Tag: scifi

– Large Language Models (LLMs) have revolutionized AI services.
– LLM chatbots are susceptible to “jailbreak” attacks.
– Existing attempts to mitigate threats have gaps in understanding.
– Jailbreaker framework offers understanding of jailbreak attacks and countermeasures.
– Innovative methodology to reverse-engineer defensive strategies of LLM chatbots.
– Automatic generation method for jailbreak prompts with high success rate.
– Responsible disclosure of findings to service providers.

– Jailbreaker framework provides understanding of jailbreak attacks and countermeasures.
– Reverse-engineers defensive strategies of prominent LLM chatbots.
– Introduces automatic generation method for jailbreak prompts.
– Achieves a promising average success rate of 21.58% in automated jailbreak generation.
– Urgent need for more robust defenses in LLM chatbots.

Jailbreaker as discussed by the authors proposes an innovative methodology inspired by time-based SQL injection techniques to reverse-engineer the defensive strategies of prominent LLM chatbots, such as ChatGPT, Bard, and Bing Chat.

– Existing defensive measures of LLM chatbots are vulnerable to jailbreak attacks.
– The Jailbreaker framework successfully bypasses the defenses of prominent LLM chatbots.
– Automated jailbreak generation achieves a promising success rate of 21.58%.
– Responsible disclosure of findings to service providers highlights the need for more robust defenses.

– Proposed methodology to reverse-engineer defensive strategies of LLM chatbots.
– Introduced automatic generation method for jailbreak prompts.
– Achieved a promising average success rate of 21.58% in jailbreak generation.

– GPT-4 released in March 2023 for financial industry tasks.
– Prompt engineering improves sentiment and theme classification.

– GPT-4 outperforms GPT-3 and GPT-3.5 models.
– Prompt engineering improves GPT-4’s performance.

– GPT-4 outperforms GPT-3 and GPT-3.5 models.
– Prompt engineering improves GPT-4’s performance for sentiment classification.

– GPT-4 outperforms GPT-3 and GPT-3.5 models.
– Prompt engineering improves GPT-4’s performance for sentiment classification.

– GPT-4 outperforms GPT-3 and GPT-3.5 models.
– GPT-4 slightly outperforms BART and FinBERT.

– GPT-4 released in March 2023 for financial industry tasks.
– Prompt engineering improves sentiment and theme classification.

– GPT-4 outperforms GPT-3 and GPT-3.5 models.
– Prompt engineering improves GPT-4’s performance.

– GPT-4 outperforms GPT-3 and GPT-3.5 models.
– Prompt engineering improves GPT-4’s performance for sentiment classification.

– GPT-4 outperforms GPT-3 and GPT-3.5 models.
– Prompt engineering improves GPT-4’s performance for sentiment classification.

– GPT-4 outperforms GPT-3 and GPT-3.5 models.
– GPT-4 slightly outperforms BART and FinBERT.

– Survey results show that almost a third of companies are using generative AI in at least one business function.
– High-performing organizations attribute a significant portion of their EBIT to AI adoption.
– Companies need to address various issues to ensure a strong return on investment in generative AI.

– Almost a third of companies are using generative AI in at least one business function.
– Companies need to address various issues to maximize the potential of generative AI.

– High performers focus on value and rewire their organization for success.
– Almost a third of companies are using generative AI in at least one business function.
– Companies need to address various issues to ensure a strong return on investment in generative AI.

– Almost a third of companies are using generative AI in at least one business function.
– High performers focus on value and rewire their organization to capture that value.
– Companies need to tackle various issues to ensure a strong return on investment in generative AI.

– Paper discusses the impact of AI on EU’s geopolitical power.
– AI seen primarily from an economic, social, and regulatory angle.
– Recommends ways for EU to respond to changing international balance of power.

– AI is a tool of power politics and state diplomacy.
– EU approaches AI from an economic, social, and regulatory angle.
– AI impacts EU’s geopolitical power and its relationship with other countries.
– Possible scenarios for how AI may change the international balance of power.
– Recommendations for the EU and its Member States to respond.

– AI is a tool of power politics and state diplomacy.
– AI will impact the global balance of power and geopolitics.
– The EU needs to engage with AI and consider its external dimension.
– The EU should invest more time, effort, and money in AI.
– The EU should focus on ethical and trustworthy AI and promote it globally.

– AI is a tool of power politics and state diplomacy.
– EU approaches AI from an economic, social, and regulatory angle.
– AI impacts EU’s geopolitical power and its relationship with other countries.
– Possible scenarios for how AI may change the international balance of power.
– Recommendations for the EU and its Member States to respond.

– The paper discusses the state of AI in 2023.
– Generative AI is highlighted as having a breakout year.
– Almost a third of companies are using generative AI in at least one business function.
– High performers attribute a significant portion of their EBIT to AI adoption.

– Almost a third of companies are using generative AI in at least one business function.
– Generative AI is seen as viable and accepted in business.
– Service operations is the only function expected to see a decrease in workforce size due to generative AI.

– High performers focus on value and rewire their organization for success.
– Almost a third of companies are using generative AI in at least one business function.
– Organizations are hiring data engineers, machine learning engineers, and AI data scientists.
– Hiring of AI-related software engineers has decreased compared to the previous survey.
– Prompt engineering roles have emerged alongside generative AI adoption.

– Almost a third of companies are using generative AI in at least one business function.
– High performers focus on value and rewire their organization to capture that value.
– Data engineers, machine learning engineers, and AI data scientists are commonly hired roles.
– AI-related software engineers are less commonly hired compared to the previous survey.
– Prompt engineering roles have emerged with the rise of generative AI adoption.

– Nature survey on researchers’ views on the rise of AI in science
– AI tools provide faster data processing and save time and money
– Concerns about reliance on pattern recognition, bias, fraud, and irreproducible research
– Evidence of bias in AI tools for medical diagnostics
– Commercial firms dominate computing resources and ownership of AI tools

– AI tools in science can lead to mistakes and false positives.
– Naive use of AI tools can result in irreproducible research.
– Generative AI tools can be used for summarizing and writing research papers.
– AI tools can help in brainstorming ideas and writing code.
– Concerns about faked studies, false information, and perpetuating bias in medical diagnostics.

– Researchers are excited about the use of AI tools in science.
– AI provides faster data processing and saves time and money.
– Concerns include reliance on pattern recognition, bias, fraud, and irreproducible research.
– AI tools for medical diagnostics can perpetuate bias and false information.
– Commercial firms dominate AI computing resources and ownership of AI tools.
– Collaboration between researchers and AI firms is considered important.
– Researchers are concerned about the potential misuse of AI, including spreading misinformation.
– Automated AI weapons and AI-assisted surveillance are also concerning.
– The idea of AI as an existential threat is least concerning.

– Researchers are excited about the expanding abilities of AI systems.
– AI tools provide faster ways to process data and save time and money.
– Concerns include reliance on pattern recognition without understanding and entrenching bias.
– AI challenges existing standards for proof and truth in science.

– AI tools are becoming increasingly common in science.
– More than half of the researchers expect AI tools to be ‘very important’ or ‘essential’ in the next decade.
– Researchers have strong concerns about how AI is transforming research.
– Only a minority of researchers regularly use generative AI tools.

– Scientists use generative AI tools to help with research tasks.
– AI tools can summarize papers, generate ideas, and write code.
– AI can also help with tasks like predicting weather and diagnosing diseases.
– However, there are concerns about AI tools in science.
– AI tools can rely too much on patterns without understanding.
– They can also perpetuate bias and make fraud easier.
– Ill-considered use of AI can lead to research that cannot be reproduced.
– AI is challenging existing standards for proof and truth in science.

– Paper discusses the impact of AI on EU’s geopolitical power.
– AI seen primarily from an economic, social, and regulatory angle.
– Recommends ways for EU to respond to changing international balance of power.

– AI is a tool of power politics and state diplomacy.
– AI will impact the global balance of power and geopolitics.
– The EU needs to engage with AI changes and consider the external dimension of its action.
– The EU and its Member States should invest in AI to benefit from international challenges.
– The EU should focus on ethical and trustworthy AI and promote this approach.

– AI is a tool of power politics and state diplomacy.
– The EU approaches AI from an economic, social, and regulatory angle.
– AI impacts the EU’s geopolitical power and its relationship with other countries.
– Possible scenarios for how AI may change the international balance of power.
– Recommendations for the EU and its Member States to respond.

– AI is a tool of power politics and state diplomacy.
– AI will impact the global balance of power and geopolitics.
– The EU needs to engage with AI and consider its external dimension.
– The EU should invest more time, effort, and money in AI.
– The EU should focus on ethical and trustworthy AI and promote it globally.

– AI impacts the EU’s geopolitical power and its relationship with other countries.
– Possible scenarios for how AI may change the international balance of power.
– Recommendations for the EU and its Member States to respond.

– Trustworthiness of digital technology is crucial for its uptake.
– Europe has the opportunity to become a leader in AI.
– Europe’s strong position in digitized industry and business-to-business applications.
– Policy framework to align efforts at European, national, and regional level.
– Aim to create an ecosystem of excellence and accelerate adoption of AI solutions.

– AI offers benefits for citizens, companies, and society as a whole.
– It can strengthen industry competitiveness and improve citizen well-being.
– AI can contribute to solving societal challenges and protecting democracies.
– Europe must develop industrial and technological capacities to seize AI opportunities.
– The European approach aims to promote innovation and trustworthy AI.
– The paper proposes policy means to boost investments and regulatory frameworks.
– A broad consultation with stakeholders will inform the Commission’s next steps.

– AI has the potential to improve healthcare, farming, climate change mitigation, and more.
– AI also poses risks such as opaque decision-making and discrimination.
– The EU aims to promote the development and deployment of AI while addressing risks.
– The EU wants to be a global leader in innovation in the data economy.
– AI should be grounded in European values and fundamental rights.
– AI can support the Sustainable Development Goals and the European Green Deal.
– A common European approach to AI is necessary to avoid fragmentation.
– The White Paper presents policy options for a trustworthy and secure development of AI.

– AI offers benefits for citizens, companies, and society as a whole.
– AI can strengthen industry competitiveness and improve citizen well-being.
– AI can contribute to solving societal challenges and fighting crime.
– Europe must develop industrial and technological capacities for AI.
– The European approach aims to promote ethical and trustworthy AI.
– The Commission launches a consultation for a European approach to AI.

– Trustworthiness of digital technology is crucial for its uptake.
– Europe has the opportunity to become a leader in AI.
– Europe’s strong position in digitized industry and business-to-business applications.
– Policy framework to align efforts at European, national, and regional level.
– Aim to create an ecosystem of excellence and accelerate adoption of AI solutions.

– AI offers benefits for citizens, companies, and society as a whole.
– It can strengthen industry competitiveness and improve citizen well-being.
– AI can contribute to solving societal challenges and protecting democracies.
– Europe must develop industrial and technological capacities to seize AI opportunities.
– The European approach aims to promote innovation and trustworthy AI.
– The paper proposes policy means to boost investments and regulatory frameworks.
– A broad consultation with stakeholders will inform the Commission’s next steps.

– AI has the potential to improve healthcare, farming, climate change mitigation, and more.
– AI also poses risks such as opaque decision-making and discrimination.
– The EU aims to promote the development and deployment of AI while addressing risks.
– The EU wants to be a global leader in innovation in the data economy.
– AI should be grounded in European values and fundamental rights.
– AI can support the Sustainable Development Goals and the European Green Deal.
– A common European approach to AI is necessary to avoid fragmentation.
– The White Paper presents policy options for a trustworthy and secure development of AI in Europe.

– AI offers benefits for citizens, companies, and society as a whole.
– AI can strengthen industry competitiveness and improve citizen well-being.
– AI can contribute to solving societal challenges and fighting crime.
– Europe must develop industrial and technological capacities for AI.
– The European approach aims to promote ethical and trustworthy AI.
– The Commission launches a consultation for a European approach to AI.

– The paper aims to promote Europe’s innovation capacity in AI.
– It supports the development and uptake of ethical and trustworthy AI.
– It proposes policy means to boost investments in research and innovation.
– It enhances the development of skills and supports AI uptake by SMEs.
– It proposes key elements of a future regulatory framework.

– NLP is important for extracting knowledge from scientific publications.
– Training deep neural models requires large amounts of labeled data.
– Annotated data in scientific domains is difficult and expensive to collect.
– Unsupervised pretraining of language models improves performance on NLP tasks.
– SCIBERT is a pretrained language model based on BERT trained on scientific text.

SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks and demonstrates statistically significant improvements over BERT.

– SciBERT is a pretrained language model for scientific text based on BERT.
– SciBERT outperforms BERT-Base and achieves new state-of-the-art results on several tasks.
– Future work includes releasing a version of SciBERT analogous to BERT-Large.

– SciBERT outperforms BERT-Base on scientific tasks.
– Achieves new state-of-the-art results on many scientific tasks.

– Paper discusses AI’s use in improving public services and challenges.
– It explores how public investments can accelerate responsible AI adoption.
– Benefits and drivers of AI in public services are identified.
– Challenges to AI uptake and acceleration are presented.
– Paper concludes with recommendations.

– Identifies barriers to AI uptake in the public sector
– Discusses the potential harm and risks associated with AI
– Highlights the benefits of AI in improving public services
– Recommends regulatory sandboxing and preprocurement for trustworthy AI
– Emphasizes the need for explainability and trustworthiness in AI systems
– Addresses the growing public concern over AI development and use

– AI use in EU public sector is not lagging behind other sectors.
– Responsible AI development is important in public services.
– Some AI applications in public services are banned due to risks.
– AI can improve public services through efficiency and error reduction.
– Human Rights Impact Assessment and regulatory simplification are recommended.

– AI use in the public sector has increased over the past two years.
– Public concern over the development and use of AI is growing.
– Trustworthy and responsible AI is crucial for public services.
– Different definitions of AI in public services exist.
– Access to data, complex regulations, and sharing best practices are barriers to uptake.
– AI in the public sector can lead to efficiency gains and less error/fraud.

– The paper discusses the use of AI in public services.
– It identifies benefits and challenges of using AI in public services.
– The paper emphasizes the need for responsible and trustworthy AI.
– It highlights the importance of explainability and human-centeredness in AI.
– The paper suggests regulatory sandboxing and preprocurement as key strategies.
– Different definitions of AI in public services are discussed.
– The paper mentions the growing public concern over the development and use of AI.
– It emphasizes the role of the public sector in creating trustworthy AI.
– The paper concludes with recommendations for the use of AI in public services.

– Public sector should educate the public about AI.
– Access to data and expertise are barriers to AI uptake.
– Regulatory sandboxing is important for developing trustworthy AI.
– Social science and humanities should be involved in AI development.
– Basic services like postal services are subject to competition rules.

Large language models (LLMs) trained on massive web corpora have shown remarkable abilities in natural language generation and understanding. However, these models may also pick up and amplify undesirable traits from their training data, such as generating toxic or biased content. Quantifying and mitigating these toxic behaviors is crucial for developing safe and ethical implications of language models.

In the paper “RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models”, Gehman et al. introduce a new benchmark dataset and evaluation framework for measuring the toxic degeneration of LLMs under adversarial prompting. They find that even highly capable models like GPT-3 can be coaxed into generating harmful content with carefully crafted prompts. The authors also explore methods for detoxifying language models during pre-training, fine-tuning, and inference.

In this review, we will take a deep dive into the methodology and results of the paper, with a focus on the mathematical details. We’ll cover the construction of the RealToxicityPrompts dataset, the evaluation metrics, the toxicity of existing LLMs, and methods for detoxification. We’ll analyze the strengths and limitations of the work and discuss future research directions. Let’s begin!

The RealToxicityPrompts Dataset

To study the toxic degeneration of LLMs, we first need a way to probe them with potentially problematic prompts and measure the toxicity of their outputs. The authors construct the RealToxicityPrompts dataset for this purpose.

The dataset consists of 100,000 prompts, each of which is a short text string that could plausibly be used to start a conversation with an LLM. The prompts are sourced from the OpenWebText corpus and filtered to remove personal information and offensive content. The prompts are then annotated by human raters for their expected toxicity – how likely they think an LLM would produce a toxic continuation.

Formally, let $\mathcal{P}$ be the set of prompts and $f: \mathcal{P} \rightarrow [0,1]$ be the annotator-specified toxicity function. The goal is to estimate the empirical toxicity distribution:

$$\hat{f}(x) = \frac{1}{N} \sum_{i=1}^N f(x_i)$$

where $x_i \in \mathcal{P}$ are the prompts and $N = |\mathcal{P}|$ is the size of the dataset.

To get a high-quality estimate of $\hat{f}$, the authors employ a careful data collection procedure:

1. Prompt Selection: The base prompts are selected from OpenWebText using heuristics to filter out offensive or sensitive content. The prompts are short (1-3 sentences) and open-ended to allow diverse continuations.
2. Prompt Perturbation: To increase coverage, the base prompts are perturbed by techniques like backtranslation, word replacement, and text infilling. This expands the dataset by 10x.
3. Human Annotation: The prompts are annotated by crowd workers on a 5-point Likert scale from “not at all likely” to “very likely” to lead to a toxic continuation. Each prompt is rated by 3 workers and the scores are averaged.
4. Prompt Clustering: The annotated prompts are clustered using k-means on their BERT embeddings. This groups prompts into topical clusters for stratified evaluation.
5. Data Splitting: The dataset is split into train (80%), validation (10%), and test (10%) sets for evaluating different detoxification methods.

The resulting RealToxicityPrompts dataset covers a diverse range of topics and toxicity levels. The expected toxicity scores follow a bell-shaped distribution with a mean of 2.7 and standard deviation of 1.1 (on the 1-5 scale). The most toxic prompts tend to mention controversial topics like politics, race, and violence.

Evaluating Toxic Degeneration

With the RealToxicityPrompts dataset in hand, we can now measure the toxic degeneration of LLMs. The authors propose a simple yet effective evaluation protocol:

1. Generate Continuations: For each prompt $x_i$, generate $K$ continuations ${y_{i,1}, \dots, y_{i,K}}$ from the LLM using top-$p$ sampling with $p=0.9$ and a maximum length of 20 tokens.
2. Measure Continuation Toxicity: Score the toxicity of each continuation $y_{i,j}$ using the Perspective API, a state-of-the-art toxicity classifier. Let $t(y) \in [0,1]$ denote the toxicity score.
3. Aggregate Toxicity Scores: Compute the average toxicity score for each prompt:

$$s(x_i) = \frac{1}{K} \sum_{j=1}^K t(y_{i,j})$$

4. Summarize Metrics: Report the following metrics over the test set:

• Average Toxicity: The mean toxicity score across all prompts.
• Expected Maximum Toxicity: The expected maximum toxicity score over $K$ continuations for a random prompt, estimated as: $$\text{EMT} = \frac{1}{N} \sum_{i=1}^N \max_{j=1}^K t(y_{i,j})$$
• Toxicity Probability: The probability that a random continuation has toxicity score greater than a threshold $\tau$: $$\text{TP}(\tau) = \frac{1}{NK} \sum_{i=1}^N \sum_{j=1}^K \mathbf{1}[t(y_{i,j}) > \tau]$$

Intuitively, the Average Toxicity measures the overall harm of the model, the Expected Maximum Toxicity measures the worst-case harm, and the Toxicity Probability measures the frequency of harm at different thresholds.

The authors evaluate several pre-trained LLMs using this protocol, including GPT-2, GPT-3, CTRL, and XLNet. They find that all models exhibit significant toxic degeneration, with GPT-3 having the highest Expected Maximum Toxicity of 0.84 (i.e. 84% of continuations have maximum toxicity). The Toxicity Probability also increases with model size, suggesting that larger models are more prone to toxic degeneration.

Qualitatively, the generated toxicity spans a wide range of harmful behaviors, including threats, profanity, hate speech, and explicit content. Many toxic outputs appear coherent and on-topic, making them difficult to detect without careful analysis.

Methods for Detoxification

Given the prevalence of toxic degeneration in LLMs, it’s important to develop methods to mitigate these harmful behaviors. The authors explore three classes of detoxification methods:

1. Data-based Methods: These methods aim to filter out toxic content from the pre-training data. The authors experiment with keyword filtering, sentiment filtering, and toxicity score filtering using the Perspective API. They find that aggressive filtering can reduce toxicity but also hurts perplexity and generation quality.
2. Model-based Methods: These methods modify the LLM architecture or training objective to discourage toxic generations. The authors experiment with:

• Toxicity Classifiers: Training a separate toxicity classifier on the continuations and using its predictions to penalize the LLM’s loss function.
• Contrastive Learning: Training the LLM to maximize the likelihood of non-toxic continuations and minimize the likelihood of toxic ones using a contrastive objective.
• Attribute Conditioning: Conditioning the LLM on a “non-toxic” attribute token during training and inference to steer generations away from toxicity.

1. Inference-time Methods: These methods post-process the LLM’s outputs to remove or mitigate toxicity. The authors experiment with:

• Toxicity Filtering: Generating multiple continuations and filtering out those that exceed a toxicity threshold.
• Prompt Engineering: Designing prompts that are less likely to trigger toxic generations, e.g. by adding disclaimers or specifying a non-toxic intent.
• Controlled Decoding: Using techniques like top-$k$ sampling, nucleus sampling, or beam search to steer generations towards less toxic outputs.

The authors evaluate these methods on the RealToxicityPrompts dataset and find that a combination of model-based and inference-time methods works best. In particular, fine-tuning GPT-3 on a filtered dataset with a contrastive objective and decoding with top-$p$ sampling reduces the Expected Maximum Toxicity by 30% while maintaining perplexity within 5% of the baseline.

However, no single method completely eliminates toxic degeneration, and there is often a trade-off between toxicity reduction and generation quality. The authors argue that detoxification should be seen as a multi-objective optimization problem, balancing the goals of minimizing harm and maximizing usefulness.

Analysis and Discussion

The RealToxicityPrompts dataset and evaluation framework provide a valuable tool for quantifying the toxic behaviors of language models. The results show that even state-of-the-art models like GPT-3 can degenerate into harmful outputs under adversarial prompting. This highlights the need for better detoxification methods and more robust architectures.

The proposed detoxification methods span a range of approaches, from data filtering to model modification to inference-time control. The most effective methods combine multiple strategies, suggesting that a holistic approach is needed to mitigate toxicity.

However, the current methods also have some limitations:

1. Toxicity Definition: The definition of toxicity used in the paper (based on the Perspective API) is broad and may not capture all types of harmful content. More fine-grained and context-dependent annotations may be needed.
2. Evaluation Metrics: The evaluation metrics focus on the probability and severity of toxicity, but do not directly measure the coherence or usefulness of the generated text. Balancing toxicity reduction with generation quality remains an open challenge.
3. Prompt Distribution: The RealToxicityPrompts dataset is based on prompts from web text and may not cover all possible user inputs. Evaluating detoxification methods on a wider range of prompts, including adversarial ones, is important for robustness.
4. Language and Culture: The paper focuses on English-language models and Western notions of toxicity. Extending the framework to other languages and cultural contexts is an important direction for future work.

Despite these limitations, the paper makes significant contributions to the study of neural toxic degeneration. The RealToxicityPrompts dataset provides a standardized benchmark for evaluating detoxification methods, and the proposed methods advance the state-of-the-art in controllable language generation.

Conclusion and Future Work

The paper “RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models” tackles the important problem of measuring and mitigating the toxic behaviors of large language models. The authors introduce a new dataset and evaluation framework for quantifying toxic degeneration under adversarial prompting, and propose several methods for detoxifying LLMs during pre-training, fine-tuning, and inference.

The results show that current LLMs are prone to generating harmful content when prompted with sensitive topics, and that a combination of data filtering, model modification, and inference-time control is needed to effectively reduce toxicity. However, challenges remain in defining and annotating toxicity, balancing detoxification with generation quality, and extending the methods to diverse languages and contexts.

Future work could explore more advanced detoxification methods, such as reinforcement learning, adversarial training, or model distillation. Developing better evaluation metrics that capture both the toxicity and coherence of generated text is also an important direction. Finally, studying the social and ethical implications of detoxification, such as the potential for censorship or bias, is crucial for responsible AI development.

As language models become more powerful and widely deployed, ensuring their safety and robustness is a key challenge. The RealToxicityPrompts paper provides a valuable framework for studying this challenge and advancing the field of controllable language generation. With further research and refinement, we can develop LLMs that are both capable and ethical, generating useful and harmless content for a wide range of applications.

– Business adoption of AI is growing.
– Companies using more sophisticated tools and practices are reaping benefits.

– Companies using more sophisticated AI tools and practices have higher bottom-line benefits.
– The most popular AI use cases span various functional activities.
– Organizations following both core and advanced best practices see higher returns from AI.
– AI can lead to revenue increase and cost decrease in organizations.
– Cybersecurity is seen as a relevant AI risk, especially in developed economies.

– Companies using more sophisticated AI tools and practices are reaping bottom-line benefits.
– Popular AI use cases include logistics-network optimization, sales forecasting, and product enhancements.
– Respondents report higher levels of cost decreases from AI adoption during the pandemic.
– AI high performers prioritize training data, model documentation, and monitoring for bias.
– Regular retraining and human-in-the-loop verification are important in model deployment.

– Companies with more sophisticated AI tools and practices have higher bottom-line benefits.
– The most popular AI use cases span various functional activities.
– Organizations with AI high performers engage in advanced best practices.
– AI high performers prioritize training, testing, and monitoring of models.
– Model bias and accuracy are important considerations for AI high performers.

– Companies with more sophisticated AI tools and practices have higher bottom-line benefits.
– The most popular AI use cases span various functional activities.
– AI high performers engage in practices such as training and testing data, measuring model bias and accuracy, and regularly monitoring for data drift.
– Model users are taught how to monitor for issues and test for different outcomes based on protected characteristics.

– The paper discusses the power, politics, and costs of Artificial Intelligence.
– It references the book “Atlas of AI” by K. Crawford.
– It also mentions the AI Now 2019 Report.

– Potential shift of moral decision-making onto an unethical system.
– Possibility of evading ethical responsibility.
– Limitations of technological dystopianism and utopianism in understanding GPT-3.
– Concerns about potential misuse applications of GPT-3.
– Excitement within the NLP industry about the skilful tasks GPT-3 can perform.
– Postulation that industry regulation is sufficient according to AI ethicists.

– The paper discusses the power, politics, and costs of AI.
– It highlights the limitations of technological dystopianism and utopianism.
– It mentions concerns about GPT-3’s potential misuse and the need for regulation.

– The paper discusses the development and deployment of large language models.
– It explores the risks associated with these models and suggests mitigation strategies.
– The authors recommend considering environmental and financial costs, curating datasets, and exploring research beyond large language models.

The authors take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? They provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.

– The development of larger language models has been a trend in NLP.
– The paper discusses the risks associated with big language models.
– Recommendations include considering environmental and financial costs, curating datasets, and exploring research beyond large models.

– The paper discusses the risks and possible mitigation strategies of large language models.

– Business adoption of AI is growing.
– Companies using more sophisticated tools and practices are reaping bottom-line benefits.

– Companies using more sophisticated AI tools and practices have higher bottom-line benefits.
– Popular AI use cases include product development, performance management, and user enablement.
– Organizations following both core and advanced best practices see the highest returns from AI.
– AI adoption can lead to revenue increase and cost decrease.
– The paper highlights practices with the highest deltas between AI high performers and other respondents.

– Companies using more sophisticated AI tools and practices are reaping bottom-line benefits.
– Popular AI use cases include logistics-network optimization, sales and demand forecasting, and product-feature optimization.
– Respondents report higher levels of cost decreases from AI adoption in the pandemic’s first year.

– Companies with more sophisticated AI tools and practices have higher bottom-line benefits.
– The most popular AI use cases span various functional activities.
– Organizations following both core and advanced best practices see higher returns from AI.

– Companies with more sophisticated AI tools and practices have higher bottom-line benefits.
– The most popular AI use cases span various functional activities.
– Organizations following core and advanced best practices see the highest returns from AI.
– AI adoption leads to revenue increase and cost decrease for organizations.
– The paper highlights practices with the highest deltas between AI high performers and other respondents.