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OpenAI API

We’re releasing an API for accessing new AI models developed by OpenAI. Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose “text in, text out” interface, allowing users to try it on virtually any English language task. You can now request access

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FAQ

Why did OpenAI decide to release a commercial product?

Ultimately, what we care about most is ensuring artificial general intelligence benefits everyone. We see developing commercial products as one of the ways to make sure we have enough funding to succeed.

We also believe that safely deploying powerful AI systems in the world will be hard to get right. In releasing the API, we are working closely with our partners to see what challenges arise when AI systems are used in the real world. This will help guide our efforts to understand how deploying future AI systems will go, and what we need to do to make sure they are safe and beneficial for everyone.

Why did OpenAI choose to release an API instead of open-sourcing the models?

There are three main reasons we did this. First, commercializing the technology helps us pay for our ongoing AI research, safety, and policy efforts.

Second, many of the models underlying the API are very large, taking a lot of expertise to develop and deploy and making them very expensive to run. This makes it hard for anyone except larger companies to benefit from the underlying technology. We’re hopeful that the API will make powerful AI systems more accessible to smaller businesses and organizations.

Third, the API model allows us to more easily respond to misuse of the technology. Since it is hard to predict the downstream use cases of our models, it feels inherently safer to release them via an API and broaden access over time, rather than release an open source model where access cannot be adjusted if it turns out to have harmful applications.

What specifically will OpenAI do about misuse of the API, given what you’ve previously said about GPT-2?

With GPT-2, one of our key concerns was malicious use of the model (e.g., for disinformation), which is difficult to prevent once a model is open sourced. For the API, we’re able to better prevent misuse by limiting access to approved customers and use cases. We have a mandatory production review process before proposed applications can go live. In production reviews, we evaluate applications across a few axes, asking questions like: Is this a currently supported use case?, How open-ended is the application?, How risky is the application?, How do you plan to address potential misuse?, and Who are the end users of your application?.

We terminate API access for use cases that are found to cause (or are intended to cause) physical, emotional, or psychological harm to people, including but not limited to harassment, intentional deception, radicalization, astroturfing, or spam, as well as applications that have insufficient guardrails to limit misuse by end users. As we gain more experience operating the API in practice, we will continually refine the categories of use we are able to support, both to broaden the range of applications we can support, and to create finer-grained categories for those we have misuse concerns about.

One key factor we consider in approving uses of the API is the extent to which an application exhibits open-ended versus constrained behavior with regard to the underlying generative capabilities of the system. Open-ended applications of the API (i.e., ones that enable frictionless generation of large amounts of customizable text via arbitrary prompts) are especially susceptible to misuse. Constraints that can make generative use cases safer include systems design that keeps a human in the loop, end user access restrictions, post-processing of outputs, content filtration, input/output length limitations, active monitoring, and topicality limitations.

We are also continuing to conduct research into the potential misuses of models served by the API, including with third-party researchers via our academic access program. We’re starting with a very limited number of researchers at this time and already have some results from our academic partners at Middlebury Institute, University of Washington, and Allen Institute for AI. We have tens of thousands of applicants for this program already and are currently prioritizing applications focused on fairness and representation research.

How will OpenAI mitigate harmful bias and other negative effects of models served by the API?

Mitigating negative effects such as harmful bias is a hard, industry-wide issue that is extremely important. As we discuss in the GPT-3 paper and model card, our API models do exhibit biases that will be reflected in generated text. Here are the steps we’re taking to address these issues:

  • We’ve developed usage guidelines that help developers understand and address potential safety issues.
  • We’re working closely with users to understand their use cases and develop tools to surface and intervene to mitigate harmful bias.
  • We’re conducting our own research into manifestations of harmful bias and broader issues in fairness and representation, which will help inform our work via improved documentation of existing models as well as various improvements to future models.
  • We recognize that bias is a problem that manifests at the intersection of a system and a deployed context; applications built with our technology are sociotechnical systems, so we work with our developers to ensure they’re putting in appropriate processes and human-in-the-loop systems to monitor for adverse behavior.

Our goal is to continue to develop our understanding of the API’s potential harms in each context of use, and continually improve our tools and processes to help minimize them.

Updated September 18, 2020

Source: https://openai.com/blog/openai-api/

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Executive Interview: Brian Gattoni, CTO, Cybersecurity & Infrastructure Security Agency 

Understanding and Advising on Cyber and Physical Risks to the Nation’s Critical Infrastructure  Brian R. Gattoni is the Chief Technology Officer for the Cybersecurity and Infrastructure Security Agency (CISA) of the Department of Homeland Security. CISA is the nation’s risk advisor, working with partners to defend against today’s threats and collaborating to build a secure and resilient […]

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As CTO of the Cybersecurity & Infrastructure Security Agency of the DHS, Brian Gattoni is charged with understanding and advising on cyber and physical risks to the nation’s critical infrastructure. 

Understanding and Advising on Cyber and Physical Risks to the Nation’s Critical Infrastructure 

Brian Gattoni, CTO, Cybersecurity & Infrastructure Security Agency

Brian R. Gattoni is the Chief Technology Officer for the Cybersecurity and Infrastructure Security Agency (CISA) of the Department of Homeland Security. CISA is the nation’s risk advisor, working with partners to defend against today’s threats and collaborating to build a secure and resilient infrastructure for the future. Gattoni sets the technical vision and strategic alignment of CISA data and mission services. Previously, he was the Chief of Mission Engineering & Technology, developing analytic techniques and new approaches to increase the value of DHS cyber mission capabilities. Prior to joining DHS in 2010, Gattoni served in various positions at the Defense Information Systems Agency and the United States Army Test & Evaluation Command. He holds a Master of Science Degree in Cyber Systems & Operations from the Naval Postgraduate School in Monterey, California, and is a Certified Information Systems Security Professional (CISSP).  

AI Trends: What is the technical vision for CISA to manage risk to federal networks and critical infrastructure? 

Brian Gattoni: Our technology vision is built in support of our overall strategy. We are the nation’s risk advisor. It’s our job to stay abreast of incoming threats and opportunities for general risk to the nation. Our efforts are to understand and advise on cyber and physical risks to the nation’s critical infrastructure.  

It’s all about bringing in the data, understanding what decisions need to be made and can be made from the data, and what insights are useful to our stakeholders. The potential of AI and machine learning is to expand on operational insights with additional data sets to make better use of the information we have.  

What are the most prominent threats? 

The Cybersecurity and Infrastructure Security Agency (CISA) of the Department of Homeland Security is the Nation’s risk advisor.

The sources of threats we frequently discuss are the adversarial actions of nation-state actors and those aligned with nation-state actors and their interests, in disrupting national critical functions here in the U.S. Just in the past month, we’ve seen increased activity from elements supporting what we refer to in the government as Hidden Cobra [malicious cyber activity by the North Korean government]. We’ve issued joint alerts with our partners overseas and the FBI and the DoD, highlighting activity associated with Chinese actors. On CISA.gov people can find CISA Insights, which are documents that provide background information on particular cyber threats and the vulnerabilities they exploit, as well as a ready-made set of mitigation activities that non-federal partners can implement.   

What role does AI play in the plan? 

Artificial intelligence has a great role to play in the support of the decisions we make as an agency. Fundamentally, AI is going to allow us to apply our decision processes to a scale of data that humans just cannot keep up with. And that’s especially prevalent in the cyber mission. We remain cognizant of how we make decisions in the first place and target artificial intelligence and machine learning algorithms that augment and support that decision-making process. We’ll be able to use AI to provide operational insights at a greater scale or across a greater breadth of our mission space.  

How far along are you in the implementation of AI at the CISA? 

Implementing AI is not as simple as putting in a new business intelligence tool or putting in a new email capability. Really augmenting your current operations with artificial intelligence is a mix of the culture change, for humans to understand how the AI is supposed to augment their operations. It is a technology change, to make sure you have the scalable compute and the right tools in place to do the math you’re talking about implementing. And it’s a process change. We want to deliver artificial intelligence algorithms that augment our operators’ decisions as a support mechanism.  

Where we are in the implementation is closer to understanding those three things. We’re working with partners in federally funded research and development centers, national labs and the departments own Science and Technology Data Analytics Tech Center to develop capability in this area. We’ve developed an analytics meta-process which helps us systemize the way we take in data and puts us in a position to apply artificial intelligence to expand our use of that data.  

Do you have any interesting examples of how AI is being applied in CISA and the federal government today? Or what you are working toward, if that’s more appropriate. 

I have a recent use case. We’ve been working with some partners over the past couple of months to apply AI to a humanitarian assistance and disaster relief type of mission. So, within CISA, we also have responsibilities for critical infrastructure. During hurricane season, we always have a role to play in helping advise what the potential impacts are to critical infrastructure sites in the affected path of a hurricane.  

We prepared to conduct an experiment leveraging AI algorithms and overhead imagery to figure out if we could analyze the data from a National Oceanic and Atmospheric Administration flight over the affected area. We compared that imagery with the base imagery from Google Earth or ArcGIS and used AI to identify any affected critical infrastructure. We could see the extent to which certain assets, such as oil refineries, were physically flooded. We could make an assessment as to whether they hit a threshold of damage that would warrant additional scrutiny, or we didn’t have to apply resources because their resilience was intact, and their functions could continue.   

That is a nice use case, a simple example of letting a computer do the comparisons and make a recommendation to our human operators. We found that it was very good at telling us which critical infrastructure sites did not need any additional intervention. To use a needle in a haystack analogy, one of the useful things AI can help us do is blow hay off the stack in pursuit of the needle. And that’s a win also. The experiment was very promising in that sense.  

How does CISA work with private industry, and do you have any examples of that?  

We have an entire division dedicated to stakeholder engagement. Private industry owns over 80% of the critical infrastructure in the nation. So CISA sits at the intersection of the private sector and the government to share information, to ensure we have resilience in place for both the government entities and the private entities, in the pursuit of resilience for those national critical functions. Over the past year we’ve defined a set of 55 functions that are critical for the nation.  

When we work with private industry in those areas we try to share the best insights and make decisions to ensure those function areas will continue unabated in the face of a physical or cyber threat. 

Cloud computing is growing rapidly. We see different strategies, including using multiple vendors of the public cloud, and a mix of private and public cloud in a hybrid strategy. What do you see is the best approach for the federal government? 

In my experience the best approach is to provide guidance to the CIO’s and CISO’s across the federal government and allow them the flexibility to make risk-based determinations on their own computing infrastructure as opposed to a one-size-fits-all approach.   

We issue a series of use cases that describeat a very high levela reference architecture about a type of cloud implementation and where security controls should be implemented, and where telemetry and instrumentation should be applied. You have departments and agencies that have a very forward-facing public citizen services portfolio, which means access to information, is one of their primary responsibilities. Public clouds and ease of access are most appropriate for those. And then there are agencies with more sensitive missions. Those have critical high value data assets that need to be protected in a specific way. Giving each the guidance they need to handle all of their use cases is what we’re focused on here. 

I wanted to talk a little bit about job roles. How are you defining the job roles around AI in CISA, as in data scientists, data engineers, and other important job titles and new job titles?  

I could spend the remainder of our time on this concept of job roles for artificial intelligence; it’s a favorite topic for me. I am a big proponent of the discipline of data science being a team sport. We currently have our engineers and our analysts and our operators. And the roles and disciplines around data science and data engineers have been morphing out of an additional duty on analysts and engineers into its own sub sector, its own discipline. We’re looking at a cadre of data professionals that serve almost as a logistics function to our operators who are doing the mission-level analysis. If you treat data as an asset that has to be moved and prepared and cleaned and readied, all terms in the data science and data engineering world now, you start to realize that it requires logistics functions similar to any other asset that has to be moved. 

If you get professionals dedicated to that end, you will be able to scale to the data problems you have without overburdening your current engineers who are building the compute platforms, or your current mission analysts who are trying to interpret the data and apply the insights to your stakeholders. You will have more team members moving data to the right places, making data-driven decisions. 

Are you able to hire the help you need to do the job? Are you able to find qualified people? Where are the gaps? 

As the domain continues to mature, as we understand more about the different roles, we begin to see gapseducation programs and training programs that need to be developed. I think maybe three, five years ago, you would see certificates from higher education in data science. Now we’re starting to see full-fledged degrees as concentrations out of computer science or mathematics. Those graduates are the pipeline to help us fill the gaps we currently have. So as far as our current problems, there’s never enough people. It’s always hard to get the good ones and then keep them because the competition is so high. 

Here at CISA, we continue to invest not only in our own folks that are re-training, but in the development of a cyber education and training group, which is looking at the partnerships with academia to help shore up that pipeline. It continually improves. 

Do you have a message for high school or college students interested in pursuing a career in AI, either in the government or in business, as to what they should study? 

Yes and it’s similar to the message I give to the high schoolers that live in my house. That is, don’t give up on math so easily. Math and science, the STEM subjects, have foundational skills that may be applicable to your future career. That is not to discount the diversity and variety of thought processes that come from other disciplines. I tell my kids they need the mathematical foundation to be able to apply the thought processes you learn from studying music or studying art or studying literature. And the different ways that those disciplines help you make connections. But have the mathematical foundation to represent those connections to a computer.   

One of the fallacies around machine learning is that it will just learn [by itself]. That’s not true. You have to be able to teach it, and you can only talk to computers with math, at the base level.  

So if you have the mathematical skills to relay your complicated human thought processes to the computer, and now it can replicate those patterns and identify what you’re asking it to do, you will have success in this field. But if you give up on the math part too earlyit’s a progressive disciplineif you give up on algebra two and then come back years later and jump straight into calculus, success is going to be difficult, but not impossible. 

You sound like a math teacher.  

A simpler way to say it is: if you say no to math now, it’s harder to say yes later. But if you say yes now, you can always say no later, if data science ends up not being your thing.  

Are there any incentives for young people, let’s say a student just out of college, to go to work for the government? Is there any kind of loan forgiveness for instance?  

We have a variety of programs. The one that I really like, that I have had a lot of success with as a hiring manager in the federal government, especially here at DHS over the past 10 years, is a program called Scholarship for Service. It’s a CyberCorps program where interested students, who pass the process to be accepted can get a degree in exchange for some service time. It used to be two years; it might be more now, but they owe some time and service to the federal government after the completion of their degree. 

I have seen many successful candidates come out of that program and go on to fantastic careers, contributing in cyberspace all over. I have interns that I hired nine years ago that are now senior leaders in this organization or have departed for private industry and are making their difference out there. It’s a fantastic program for young folks to know about.  

What advice do you have for other government agencies just getting started in pursuing AI to help them meet their goals? 

My advice for my peers and partners and anybody who’s willing to listen to it is, when you’re pursuing AI, be very specific about what it can do for you.   

I go back to the decisions you make, what people are counting on you to do. You bear some responsibility to know how you make those decisions if you’re really going to leverage AI and machine learning to make decisions faster or better or some other quality of goodnessThe speed at which you make decisions will go both ways. You have to identify your benefit of that decision being made if it’s positive and define your regret if that decision is made and it’s negative. And then do yourself a simple HIGH-LOW matrix; the quadrant of high-benefit, low-regret decisions is the target. Those are ones that I would like to automate as much as possible. And if artificial intelligence and machine learning can help, that would be great. If not, that’s a decision you have to make. 

I have two examples I use in our cyber mission to illustrate the extremes here. One is for incident triage. If a cyber incident is detected, we have a triage process to make sure that it’s real. That presents information to an analyst. If that’s done correctly, it has a high benefit because it can take a lot of work off our analysts. It has lowtomedium regret if it’s done incorrectly, because the decision is to present information to an analyst who can then provide that additional filter. So that’s a high benefit, low regret. That’s a no-brainer for automating as much as possible. 

On the other side of the spectrum is protecting next generation 911 call centers from a potential telephony denial of service attack. One of the potential automated responses could be to cut off the incoming traffic to the 911 call center to stunt the attack. Benefit: you may have prevented the attack. Regret: potentially you’re cutting off legitimate traffic to a 911 call center, and that has life and safety implications. And that is unacceptable. That’s an area where automation is probably not the right approach. Those are two extreme examples, which are easy for people to understand, and it helps illustrate how the benefit regret matrix can work. How you make decisions is really the key to understanding whether to implement AI and machine learning to help automate those decisions using the full breadth of data.  

Learn more about the Cybersecurity & Infrastructure Security Agency.  

Source: https://www.aitrends.com/executive-interview/executive-interview-brian-gattoni-cto-cybersecurity-infrastructure-security-agency/

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Making Use Of AI Ethics Tuning Knobs In AI Autonomous Cars 

By Lance Eliot, the AI Trends Insider   There is increasing awareness about the importance of AI Ethics, consisting of being mindful of the ethical ramifications of AI systems.    AI developers are being asked to carefully design and build their AI mechanizations by ensuring that ethical considerations are at the forefront of the AI systems development […]

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Ethical tuning knobs would be a handy addition to self-driving car controls, the author suggests, if for example the operator was late for work and needed to exceed the speed limit. (Credit: Getty Images) 

By Lance Eliot, the AI Trends Insider  

There is increasing awareness about the importance of AI Ethics, consisting of being mindful of the ethical ramifications of AI systems.   

AI developers are being asked to carefully design and build their AI mechanizations by ensuring that ethical considerations are at the forefront of the AI systems development process. When fielding AI, those responsible for the operational use of the AI also need to be considering crucial ethical facets of the in-production AI systems. Meanwhile, the public and those using or reliant upon AI systems are starting to clamor for heightened attention to the ethical and unethical practices and capacities of AI.   

Consider a simple example. Suppose an AI application is developed to assess car loan applicants. Using Machine Learning (ML) and Deep Learning (DL), the AI system is trained on a trove of data and arrives at some means of choosing among those that it deems are loan worthy and those that are not. 

The underlying Artificial Neural Network (ANN) is so computationally complex that there are no apparent means to interpret how it arrives at the decisions being rendered. Also, there is no built-in explainability capability and thus the AI is unable to articulate why it is making the choices that it is undertaking (note: there is a movement toward including XAI, explainable AI components to try and overcome this inscrutability hurdle).   

Upon the AI-based loan assessment application being fielded, soon thereafter protests arose by some that assert they were turned down for their car loan due to an improper inclusion of race or gender as a key factor in rendering the negative decision.   

At first, the maker of the AI application insists that they did not utilize such factors and professes complete innocence in the matter. Turns out though that a third-party audit of the AI application reveals that the ML/DL is indeed using race and gender as core characteristics in the car loan assessment process. Deep within the mathematically arcane elements of the neural network, data related to race and gender were intricately woven into the calculations, having been dug out of the initial training dataset provided when the ANN was crafted. 

That is an example of how biases can be hidden within an AI system. And it also showcases that such biases can go otherwise undetected, including that the developers of the AI did not realize that the biases existed and were seemingly confident that they had not done anything to warrant such biases being included. 

People affected by the AI application might not realize they are being subjected to such biases. In this example, those being adversely impacted perchance noticed and voiced their concerns, but we are apt to witness a lot of AI that no one will realize they are being subjugated to biases and therefore not able to ring the bell of dismay.   

Various AI Ethics principles are being proffered by a wide range of groups and associations, hoping that those crafting AI will take seriously the need to consider embracing AI ethical considerations throughout the life cycle of designing, building, testing, and fielding AI.   

AI Ethics typically consists of these key principles: 

1)      Inclusive growth, sustainable development, and well-being 

2)      Human-centered values and fairness 

3)      Transparency and explainability 

4)      Robustness, security, and safety 

5)      Accountability   

We certainly expect humans to exhibit ethical behavior, and thus it seems fitting that we would expect ethical behavior from AI too.   

Since the aspirational goal of AI is to provide machines that are the equivalent of human intelligence, being able to presumably embody the same range of cognitive capabilities that humans do, this perhaps suggests that we will only be able to achieve the vaunted goal of AI by including some form of ethics-related component or capacity. 

What this means is that if humans encapsulate ethics, which they seem to do, and if AI is trying to achieve what humans are and do, the AI ought to have an infused ethics capability else it would be something less than the desired goal of achieving human intelligence.   

You could claim that anyone crafting AI that does not include an ethics facility is undercutting what should be a crucial and integral aspect of any AI system worth its salt. 

Of course, trying to achieve the goals of AI is one matter, meanwhile, since we are going to be mired in a world with AI, for our safety and well-being as humans we would rightfully be arguing that AI had better darned abide by ethical behavior, however that might be so achieved.   

Now that we’ve covered that aspect, let’s take a moment to ponder the nature of ethics and ethical behavior.  

Considering Whether Humans Always Behave Ethically   

Do humans always behave ethically? I think we can all readily agree that humans do not necessarily always behave in a strictly ethical manner.   

Is ethical behavior by humans able to be characterized solely by whether someone is in an ethically binary state of being, namely either purely ethical versus being wholly unethical? I would dare say that we cannot always pin down human behavior into two binary-based and mutually exclusive buckets of being ethical or being unethical. The real-world is often much grayer than that, and we at times are more likely to assess that someone is doing something ethically questionable, but it is not purely unethical, nor fully ethical. 

In a sense, you could assert that human behavior ranges on a spectrum of ethics, at times being fully ethical and ranging toward the bottom of the scale as being wholly and inarguably unethical. In-between there is a lot of room for how someone ethically behaves. 

If you agree that the world is not a binary ethical choice of behaviors that fit only into truly ethical versus solely unethical, you would therefore also presumably be amenable to the notion that there is a potential scale upon which we might be able to rate ethical behavior. 

This scale might be from the scores of 1 to 10, or maybe 1 to 100, or whatever numbering we might wish to try and assign, maybe even including negative numbers too. 

Let’s assume for the moment that we will use the positive numbers of a 1 to 10 scale for increasingly being ethical (the topmost is 10), and the scores of -1 to -10 for being unethical (the -10 is the least ethical or in other words most unethical potential rating), and zero will be the midpoint of the scale. 

Please do not get hung up on the scale numbering, which can be anything else that you might like. We could even use letters of the alphabet or any kind of sliding scale. The point being made is that there is a scale, and we could devise some means to establish a suitable scale for use in these matters.   

The twist is about to come, so hold onto your hat.   

We could observe a human and rate their ethical behavior on particular aspects of what they do. Maybe at work, a person gets an 8 for being ethically observant, while perhaps at home they are a more devious person, and they get a -5 score. 

Okay, so we can rate human behavior. Could we drive or guide human behavior by the use of the scale? 

Suppose we tell someone that at work they are being observed and their target goal is to hit an ethics score of 9 for their first year with the company. Presumably, they will undertake their work activities in such a way that it helps them to achieve that score.   

In that sense, yes, we can potentially guide or prod human behavior by providing targets related to ethical expectations. I told you a twist was going to arise, and now here it is. For AI, we could use an ethical rating or score to try and assess how ethically proficient the AI is.   

In that manner, we might be more comfortable using that particular AI if we knew that it had a reputable ethical score. And we could also presumably seek to guide or drive the AI toward an ethical score too, similar to how this can be done with humans, and perhaps indicate that the AI should be striving towards some upper bound on the ethics scale. 

Some pundits immediately recoil at this notion. They argue that AI should always be a +10 (using the scale that I’ve laid out herein). Anything less than a top ten is an abomination and the AI ought to not exist. Well, this takes us back into the earlier discussion about whether ethical behavior is in a binary state.   

Are we going to hold AI to a “higher bar” than humans by insisting that AI always be “perfectly” ethical and nothing less so?   

This is somewhat of a quandary due to the point that AI overall is presumably aiming to be the equivalent of human intelligence, and yet we do not hold humans to that same standard. 

For some, they fervently believe that AI must be held to a higher standard than humans. We must not accept or allow any AI that cannot do so. 

Others indicate that this seems to fly in the face of what is known about human behavior and begs the question of whether AI can be attained if it must do something that humans cannot attain.   

Furthermore, they might argue that forcing AI to do something that humans do not undertake is now veering away from the assumed goal of arriving at the equivalent of human intelligence, which might bump us away from being able to do so as a result of this insistence about ethics.   

Round and round these debates continue to go. 

Those on the must-be topnotch ethical AI are often quick to point out that by allowing AI to be anything less than a top ten, you are opening Pandora’s box. For example, it could be that AI dips down into the negative numbers and sits at a -4, or worse too it digresses to become miserably and fully unethical at a dismal -10. 

Anyway, this is a debate that is going to continue and not be readily resolved, so let’s move on. 

If you are still of the notion that ethics exists on a scale and that AI might also be measured by such a scale, and if you also are willing to accept that behavior can be driven or guided by offering where to reside on the scale, the time is ripe to bring up tuning knobs. Ethics tuning knobs. 

Here’s how that works. You come in contact with an AI system and are interacting with it. The AI presents you with an ethics tuning knob, showcasing a scale akin to our ethics scale earlier proposed. Suppose the knob is currently at a 6, but you want the AI to be acting more aligned with an 8, so you turn the knob upward to the 8. At that juncture, the AI adjusts its behavior so that ethically it is exhibiting an 8-score level of ethical compliance rather than the earlier setting of a 6. 

What do you think of that? 

Some would bellow out balderdash, hogwash, and just unadulterated nonsense. A preposterous idea or is it genius? You’ll find that there are experts on both sides of that coin. Perhaps it might be helpful to provide the ethics tuning knob within a contextual exemplar to highlight how it might come to play. 

Here’s a handy contextual indication for you: Will AI-based true self-driving cars potentially contain an ethics tuning knob for use by riders or passengers that use self-driving vehicles?   

Let’s unpack the matter and see.   

For my framework about AI autonomous cars, see the link here: https://aitrends.com/ai-insider/framework-ai-self-driving-driverless-cars-big-picture/ 

Why this is a moonshot effort, see my explanation here: https://aitrends.com/ai-insider/self-driving-car-mother-ai-projects-moonshot/ 

For more about the levels as a type of Richter scale, see my discussion here: https://aitrends.com/ai-insider/richter-scale-levels-self-driving-cars/ 

For the argument about bifurcating the levels, see my explanation here: https://aitrends.com/ai-insider/reframing-ai-levels-for-self-driving-cars-bifurcation-of-autonomy/   

Understanding The Levels Of Self-Driving Cars   

As a clarification, true self-driving cars are ones that the AI drives the car entirely on its own and there isn’t any human assistance during the driving task.   

These driverless vehicles are considered a Level 4 and Level 5, while a car that requires a human driver to co-share the driving effort is usually considered at a 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-on’s that are referred to as ADAS (Advanced Driver-Assistance Systems).   

There is not yet a true self-driving car at Level 5, which we don’t yet even know if this will be possible to achieve, and 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). 

Since semi-autonomous cars require a human driver, the adoption of those types of cars won’t be markedly different than driving conventional vehicles, so there’s not much new per se to cover about them on this topic (though, as you’ll 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 that’s 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. 

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here: https://aitrends.com/ai-insider/remote-piloting-is-a-self-driving-car-crutch/ 

To be wary of fake news about self-driving cars, see my tips here: https://aitrends.com/ai-insider/ai-fake-news-about-self-driving-cars/ 

The ethical implications of AI driving systems are significant, see my indication here: https://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/   

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: https://aitrends.com/ai-insider/normalization-of-deviance-endangers-ai-self-driving-cars/   

Self-Driving Cars And Ethics Tuning Knobs 

For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task. All occupants will be passengers. The AI is doing the driving.   

This seems rather straightforward. You might be wondering where any semblance of ethics behavior enters the picture. Here’s how. Some believe that a self-driving car should always strictly obey the speed limit. 

Imagine that you have just gotten into a self-driving car in the morning and it turns out that you are possibly going to be late getting to work. Your boss is a stickler and has told you that coming in late is a surefire way to get fired.   

You tell the AI via its Natural Language Processing (NLP) that the destination is your work address. 

And, you ask the AI to hit the gas, push the pedal to the metal, screech those tires, and get you to work on-time.

But it is clear cut that if the AI obeys the speed limit, there is absolutely no chance of arriving at work on-time, and since the AI is only and always going to go at or less than the speed limit, your goose is fried.   

Better luck at your next job.   

Whoa, suppose the AI driving system had an ethics tuning knob. 

Abiding strictly by the speed limit occurs when the knob is cranked up to the top numbers like say 9 and 10. 

You turn the knob down to a 5 and tell the AI that you need to rush to work, even if it means going over the speed limit, which at a score of 5 it means that the AI driving system will mildly exceed the speed limit, though not in places like school zones, and only when the traffic situation seems to allow for safely going faster than the speed limit by a smidgen.   

The AI self-driving car gets you to work on-time!   

Later that night, when heading home, you are not in as much of a rush, so you put the knob back to the 9 or 10 that it earlier was set at. 

Also, you have a child-lock on the knob, such that when your kids use the self-driving car, which they can do on their own since there isn’t a human driver needed, the knob is always set at the topmost of the scale and the children cannot alter it.   

How does that seem to you? 

Some self-driving car pundits find the concept of such a tuning knob to be repugnant. 

They point out that everyone will “cheat” and put the knob on the lower scores that will allow the AI to do the same kind of shoddy and dangerous driving that humans do today. Whatever we might have otherwise gained by having self-driving cars, such as the hoped-for reduction in car crashes, along with the reduction in associated injuries and fatalities, will be lost due to the tuning knob capability.   

Others though point out that it is ridiculous to think that people will put up with self-driving cars that are restricted drivers that never bend or break the law. 

You’ll end-up with people opting to rarely use self-driving cars and will instead drive their human-driven cars. This is because they know that they can drive more fluidly and won’t be stuck inside a self-driving car that drives like some scaredy-cat. 

As you might imagine, the ethical ramifications of an ethics tuning knob are immense. 

In this use case, there is a kind of obviousness about the impacts of what an ethics tuning knob foretells.   

Other kinds of AI systems will have their semblance of what an ethics tuning knob might portend, and though it might not be as readily apparent as the case of self-driving cars, there is potentially as much at stake in some of those other AI systems too (which, like a self-driving car, might entail life-or-death repercussions).   

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here: https://aitrends.com/ai-insider/remote-piloting-is-a-self-driving-car-crutch/   

To be wary of fake news about self-driving cars, see my tips here: https://aitrends.com/ai-insider/ai-fake-news-about-self-driving-cars/ 

The ethical implications of AI driving systems are significant, see my indication here: https://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/   

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: https://aitrends.com/ai-insider/normalization-of-deviance-endangers-ai-self-driving-cars/   

Conclusion   

If you really want to get someone going about the ethics tuning knob topic, bring up the allied matter of the Trolley Problem.   

The Trolley Problem is a famous thought experiment involving having to make choices about saving lives and which path you might choose. This has been repeatedly brought up in the context of self-driving cars and garnered acrimonious attention along with rather diametrically opposing views on whether it is relevant or not. 

In any case, the big overarching questions are will we expect AI to have an ethics tuning knob, and if so, what will it do and how will it be used. 

Those that insist there is no cause to have any such device are apt to equally insist that we must have AI that is only and always practicing the utmost of ethical behavior. 

Is that a Utopian perspective or can it be achieved in the real world as we know it?   

Only my crystal ball can say for sure.  

Copyright 2020 Dr. Lance Eliot  

This content is originally posted on AI Trends.  

[Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/] 

http://ai-selfdriving-cars.libsyn.com/website 

Source: https://www.aitrends.com/ai-insider/making-use-of-ai-ethics-tuning-knobs-in-ai-autonomous-cars/

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Application of AI to IT Service Ops by IBM and ServiceNow Exemplifies a Trend 

By John P. Desmond, AI Trends Editor  The application of AI to IT service operations has the potential to automate many tasks and drive down the cost of operations.  The trend is exemplified by the recent agreement between IBM and ServiceNow to leverage IBM’s AI-powered cloud infrastructure with ServiceNow’s intelligent workflow systems, as reported in Forbes.  […]

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AI combined with IT service operations is seen as having the potential to automate many tasks while improving response times and decreasing costs (Credit: Getty Images) 

By John P. Desmond, AI Trends Editor 

The application of AI to IT service operations has the potential to automate many tasks and drive down the cost of operations. 

The trend is exemplified by the recent agreement between IBM and ServiceNow to leverage IBM’s AI-powered cloud infrastructure with ServiceNow’s intelligent workflow systems, as reported in Forbes. 

The goal is to reduce resolution times and lower the cost of outages, which according to a recent report from Aberdeen, can cost a company $260,000 per hour.  

David Parsons, Senior Vice President of Global Alliances and Partner Ecosystem at ServiceNow

“Digital transformation is no longer optional for anyone, and AI and digital workflows are the way forward,” stated David Parsons, Senior Vice President of Global Alliances and Partner Ecosystem at ServiceNow. “The four keys to success with AI are the ability 1) to automate IT, 2) gain deeper insights, 3) reduce risks, and 4) lower costs across your business,” Parsons said.   

The two companies plan to combine their tools in customer engagement to address each of these factors. “The first phase will bring together IBM’s AIOps software and professional services with ServiceNow’s intelligent workflow capabilities to help companies meet the digital demands of this moment,” Parsons stated. 

Arvind Krishna, Chief Executive Officer of IBM stated in a press release on the announcement, “AI is one of the biggest forces driving change in the IT industry to the extent that every company is swiftly becoming an AI company.” ServiceNow’s cloud computing platform helps companies manage digital workflows for enterprise IT operations.  

By partnering with ServiceNow and their market leading Now Platform, clients will be able to use AI to quickly mitigate unforeseen IT incident costs. “Watson AIOps with ServiceNow’s Now Platform is a powerful new way for clients to use automation to transform their IT operations and mitigate unforeseen IT incident costs,” Krishna stated. 

The IT service offering squarely positions IBM at aiming for AI in business. “When we talk about AI, we mean AI for business, which is much different than consumer AI,” stated Michael Gilfix of IBM in the Forbes account. He is the Vice President of Cloud Integration and Chief Product Officer of Cloud Paks at IBM. “AI for business is all about enabling organizations to predict outcomes, optimize resources, and automate processes so humans can focus their time on things that really matter,” he stated.   

IBM Watson has handled more than 30,000 client engagements since inception in 2011, the company reports. Among the benefits of this experience is a vast natural language processing vocabulary, which can parse and understand huge amounts of unstructured data. 

Ericsson Scientists Develop AI System to Automatically Resolve Trouble Tickets 

Another experience involving AI in operations comes from two AI scientists with Ericsson, who have developed a machine learning algorithm to help application service providers manage and automatically resolve trouble tickets. 

Wenting Sun, senior data science manager, Ericsson

Wenting Sun, senior data science manager at Ericsson in San Francisco, and Alka Isac, data scientist in Ericsson’s Global AI Accelerator outside Boston, devised the system to help quickly resolve issues with the complex infrastructure of an application service provider, according to an account on the Ericsson BlogThese could be network connection response problems, infrastructure resource limitations, or software malfunctioning issues. 

The two sought to use advanced NLP algorithms to analyze text information, interpret human language and derive predictions. They also took advantage of features/weights discovered from a group of trained models. Their system uses a hybrid of an unsupervised clustering approach and supervised deep learning embedding. “Multiple optimized models are then ensembled to build the recommendation engine,” the authors state.  

The two describe current trouble ticket handling approaches as time-consuming, tedious, labor-intensive, repetitive, slow, and prone to error. Incorrect triaging often results, which can lead to a reopening of a ticket and more time to resolve, making for unhappy customers. When personnel turns over, the human knowledge gained from years of experience can be lost.  

Alka Isac, data scientist in Ericsson’s Global AI Accelerator

We can replace the tedious and time-consuming triaging process with intelligent recommendations and an AI-assisted approach,” the authors stated, with a time to resolution expected to be reduced up to 75% and avoidance of multiple ticket reopenings  

Sun leads a team of data scientists and data engineers to develop AI/ML applications in the telecommunication domain. She holds a bachelor’s degree in electrical and electronics engineering and a PhD degree in intelligent control. She also drives Ericsson’s contributions to the AI open source platform Acumos (under Linux foundation’s Deep Learning Foundation).  

As a Data Scientist in Ericsson’s Global AI Accelerator, Isac is part of a team of Data Scientists focusing on reducing the resolution time of tickets for Ericsson’s Customer Support Team. She holds a master’s degree in Information Systems Management majoring in Data Science. 

Survey Finds AI Is Helpful to IT 

In a survey of 154 IT and business professionals at companies with at least one AI-related project in general production, AI was found to deliver impressive results to IT departments, enhancing the performance of systems and making help desks more helpful, according to a recent account in ZDNet.  

The survey was conducted by ITPro Today working with InformationWeek and Interop. 

Beyond benefits of AI for the overall business, many respondents could foresee the greatest benefits going right to the IT organization itself63% responded that they hope to achieve greater efficiencies within IT operations. Another 45% aimed for improved product support and customer experience, and another 29% sought improved cybersecurity systems.   

The top IT use case was security analytics and predictive intelligence, cited by 71% of AI leaders. Another 56% stated AI is helping with the help desk, while 54% have seen a positive impact on the productivity of their departments. “While critics say that the hype around AI-driven cybersecurity is overblown, clearly, IT departments are desperate to solve their cybersecurity problems, and, judging by this question in our survey, many of them are hoping AI will fill that need,” stated Sue Troy, author of the survey report.   

AI expertise is in short supply. More than two in three successful AI implementers, 67%, report shortages of candidates with needed machine learning and data modeling skills, while 51seek greater data engineering expertise. Another 42% reported compute infrastructure skills to be in short supply.    

Read the source articles and information in Forbes, the IBM press release on the alliance with ServiceNow, on the Ericsson Blog, in ZDNet and from ITPro Today . 

Source: https://www.aitrends.com/aiops/application-of-ai-to-it-service-ops-by-ibm-and-servicenow-exemplifies-a-trend/

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