Now Next Later AI - Blog , Uncategorized

Navigating the Murky Waters of AI and Copyright

Fri, 15 Sep 2023 09:33:55 +1000

Powerful Generative AI systems can now generate stunning works of art, human-sounding text, and original music with the click of a button. This emerging technology holds immense promise, yet also surfaces intricate legal questions around copyright protections. How exactly should business leaders navigate the complex intersection between AI creation and existing copyright laws? A new research paper by legal scholar Dr Andres Guadamuz provides an enlightening analysis of this murky terrain.

Guadamuz explains that modern AI relies heavily on a process called machine learning. Here, algorithms are fed vast troves of data—such as text corpuses, images, or audio samples - which they analyze to discern patterns and complete tasks. As the AI ingests more data, its performance improves. This data serves as the lifeblood for systems like ChatGPT, DALL-E 2, and Midjourney to produce their creative outputs.

Of course, much of this training data consists of copyrighted works. And herein lies the crux of the issue. Does an AI system infringe copyright through its utilization of such data? Are laws adequately calibrated to protect rights holders while also giving space for AI innovation to blossom? Guadamuz's research suggests we are in a legal gray zone lacking definitive precedents.

One fundamental question is whether the data used to train AI systems is eligible for copyright protection in the first place. Raw facts, statistics, and randomly generated information are not subject to copyright laws as they lack originality. However, some training datasets do involve meaningful creative choices by humans in the selection and arrangement of data. For example, a dataset of images captioned with descriptive text would have more original compilation than a random assortment of photos. These types of datasets with creative selection potentially clear the originality bar needed for copyright protection.

That said, many AI models utilize purely factual data, public domain content, or freely licensed works that do not warrant copyright restrictions. According to Guadamuz's analysis, there are plenty of legitimate large-scale datasets available that teach AI systems without necessarily infringing on copyrighted source material. For instance, collections of Shakespeare's works or Van Gogh's paintings that are in the public domain can train models without legal concerns. Additionally, open access datasets like those under Creative Commons licenses offer content that creators have explicitly authorized for reuse. So there are many lawful paths for feeding data to AI systems without trampling on copyright protections.

What about the actual training process? Here Guadamuz explains there is considerable uncertainty. Widely adopted machine learning methods require the AI to intake copies of data to extract patterns. Guadamuz notes this likely constitutes reproduction under copyright law and thus requires permission. However, the research highlights that temporary copies or text and data mining exceptions in some jurisdictions may permit this usage without authorization. The EU specifically created new exceptions for text and data mining for both non-commercial and commercial purposes. But their precise boundaries remain untested so far.

Analyzing copyright issues around AI outputs adds further Complexity according to Guadamuz. Three main requirements must be fulfilled to show infringement: 1) violation of exclusive rights, 2) a causal connection to copyrighted inputs, and 3) substantially similar copying.

Guadamuz suggests the second and third factors make infringement difficult to prove outside verbatim re-creations. With vast datasets and compressed latent representations, directly connecting outputs to specific inputs poses challenges. Similarly, replication of broad styles and ideas is not protected by copyright. Substantial similarity requires qualitatively important expressions to be copied. But Guadamuz notes that character copyright issues could arise with AI generations. He argues current fair dealing style exceptions around parody and pastiche may shield some AI outputs.

In conclusion, Guadamuz paints a complex landscape filled with legal uncertainty. With few definitive court precedents so far, business leaders should closely track how laws are interpreted as AI copyright cases inevitably unfold. In the meantime, pursuing ethical approaches that respect rights holder interests appears prudent. Additionally, supporting collaborative initiatives and technological solutions like opt-out databases could help ease emerging tensions. But the path forward will require nuance, cooperation and openness to new models between all stakeholders.

Footnotes:

A Scanner Darkly: Copyright Liability and Exceptions in Artificial Intelligence Inputs and Outputs by Dr Andres Guadamuz

Transformers Expressible in Simple Logic

Sun, 13 Aug 2023 19:50:20 +1000

A new study from New York University and the Allen Institute for AI has shown that large language models called transformers can be expressed in a simple logic formalism. This finding challenges the perception that transformers are inscrutable black boxes and suggests avenues for interpreting how they work.

Transformers are a type of neural network behind major AI achievements like chatbots and language translation. They are trained on massive datasets to generate human-like text. Despite their impressive capabilities, how transformers arrive at their outputs has remained poorly understood.

The researchers proved transformers can be translated into symbolic logic sentences that replicate their function. Specifically, they showed transformers fit within a logic called first-order logic with majority quantifiers. This logic allows logical sentences with familiar constructs like "AND", "OR", and "IF-THEN", as well as majority quantifiers that check if a condition holds for over half of the elements.

While real-world transformers are complex neural networks, this study theoretically shows their reasoning can be captured by simple logical expressions. For instance, the logic could recognize patterns like "three As followed by three Bs", which transformers are known to identify.

The findings disprove the notion that transformers are inscrutable black boxes. Instead, they suggest transformers implement a form of reasoning not radically different from familiar logical formalisms. The possibility of expressing transformers in interpretable logic could enable explaining how they arrive at outputs, like detecting biases.

For business leaders deploying AI, this research opens possibilities for making transformers more transparent and accountable. It provides a path toward debugging models to avoid failures or bias. The ability to translate transformers into logical sentences could allow systematically checking if undesirable reasoning patterns occur.

Overall, this theoretical advance challenges prevailing views of transformers as hopelessly opaque. It demonstrates their thinking can be characterized in understandable logic, unlocking new ways for technologists to interpret these increasingly critical AI models. The research brings transformers closer to human-level reasoning by showing their outputs are not ineffable, but rather can be explained through logic.

Sources:

A Logic for Expressing Log-Precision Transformers

William Merrill and Ashish Sabharwal

Study Uncovers Bias in AI Text Detectors Against Non-Native Writers

Sun, 13 Aug 2023 05:47:29 +1000

A new study reveals troubling bias in AI detectors of machine-generated text against non-native English speakers. The findings raise important questions around AI fairness and underscore the need for more inclusive technologies.

With models like ChatGPT attracting millions of users, risks emerge of AI text being passed off as human-written. Several detectors aim to differentiate AI versus human content, but their effectiveness and fairness are uncertain. This study by researchers at Stanford evaluates popular detectors on essays by native English 8th graders versus Chinese English learners.

Alarmingly, over half of non-native essays were mislabeled as AI-generated, while native essays were accurately classified. The study shows detectors consistently penalize non-native writers' limited vocabulary and linguistic complexity. When the researchers used ChatGPT to enrich the non-native essays with more native-like word choices, misclassifications plummeted.

The implications are stark. As detectors become more stringent, non-native authors may rely on AI editing just to avoid false accusations of cheating or fake news. This risks further marginalizing diverse voices in education, media, and public discourse. The study highlights the urgent need to address bias in AI systems that increasingly mediate communication.

The researchers also demonstrate a simple technique for bypassing detectors, editing machine-generated essays and abstracts to dodge detection. This casts doubt on current methods overly reliant on statistical measures like perplexity. More robust techniques and human-in-the-loop validation will likely be imperative.

For business leaders, this study is a wake-up call on emerging risks of unfairness as AI proliferates. Consider recruiting and hiring, where text analysis aids decision-making. Bias against non-native speech could lead to unjust screening out of qualified talent. Proactively auditing for fairness and enabling redress will be key.

Customer service chatbots present another concern. If detectors disproportionately flag non-native customers as “bots,” will they receive lower quality service? Fostering trust requires ensuring AI interacts equitably with all users.

As for content moderation, faulty AI could censor non-native writers sharing ideas or reporting on social media. Humans must remain in the loop to prevent silencing marginalized voices.

While detectors aim to manage risks of AI content, overlooking their own limitations poses risks of disenfranchisement. Study co-author James Zou states: “Our findings emphasize the need for increased focus on the fairness and robustness of these detectors.”

Indeed, achieving AI’s promise requires centering inclusion from the start, not as an afterthought. Building balanced training data, testing for disparate impacts, and enabling redress of unfair outcomes will be key priorities for responsible innovation.

The rapid pace of AI progress demands proactive engagement on ethics and governance. Thoughtful development today will lead to more just and empowering outcomes as these technologies continue transforming society.

Sources:

GPT detectors are biased against non-native English writers

Protecting LLMs from Theft with Watermarks

Sat, 12 Aug 2023 10:41:41 +1000

AI models, like GPT-4, are like gold in the tech world. Companies use these models to turn text into a special format called vectors. But there's a problem: some people are copying these models without permission, which is bad for businesses that spent a lot of money creating them.

Some experts from big companies like Microsoft and Sony came up with a smart solution. They found a way to put a secret mark inside the model, like an invisible tattoo. This mark is made by slightly changing the way the model handles certain words. So, if someone tries to copy the model, the mark will also be copied. This way, the original company can prove they own the model.

How does it work? These secret words (let's call them 'trigger words') are chosen carefully. They're not super common, so they don't mess up the model's usual tasks. But they're not too rare either, so the mark is likely to show up in copied models. The great thing is, these marks are very hard to find or remove if you don’t know what to look for.

Why is this important for businesses?

Companies can prove they own a model, protecting their hard work and money.
It stops others from copying models without permission, which keeps the market fair.
Customers using the original service won't notice any difference, so they still get top-quality service.
This method can be used in many different AI models and situations.
It could also help companies track if their own employees are sharing things they shouldn’t.

In summary, this invisible marking system is like a shield for AI models in the cloud. It makes sure companies' hard work is safe, stops people from cheating, and helps the whole AI industry stay fair and trustworthy. While it's not perfect, it's a big step forward in keeping AI models secure.

Critically Analyzing the Priorities of Companies Like Microsoft

While the invisible marking system is an innovative way to safeguard AI models, there's a more fundamental issue many companies are overlooking: the ethical and legal implications of training these models on copyrighted data. Often, AI models like GPT-4 are trained on vast datasets that include copyrighted materials, like books, articles, or artwork. This training process might infringe on the rights of artists, authors, and other content creators, leading to significant legal and ethical quandaries.

These creators often don't consent to their work being used in such a manner, and it denies them the rightful recognition or compensation they deserve. It's imperative that companies prioritize the sourcing of their training data ethically, ensuring it respects copyrights and intellectual property rights.

Before adopting advanced protection measures for the models, the first step should be to ensure that these models aren't built upon the unrecognized or uncompensated work of others. The industry must acknowledge and address this foundational issue, ensuring AI advancements are both technologically and ethically sound.

Sources:

ACL 2023 — Area Chair Awards — NLP Applications: Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark

Our Pedagogic Design

Mon, 10 Jul 2023 23:18:13 +1000

Hi there!

Today I want to share with you our AI academy’s pedagogic design.

As you may know, designing an effective online course involves careful planning, thoughtful choices, and a commitment to ongoing refinement.

Our design aligns with the National Microcredentials Framework, aimed at addressing the growing demand for shorter-form courses that enable rapid upskilling and lifelong learning.

Here is our approach:

We set clear learning objectives. Each one of our lessons includes a set of SMART learning outcomes. These are measurable and tested at the end of each lesson and course. A certificate is issued upon successful completion.
We design for engagement by catering to diverse learning styles. Each one of our lessons integrates video, audio, and images. And we also enable community discussion, and group assignments (when applicable).
We structure our courses by breaking down content into manageable chunks, and sequence them in ways that build understanding. Each course has a learning roadmap designed to be logical and intuitive.
All assignments, assessments, and activities directly support the achievement of the learning outcomes. This is called constructive alignment.
Our materials are designed for accessibility: video transcripts, images alt text, and screen-reader friendly documents. Courses are available on mobile app and on a variety of browsers.
We support self-regulated learning. Drip lesson scheduling and ongoing comms can be enabled to help learners manage their time, set goals, stay motivated, and reflect on their learning.
We provide immediate and meaningful feedback. Our interactive quizzes at the end of each lesson provide an opportunity to revise and measure learning outcomes. Plus, the course’s community site enables discussion and peer support.
Our content is reviewed and updated monthly to keep up with the fast pace evolution of the AI landscape. We also solicit feedback by enabling course reviews and surveys to identify areas for improvement.
Our human instructors regularly engage in the discussion forums, providing learning support. They also deliver weekly live sessions which are a great opportunity for real-time updates on the latest news and discussion.
We build a long-lasting community. In addition to the course forum, we give learners access to our discord site enabling voice, video, and chat avenues for engagement beyond the course timeline.

This approach is important to us, but we are looking for opportunities to improve it.

I have a few other things to share with you. You see, we live what we teach.

Our platform is intelligent and automated to drive the best customer experience and operational productivity.

Our use of AI avatars, aka me, in self-paced lesson content enables us to:

update content fast and effectively, keeping up to date with a fast moving AI landscape.
deliver content at an optimum pace and to the highest standards of enunciation.
and also releases instructor time to provide one-to-one support, engage in live sessions, and create more courses.

Support is available at every step along the way. Chatbot, knowledge base, and help desk—are all just one click away.

Also, privacy and security are always top of mind. Each student has a dedicated portal, a secure login, and a console to track progress. Gated corporate courses can be setup to support groups of students from the same business.

And at least once a week we organise live sessions, delivered on our platform, which supports breakout rooms, whiteboard, group video sharing, polls, chat, Q&A, co-annotation, and live quizzes. All our events can be accessed via mobile app.

What are you waiting for? Go check out our courses, and I’ll see you in one of our classes.

Bye for now and… Stay human!

Artificial Intelligence Risk Management

Sun, 02 Jul 2023 02:39:36 +1000

Are you looking to adopt Artificial Intelligence in your organization? Do you have a risk management framework in place?

According to PwC’s Global Artificial Intelligence Study, the potential contribution of artificial intelligence to the global economy by 2030 is as high as 15 trillion US dollars. And according to McKinsey, AI adoption has more than doubled since 2017. However, it has plateaued between 50 and 60 percent for the past few years. AI capabilities, such as natural-language generation and computer vision, have also doubled.

McKinsey has found that, while AI use has increased, risk mitigation to bolster digital trust has remained concerningly consistent since 2019.

Today, there is a lot of fear, uncertainty, and doubt. Still, Artificial Intelligence is not even close to delivering a super intelligence, but highly specialized, what we call narrow AI, is transforming entire industries.

A portfolio approach can help companies successfully unleash the power of machine intelligence. And a well-balanced portfolio will include some quick wins, focused, for example, on the optimization of a touch-point, and some long-term projects transforming end-to-end processes.

The quick wins won’t transform your business, but they expose staff to the benefits and opportunities AI presents, and build confidence and momentum with key stakeholders like the board and management. For example, a quick win could involve a tool to schedule internal meetings, which allows you to use off-the-shelf packages, while you simultaneously build capability in areas such as hiring and training staff, large-scale data gathering, processing, and labeling.

Training staff and gaining AI capabilities over time is important, but to reduce risk and drive some momentum, we suggest that, instead of front-loading your costs, you scale them slowly and consistently, making use of off-the-shelf solutions (with suitable adaptations) to help keep costs manageable.

Data is without a doubt the key to machine learning projects, and the virtuous cycle of data collection means the rich get richer. So, a key risk of this new wave of transformation is the concentration of power in the hands of a few platforms. Knowing where to place your bets is tied to the data you can access that is of competitive advantage to your business. However, issues related to bias and potential misuse will be amplified if leaders have little or no understanding of how algorithms are built.

Misconceptions can also be costly, with several studies suggesting that product-related AI innovation still struggles to deliver value. Knowing where to place innovation bets is less risky when management understands what types of innovations produce best returns.

Misconceptions also occur in regards to what to centralize and why, and related operating model choices. An AI Era business looks very different from an Internet Era one, and this transformation requires a considered approach and AI knowledge. The product-innovation focus and incrementality of the agile internet companies is replaced by strategic data acquisition, unified data platforms, end-to-end process automation and the adoption of new roles within the business.

Another misconception relates to expected job losses. Job numbers will remain about the same, but a rebalancing of roles will occur and you should expect to see fewer requirements for managerial roles as you scale AI within the organization.

Many of the best performing Machine Learning models are often highly opaque. Explainable AI is becoming a key requirement and in high demand. Machine Learning requires its own governance frameworks, with key lines of defense established right up front.

In addition, AI presents major ethical concerns related to privacy, bias, and discrimination. An organization must define the principles guiding their AI initiatives, ensuring they align with their values, industry, culture, geography, and other factors.

A 2020 global report from Harvard University evaluated 36 AI Ethics Frameworks from big companies, standards bodies, industry coalitions, and governments to identify eight common themes related to AI Ethics that you should consider:

Privacy,
Accountability,
Safety and security,
Transparency and explainability,
Fairness and non-discrimination,
Human control of technology,
Professional responsibility, and
Promotion of human values.

AI presents tremendous opportunity and also high risks. The right risk management approach is critical, as the perils presented by AI differ significantly from previous waves of digital transformation.

A solid AI Risk Management Framework enables dialogue, understanding, and activities to manage AI risks, and responsibly develop trustworthy AI systems. It should address four functions:

Govern,
Map,
Measure, and
Manage.

Each broken down into specific actions and outcomes.

If you enjoyed this article, subscribe so that we can share with you Artificial Intelligence Risk Management approaches and frameworks. We’ll help you identify what you should prioritize now, next, and later.

Stay human!

Inês

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Now Next Later AI - Blog , Uncategorized

Navigating the Murky Waters of AI and Copyright

Transformers Expressible in Simple Logic

Study Uncovers Bias in AI Text Detectors Against Non-Native Writers

Protecting LLMs from Theft with Watermarks

Our Pedagogic Design

Artificial Intelligence Risk Management

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