Ghostbuster: Unprecedented Accuracy in AI-Generated Text Detection

Posted on by Kedist

Text generated by language models, like ChatGPT, is getting better and better at mimicking human language. But doubts have been raised about the authenticity and trustworthiness of writing produced by AI. In response, scientists at the University of California, Berkeley have created Ghostbuster, a sophisticated technique for identifying text written by artificial intelligence.

Methodology

Ghostbuster uses an innovative technique that involves using a number of less powerful language models and running a systematic search over their features. It may determine if a document is artificial intelligence (AI) created by training a linear classifier on specific attributes. Interestingly, Ghostbuster can identify text produced by unknown or black-box models because it doesn’t need token probabilities from the target model. Three additional datasets were made available by the researchers for benchmarking detection across different domains.

Figure 1: An outline of the Ghostbuster model training procedure. The researchers fed each document into a series of weaker language models to obtain token probabilities. Then, they ran a structured search over combinations of the model outputs and trained a linear classifier on the selected features. (Credit: Berkeley Artificial Intelligence Research (BAIR))

Performance and Comparison

Ghostbuster performed exceptionally well in assessments, outperforming competing detectors like DetectGPT and GPTZero by a wide margin with an in-domain classification score of 99.0 F1. It showed better generality over language models, prompting techniques, and writing domains. These astounding results demonstrate Ghostbuster’s dependability and its capacity to identify AI-generated material.

Ethics

There are a lot of ethical questions raised by the use of AI-generated text detection methods. Such models’ false positive rates, which mistakenly identify genuine human work as AI-generated, can have serious consequences. Prior research has revealed some biases, such as the disproportionate marking of writings written by non-native English speakers as AI-generated. Nonetheless, Ghostbuster helps to address these ethical issues thanks to its enhanced performance and generalization skills. Ghostbuster is a technological and moral advance since it ensures more accurate identification while lowering false positives.

Challenges and Future Directions

The paper notes that there are still difficulties in identifying language produced by artificial intelligence, especially when dealing with hostile prompting and paraphrasing attacks. But Ghostbuster’s emphasis on full paragraphs or papers produced by language models offers a viable direction for further investigation. It is imperative to prioritize transparency and fairness in the creation and implementation of AI-generated text detection systems to guarantee impartial treatment and prevent unwarranted harm.

Constraints

Despite Ghostbuster’s outstanding performance, it’s important to recognize its limitations. The quality and diversity of the weaker language models utilized in the detection process can affect the efficacy of the system. Furthermore, adversarial strategies might develop and provide problems for the accuracy of the system. To overcome these restrictions and further expand the system’s capabilities, more research is required.

Credit: Tesfu Assefa

Conclusion

In summary, Ghostbuster is a noteworthy development in the area of artificial intelligence-generated text detection. Its exceptional performance and ethical advancements make it an effective tool for recognizing text generated by artificial intelligence in a variety of sectors. Ghostbuster addresses potential biases and lowers false positives, promoting the safe usage of AI-generated text detection systems. Continued research and development is essential to overcoming obstacles, enhancing system performance, and guaranteeing the moral use of AI-generated text identification tools. With the prevalence of text produced by artificial intelligence (AI), Ghostbuster provides a useful way to ensure the reliability and trustworthiness of written material while giving ethical issues priority.

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Crypto AI Revenues Could Exceed $10 Billion by 2030

Posted on by Werner

The integration of crypto incentives and blockchain technology with artificial intelligence (AI) could drive widespread adoption and advancement of decentralized AI and AI cryptos over the next decade, according to a new report by digital asset manager VanEck. Its timing couldn’t be better as we’ve been seeing an absolute explosion of AI-related cryptocurrency prices so far in 2024, the most recent rally coming on the back of OpenAI’s Sora launch.

In a recent in-depth report ‘Crypto AI Revenue Predictions by 2030’, VanEck analysts made several bullish predictions about the growth and the revenue potential of businesses operating at the intersection of crypto and AI. While any predictions should be taken with a grain of salt, considering where we’re at in the current crypto 2024 bull cycle, the report asks a lot of important questions and offers some surprising answers in the process. 

Van Eck analyst Gabor Gurbacs has taken a controversial stand against AI, predicting its misuse will result in the biggest spam attacks the world has ever seen. Gurbacs sees Bitcoin as its only salvation, so read on to find why. 

Public Blockchains Uniquely Positioned to Aid AI Progress

The Van Eck report is extremely bullish on the benefits that blockchain can bring to AI. He feels that AI and AI Agents “provide the raison d’être” for blockchain technology. It’s a big statement, but backed up by some sound reasoning: blockchain networks like Bitcoin and Ethereum possess several key attributes that make them well-suited to address existing challenges facing the AI industry, most notably:

  • Transparency – Being public ledgers recording all transactions, blockchains enable oversight related to data usage, model ownership, etc. This supports trust and accountability.
  • Immutability – Records made on blockchains cannot be altered afterwards. This immutability lends integrity to data used for critical model training and testing.
  • Ownership – Tokens, NFTs, and smart contracts allow us to clearly define ownership rights related to data, models, and even model outputs.
  • Adversarial Testing Environment – The adversarial, incentivized environments of crypto networks that financially reward hacking and optimization force rigorous, real-world testing of systems.

He projects that these properties of blockchain will speed up the adoption of, and trust in, AI over the next seven years.

Crypto Market Share of AI Could Reach Billions Annually

Applying economic and productivity growth assumptions to previous McKinsey research, Van Eck predicts a total addressable market (TAM) for global productivity gains enabled by AI automation will clock a crazy $5.85 trillion in 2030. Assuming 33% adoption by businesses worldwide, annual AI revenues could top $250 billion within the decade.

Moreover, the report forecasts crypto’s potential market share across major AI business categories:

  • Software: $6.27 billion
  • Infrastructure/Compute: $1.9 billion
  • Identity: $878 million
  • Safety/Compliance: $1.12 billion

Tallying these up, it’s predicted that annual crypto AI revenues could approach $10.2 billion by 2030 in VanEck’s base case. Under more aggressive assumptions about adoption and market capture, AI cryptocurrencies’ stake could exceed $50 billion.

How blockchain helps AI solve identity issues 

Artificial intelligence is increasingly critical to the global economy, akin to an essential utility. Web2 giants like Amazon and Google dominate current AI infrastructure, while blockchain technology supports specific, high-demand needs with its decentralized approach, offering flexibility and customization for AI development. This scenario positions blockchain as a dynamic adjunct in the AI infrastructure market, similar to how ride-sharing platforms like Uber complement, rather than replace, traditional transport services.

The importance of secure AI identities is rising, with blockchain playing a key role in preventing Sybil attacks by establishing verifiable digital identities. This defense is crucial as AI applications extend into areas like autonomous vehicles and healthcare, where safety is paramount. 

Blockchain’s immutable records serve as reliable “proofs of safety,” essential for high-stakes accountability and compliance. Despite blockchain’s potential to revolutionize AI safety and identity verification, a significant portion of this market is expected to stay centralized, preferring established reliability and trust in sensitive sectors.

Bitcoin miners find surprising synergy with the AI sector

Bitcoin miners, traditionally focused on mining proof-of-work cryptocurrencies like Bitcoin, are diversifying into the AI sector due to their shared high energy consumption needs. These miners have historically invested heavily in energy infrastructure, often utilizing cost-effective but carbon-intensive power sources. This positions them uniquely to offer lower-cost energy solutions for AI’s backend infrastructure, contrasting with Big Tech’s move towards renewable energy and vertically integrated operations. 

As AI’s energy demands potentially outpace current projections, bitcoin miners’ cost advantage in electricity could become increasingly significant, prompting a shift towards providing high-margin AI services, particularly in GPU provisioning.

Companies like Hive, Hut 8, and Applied Digital are leading this transition, with some reporting substantial revenue growth from AI-related operations compared to traditional bitcoin mining. 

For instance, Hive’s AI operations are notably more profitable on a per-megawatt basis. However, despite the promising shift and potential for revenue diversification, bitcoin miners face challenges in scaling up for AI, including skills gaps in data center construction, the need for a specialized salesforce, and limitations imposed by network latency and bandwidth in remote locations. These hurdles could impede their pivot to AI, despite the opportunities in the sector.

Credit: Tesfu Assefa

Notable Blockchain Use Cases Emerging Across the AI Landscape

Public blockchains and crypto token incentives have already sparked solutions addressing several pressing needs for progress across the AI landscape:

Decentralized Compute/Infrastructure

Projects like Akash, Render and Bittensor provide decentralized cloud computing platforms and infrastructure to help supply scarce GPUs for AI model training and deployment. This helps to pull down current limitations around access, cost, security, and customization options.

Model Optimization

Protocols like Numerai use tokenized incentives to organize data science competitions aimed at building optimized models for tasks like quantitative finance or natural language processing. Crypto tokens reward the most accurate and effective models.

Data Integrity

Emerging zero-knowledge proof solutions from startups like MODA allow AI model owners to mathematically prove certain claims about data usage or model performance without revealing proprietary intellectual property or sensitive information. This supports copyright protections and model accountability.

Digital Identity

Initiatives like WorldID (with its Worldcoin cryptocurrency) – spearheaded by AI thought leader Sam Altman from OpenAI and Sora – controversially leverage blockchain and biometrics to establish verified digital identity firmly linked to real humans. As automation increases, reliably determining humanity could help ensure security for computer networks and systems.

Conclusion

In summary, crypto-based networks already demonstrate clear potential to overcome some of the hardest barriers holding back innovation and mainstream adoption in artificial intelligence. However, integrating these two exponentially advancing technologies comes with persistent technical and adoption challenges.

If solutions continue maturing at their current brisk pace, crypto and AI seem well positioned to drive tremendous value for one another over the next 7 to 10 years. But uncertainties and speculation still cloud the most aggressive growth projections put forth. According to VanEck, the base case for 2030 remains strong, but the roadmap to billions in annual revenue still requires some visionary bets on both technologies to pay off.

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Universal Human Values and Artificial General Intelligence

Posted on by Jay Friedenberg

The field of value alignment is becoming increasingly important as AGI developments accelerate. By alignment we mean giving a generally intelligent software system the capability to act in a way that is beneficial to humans. One approach to this is to instill AI programs with human values. 

However, the research in this field tends to focus on algorithms for maximizing metrics like pleasure or happiness. Much less attention has been devoted to the actual set of values they should follow.

I propose that the evidence from worldwide religions, traditional philosophy, evolutionary psychology and survey research finds surprising agreement on basic human values. Abstracting from this work, I propose a five-tier system of values that can be applied to an AGI. 

Level 1 is the values of life, survival, and persistence. It evolves from a concern for living and reproducing. An agent acts to avoid destruction, seek out energy and pass on characteristics to a future generation. 

Level 2 contains truth, wisdom, and knowledge. It is about agents that value truth and the ability to understand and interact well with their immediate surroundings.

Level 3 is a bundle of three sets of values concerning oneself: freedom, temperance, and growth. These values affect the agent’s internal state and behavior. The ‘self’ in question might be a human being or an AI designed as an autonomous moral agent. 

Level 4 is a bundle of five values: empathy, authority, security, justice, and conformity. These are all concerned with group interaction. They apply to biological fellow beings or to multi-agent AI systems. 

Level 5 contains values for dealing with nature and the surrounding ecosystem as well as the universe beyond. A sufficiently powerful general intelligence could have a terrestrial and extra-terrestrial impact and so needs to be concerned with the larger environment.

Credit: Tesfu Assefa

The values are concentric – they start with fundamental metaphysical and epistemological assumptions and radiate outwards to be more inclusive in space, in time, and in variety.

However there are a number of unresolved issues:

  1. Universality – The debate of moral relativism vs. moral universalism in which different values are emphasized or traded off.
  2. Hierarchy – The ranked order of the values in terms of their importance or goal relevance.
  3. Change – Ethics for a transhuman or posthuman society might necessitate alterations due to changes in the nature of humanity or other AIs.
  4. Human centricity – These values are human and created by our unique evolution and societal organizations.
  5. Consciousness – Agents may need to feel – to have qualia – in order to be morally responsible. This does not mean that an AGI cannot act morally.
  6. Implementation – This listing of said values does not describe the mechanisms by which they can be instantiated.

In light of these challenges and opportunities within the realm of value alignment in AGI, the upcoming Beneficial AGI Summit emerges as a pivotal platform. The BGI Summit, with its gathering of leading minds in artificial intelligence, ethics, and related fields, presents an unparalleled opportunity to delve deeper into these unresolved issues.

The universality of values, the hierarchy of ethical considerations, the evolving nature of ethics in a transhuman society, the human-centric viewpoint, consciousness, and the practical implementation of these values in AGI systems – all these topics are ripe for discussion at the BGI Summit. 

This event could mark a significant step forward in our understanding and ability to integrate these complex value systems into AGI, fostering a future where artificial intelligence compliments and enhances human values and ethics. Join me at the BGI Summit to explore these topics. For more information and to register visit bgi24.ai

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NEOLAF: Introducing a Never-Ending Learning Framework for Intelligent Agents

Posted on by Tensae

The article “NEOLAF: A Neural-Symbolic Cognitive Architecture for Generalized Intelligence”  introduces an integrated neural-symbolic cognitive architecture aimed at modeling and constructing intelligent agents. Unlike traditional approaches such as pure connectionist or symbolic models, NEOLAF stands out for its unique features, including superior explainability, incremental learning, efficiency, collaborative and distributed learning, human-in-the-loop enablement, and self-improvement. This study highlights the advanced learning capabilities of the framework with a captivating experiment featuring a NEOLAF agent tasked with tackling challenging math questions from the MATH dataset.

NEOLAF serves a broad purpose in constructing intelligent agents, particularly self-improving intelligent tutor agents within adaptive instructional systems. Inspired by human cognitive development, NEOLAF combines the best features of both connectionist (ChatGPT, for example) and symbolic (SOAR, ACTR) techniques to overcome the shortcomings of each paradigm. The framework is a flexible tool for creating intelligent agents because of its unique benefits, which include explainability, incremental learning, efficiency, collaborative learning, and self-improvement.

The methodology behind NEOLAF involves instantiating learning agents from a DNA-like starter kit, leveraging pre-trained large language models (LLMs) for foundational reasoning. Like human cognition, NEOLAF agents function on two cognitive levels: rapid and slow. As a depiction of the knowledge-experience duality, the KSTAR framework (Knowledge, Situation, Task, Action, Result) is presented, enabling agents to learn via ongoing, iterative, and multitasking procedures.

NEOLAF agents exhibit two types of memory: implicit memory, which involves offline knowledge injection for model fine-tuning, and explicit memory, which represents past knowledge stored through the KSTAR process for each encounter. Like humans consolidate their memories as they sleep, NEOLAF agents can consolidate knowledge thanks to this dual-memory architecture.

Credit: Mindplex

Building on related work in the chain of thought reasoning, NEOLAF incorporates recent advances in LLM, reinforcement learning (RL), multitask learning, and planning to conduct tasks in the KSTAR process. The study describes a preliminary implementation of NEOLAF for a math problem-solving agent and evaluates its effectiveness against other models, like ChatGPT. The experiment uses difficult questions from the AIME and USAMO Math Competitions to assess different performance measures.

Beyond its application to math problem-solving, NEOLAF is envisioned as a cognitive architecture for an agent-based learning environment (Open Learning Adaptive Framework – OLAF). OLAF creates a dynamic and interactive learning environment by integrating three types of agents: learners, human teachers, and AI agents.

In summary, the NEOLAF architecture combines system-1 LLM capabilities with system-2 explicit reasoning and external services, which is a breakthrough approach to AI. NEOLAF solves significant issues with conventional methods by utilizing a dual-memory architecture and the KSTAR representation for problem-solving. Beyond addressing mathematical problems, the framework may be used to develop co-habitat ecosystems called BotLand and multimodal reasoning, which will promote interaction and evolution between intelligent agents and humans.  NEOLAF emerges as a lightweight and continually improving AI model, offering a promising alternative to current leading LLMs that are expensive to train and maintain.

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