Category Archives: Machine Learning

Link Machine Learning (LML) gets a neutral rating from InvestorsObserver Sunday. The token is up 82.14% to $0.004303122367 while the broader crypto market is up 2.46%.

The Sentiment Score provides a quick, short-term look at the cryptos recent performance. This can be useful for both short-term investors looking to ride a rally and longer-term investors trying to buy the dip.

Link Machine Learning price is currently above resistance. With support set around $0.00162854200285358 and resistance at $0.00377953835819346, Link Machine Learning is potentially in a volatile position if the rally burns out.

Link Machine Learning has traded on low volume recently. This means that today's volume is below its average volume over the past seven days.

Due to a lack of data, this crypto may be less suitable for some investors.

Click here to unlock the rest of the report on Link Machine Learning

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Link Machine Learning (LML) has a Neutral Sentiment Score, is Rising, and Outperforming the Crypto Market Sunday: What's Next? - InvestorsObserver

Research and innovation in machine learning in the power industry operations and technologies sector has declined in the last quarter but remains higher than it was a year ago.

The most recent figures show that the number of related patent applications in the industry stood at 108 in the three months ending March up from 103 over the same period in 2021.

Figures for patent grants related to followed a similar pattern to filings growing from 15 in the three months ending March 2021 to 19 in the same period in 2022.

The figures are compiled by GlobalData, which tracks patent filings and grants from official offices around the world. Using textual analysis, as well as official patent classifications, these patents are grouped into key thematic areas and linked to key companies across various industries.

Machine learning is one of the key areas tracked by GlobalData. It has been identified as being a key disruptive force facing companies in the coming years, and is one of the areas that companies investing resources in now are expected to reap rewards from. The figures also provide an insight into the largest innovators in the sector.

Siemens was the top innovator in the power industry operations and technologies sector in the latest quarter. The company, which has its headquarters in Germany, filed 83 related patents in the three months ending March. That was up from 77 over the same period in 2021.

It was followed by the Switzerland-based ABB with 11 patent applications, South Korea-based Korea Electric Power Corp (9 applications), and the US-based Honeywell International Inc (9 applications).

ABB has recently ramped up R&D in machine learning. It saw growth of 36.4% in related patent applications in the three months ending March compared to the same period in 2021 the highest percentage growth out of all companies tracked with more than 10 quarterly patents in the power industry operations and technologies sector.

Fabric Expansion Joints, Metal Expansion Joints and Elastomer Expansion Joints

Excerpt from:
Machine learning innovation among power industry companies dropped off in the last quarter - Power Technology

AiM Future, a leader in embedded machine learning intellectual property (IP) for edge computing devices, announced it has joined the Edge AI and Vision Alliance.

AiM Future is accelerating the transition from centralized cloud-native AI to the distributed intelligent edge. Its market-proven NeuroMosAIc Processor (NMP) family of machine learning hardware accelerators and software, NeuroMosAIc Studio, enables the efficient execution of deep learning models common to computer vision applications. Shipping in smart home devices since 2019, the co-designed hardware and software offer a highly flexible and scalable solution meeting end-application performance, power, and cost requirements from always-on, battery-operated cameras to high-performance edge infrastructure.

Recommended AI News: How Startups are Leveraging the Cloud to Scale

It is our companys pleasure to join the Edge AI and Vision Alliance, said ChangSoo Kim, founder, and CEO of AiM Future. As a premier organization for technology innovators revolutionizing artificial intelligence across the edge computing spectrum, the partnership is a natural fit. It is clear AiM Futures vision of bringing the impossible to reality is shared by the Alliance and its ecosystem. The field of edge AI is rapidly advancing and partnerships are fundamental to addressing the many challenges and limitations of todays edge devices.

Today, more and more devices and systems are gaining the ability to see and understand their environments, said Jeff Bier, founder of the Edge AI and Vision Alliance. And thanks to visual perception, these machines are becoming more autonomous, safer, more capable and easier to use. With its processing architecture and accompanying toolset, AiM Future is implementing an intriguing approach to deep learning inference acceleration. We welcome AiM Future as one of the Edge AI and Vision Alliances newest members and look forward to their participation at the Embedded Vision Summit.

Recommended AI News: Nintex Named a Leader in Workflow and Content Automation by Aragon Research

[To share your insights with us, please write to sghosh@martechseries.com]

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AiM Future Joins the Edge AI and Vision Alliance - AiThority

The $20 million National Science Foundation award will help UAB and eight other Alabama-based universities build research infrastructure. UABs share will be about $2 million.

Yogesh Vohra Yogesh Vohra, Ph.D., is a co-principal investigator on a National Science Foundation award that will bring the University of Alabama at Birmingham about $2 million over five years.

The total NSF EPSCoR Research Infrastructure Improvement Program award of $20 million with its principal investigator Gary Zank, Ph.D., based at the University of Alabama in Huntsville will help strengthen research infrastructure at UAB, UAH, Auburn University, Tuskegee University, the University of South Alabama, Alabama A&M University, Alabama State University, Oakwood University, and the University of Alabama.

The award, Future technologies and enabling plasma processes, or FTPP, aims to develop new technologies using plasma in hard and soft biomaterials, food safety and sterilization, and space weather prediction. This project will build plasma expertise, research and industrial capacity, as well as a highly trained and capable plasma science and engineering workforce, across Alabama.

Unlike solids, liquids and gas, plasma the fourth state of matter does not exist naturally on Earth. This ionized gaseous substance can be made by heating neutral gases. At UAB, Vohra, a professor and university scholar in the UAB Department of Physics, has employed microwave-generated plasmas to create thin diamond films that have many potential uses, including super-hard coatings and diamond-encapsulated sensors for extreme environments. This new FTPP grant will support research into plasma synthesis of materials that maintain their strength at high temperatures, superconducting thin films and developing plasma surface modifications that incorporate antimicrobial materials in biomedical implants.

Vohra says the UAB Department of Physics will mostly use its share of the award to support faculty in the UAB Center for Nanoscale Materials and Biointegration and two full-time postdoctoral scholars, and support hiring of a new faculty member in computational physics with a background in machine-learning. The machine-learning predictions using the existing databases on materials properties will enable our research team to reduce the time from materials discovery to actual deployment in real-world applications, Vohra said.

The NSF EPSCoR Research Infrastructure Improvement Program helps establish partnerships among academic institutions to make sustainable improvements in research infrastructure, and research and development capacity. EPSCoR is the acronym for Established Program to Stimulate Competitive Research, an effort to level the playing field for states, territories and a commonwealth that historically have received lesser amounts of federal research and development funding.

Jurisdictions can compete for NSF EPSCoR awards if their five-year level of total NSF funding is less than 0.75 percent of the total NSF budget. Current qualifiers include Alabama, 22 other states, and Guam, the U.S. Virgin Islands and Puerto Rico.

Besides Alabama, the other four 2022 EPSCoR Research Infrastructure Improvement Program awardees are Hawaii, Kansas, Nevada and Wyoming.

In 2017, UAB was part of another five-year, $20 million NSF EPSCoR award to Alabama universities.

The Department of Physics is part of the UAB College of Arts and Sciences.

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NSF award will boost UAB research in machine-learning-enabled plasma synthesis of novel materials - University of Alabama at Birmingham

In a welcome move, the Indian Institute of Technology (IIT) has partnered with the online learning platform, Coursera. You can now access several courses from the comfort of your home while getting degrees certified by the premium institute.

Here are five courses that you may want to check out.

The course offers a strong foundation in business and technology. This is an opportunity to learn from industry experts at the B school. Dive into your area of specialisation, after choosing from over 55 electives. The curriculum spans business, management, data science and data analytics.

Eligibility criteria: A Bachelors degree with 65 per cent; four years of relevant work experience after graduation.Fees: Rs. 10,93,000/Duration: 24 months to 60 months

For more details, click here.

As algorithms shape our world and businesses, it is becoming more important to keep pace. In the course, you will gain exposure to the different algorithms that are needed for machine learning. You will also be trained in the application of Python programming in solving real-world financial problems.

Eligibility criteria: Knowledge of basic mathematics, linear algebra, calculus, statistics and spreadsheetsFees: Rs. 90,000/Duration: 6 months

For more details, click here.

Learn from industry experts who share with you their knowledge of mechatronics. In this course, you will be introduced to manufacturing processes and how these can be enhanced through computer technology. You will be trained in computer-aided design (CAD) and computer-aided manufacturing (CAM) softwares.

Eligibility criteria: Bachelors degree in any technology or engineering field; basic knowledge of programming. Students pursuing BE or BTech may also enrol.Fees: 1,12,500/Duration: 6 months

For more details, click here.

If you have been intrigued by the world of virtual reality, this course will help you delve deeper into understanding it. The course gives you a firm footing in the design and development of these technologies. Get expert insights into how to build deep learning models such as Encoder-Decoder.

Eligibility criteria: Bachelors degree in a related field with basic knowledge of programming.Fees: 1,12,500/Duration: 6 months

For more details, click here.

Understand the computational properties of Natural Language Processing with this course. Learn how to integrate machine learning and natural language processing to solve real-world problems across industries.

Eligibility criteria: Bachelors degree in a related field; a mathematics background in linear algebra, calculus, probability, statistics, data structures and algorithms; knowledge of Python.Fees: 1,12,500/Duration: 6 months

For more details, click here.

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Machine Learning to Virtual Reality: Learn from Anywhere with 5 Online Courses by IITs - The Better India

AI Ethics quandary about using adversarial attacks against Machine Learning even if done for ... [+] purposes of goodness.

It is widely accepted sage wisdom to garner as much as you can about your adversaries.

Frederick The Great, the famous king of Prussia and a noted military strategist, stridently said this: Great advantage is drawn from knowledge of your adversary, and when you know the measure of their intelligence and character, you can use it to play on their weakness.

Astutely leveraging the awareness of your adversaries is both a vociferous defense and a compelling offense-driven strategy in life. On the one hand, you can be better prepared for whatever your adversary might try to destructively do to you. The other side of that coin is that you are likely able to carry out better attacks against your adversary via the known and suspected weaknesses of any vaunted foe.

Per the historically revered statesman and ingenious inventor Benjamin Franklin, those that are on their guard and appear ready to receive their adversaries are in much less danger of being attacked, much more so than otherwise being unawares, supine, and negligent in preparation.

Why all this talk about adversaries?

Because one of the biggest concerns facing much of todays AI is that cyber crooks and other evildoers are deviously attacking AI systems using what is commonly referred to as adversarial attacks. This can cause an AI system to falter and fail to perform its designated functions. As youll see in a moment, there are a variety of vexing AI Ethics and Ethical AI issues underlying the matter, such as ensuring that AI systems are protected against such scheming adversaries, see my ongoing and extensive coverage of AI Ethics at the link here and the link here, just to name a few.

Perhaps even worse than getting the AI to simply stumble, the adversarial attack can sometimes be used to get AI to perform as the wrongdoer wishes the AI to perform. The attacker can essentially trick the AI into doing the bidding of the malefactor. Whereas some adversarial attacks seek to disrupt or confound the AI, another equally if not more insidious form of deception involves getting the AI to act on the behalf of the attacker.

It is almost as though one might use a mind trick or hypnotic means to get a human to do wrong acts and yet the person is blissfully unaware that they have been fooled into doing something that they should not particularly have done. To clarify, the act that is performed does not necessarily have to be wrong per se or illegal in its merits. For example, conning a bank teller to open the safe or vault for you is not in itself a wrong or illegal act. The bank teller is doing what they legitimately are able to perform as a valid bank-approved task. Of course, if they open the vault and doing so allows a robber to steal the money and all of the gold bullion therein, the bank teller has been tricked into performing an act that they should not have undertaken in the given circumstances.

The use of adversarial attacks against AI has to a great extent arisen because of the way in which much of contemporary AI is devised. You see, this latest era of AI has tended to emphasize the use of Machine Learning (ML) and Deep Learning (DL). These are computational pattern matching techniques and technologies which have dramatically aided the advancement of modern-day AI systems. ML/DL is often used as a key element in many of the AI systems that you interact with daily, such as the use of conversational interactive systems or Natural Language Processing (NLP) akin to Alexa and Siri.

The manner in which ML/DL is designed and fielded provides a fertile opening for the leveraging of adversarial attacks. Cybercrooks generally can guess how the ML/DL was built. They can make reasoned guesses about how the ML/DL will react when put into use. There are only so many ways that ML/DL is usually constructed. As such, the evildoer hackers can try a slew of underhanded ML/DL adversarial tricks to get the AI to either go awry or do their bidding.

In contrast, during the prior era of AI systems, it was somewhat harder to undertake adversarial attacks since much of the AI was more idiosyncratic and written in a more proprietary or individualistic manner. You would have had a more challenging time trying to guess how the AI was constructed and also how it might react when placed into active use. In comparison, ML/DL is largely more predictable as to its susceptibilities (this is not always the case, and please know that I am broadly generalizing).

You might be thinking that if adversarial attacks are relatively able to be targeted specifically at ML/DL then certainly there be should a boatload of cybersecurity measures available to protect against those attacks. One would hope that those devising and releasing their AI applications would ensure that the app was securely able to fight against those adversarial attacks.

The answer is yes and no.

Yes, there exist numerous cybersecurity protections that can be used by and within ML/DL to guard against adversarial attacks. Unfortunately, the answer is also somewhat a no in that many of the AI builders are not especially versed in those protections or are not explicitly including those protections.

There are lots of reasons for this.

One is that some AI software engineers concentrate solely on the AI side and are not particularly caring about the cybersecurity elements. They figure that someone else further along in the chain of making and releasing the AI will deal with any needed cybersecurity protections. Another reason for the lack of protection against adversarial attacks is that it can be a burden of sorts to the AI project. An AI project might be under a tight deadline to get the AI out the door. Adding into the mix a bunch of cybersecurity protections that need to be crafted or set up will potentially delay the production cycle of the AI. Furthermore, the cost of creating AI is bound to go up too.

Note that none of those are satisfactory as to allow an AI system to be vulnerable to adversarial attacks. Those that are in the know would say the famous line of either pay me now or pay me later would come to play in this instance. You can skirt past the cybersecurity portions to get an AI system sooner into production, but the chances are that it will then suffer an adversarial attack. A cost-benefit analysis and ROI (return on investment) needs to be properly assessed as to whether the cost upfront and the benefits thereof are going to be more profitable against the costs to repair and deal with cybersecurity intrusions further down the pike.

There is no free lunch when it comes to making ML/DL that is well-protected against adversarial attacks.

That being said, you dont necessarily need to move heaven and earth to be moderately protected against those evildoing tricks. Savvy specialists that are versed in cybersecurity protections can pretty much sit side-by-side with the AI crews and dovetail the security into the AI as it is being devised. There is also the assumption that a well-versed AI builder can readily use AI constructing techniques and technologies that simultaneously aid their AI building and that seamlessly encompasses adversarial attack protections. To adequately do so, they usually need to know about the nature of adversarial attacks and how to best blunt or mitigate them. This is something only gradually becoming regularly instituted as part of devising AI systems.

A twist of sorts is that more and more people are getting into the arena of developing ML/DL applications. Regrettably, some of those people are not versed in AI per se, and neither are they versed in cybersecurity. The idea overall is that perhaps by making the ability to craft AI systems with ML/DL widely available to all we are aiming to democratize AI. That sounds good, but there are downsides to this popular exhortation, see my analysis and coverage at the link here.

Speaking of twists, I will momentarily get to the biggest twist of them all, namely, I am going to shock you with a recently emerging notion that some find sensible and others believe is reprehensible. Ill give you a taste of where I am heading on this heated and altogether controversial matter.

Are you ready?

There is a movement toward using adversarial attacks as a means to disrupt or fool AI systems that are being used by wrongdoers.

Let me explain.

So far, I have implied that AI is seemingly always being used in the most innocent and positive of ways and that only miscreants would wish to confound the AI via the use of adversarial attacks. But keep in mind that bad people can readily devise AI and use that AI for doing bad things.

You know how it is, whats good for the goose is good for the gander.

Criminals and cybercrooks are eagerly wising up to the building and using AI ML/DL to carry out untoward acts. When you come in contact with an AI system, you might not have any means of knowing whether it is an AI For Good versus an AI For Bad type of system. Be on the watch! Just because AI is being deployed someplace does not somehow guarantee that the AI will be crafted by well-intended builders. The AI could be deliberately devised for foul purposes.

Here then is the million-dollar question.

Should we be okay with using adversarial attacks on purportedly AI For Bad systems?

Im sure that your first thought is that we ought to indeed be willing to fight fire with fire. If AI For Good systems can be shaken up via adversarial attacks, we can use those same evildoing adversarial attacks to shake up those atrocious AI For Bad systems. We can rightfully turn the attacking capabilities into an act of goodness. Fight evil using the appalling trickery of evil. The net result would seem to be an outcome of good.

Not everyone agrees with that sentiment.

From an AI Ethics perspective, there is a lot of handwringing going on about this meaty topic. Some would argue that by leveraging adversarial attacks, even when the intent is for the good, you are perpetuating the use of adversarial attacks all-told. You are basically saying that it is okay to launch and promulgate adversarial attacks. Shame on you, they exclaim. We ought to be stamping out evil rather than encouraging or expanding upon evil (even if the evil is ostensibly aiming to offset evil and carry out the work of the good).

Those against the use of adversarial attacks would also argue that by keeping adversarial attacks in the game that you are going to merely step into a death knell of quicksand. More and stronger adversarial attacks will be devised under the guise of attacking the AI For Bad systems. That seems like a tremendously noble pursuit. The problem is that the evildoers will undoubtedly also grab hold of those emboldened and super-duper adversarial attacks and aim them squarely at the AI For Good.

You are blindly promoting the cat and mouse gambit. We might be shooting our own foot.

A retort to this position is that there are no practical means of stamping out adversarial attacks. No matter whether you want them to exist or not, the evildoers are going to make sure they do persist. In fact, the evildoers are probably going to be making the adversarial attacks more resilient and potent, doing so to overcome whatever cyber protections are put in place to block them. Thus, a proverbial head-in-the-sand approach to dreamily pretending that adversarial attacks will simply slip quietly away into the night is pure nonsense.

You could contend that adversarial attacks against AI are a double-edged sword. AI researchers have noted this quandary, as stated by these authors in a telling article in AI And Ethics journal: Sadly, AI solutions have already been utilized for various violations and theft, even receiving the name AI or Crime (AIC). This poses a challenge: are cybersecurity experts thus justified to attack malicious AI algorithms, methods and systems as well, to stop them? Would that be fair and ethical? Furthermore, AI and machine learning algorithms are prone to be fooled or misled by the so-called adversarial attacks. However, adversarial attacks could be used by cybersecurity experts to stop the criminals using AI, and tamper with their systems. The paper argues that this kind of attacks could be named Ethical Adversarial Attacks (EAA), and if used fairly, within the regulations and legal frameworks, they would prove to be a valuable aid in the fight against cybercrime (article by Micha Chora and Micha Woniak, The Double-Edged Sword Of AI: Ethical Adversarial Attacks To Counter Artificial Intelligence For Crime).

Id ask you to mull this topic over and render a vote in your mind.

Is it unethical to use AI adversarial attacks against AI For Bad, or can we construe this as an entirely unapologetic Ethical AI practice?

You might be vaguely aware that one of the loudest voices these days in the AI field and even outside the field of AI consists of clamoring for a greater semblance of Ethical AI. Lets take a look at what it means to refer to AI Ethics and Ethical AI. On top of that, we can set the stage by looking at some examples of adversarial attacks to establish what I mean when I speak of Machine Learning and Deep Learning.

One particular segment or portion of AI Ethics that has been getting a lot of media attention consists of AI that exhibits untoward biases and inequities. You might be aware that when the latest era of AI got underway there was a huge burst of enthusiasm for what some now call AI For Good. Unfortunately, on the heels of that gushing excitement, we began to witness AI For Bad. For example, various AI-based facial recognition systems have been revealed as containing racial biases and gender biases, which Ive discussed at the link here.

Efforts to fight back against AI For Bad are actively underway. Besides vociferous legal pursuits of reining in the wrongdoing, there is also a substantive push toward embracing AI Ethics to righten the AI vileness. The notion is that we ought to adopt and endorse key Ethical AI principles for the development and fielding of AI doing so to undercut the AI For Bad and simultaneously heralding and promoting the preferable AI For Good.

On a related notion, I am an advocate of trying to use AI as part of the solution to AI woes, fighting fire with fire in that manner of thinking. We might for example embed Ethical AI components into an AI system that will monitor how the rest of the AI is doing things and thus potentially catch in real-time any discriminatory efforts, see my discussion at the link here. We could also have a separate AI system that acts as a type of AI Ethics monitor. The AI system serves as an overseer to track and detect when another AI is going into the unethical abyss (see my analysis of such capabilities at the link here).

In a moment, Ill share with you some overarching principles underlying AI Ethics. There are lots of these kinds of lists floating around here and there. You could say that there isnt as yet a singular list of universal appeal and concurrence. Thats the unfortunate news. The good news is that at least there are readily available AI Ethics lists and they tend to be quite similar. All told, this suggests that by a form of reasoned convergence of sorts that we are finding our way toward a general commonality of what AI Ethics consists of.

First, lets cover briefly some of the overall Ethical AI precepts to illustrate what ought to be a vital consideration for anyone crafting, fielding, or using AI.

For example, as stated by the Vatican in the Rome Call For AI Ethics and as Ive covered in-depth at the link here, these are their identified six primary AI ethics principles:

As stated by the U.S. Department of Defense (DoD) in their Ethical Principles For The Use Of Artificial Intelligence and as Ive covered in-depth at the link here, these are their six primary AI ethics principles:

Ive also discussed various collective analyses of AI ethics principles, including having covered a set devised by researchers that examined and condensed the essence of numerous national and international AI ethics tenets in a paper entitled The Global Landscape Of AI Ethics Guidelines (published in Nature), and that my coverage explores at the link here, which led to this keystone list:

As you might directly guess, trying to pin down the specifics underlying these principles can be extremely hard to do. Even more so, the effort to turn those broad principles into something entirely tangible and detailed enough to be used when crafting AI systems is also a tough nut to crack. It is easy to overall do some handwaving about what AI Ethics precepts are and how they should be generally observed, while it is a much more complicated situation in the AI coding having to be the veritable rubber that meets the road.

The AI Ethics principles are to be utilized by AI developers, along with those that manage AI development efforts, and even those that ultimately field and perform upkeep on AI systems. All stakeholders throughout the entire AI life cycle of development and usage are considered within the scope of abiding by the being-established norms of Ethical AI. This is an important highlight since the usual assumption is that only coders or those that program the AI are subject to adhering to the AI Ethics notions. As earlier stated, it takes a village to devise and field AI, and for which the entire village has to be versed in and abide by AI Ethics precepts.

Lets also make sure we are on the same page about the nature of todays AI.

There isnt any AI today that is sentient. We dont have this. We dont know if sentient AI will be possible. Nobody can aptly predict whether we will attain sentient AI, nor whether sentient AI will somehow miraculously spontaneously arise in a form of computational cognitive supernova (usually referred to as the singularity, see my coverage at the link here).

The type of AI that I am focusing on consists of the non-sentient AI that we have today. If we wanted to wildly speculate about sentient AI, this discussion could go in a radically different direction. A sentient AI would supposedly be of human quality. You would need to consider that the sentient AI is the cognitive equivalent of a human. More so, since some speculate we might have super-intelligent AI, it is conceivable that such AI could end up being smarter than humans (for my exploration of super-intelligent AI as a possibility, see the coverage here).

Lets keep things more down to earth and consider todays computational non-sentient AI.

Realize that todays AI is not able to think in any fashion on par with human thinking. When you interact with Alexa or Siri, the conversational capacities might seem akin to human capacities, but the reality is that it is computational and lacks human cognition. The latest era of AI has made extensive use of Machine Learning (ML) and Deep Learning (DL), which leverage computational pattern matching. This has led to AI systems that have the appearance of human-like proclivities. Meanwhile, there isnt any AI today that has a semblance of common sense and nor has any of the cognitive wonderment of robust human thinking.

ML/DL is a form of computational pattern matching. The usual approach is that you assemble data about a decision-making task. You feed the data into the ML/DL computer models. Those models seek to find mathematical patterns. After finding such patterns, if so found, the AI system then will use those patterns when encountering new data. Upon the presentation of new data, the patterns based on the old or historical data are applied to render a current decision.

I think you can guess where this is heading. If humans that have been making the patterned upon decisions have been incorporating untoward biases, the odds are that the data reflects this in subtle but significant ways. Machine Learning or Deep Learning computational pattern matching will simply try to mathematically mimic the data accordingly. There is no semblance of common sense or other sentient aspects of AI-crafted modeling per se.

Furthermore, the AI developers might not realize what is going on either. The arcane mathematics in the ML/DL might make it difficult to ferret out the now hidden biases. You would rightfully hope and expect that the AI developers would test for the potentially buried biases, though this is trickier than it might seem. A solid chance exists that even with relatively extensive testing that there will be biases still embedded within the pattern matching models of the ML/DL.

You could somewhat use the famous or infamous adage of garbage-in garbage-out. The thing is, this is more akin to biases-in that insidiously get infused as biases submerged within the AI. The algorithm decision-making (ADM) of AI axiomatically becomes laden with inequities.

Not good.

I trust that you can readily see how adversarial attacks fit into these AI Ethics matters. Evildoers are undoubtedly going to use adversarial attacks against ML/DL and other AI that is supposed to be doing AI For Good. Meanwhile, those evildoers are indubitably going to be devising AI For Bad that they foster upon us all. To try and fight against those AI For Bad systems, we could arm ourselves with adversarial attacks. The question is whether we are doing more good or more harm by leveraging and continuing the advent of adversarial attacks.

Time will tell.

One vexing issue is that there is a myriad of adversarial attacks that can be used against AI ML/DL. You might say there are more than you can shake a stick at. Trying to devise protective cybersecurity measures to negate all of the various possible attacks is somewhat problematic. Just when you might think youve done a great job of dealing with one type of adversarial attack, your AI might get blindsided by a different variant. A determined evildoer is likely to toss all manner of adversarial attacks at your AI and be hoping that at least one or more sticks. Of course, if we are using adversarial attacks against AI For Bad, we too would take the same advantageous scattergun approach.

Some of the most popular types of adversarial attacks include:

At this juncture of this weighty discussion, Id bet that you are desirous of some illustrative examples that might showcase the nature and scope of adversarial attacks against AI and particularly aimed at Machine Learning and Deep Learning. There is a special and assuredly popular set of examples that are close to my heart. You see, in my capacity as an expert on AI including the ethical and legal ramifications, I am frequently asked to identify realistic examples that showcase AI Ethics dilemmas so that the somewhat theoretical nature of the topic can be more readily grasped. One of the most evocative areas that vividly presents this ethical AI quandary is the advent of AI-based true self-driving cars. This will serve as a handy use case or exemplar for ample discussion on the topic.

Heres then a noteworthy question that is worth contemplating: Does the advent of AI-based true self-driving cars illuminate anything about the nature of adversarial attacks against AI, and if so, what does this showcase?

Allow me a moment to unpack the question.

First, note that there isnt a human driver involved in a true self-driving car. Keep in mind that true self-driving cars are driven via an AI driving system. There isnt a need for a human driver at the wheel, nor is there a provision for a human to drive the vehicle. For my extensive and ongoing coverage of Autonomous Vehicles (AVs) and especially self-driving cars, see the link here.

Id like to further clarify what is meant when I refer to true self-driving cars.

Understanding The Levels Of Self-Driving Cars

As a clarification, true self-driving cars are ones where the AI drives the car entirely on its own and there isnt any human assistance during the driving task.

These driverless vehicles are considered Level 4 and Level 5 (see my explanation at this link here), while a car that requires a human driver to co-share the driving effort is usually considered at Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-ons that are referred to as ADAS (Advanced Driver-Assistance Systems).

There is not yet a true self-driving car at Level 5, and we dont yet even know if this will be possible to achieve, nor how long it will take to get there.

Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se (we are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some contend, see my coverage at this link here).

Since semi-autonomous cars require a human driver, the adoption of those types of cars wont be markedly different than driving conventional vehicles, so theres not much new per se to cover about them on this topic (though, as youll see in a moment, the points next made are generally applicable).

For semi-autonomous cars, it is important that the public needs to be forewarned about a disturbing aspect thats been arising lately, namely that despite those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car.

You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3.

Self-Driving Cars And Adversarial Attacks Against AI

For Level 4 and Level 5 true self-driving vehicles, there wont be a human driver involved in the driving task.

All occupants will be passengers.

The AI is doing the driving.

One aspect to immediately discuss entails the fact that the AI involved in todays AI driving systems is not sentient. In other words, the AI is altogether a collective of computer-based programming and algorithms, and most assuredly not able to reason in the same manner that humans can.

Why is this added emphasis about the AI not being sentient?

Because I want to underscore that when discussing the role of the AI driving system, I am not ascribing human qualities to the AI. Please be aware that there is an ongoing and dangerous tendency these days to anthropomorphize AI. In essence, people are assigning human-like sentience to todays AI, despite the undeniable and inarguable fact that no such AI exists as yet.

With that clarification, you can envision that the AI driving system wont natively somehow know about the facets of driving. Driving and all that it entails will need to be programmed as part of the hardware and software of the self-driving car.

Lets dive into the myriad of aspects that come to play on this topic.

First, it is important to realize that not all AI self-driving cars are the same. Each automaker and self-driving tech firm is taking its approach to devising self-driving cars. As such, it is difficult to make sweeping statements about what AI driving systems will do or not do.

Furthermore, whenever stating that an AI driving system doesnt do some particular thing, this can, later on, be overtaken by developers that in fact program the computer to do that very thing. Step by step, AI driving systems are being gradually improved and extended. An existing limitation today might no longer exist in a future iteration or version of the system.

I hope that provides a sufficient litany of caveats to underlie what I am about to relate.

As earlier mentioned, some of the most popular types of adversarial attacks include:

We can showcase the nature of each such adversarial attack and do so in the context of AI-based self-driving cars.

Adversarial Falsification Attacks

Consider the use of adversarial falsifications.

There are generally two such types: (1) false-positive attacks, and (2) false-negative attacks. In the false-positive attack, the emphasis is on presenting to AI a so-called negative sample that is then incorrectly classified by the ML/DL as a positive one. The jargon for this is that it is a Type I effort (this is reminiscent perhaps of your days of taking a statistics class in college). In contrast, the false-negative attack entails presenting a positive sample for which the ML/DL incorrectly classifies as a negative instance, known as a Type II error.

Suppose that we had trained an AI driving system to detect Stop signs. We used an ML/DL that we had trained beforehand with thousands of images that contained Stop signs. The idea is that we would be using video cameras on the self-driving car to collect video and images of the roadway scene surrounding the autonomous vehicle during a driving journey. As the digital imagery real-time streams into an onboard computer, the ML/DL scans the digital data to detect any indication of a nearby Stop sign. The detection of a Stop sign is obviously crucial for the AI driving system. If a Stop sign is detected by the ML/DL, this is conveyed to the AI driving system and the AI would need to ascertain a suitable means to use the driving controls to bring the self-driving car to a proper and safe stop.

Humans seem to readily be able to detect Stop signs, at least most of the time. Our human perception of such signs is keenly honed by our seemingly innate cognitive pattern matching capacities. All we need to do is learn what a Stop sign looks like and we take things from there. A toddler learns soon enough that a Stop sign is typically red in color, contains the word STOP in large letters, has a special rectangular shape, usually is posted adjacent to the roadway and resides at a persons height, and so on.

Imagine an evildoer that wants to make trouble for self-driving cars.

In a false-positive adversarial attack, the wrongdoer would try to trick the ML/DL into computationally calculating that a Stop sign exists even when there isnt a Stop sign present. Maybe the wrongdoer puts up a red sign along a roadway that looks generally similar to a Stop sign but lacks the word STOP on it. A human would likely realize that this is merely a red sign and not a driving directive. The ML/DL might though calculate that the sign resembles sufficiently enough a Stop sign to the degree that the AI ought to consider the sign as in fact a Stop sign.

You might be tempted to think that this is not much of an adversarial attack and that it seems rather innocuous. Well, suppose that you are driving in a car and meanwhile a self-driving car that is ahead of you suddenly and seemingly without any basis for doing so comes to an abrupt stop (due to having misconstrued a red sign near the roadway as being a Stop sign). You might ram into that self-driving car. It could be that the AI was fooled into computationally calculating that a non-stop sign was a Stop sign, thus committing a false-positive error. You get injured, the passengers in the self-driving car get injured, and perhaps even pedestrians get injured by this dreadful false-positive adversarial attack.

A false-negative adversarial attack is somewhat akin to this preceding depiction though based on tricking the ML/DL into incorrectly misclassifying in the other direction, as it were. Imagine that a Stop sign is sitting next to the roadway and for all usual visual reasons seems to be a Stop sign. Humans accept that this is indeed a valid Stop sign.

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AI Ethics Tempted But Hesitant To Use AI Adversarial Attacks Against The Evils Of Machine Learning, Including For Self-Driving Cars - Forbes