Imagine asking your robot assistant to “go grab my keys from the kitchen table and bring them to me on the couch.” For many years, this type of request only existed in science fiction. But rapid advances in artificial intelligence are bringing this vision closer to reality.

Researchers at New York University recently developed a robotic system called OK-Robot that can understand natural language commands and complete tasks like moving objects around typical home environments. 

This new system demonstrates how the latest AI capabilities can be combined to create truly useful robot assistants.

Understanding Natural Language

A key innovation that enables OK-Robot’s abilities is the use of neural networks - AI systems loosely inspired by the human brain - that have been trained on huge datasets to understand language. Systems called Vision-Language Models can now identify over 20,000 different objects when shown images and can understand written descriptions and questions about those images.

The researchers used these models to give OK-Robot the ability to interpret natural language commands using common words to describe objects, places they can be found, and where they should be moved. This gives untrained users the ability to give instructions without needing to learn a rigid syntax or command structure.

Navigating Like a Human

But understanding language is only the first step to completing tasks - the robot also needs to be able to navigate environments and manipulate objects. Drawing inspiration from technologies self-driving cars use to "see" and move through spaces, the team gave OK-Robot the ability to build a 3D map of rooms using images captured from phone cameras.

This allows OK-Robot to create navigation plans to move around obstacles and get near requested items. It also uses algorithms that simulate human visual and physical reasoning abilities to identify flat surfaces, avoid collisions with clutter, and select optimal paths. The result is fluid navigation using the same sort of common-sense logic humans implicitly understand about moving through home environments.

Manipulating Household Objects 

Finally, to pick up and move everyday items, OK-Robot employs AI recognition capabilities to identify graspable points on target objects. It considers shape, size, and physical properties learned from experience grasping thousands of objects to select a suitable gripper pose. This allows OK-Robot to handle items ranging from boxes and bottles to clothing and coffee mugs.

The system combines its language interpretation, navigation system, and grasping abilities to fulfill requests like “Put my phone on the nightstand” or “Throw this soda can in the recycling”. It even handles specifying destinations using relationships like “on top of” or “next to”.

Real-World Robot Challenges

Evaluating their new system across 10 real homes, the NYU team found OK-Robot could fulfill requests like moving common household items nearly 60% of the time with no prior training or exposure to the environment. This major leap towards capable home robots highlights the progress AI is making.

However, it also uncovered real-world challenges robots still face operating in human spaces. Items placed in difficult to reach locations, clutter blocking paths or grasps, and requests involving heavy, fragile, or transparent objects remain problematic areas. Quirks of language interpretation can also lead to confusion over which specific item is being indicated or where it should be moved.

Still, by integrating the latest AI in an adaptable framework, OK-Robot sets a new high bar for language-driven robot competency. And its failures help illustrate remaining gaps researchers must close to achieve fully capable assistants.

The Path to Robot Helpers

The natural language understanding and navigation capabilities demonstrated by OK-Robot lend hope that AI and robotics are stepping towards the dreamed-of era of useful automated helpers. Continued progress pairing leading-edge statistical learning approaches with real-world robotic systems seems likely to make this a reality.

Key insights from this research illustrating existing strengths and limitations include:

• Modern natural language AI allows untrained users to give robots useful instructions 
• Advanced perception and planning algorithms enable feasible navigation of home spaces  
• Data-driven grasping models generalize reasonably well to new household objects
• Real-world clutter, occlusion, and ambiguity still frequently thwart capable robots
• Careful system integration is crucial to maximize performance from imperfect components

So while the robot revolution still faces hurdles in reliably handling everyday situations, projects like OK-Robot continue pushing towards convenient and affordable automation in our homes, workplaces, and daily lives.

Reference Paper

https://arxiv.org/abs/2401.12202

We are in the midst of an AI revolution. Systems like ChatGPT and DALL-E 2 capture headlines and imaginations with their ability to generate remarkably human-like text, images, and more. But beyond the hype, companies like Shopify thoughtfully integrate this technology to solve real business challenges.

Shopify provides the infrastructure for entrepreneurs to build thriving businesses. As Director of Product Miqdad Jaffer explains, "AI is an opportunity to make entrepreneurship accessible for everyone." He envisions AI as a "powerful assistant" to help with administrative tasks so entrepreneurs can focus on developing standout products.

Much like past innovations such as calculators boosting productivity in their domains, AI promises to eliminate digital struggle: "We've seen that we've created a suite of products called Shopify Magic. And the idea behind this was, how do we embed this directly into the workflows our merchants must go through?"

The key is designing AI to enhance human capabilities rather than replace them. Shopify's first offering, Shopify Magic, helps write product descriptions, email campaigns, customer service responses, and more while giving business owners final approval. 

Keeping humans in charge eases concerns around brands losing control over messaging or AI sharing inaccurate product information. Merchants are quickly finding ways to customize the AI's output to their brand voice and specialty offerings. Despite risks with rapidly adopting new AI systems, Shopify leaned into experimentation, knowing the tools can solve problems today: "We wanted to lean in for a couple of reasons. One, it's important to get this in the hands of merchants as fast as possible, staying with the cutting edge of the technology side." Their risk tolerance traces directly back to who their users are.

Entrepreneurs tend to have high-risk tolerances, willing to sacrifice stability to turn ideas into reality. Shopify realized Magic aligned better with this hunger to try new things than a cautious rollout. The results reveal shop owners enthusiastically use Magic, from launching online outlets to translating product info for international audiences.

Rather than replace humans, Shopify aims to build AI products that enable entrepreneurs to excel: "This is always going to be something that augments and isn't a replacement. So this will always be something that helps a user be the best version of themselves." Much like past innovations such as tractors and computers overcame limitations to empower more extraordinary human achievement, AI promises another leap by eliminating digital drudgery.

FAQ

Q: Why did Shopify move so quickly to adopt risky new AI tools? 

They knew entrepreneurs tend to have high-risk tolerance and are eager to gain any possible advantage using cutting-edge technology.

Q: Does Shopify's AI write final emails and product descriptions by itself?

No, their Magic assistant makes intelligent suggestions, but business owners have final approval over client-facing messaging. This maintains human control.

Q: What are some ways merchants customize AI outputs?

Users employed AI to translate product info into different languages, rapidly create content for new campaign sites, and tailor its suggestions to fit their brand style.

Q: How has AI started impacting other Shopify product areas?

Shopify recently launched visual AI tools to let merchants effortlessly customize backdrops for product images to create tailored campaigns.

Q: How do AI innovations compare historically? 

Much like farm equipment boosted production capacity, and computers increased information access, AI eliminates tedious tasks so humans can better pursue their passions.

Glossary

• Generative AI: AI systems capable of generating original content like text, images, audio, and video rather than just classifying data. Example: ChatGPT.
• Natural Language Processing (NLP): Subfield of AI focused on understanding, interpreting, and generating human languages. Enables capabilities like text summarization.  
• Prompt engineering: Crafting the text prompts provided to generative AI models to influence their outputs. Requires human skill.
• Overfitting: When an AI algorithm performs very well on its training data but fails to generalize to new situations. Leads to fragility.

Transformers have become the backbone behind many recent advances in AI, powering systems like ChatGPT for natural language tasks. Yet the standard transformer architecture has many intricacies that make it complex and inefficient. In a new paper, researchers Bobby He and Thomas Hofmann explore how to streamline transformers by removing unnecessary components, making them more straightforward, faster, and practical. 

The core idea is that several aspects of the standard transformer block—the primary building block transformers are made of—can be simplified or removed entirely without hampering performance. Specifically, He and Hofmann identify and eliminate excess baggage in terms of 1) skip connections, 2) value and projection parameters, 3) sequential sub-blocks, and 4) normalization layers.  

Skip connections are links between layers that help information flow across the network. The researchers find these can be discarded in each transformer layer's attention and feedforward sub-blocks. The key is to initialize the attention mechanism to have a vital identity component, allowing tokens to retain information about themselves better as signals pass through the network.

The value and projection parameters help transform representations as they enter and exit the multi-head attention module in each layer. Surprisingly, He and Hofmann reveal these extra transform matrices can be fixed to the identity without affecting results. This eliminates half of the matrix multiplications required in the attention layer.  

Similarly, the standard transformer computes attention and feedforward sequential sub-blocks in order. By parallelizing these computations instead, skip connections linking the sub-blocks become unnecessary.

Finally, normalization layers that help regulate activations can also be removed with the proper architecture adjustments, albeit with a minor drop in per-step speeds. 

Together, these modifications lead to a radically simplified transformer block that matches, if not exceeds, the performance and efficiency of the original complex block. For example, the simplified model attains over 15% faster throughput and uses 15% fewer parameters, yielding practical savings.

Real-World Impact

The work has both theoretical and practical implications. On the theory side, it reveals limitations in current tools like signal propagation for explaining and designing networks, motivating more nuanced dynamic theories that capture training intricacies. Practically, simpler transformer architectures can translate to significant efficiency gains in AI systems, reducing computational demands to deploy large language models.

For example, consider CPROMPT.AI, a platform allowing everyday users to build and share custom AI applications easily. The apps tap into capacities like text generation from prompts, with no coding needed. Simpler and faster transformers directly enable deploying more powerful capacities to more people for less cost—crucial as advanced AI diffuses across society.  

He and Hofmann’s simplifications also compound the work of other researchers pursuing efficient transformers, bringing us closer to practical transformers at the scales and accuracies necessary to push AI forward. So, while recent models boast hundreds of billions of parameters, streamlined architectures could pack comparable performance in packages sized for broad access and impact.

The quest for AI that is not only capable but accessible and responsible continues. Reducing transformer complexity provides one path to more efficient, economical, and beneficial AI development and deployment.

The key facts from the paper

• Skip connections in both the attention and feedforward modules of transformers can be removed without hampering performance by initializing attention to have a vital identity component.  
• The value and projection parameters in multi-head attention are unnecessary and can be fixed to the identity matrix.
• By parallelizing the attention and feedforward computations, sequential sub-blocks can also be eliminated.
• Simplifying transformers in these ways yields models with 15% higher throughput and 15% fewer parameters.
• Limitations of signal propagation theories for neural network design are revealed, motivating more refined dynamic theories.

Glossary

• Skip connection - a connection between non-consecutive layers in a neural network. 
• Value parameters - weights in the transformer attention mechanism 
• Projection parameters - weights that transform attention outputs
• Sequential sub-blocks - the standard process of computing attention and then feedforward blocks
• Normalization layer - a layer that regulates activation values 

We've all seen the recent feats of AI, like ChatGPT and DALL-E 2, churning out essays, computer code, artworks, and more with a simple text prompt. The outputs seem intelligent, creative even. But are these AI systems innovative in the way humans are? Developmental psychologists argue there's a fundamental difference. 

In a recent paper published in Perspectives on Psychological Science, researchers Eunice Yiu, Eliza Kosoy, and Alison Gopnik make the case that while today's large language models excel at imitating existing patterns in data, they lack the flexible, truth-seeking abilities that allow even young children to innovate tools and discover new causal structures in the world.  

The Core Idea: Imitation Versus Innovation

The authors explain that AI systems like ChatGPT are best understood not as intelligent agents but as "cultural technologies" that enhance the transmission of information from person to person. Much like writing, print, and the Internet before them, large language models are highly skilled at extracting patterns from vast datasets of text and images created by humans. They are, in effect, "giant imitation engines" in language and visual creativity.  

However, cultural evolution depends on imitation and innovation – the ability to expand on existing ideas or create new ones. This capacity for innovation requires more than statistical analysis; it demands interacting with the world in an exploratory, theory-building way to solve what scientists call "the inverse problem." Children as young as four can innovate essential tools and discover new causal relationships through active experimentation, going beyond the patterns they've observed.

So, while AI models can skillfully continue trends and genres created by humans, they need more flexible reasoning skills to push boundaries and explore new creative territory. As Gopnik told the Wall Street Journal, "To be truly creative means to break out of previous patterns, not to fulfill them." 

Evidence: Comparing AI and Child Tool Innovation 

To test this imitation versus innovation hypothesis, the researchers conducted experiments comparing how children, adults, and significant AI models like Claude and GPT-4 handled tool innovation tasks. 

In one scenario, participants were asked to select an object to draw a circle without the usual compass tool, choosing from either:

• An associated but irrelevant item – a ruler 
• A visually dissimilar but functionally relevant item – a round-bottomed teapot
• An irrelevant item – a stove

The results showed:

• Both kids and adults excelled at selecting the teapot, demonstrating an ability to discover new causal affordances in objects.
• The AI models struggled, often picking the associated ruler instead of realizing the teapot's potential.  

This suggests that while statistical learning from text can capture superficial relationships between objects, it falls short when more creative abstraction is needed.

This research shows that today's AI still can't match a child's innate curiosity and drive to experiment. We see this on the CPROMPT.AI platform, where users ideate and iterate prompt apps to explore topics and share perspectives without external incentives or curation. It's a case where human creativity shines!

AI models provide an incredible tool for enhancing human creativity through more accessible access to knowledge and quick iterations. The CPROMPT.AI no-code interface lets anyone transform AI chat into usable web apps for free. You dream it, you build it, no programming required.  

The interplay between human and artificial intelligence promises even more innovation. But the next giant leap will likely come from AI that, like children, actively learns by doing rather than purely analyzing patterns. Budding young scientists have a lesson for the best minds in AI!

Glossary

• Large language models - AI systems trained on massive text or image datasets, like ChatGPT and DALL-E, to generate new text or images. 
• Inverse problem - The challenge of inferring causes from observed effects and making predictions. Solving it requires building models of the external world through exploration.
• Affordance - The possible uses and actions latent in an object based on its properties. Recognizing affordances allows innovative tool use.
• Overimitation - Copying all details of a task, even non-causal ones. AI models have high-fidelity imitation but may lack human social imitation abilities.
• Causal over hypotheses: An abstract hypothesis that reduces hypotheses about more concrete causal relationships. Discovering these allows generalization.

In artificial intelligence, an intriguing phenomenon lies beneath the surface - extractable memorization. This term refers to an AI model's tendency to inadvertently retain fragments of training data, which a third party can later extract. Understanding this concept is vital for safeguarding privacy in AI systems. 

What is Extractable Memorization?

Extractable memorization occurs when parts of an AI model's training data can be efficiently recovered by an external "attacker," intentionally or unintentionally. Also called data extraction attacks, these exploits pose serious privacy risks if personal or sensitive data is revealed. Recent research analyzed extractable memorization across various language models - from open-source tools like GPT-Neo to private APIs like ChatGPT. The findings were troubling:

• Open models memorized up to 1% of training data. More data was extracted as the model size increased.
• Closed models also showed vulnerability. ChatGPT leaked personal details with simple attacks despite privacy measures.

With prompts costing $0.002, spending just $200 yielded over 10,000 private training examples from ChatGPT. Extrapolations estimate adversaries could extract far more for higher budgets.

What Does This Mean for Developers and Users?

This signals the urgent need for rigorous testing and mitigation of risks from extractable memorization for developers. As models grow more capable, so does the quantity of sensitive data they accumulate and the potential for exposure. Responsible AI requires acknowledging these failure modes. It challenges users' assumptions that personal information is protected when engaging with AI. Even robust models have exhibited critical flaws, enabling data leaks. I'd like to point out that caution is warranted around data security with existing systems.

Progress in AI capabilities brings immense potential and complex challenges surrounding transparency and privacy. Extractable memorization is the tip of the iceberg. Continued research that responsibly probes model vulnerabilities is crucial for cultivating trust in emerging technologies. Understanding the hidden memories within language models marks an essential step.

Self-supervised learning (SSL) has become an increasingly powerful tool for training AI models without requiring manual data labeling. But while SSL methods like contrastive learning produce state-of-the-art results on many tasks, interpreting what these models have learned remains challenging.  A new paper from Dr. Yann LeCun and other researchers helps peel back the curtain on SSL by extensively analyzing standard algorithms and models. Their findings reveal some surprising insights into how SSL works its magic.

At its core, SSL trains models by defining a "pretext" task that does not require labels, such as predicting image rotations or solving jigsaw puzzles with cropped image regions. The key innovation is that by succeeding at these pretext tasks, models learn generally useful data representations that transfer well to downstream tasks like classification.

Digging Into the Clustering Process

A significant focus of the analysis is how SSL training encourages input data to cluster based on semantics. For example, with images, SSL embeddings tend to get grouped into clusters corresponding to categories like animals or vehicles, even though category labels are never provided. The authors find that most of this semantic clustering stems from the "regularization" component commonly used in SSL methods to prevent representations from just mapping all inputs to a single point. The invariance term that directly optimizes for consistency between augmented samples plays a lesser role.

Another remarkable result is that semantic clustering reliably occurs across multiple hierarchies - distinguishing between fine-grained categories like individual dog breeds and higher-level groupings like animals vs vehicles.

Preferences for Real-World Structure 

However, SSL does not cluster data randomly. The analysis provides substantial evidence that it prefers grouping samples according to patterns reflective of real-world semantics rather than arbitrary groupings. The authors demonstrate this by generating synthetic target groupings with varying degrees of randomness. The embeddings learned by SSL consistently align much better with less random, more semantically meaningful targets. This preference persists throughout training and transfers across different layers of the network.

The implicit bias towards semantic structure explains why SSL representations transfer so effectively to real-world tasks. Here are some of the key facts:

• SSL training facilitates clustering of data based on semantic similarity, even without access to category labels
• Regularization loss plays a more significant role in semantic clustering than invariance to augmentations 
• Learned representations align better with semantic groupings vs. random clusters
• Clustering occurs across multiple hierarchies of label granularity
• Deeper network layers capture higher-level semantic concepts 

By revealing these inner workings of self-supervision, the paper makes essential strides toward demystifying why SSL performs so well. 

Glossary

• Self-supervised learning (SSL) - Training deep learning models through "pretext" tasks on unlabeled data
• Contrastive learning - Popular SSL approach that maximizes agreement between differently augmented views of the same input
• Invariance term - SSL loss component that encourages consistency between augmented samples 
• Regularization term - SSL loss component that prevents collapsed representations
• Neural collapse - Tendency of embeddings to form tight clusters around class means

As consumer AI -- Large Language Models (LLMs) become increasingly capable, evaluating them is crucial yet challenging; how can we effectively benchmark AI's performance, especially in the open-ended, free-form conversations preferred by users? Researchers from UC Berkeley, Stanford, and other institutions explore using strong LLMs as judges to evaluate chatbots in a new paper titled "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena." The core premise is that well-trained LLMs already exhibit alignment with human preferences so that they can act as surrogates for expensive and time-consuming human ratings. 

This LLM-as-a-judge approach offers immense promise in accelerating benchmark development. Let's break down the critical details from the paper.

The Challenge of Evaluating Chatbots

While benchmarks abound for assessing LLMs' core capabilities like knowledge and logic, they focus primarily on closed-ended questions with short, verifiable responses. Yet modern chatbots handle free-form conversations across diverse topics. Evaluating their helpfulness and alignment with user expectations is vital but profoundly challenging.

Obtaining robust human evaluations is reliable but laborious and costly. Crowdsourcing ratings from average users for each new model revision could be more practical. At the same time, existing standardized benchmarks often fail to differentiate between base LLMs and aligned chatbots preferred by users. 

For instance, the researchers demonstrate that human users strongly favor Vicuna, a chatbot fine-tuned to mimic ChatGPT conversations, over the base LLaMA model it's built on. Yet differences in benchmark scores on datasets like HellaSwag remain negligible. This discrepancy highlights the need for better benchmarking paradigms tailored to human preferences.

Introducing MT-Bench and Chatbot Arena

To address this evaluation gap, the researchers construct two new benchmarks with human ratings as key evaluation metrics:

• MT-Bench: A set of 80 open-ended, multi-turn questions testing critical user-facing abilities like following instructions over conversations. Questions fall into diverse domains like writing, reasoning, math, and coding.
• Chatbot Arena: A live platform where anonymous users chat simultaneously with two models, then vote on preferred responses without knowing model identities. This allows gathering unconstrained votes based on personal interests.

These human-centered benchmarks offer more realistic assessments grounded in subjective user preferences versus technical accuracy alone.  Here I have run a prompt for two versions of Claude LLMs and I found one answer (B) to be more interesting than the other one (A).

You can try this at: https://chat.lmsys.org

LLMs as Surrogate Judges 

The paper investigates using strong LLMs like Claude and GPT-4 as surrogate judges to approximate human ratings. The fundamental hypothesis is that because these models are already trained to match human preferences (e.g., through reinforcement learning from human feedback), their judgments should closely correlate with subjective user assessments. Advantages of this LLM-as-a-judge approach include:

• Scalability: Automated LLM judgments require minimal human involvement, accelerating benchmark iteration.
• Explainability: LLMs provide explanatory judgments, not just scores. This grants model interpretability, as illustrated in examples later.

The paper systematically analyzes this method by measuring LLM judge agreement with thousands of controlled experts and unconstrained crowd votes from the two new benchmarks. But first, let's examine some challenges.

Position Bias and Other Limitations

LLM judges exhibit certain biases that can skew evaluations:

• Position bias: Tendency to favor responses based just on order presented rather than quality. All LLM judges here demonstrate significant position bias.
• Verbosity bias: Longer responses seem rated higher regardless of clarity or accuracy. When researchers artificially expanded model responses via repetition without adding new information, all but GPT-4 judges failed to detect this distortion.
• Self-enhancement bias: Some hints exist of judges preferring responses stylistically similar to their own, but limited evidence prevents clear conclusions.
• Reasoning limitations: Since math/logic capabilities in LLMs still need improvement, their competency grading such questions unsurprisingly needs to be revised. But even on problems they can solve independently, providing incorrect candidate answers can mislead judges.

Despite these biases, agreement between LLM and human judgments ultimately proves impressive, as discussed next. And researchers propose some techniques to help address limitations like position bias, which we'll revisit later.

Key Finding: LLM Judges Match Human Preferences  

Across both controlled and uncontrolled experiments, GPT-4 achieves over 80% judgment agreement with human assessors - on par even with the ~81% inter-rater agreement between random human pairs. This suggests LLMs can serve as cheap and scalable substitutes for costly human evaluations. In particular, here's a sample highlight:

MT-Bench: On 1138 pairwise comparisons from multi-turn dialogues, GPT-4 attained 66% raw agreement and 85% non-tie agreement with experts. The latter excludes tied comparisons where neither response was favored.

Remarkably, when human experts disagreed with GPT-4 judgments, they still deemed its explanations reasonable 75% of the time. And 34% directly changed their original choice to align with the LLM assessment after reviewing its analysis. This further validates the reliability of LLM surrogate judging.

LLM agreement rates grow even higher on model pairs exhibiting sharper performance differences. When responses differ significantly in quality, GPT-4 matches experts almost 100% of the time. This suggests alignment improves for more extreme cases that should be easier for both humans and LLMs to judge consistently.

Mitigating LLM Judge Biases 

While the paper demonstrates impressive LLM judge performance mainly on par with average human consistency, biases like position bias remain crucial for improvement.  Researchers propose a few bias mitigation techniques with preliminary success:

• Swapping positions: Running judgments twice with responses flipped and only keeping consistent verdicts can help control position bias.
• Few-shot examples: Priming LLM judges with a handful of illustrative examples significantly boosts consistency on position bias tests from 65% to 77% for GPT-4, mitigating bias.
• Reference guidance: For mathematical problems, providing LLM judges with an independently generated reference solution drastically cuts failure rates in assessing candidate answers from 70% down to just 15%. This aids competency on questions requiring precise analysis.

So, while biases exist, simple strategies can help minimize their impacts. And overall agreement rates already match or even exceed typical human consistency.

Complementing Standardized Benchmarks   

Human preference benchmarks like MT-Bench and Chatbot Arena assess different dimensions than existing standardized tests of knowledge, reasoning, logic, etc. Using both together paints a fuller picture of model strengths.

For example, the researchers evaluated multiple variants of the base LLaMA model with additional conversation data fine-tuning. Metrics like accuracy on the standardized HellaSwag benchmark improved steadily with more fine-tuning data. However, small high-quality datasets produced models strongly favored by GPT-4 judgments despite minimal gains on standardized scores.

This shows both benchmark types offer complementary insights. Continued progress will also require pushing beyond narrowly defined technical metrics to capture more subjective human preferences.

Democratizing LLM Evaluation 

Accessibly evaluating sophisticated models like ChatGPT requires expertise today. But platforms like CPROMPT.AI open LLM capabilities to everyone by converting text prompts into accessible web apps.  With intuitive visual interfaces, anyone can tap into advanced LLMs to create AI-powered tools for education, creativity, productivity, etc. No coding is needed. And the apps can be shared publicly or sold without any infrastructure or scaling worries.  

By combining such no-code platforms with the automated LLM judge approaches above, benchmarking model quality could also become democratized. Non-experts can build custom benchmark apps to evaluate evolving chatbots against subjective user criteria.  

More comprehensive access can help address benchmark limitations like overfitting on standardized tests by supporting more dynamic, personalized assessments. This is aligned with emerging paradigms like Dynabench that emphasize continuous, human-grounded model evaluations based on actual use cases versus narrow accuracy metrics alone.

Lowering barriers facilitates richer, real-world measurements of AI progress beyond expert evaluations.

Key Takeaways

Let's recap the critical lessons around using LLMs as judges to evaluate chatbots:

• Aligning AI with subjective user preferences is crucial yet enormously challenging to measure effectively.
• New human preference benchmarks like MT-Bench demonstrate failed alignment despite standardized solid test performance.
• Employing LLMs as surrogate judges provides a scalable and automated way to approximate human assessments.
• LLMs like GPT-4 can match expert consistency levels above 80%, confirming efficacy.
• Certain biases affect LLM judges, but mitigation strategies like swapping response positions and few-shot examples help address those.
• Maximizing progress requires hybrid evaluation frameworks combining standardized benchmarks and human preference tests.

As chatbot quality continues improving exponentially, maintaining alignment with user expectations is imperative. Testing paradigms grounded in human judgments enable safe, trustworthy AI development. Utilizing LLMs as judges offers a tractable path to effectively keep pace with accelerating progress in this domain.

Glossary

• MT-Bench: Suite of open-ended, multi-turn benchmark questions with human rating comparisons  
• Chatbot Arena: Platform to gather unconstrained conversations and votes pitting anonymous models 
  against each other
• Human preference benchmark: Tests targeting subjective user alignments beyond just technical accuracy
• LLM-as-a-judge: Approach using large language models to substitute for human evaluation and preferences
• Position bias: Tendency for language models to favor candidate responses based simply on the order presented rather than quality

Artificial intelligence (AI) has brought astounding advances, from self-driving cars to personalized medicine. However, it also poses novel risks. How can we manage the downsides so AI's upsides shine through? The US National Institute of Standards and Technology (NIST) offers a pioneering perspective in its AI Risk Management Framework. 

At its heart, the framework views AI risks as socio-technical - arising from the interplay of technical factors and social dynamics. If deployed crudely, an AI system designed with the best intentions could enable harmful discrimination. And even a technically sound system might degrade performance over time as society changes. Continual adjustment is critical. The framework outlines four core functions - govern, map, measure, and manage. 

"Govern" focuses on accountability, culture, and policies. It asks organizations to clearly define roles for governing AI risks, foster a culture of responsible AI development, and institute policies that embed values like fairness into workflows. Wise governance enables the rest.

"Map" then surveys the landscape of possibilities - both beneficial uses and potential downsides of a planned AI system. Mapping elucidates the real-world context where a system might operate, illuminating risks.

"Measure" suggests concrete metrics to track those risks over an AI system's lifetime, enabling ongoing vigilance. Relevant metrics span technical dimensions like security vulnerabilities to societal measures like discriminatory impacts. 

Finally, "manage" closes the loop by prioritizing risks that surfaced via mapping and measurement, guiding mitigation efforts according to tolerance levels. Management also includes communication plans for transparency.

At CPROMPT.AI, these functions tangibly guide the development of our easy-to-use platform for no-code AI. We continually map end-user needs and potential misuses, instituting governance policies that embed beneficial values upfront. We measure via feedback loops to catch emerging issues fast. We actively manage - and adjust policies based on user input to keep risks low while enabling broad access to AI's benefits.

The framework highlights that AI risks can never be "solved" once and for all. Responsible AI requires a sustained, collaborative effort across technical and social spheres - achieving trust through ongoing trustworthiness. Top Takeaways:

• AI risks are socio-technical - arising from technology and social dynamics. Both angles need addressing.
• Core risk management functions span governing, mapping, measuring, and managing. Each enables managing AI's downsides amid its upsides.
• Mapping helps reveal risks and opportunities early by understanding the context thoroughly.
• Measurement tracks technical and societal metrics to catch emerging issues over time.
• Management closes the loop - mitigating risks based on tolerance levels and priorities.

At CPROMPT.AI, we're putting these ideas into practice - enabling anyone to build AI apps quickly while governing use responsibly. The future remains unwritten. We can shape AI for good through frameworks like NIST's guiding collective action.

Recommended Reading

Managing AI Risks: A Framework for Organizations

FAQ

Q: What is the NIST AI Risk Management Framework?

The NIST AI Risk Management Framework guides organizations in managing the potential risks of developing, deploying, and using AI systems. It outlines four core functions – govern, map, measure, and control – to help organizations build trustworthy and responsible AI. 

Q: Who can use the NIST AI Risk Management Framework? 

The framework is designed to be flexible for any organization working with AI, including companies, government agencies, non-profits, etc. It can be customized across sectors, technologies, and use cases.

Q: What are some unique AI risks the framework helps address?

The framework helps manage amplified or new risks with AI systems compared to traditional software. This includes risks related to bias, opacity, security vulnerabilities, privacy issues, and more arising from AI's statistical nature and complexity.

Q: Does the framework require specific laws or regulations to be followed?

No, the NIST AI Risk Management Framework is voluntary and complements existing laws, regulations, and organizational policies related to AI ethics, safety, etc. It provides best practices all organizations can apply.

Q: How was the NIST AI Risk Management Framework created?

NIST developed the framework based on industry, academia, civil society, and government input. It aligns with international AI standards and best practices. As a "living document," it will be updated regularly based on user feedback and the evolving AI landscape.

Glossary

• Socio-technical - relating to the interplay of social and technological factors
• Governance - establishing policies, accountability, and culture to enable effective risk management 
• Mapping - analyzing the landscape of possibilities, risks, and benefits for a particular AI system
• Measurement - creating and tracking metrics that shed light on a system's technical and societal performance

Artificial intelligence (AI) systems hold tremendous promise to enhance our lives but also come with risks. How should organizations approach governing AI systems to maximize benefits and minimize harms? The AI Risk Management Framework (RMF) Playbook created by the National Institute of Standards and Technology (NIST) offers practical guidance. NIST s a U.S. federal agency within the Department of Commerce. It's responsible for developing technology, metrics, and standards to drive innovation and economic competitiveness at national and international levels. NIST's work covers various fields, including cybersecurity, manufacturing, physical sciences, and information technology. It plays a crucial role in setting standards that ensure product and system reliability, safety, and security, especially in new technology areas like AI.

At its core, the Playbook provides suggestions for achieving outcomes in the AI RMF Core Framework across four essential functions: Govern, Map, Measure, and Manage. The AI RMF was developed through a public-private partnership to help organizations evaluate AI risks and opportunities. 

The Playbook is not a checklist of required steps. Instead, its voluntary suggestions allow organizations to borrow and apply ideas relevant to their industry or interests. By considering Playbook recommendations, teams can build more trustworthy and responsible AI programs. Here are three top-level takeaways from the AI RMF Playbook:

Start with strong governance policies 

The Playbook emphasizes getting governance right upfront by establishing policies, procedures, roles, and accountability structures. This includes outlining risk tolerance levels, compliance needs, stakeholder participation plans, and transparency requirements. These guardrails enable the subsequent mapping, measurement, and management of AI risks.

For example, the Playbook suggests creating standardized model documentation templates across development projects. This supports consistently capturing limitations, test results, legal reviews, and other data to govern systems.

Continuously engage stakeholders

Given AI's broad societal impacts, the Playbook highlights regular engagement with end users, affected communities, independent experts, and other stakeholders. Their input informs context mapping, impact assessments, and the suitability of metrics. 

Participatory design research and gathering community insights are highlighted as ways to enhance measurement and response plans. The goal is to apply human-centered methods to make systems more equitable and trustworthy.

Adopt iterative, data-driven improvements  

The Playbook advocates iterative enhancements informed by risk-tracking data, metrics, and stakeholder feedback. This means continually updating performance benchmarks, fairness indicators, explainability measures, and other targets. Software quality protocols like monitoring for bug severity and system downtime are also suggested.

This measurement loop aims to spur data-driven actions and adjustments. Tying metrics to potential harms decreases the likelihood of negative impacts over an AI system's lifecycle. Documentation also builds institutional knowledge.

Creating Trustworthy AI

Organizations like CPROMPT.AI, enabling broader access to AI capabilities, have an opportunity to integrate ethical design. While risks exist, the Playbook's voluntary guidance provides a path to developing, deploying, and monitoring AI thoughtfully.

Centering governance, engagement, and iterative improvements can help machine learning teams act responsibly. Incorporating feedback ensures AI evolves to serve societal needs best. Through frameworks like the AI RMF, we can build AI that is not only powerful but also deserving of trust.

FAQ

What is the AI RMF Playbook?

The AI RMF Playbook provides practical guidance aligned to the AI Risk Management Framework (AI RMF) Core. It suggests voluntary actions organizations can take to evaluate and manage risks across the AI system lifecycle areas of government, mapping, measuring, and managing.

Who developed the AI RMF Playbook?

The Playbook was developed through a public-private partnership between industry, academia, civil society, government, international organizations, and impacted communities. The goal was to build consensus around AI risk management best practices.

Does my organization have to follow all Playbook recommendations?

No, the Playbook is not a required checklist. Organizations can selectively apply suggestions relevant to their industry use case interests based on their risk profile and resources. It serves as a reference guide.

What are some key themes in the Playbook?

• Establishing strong AI governance.
• Continually engaging stakeholders for input.
• Conducting impact assessments.
• Tracking key risk metrics.
• Adopting iterative data-driven enhancements to systems.

How can following the Playbook guidance help my AI systems?

By considering Playbook suggestions, organizations can better anticipate risks across fairness, safety, privacy, and security. This empowers teams to build more trustworthy, transparent, and responsible AI systems that mitigate harm.

When tackling tricky challenges that require complex reasoning – like solving a math puzzle or writing a coherent story – how we structure our thought process greatly impacts the outcome. Typically, there are two frameworks people use: 

• Chain of Thought (CoT): Linear, step-by-step thinking;
• Tree of Thought (ToT): Branching, exploring many sub-ideas.  

Intuitively, mapping out all facets of an issue enables deeper analysis than a single train of logic. An intriguing AI technique called Tree of Thoughts formally integrates this concept into advanced systems known as large language models. 

Inside the AI: Tree of Thoughts 

In a paper from Princeton and Google AI researchers, a framework dubbed "Tree of Thoughts" (ToT) enhances deliberate planning and problem solving within language models – AI systems trained on vast texts that can generate writing or answer questions when prompted. 

Specifically, ToT formulates thinking as navigating a tree, where each branch represents exploring another consideration or intermediate step toward the final solution. For example, the system logically breaks down factors like space, allergies, and care needs to recommend the best family pet, gradually elaborating the options. This branching structure resembles visual concept maps that aid human creativity and comprehension.

Crucially, ToT incorporates two integral facets of higher-level cognition that sets it apart from standard AI:

• Evaluating ideas: The system assesses each branch of reasoning via common sense and looks a few steps ahead at possibilities.
• Deciding and backtracking: It continually judges the most promising path to continue thinking through, backtracking as needed.  

This deliberate planning technique enabled significant advances in challenging puzzles requiring creative mathematical equations or coherent story writing that stump today's best AIs.

Chain vs. Tree: A Superior Way to Reason 

Compared to a chain of thought's linear, one-track reasoning, experiments reveal ToT's branching approach to thinking:

• Better handles complexity as ideas divide into sub-topics
• Allows more comprehensive exploration of alternatives  
• Keeps sight of the central issue as all branches connect to the main trunk

Yet the chain of thought's simplicity has merits, too, in clearly conveying ideas step-by-step.

In essence, ToT combines people's innate tree-like conceptualization with AI's scaling computational power for more brilliant exploration. Its versatility also allows customizing across different tasks and systems.

So, while both frameworks have roles depending on needs and individual thinking style preferences, ToT's deliberate branching is uniquely suited to steering AI's problem-solving today. 

As AI becomes more autonomous in real-world decision-making, ensuring deliberate, structured thinking will only grow in importance – making the tree of thought an increasingly essential capability that today's promising explorations point toward.

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