOIA and FACA, EPIC brought suit under the two laws.

EPICs complaint alleged that the NSCAI had conducted its operations opaquely in its short lifespan. Since its establishment, the commission has operated almost entirely in secret with meetings behind closed doors[,] and has failed to publish or disclose any notices, agendas, minutes, or materials. If Congress had intended the NSCAI to comply with FOIA and FACA, such activity would not satisfy the statutes requirements. Given the potential implications of federal artificial intelligence decisions for privacy, cybersecurity, human rights, and algorithmic bias, EPIC argued that [p]ublic access to the records and meetings of the AI Commission is vital to ensure government transparency and democratic accountability. The complaint also noted the potential ramifications of commission activities for the government, private sector, and public, as well as the importance of artificial intelligence safeguards in the national security context due to limited public oversight. According to EPIC, increasing public participation would permit greater input into the development of national AI policy by those whose privacy and data security could potentially be affected.

The U.S. District Court for the District of Columbia addressed EPICs FOIA claim in a December 2019 decision. FOIA requires agencies to disclose their records to a party upon request, barring exemptions (including for information classified to protect national security). EPIC alleged that the NSCAI failed to uphold its obligations under FOIA to process FOIA requests in a timely fashion; to process EPICs FOIA requests in an expedited manner, in accordance with EPICs claims of urgency; and to make available for public inspection and copying its records, reports, transcripts, minutes, appendixes, working papers, drafts, studies, agenda, or other documents. The commission, which at the time did not have a FOIA processing mechanism in place or other pending FOIA requests, argued that it was not an agency subject to FOIA.

The courts inquiry centered on whether the NSCAI is an agency under FOIA. Comparing the language establishing the NSCAI with FOIAs definition of agency, the court held that the NSCAI is subject to FOIA. In his decision, District Judge Trevor McFadden noted that Congress could have hardly been clearer. As a result, since that time, the commission has had to produce historical documents in response to FOIA requests.

FACA, by contrast, applies forward-looking requirements specifically to federal advisory committees. These mandates include requiring committees to open meetings to the public and announce them in the Federal Register, and to make reports, transcripts and other commission materials publicly available. The measures aim to inform the public about and invite public engagement with the committees that provide expertise to the executive branch. EPIC alleged that the NSCAI violated FACA by failing to hold open meetings and provide notice of them, and by failing to make records available to the public. EPIC sought mandamus relief pursuant to the alleged FACA violations.

In its June decision, the district court ruled that FACA applies to the NSCAI. The commission had filed a motion to dismiss the FACA claims, arguing that it could not be subject to both FOIA and FACA. Since the court had previously held the NSCAI to be an agency for purposes of FOIA, the commission reasoned that it could not simultaneously be an advisory committee under FACA. McFadden disagreed. Invoking the Roman God Januss two facesone forward-looking and the other backward-facinghe wrote, [L]ike Janus, the Commission does indeed have two faces, and ... Congress obligated it to comply with FACA as well as FOIA. The court could not identify a conflict between the requirements of the two statutes, despite differences in their obligations and exceptions. Rather, it noted that if such conflicts arise, it will be incumbent on the parties and the Court to resolve any difficulties. The court dismissed additional claims under the Administrative Procedure Act (APA) for lack of subject matter jurisdiction, as it determined that the commission is not an agency under the APA definition.

The courts decision turned on whether the NSCAI is an advisory committee subject to FACA. The court determined that the statutory text of the 2019 NDAA establishing the NSCAI fit[s] the [FACA] definition of advisory committee like a glove. Furthermore, turning to the full text of the 2019 NDAA, the court noted that the law contains at least two instances in which it explicitly exempts a government body from FACA. The court read the 2019 NDAA as silent when FACA applies and explicit when FACA does not apply. Given Congresss silence on the applicability of FACA to the NSCAI in the 2019 NDAAand again in the 2020 NDAAthe court reasoned that Congress intended the NSCAI to be subject to FACA.

In determining the NSCAI to be subject to FACA, in addition to FOIA, the court has compelled the commission to adopt a more transparent operating posture going forward. Since the December 2019 decision on FOIA, the NSCAI has produced a number of historical records in response to FOIA requests. The recent ruling on FACA grounds requires the NSCAI to hold open meetings, post notice of meetings in advance and make documents publicly available. As a result, the commissions process of compiling findings and developing recommendations for government action related to artificial intelligence and machine learning will likely become more accessible to the public.

The two court decisions come in time to have a noticeable impact on the remaining term of the temporary commission. While the NSCAI was previously due to disband later in 2020, the NDAA for fiscal 2020 1735 extended the commissions lifespan by one year, to October 1, 2021. Citing federal budgetary timelines and the pace of AI development, the commission released its first set of recommendations in March 2020 and expressed its intent to publish additional recommendations on a quarterly basis thereafter. The commission is due to submit its final report to Congress by March 1, 2021. As the NSCAI prepares to enter its final year of operations and develop its closing recommendations, the public will have a clearer window into the commissions work.

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Increasing Transparency at the National Security Commission on Artificial Intelligence - Lawfare

Just like there are eight levels to analytics as mentioned in a recent Spend Matters PRO brief, artificial intelligence (AI) has various stages of the technology today even though there is no such thing as true AI by any standard worth its technical weight.

But just because we dont yet have true AI doesnt mean todays AI cant help procurement improve its performance. We just need enough computational intelligence to allow software to do the tactical and non-value-added tasks that software should be able to perform with all of the modern computational power available to us. As long as the software can do the tasks as well as an average human expert the vast majority of the time (and kick up a request for help when it doesnt have enough information or when the probability it will outperform a human expert is less than the expert performing a task) thats more than good enough.

The reality is, for some basic tactical tasks, there are plenty of software options today (e.g., intelligent invoice processing). And even for some highly specialized tasks that we thought could never be done by a computer, we have software that can do it better, like early cancerous growth detection in MRIs and X-rays.

That being said, we also have a lot of software on the market that claims to be artificial intelligence but that is not even remotely close to what AI is today, let alone what useful software AI should be. For software to be classified as AI today, it must be capable of artificial learning and evolving its models or codes and improve over time.

So, in this PRO article, we are going to define the levels of AI that do exist today, and that may exist tomorrow. This will allow you to identify what truth there is to the claims that a vendor is making and whether the software will actually be capable of doing what you expect it to.

Not counting true AI, there are five levels of AI that are available today or will likely be available tomorrow:

Lets take a look at each group.

See the original post:
Artificial intelligence levels show AI is not created equal. Do you know what the vendor is selling? - Spend Matters