In image recognition, researchers and developers constantly seek innovative approaches to enhance the accuracy and efficiency of computer vision systems. Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and classify visual information. However, recent advancements have paved the way for exploring alternative architectures, prompting the integration of Transformer-based models into visual data analysis.

 One such groundbreaking development is the Vision Transformer (ViT) model, which reimagines the way images are processed by transforming them into sequences of patches and applying standard Transformer encoders, initially used for natural language processing (NLP) tasks, to extract valuable insights from visual data. By capitalizing on self-attention mechanisms and leveraging sequence-based processing, ViT offers a novel perspective on image recognition, aiming to surpass the capabilities of traditional CNNs and open up new possibilities for handling complex visual tasks more effectively.

The ViT model reshapes the traditional understanding of handling image data by converting 2D images into sequences of flattened 2D patches, allowing the application of the standard Transformer architecture, originally devised for natural language processing tasks, to process visual information. Unlike CNNs, which heavily rely on image-specific inductive biases baked into each layer, ViT leverages a global self-attention mechanism, with the model utilizing constant latent vector size throughout its layers to process image sequences effectively. Moreover, the model’s design integrates learnable 1D position embeddings, enabling the retention of positional information within the sequence of embedding vectors. Through a hybrid architecture, ViT also accommodates the input sequence formation from feature maps of a CNN, further enhancing its adaptability and versatility for different image recognition tasks.

The proposed Vision Transformer (ViT), demonstrates promising performance in image recognition tasks, rivaling the conventional CNN-based models in terms of accuracy and computational efficiency. By leveraging the power of self-attention mechanisms and sequence-based processing, ViT effectively captures complex patterns and spatial relations within image data, surpassing the image-specific inductive biases inherent in CNNs. The model’s capability to handle arbitrary sequence lengths, coupled with its efficient processing of image patches, enables it to excel in various benchmarks, including popular image classification datasets like ImageNet, CIFAR-10/100, and Oxford-IIIT Pets. 

The experiments conducted by the research team demonstrate that ViT, when pre-trained on large datasets such as JFT-300M, outperforms the state-of-the-art CNN models while utilizing significantly fewer computational resources for pre-training. Furthermore, the model showcases a superior ability to handle diverse tasks, ranging from natural image classifications to specialized tasks requiring geometric understanding, thus solidifying its potential as a robust and scalable image recognition solution.

In conclusion, the Vision Transformer (ViT) model presents a groundbreaking paradigm shift in image recognition, leveraging the power of Transformer-based architectures to process visual data effectively. By reimagining the traditional approach to image analysis and adopting a sequence-based processing framework, ViT demonstrates superior performance in various image classification benchmarks, outperforming traditional CNN-based models while maintaining computational efficiency. With its global self-attention mechanisms and adaptive sequence processing, ViT opens up new horizons for handling complex visual tasks, offering a promising direction for the future of computer vision systems.

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A team of researchers from Lehigh University, Massachusetts General Hospital, and Harvard Medical School recently performed a thorough evaluation of GPT-4V, a state-of-the-art multimodal language model, particularly in Visual Question Answering tasks. The assessment aimed to determine the model’s overall efficiency and performance in handling complex queries requiring text and visual inputs. The study’s findings reveal the potential of GPT-4V for enhancing natural language processing and computer vision applications.

Based on the latest research, the current version of GPT-4V is not suitable for practical medical diagnostics due to its unreliable and suboptimal responses. GPT-4V heavily relies on textual input, which often results in inaccuracies. The study does highlight that GPT-4V can provide educational support and can produce accurate results for different question types and levels of complexity. The study also emphasizes that more precise and concise responses are needed for GPT-4V to be more effective.

The approach underscores the multimodal nature of medicine, where clinicians integrate diverse data types, including medical images, clinical notes, lab results, electronic health records, and genomics. While various AI models have demonstrated promise in biomedical applications, many are tailored to specific data types or tasks. It also highlights the potential of ChatGPT in offering valuable insights to patients and doctors, exemplifying a case where it accurately diagnosed a patient after multiple medical professionals couldn’t. 

The GPT-4V evaluation entails utilizing pathology and radiology datasets encompassing eleven modalities and fifteen objects of interest, where questions are posed alongside relevant images. Textual prompts are carefully designed to guide GPT-4V in integrating visual and textual information effectively. The evaluation employs GPT-4V’s dedicated chat interface, initiating separate chat sessions for each QA case to ensure impartial results. Performance is quantified using the accuracy metric, encompassing closed-ended and open-ended questions.

Experiments involving GPT-4V within the medical domain’s Visual Question Answering task reveal that the current version could be more suitable for real-world diagnostic applications and is characterized by unreliable and subpar accuracy in responding to diagnostic medical queries. GPT-4V consistently advises users to seek direct consultation with medical experts in cases of ambiguity, underscoring the importance of expert medical guidance and adopting a cautious approach to medical analysis.

The study needs to conduct a comprehensive examination of GPT-4V’s limitations within the medical Visual Question Answering task. It does mention specific challenges, such as GPT-4V’s difficulty in interpreting size relationships and contextual contours within CT images. GPT-4V tends to overemphasize image markings and may need help differentiating between queries solely based on these markings. The current study needs to explicitly address limitations related to handling complex medical inquiries or providing exhaustive answers.

In conclusion, the GPT-4V language model is unreliable or accurate enough for medical diagnostics. Its limitations highlight the need for collaboration with medical experts to ensure precise and nuanced results. Seeking expert advice and consulting with medical professionals is essential for achieving clear and comprehensive answers. GPT-4V consistently emphasizes the significance of expert guidance, particularly in cases of uncertainty.

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Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4Vabs: https://t.co/By37lYtaEi”…the current version of GPT-4V is not recommended for real-world diagnostics due to its unreliable and suboptimal accuracy in responding to diagnostic medical questions” pic.twitter.com/WMb6kEXo7m— Tanishq Mathew Abraham, PhD (@iScienceLuvr) October 31, 2023

Researchers introduced hand-drawn sketches as an unexplored modality for specifying goals in visual imitation learning. The sketches offer a balance between the ambiguity of natural language and the over-specification of images, enabling users to convey task objectives swiftly. Their research proposes RT-Sketch, a goal-conditioned manipulation policy that takes hand-drawn sketches of desired scenes as input and generates corresponding actions. Training on paired trajectories and synthetic sketches, RT-Sketch demonstrates robust performance in various manipulation tasks, outperforming language-based agents in scenarios with ambiguous goals or visual distractions. 

The study delves into existing approaches in goal-conditioned imitation learning, focusing on conventional goal representations like natural language and images. It underscores the limitations of the representations, emphasizing the need for more abstract and precise alternatives, such as sketches. It acknowledges ongoing work in converting images to sketches to integrate them into goal-based imitation learning. It references previous research that relies on language or images for goal conditioning and explores multimodal approaches combining both. The use of image-to-sketch conversion for hindsight relabeling of terminal images in demonstration data is discussed. 

The approach points out the drawbacks of natural language commands, which can be imprecise, and goal images, which tend to be overly detailed and challenging to generalize. It proposes hand-drawn sketches as a promising alternative for specifying goals in visual imitation learning, offering more specificity than language and aiding in disambiguating task-relevant objects. The sketches are user-friendly and integrated into existing policy architectures RT-Sketch. This goal-conditioned policy takes hand-drawn sketches of desired scenes as input and produces corresponding actions. 

RT-Sketch is a manipulation policy that takes hand-drawn scene sketches as input and is trained on a dataset of paired trajectories and synthetic goal sketches. It modifies the original RT-1 policy, removing FiLM language tokenization and replacing it with concatenating goal images or sketches with image history as input to EfficientNet. Training employs behavioral cloning to minimize action log-likelihood given observations and the sketch goal. An image-to-sketch generation network augments the RT-1 dataset with goal sketches for RT-sketch training. The study evaluates RT-Sketch’s proficiency in handling sketches of varying detail, including free-hand, line, and colorized representations.

The study has demonstrated that RT-Sketch performs competitively, comparable to agents conditioned on images or language in simple scenarios. Its proficiency in achieving goals from hand-drawn sketches is especially noteworthy. RT-Sketch exhibits greater robustness than language-based goals when dealing with ambiguity or visual distractions. The assessment includes measuring spatial precision using pixel-wise distance and human-rated semantic and spatial alignment using a 7-point Likert scale. While acknowledging its limitations, the study underscores the need to test RT-Sketch’s generalization across sketches from various users and occasional incorrect skill execution.

In conclusion, the introduced RT-Sketch, a goal-conditioned manipulation policy utilizing hand-drawn sketches, exhibits performance comparable to established language or goal-image-based policies across various manipulation tasks. It demonstrates heightened resilience against visual distractions and goal ambiguities. RT-Sketch’s versatility is evident in its ability to comprehend sketches of varying specificity, from simple line drawings to intricate, colored depictions. Future research may expand the utility of hand-drawn illustrations to encompass more structured representations, such as schematics or diagrams, for assembly tasks.

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We can tell our robots what we want them to do, but language can be underspecified. Goal images are worth 1,000 words, but can be overspecified.Hand-drawn sketches are a happy medium for communicating goals to robots!🤖✏️Introducing RT-Sketch: https://t.co/EJAaWnxkdg🧵1/11 pic.twitter.com/sj1cdoYlGP— Priya Sundaresan (@priyasun_) November 3, 2023

In computer vision and robotics, simultaneous localization and mapping (SLAM) systems enable machines to navigate and understand their surroundings. However, the accurate mapping of dynamic environments, particularly the reconstruction of moving objects, has posed a significant challenge for traditional SLAM approaches. In a recent breakthrough, a research team has introduced a pioneering solution, the TiV-NeRF framework, that harnesses neural implicit representations in the dynamic domain, thereby revolutionizing dense SLAM technology. By mitigating the reliance on pre-trained models and incorporating an innovative keyframe selection strategy based on overlap ratios, this approach marks a significant advancement in 3D environment understanding and reconstruction.

In their pursuit to address the limitations of existing methods, a team of researchers from China adopted a forward-thinking strategy that extends 3D spatial positions to 4D space-temporal positions. By integrating this time-varying representation into their SLAM system, they enable more precise reconstruction of dynamic objects within the environment. This innovation represents a significant step forward in the field, opening up new possibilities for accurate and comprehensive mapping of dynamic scenes.

One of the key highlights of the proposed method is the introduction of the overlap-based keyframe selection strategy, which greatly enhances the system’s capability to construct complete dynamic objects. Unlike conventional approaches, this strategy ensures a more robust and stable reconstruction process, mitigating the issues often encountered with traditional SLAM systems, such as ghost trail effects and gaps. By accurately calculating the overlap ratio between the current frame and the keyframes database, the system achieves more comprehensive and accurate dynamic object reconstruction, thereby setting a new standard in the field of SLAM.

Although the proposed method demonstrates promising performance on synthetic datasets, the research team acknowledges the need to evaluate real-world sequences further. They recognize the challenges posed by environments with high-speed dynamic objects, which can impact the accuracy of camera pose estimation. As a result, the team emphasizes the importance of ongoing research to refine the system’s performance and address these challenges effectively.

This innovative approach represents a significant contribution to dense SLAM, offering a viable solution to the limitations posed by existing methods. By leveraging neural implicit representations and implementing an overlap-based keyframe selection strategy, the research team has paved the way for more accurate and comprehensive reconstruction of dynamic scenes. However, the quest for further advancements continues, with the need for more extensive real-world evaluations and enhancements in camera pose estimation in dynamic environments with fast-moving objects.

In conclusion, this research represents a significant step forward in evolving SLAM systems, with its unique focus on dynamic environments and comprehensive object reconstruction. The proposed method’s reliance on neural implicit representations and the efficient overlap-based keyframe selection strategy signifies a shift in the paradigm of SLAM systems, offering a more robust and stable approach to handling dynamic scenes. Despite the current limitations, the potential for further advancements and applications in real-world scenarios holds great promise for the future of dense SLAM technology.

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The GPT-4 was tested using a public Turing test on the internet by a group of researchers from UCSD. The best performing GPT-4 prompt was successful in 41% of games, which was better than the baselines given by ELIZA (27%), GPT-3.5 (14%), and random chance (63%), but it still needs to be quite there. The results of the Turing Test showed that participants judged primarily on language style (35% of the total) and social-emotional qualities (27%). Neither participants’ education nor their prior experience with LLMs predicted their ability to spot the deceit, demonstrating that even persons who are well-versed in such matters may be vulnerable to trickery. While the Turing Test has been widely criticized for its shortcomings as a measure of intellect, two researchers from the San Diego (University of California) maintain that it remains useful as a gauge of spontaneous communication and deceit. They have artificial intelligence models that can pass as humans, which might have far-reaching social effects. Thus, they examine the efficacy of various methodologies and criteria for determining human likeness. 

The Turing Test is interesting for reasons unrelated to the controversy over its sensitivity to intelligence. To begin, it’s crucial to analyze the ability being tested, which is whether or not a system can fool an interlocutor into thinking it is human. The Turing Test offers a reliable method of monitoring the development of such abilities in models. In addition, it enables us to learn about the various aspects that play a role in lying, such as model size and performance, prompting strategies, ancillary infrastructure like access to real-time information, and the Interrogator’s level of expertise.

Second, the Turing Test can be used as a research tool to probe the common perception of what it means for a machine to appear human. Machines’ performance is assessed, but human participants’ cultural, ethical, and psychological presuppositions are also investigated. Interrogators’ underlying opinions about the attributes that constitute being human and which of those qualities would be hardest to possess are revealed as they craft and refine inquiries.

The Turing Test is modified by a single interrogator and a single witness to determine if the latter is human or artificial intelligence. This contrasts Turing’s original three-person formulation, but it has become fairly standard because it eliminates the need to have multiple human participants online for each game.

Using the OpenAI API, they developed 25 LLM Witnesses that could answer questions posed by users. Each LLM witness had a temperature setting (0.2, 0.5, or 1.0), a model (GPT-3.5 or GPT-4), and a prompt. The alert explained the situation and gave the agent specific guidance on proceeding.

The chat app’s user interface was made to resemble a standard messaging service. The Interrogator sent the first message, and each user could only send one message simultaneously. Each communication could only be 300 characters long, and the total discussion time was 5 minutes. Users were restricted from pasting into the text box, and a content filter was applied to prevent abusive messages. In the upper right corner of the screen, you’ll find a countdown timer, buttons that reveal instructions overlays, and a form for reporting abusive people.

The questioner used the decision interface to provide the following:

i) a verdict (machine or human witness)

ii) how sure they are that they made the right call (0-100).

iii) a justification (free-form language) for their choice.

IRB at UC San Diego gave their clearance to the study—the Turing test. The live website played host to the experiment. Online social media platforms were used to enlist participants. They read instructions that outlined the rules of the game and consented to their data being used for scientific research.

They recruited 650 people and made 1,400 “games” in which they had short talks with either a human or a GPT model. The subjects were given a dialogue partner and asked to identify them.

Study participants were tricked 41% of the time by GPT-4 models but only 5% to 14% of the time by GPT -3.5 models. Only in 63% of the trials could people convince participants they were not machines.

Limitations

Due to its nature as an open, online experiment, this study has several caveats that may make its findings less than solid. The first problem is that the sample could be more representative of the community because participants were recruited through social media. Second, there needed to be incentives for the participants. Thus, there’s a chance that the interrogators and the witnesses could have done their best. Human witnesses have been ‘trolling’ by acting like they are artificial intelligence. Some investigators used this kind of conduct as justification for human verdicts as well. Therefore, the results may understate human performance while overestimating AI’s. Third, some of the questioners admitted that they had prior knowledge about the witness.

To sum it up –

The Turing Test has been frequently condemned as an imperfect measure of intelligence: both for being too, but to the extent that this occurred and interrogators did not reveal it, researchers may have overestimated human performance. Finally, as there was only one online user at a time, they were frequently paired with the same artificial intelligence witness. As a result, people had a preconceived notion that any given testimony was an AI, which could have resulted in lower SR across the board. This bias likely affected the results despite efforts to counteract it by removing games where an interrogator had played against an AI more than three times in succession. Finally, they only employed a small subset of the available prompts, which were developed without knowing how real people would interact with the game. The results certainly understate GPT-4’s potential performance on the Turing Test because there are more effective prompts.

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From ChatGPT to GPT4 to DALL-E 2/3 to Midjourney, the latest wave of generative AI has garnered unprecedented attention worldwide. This fascination is tempered with serious worry about the risks associated with “intelligence” that appears to be even beyond human capacity. Current generative models may yield results that are capable of challenging specialists with years of experience and expertise in both the language and visual domains, and this provides persuasive support for assertions that machines have exceeded human intelligence. Simultaneously, examining the model outputs further reveals fundamental comprehension mistakes that are surprising even to non-expert people. 

This raises what seems to be a paradox: how can they explain these models’ apparently superhuman powers while maintaining a core set of mistakes that most people could fix? They suggest that this conflict results from the differences between how human intelligence is configured and how capabilities are configured in today’s generative models. In particular, researchers from the University of Washington and Allen Institute for Artificial Intelligence put forth and investigated the Generative AI Paradox hypothesis in this work, which states that generative models can be more creative than expert-like output interpreters because they have been trained to produce expert-like outputs directly. 

In comparison, people almost always require a foundational understanding before providing expert-level results. They examine generation and understanding capacities in generative models spanning verbal and visual modalities in controlled studies to evaluate this idea. Two perspectives are used to construct “understanding” in relation to generation: 1) given a generating task, how well can models choose appropriate answers in a discriminative version of the same task? and 2) To what degree can models respond to queries on the nature and suitability of a generated response, provided that it is correct? As a result, there are two distinct experimental settings: interrogative and selected. 

Despite the fact that their findings vary between tasks and modalities, certain distinct patterns crop up. When it comes to selective evaluation, models frequently perform on par with or even better than people in generative task contexts. Still, they are not as well as humans in discriminative situations. Subsequent investigation reveals that human discrimination performance is more resilient to hostile inputs and that it is more closely correlated with generation performance than it is with GPT4. The model-human discrimination gap also grows as task complexity increases. Similar to this, models are able to provide high-quality outputs for a variety of tasks in interrogative evaluation, but they frequently make mistakes when answering questions about the same generations, and their understanding performance needs to be improved in human comprehension. 

The authors examine many possible explanations for the differences in capacity configurations between generative models and humans, such as the goals of model training and the kind and quantity of input. Their conclusions have several further ramifications. Firstly, it suggests that current conceptions of intelligence, which are based on human experience, might not translate to artificial intelligence. While AI capabilities resemble or surpass human intelligence in many aspects, their actual characteristics may deviate significantly from anticipated patterns in human thought processes. Conversely, their results caution against drawing conclusions about human intelligence and cognition from generative models since their expert human-like outputs might mask non-human-like mechanisms. Overall, rather than viewing models as comparable to human intelligence, the generative AI conundrum suggests viewing them as a fascinating contrast.

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Clustering serves as a fundamental and widespread challenge in the realms of data mining and unsupervised machine learning. Its objective is to assemble similar items into distinct groups. There are two types of clustering: metric clustering and graph clustering. Metric clustering involves using a specified metric space, which establishes the distances between various data points. These distances serve as the basis for grouping data points, with the clustering process relying on the separation between them. On the other hand, graph clustering employs a given graph that connects similar data points through edges. The clustering process then organizes these data points into groups based on the connections existing between them.

One clustering strategy involves embedding models like BERT or RoBERTa to formulate a metric clustering problem. Alternatively, another approach utilizes cross-attention (CA) models such as PaLM or GPT to establish a graph clustering problem. While CA models can be highly precise similarity scores, constructing the input graph may necessitate an impractical quadratic number of inference calls to the model. Conversely, the distances between embeddings produced by embedding models can effectively define a metric space.

Researchers introduced a clustering algorithm named KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals. This innovative algorithm effectively merges the scalability advantages of embedding models with the superior quality CA models provide. The algorithm for graph clustering possesses query access to both the CA model and the embedding model. However, a constraint is imposed on the number of queries made to the CA model. This algorithm employs the CA model to address edge queries and takes advantage of unrestricted access to similarity scores from the embedding model.

The process involves first identifying a set of documents known as centers that do not share similarity edges and then creating clusters based on these centers. A method named the combo similarity oracle is presented to balance the high-quality information offered by Cross-Attention (CA) models and the effective operations of embedding models.

In this methodology, the embedding model is employed to guide the selection of queries directed to the CA model. When presented with a set of center documents and a target document, the combo similarity oracle mechanism generates an output by identifying a center from the set similar to the target document if such similarity exists. The combo similarity oracle proves valuable in conserving the allocated budget by restricting the number of query calls to the CA model during the selection of centers and the formation of clusters. This is achieved by initially ranking centers based on their embedding similarity to the target document and subsequently querying the CA model for the identified pair.

Following the initial clustering, there is also a subsequent post-processing step in which clusters undergo merging. This merging occurs when a strong connection is identified between two clusters, specifically when the number of connecting edges exceeds the number of missing edges between the two clusters.

The researchers tested the algorithm on several datasets with different features. The performance of the algorithm is tested against the two best-performing baseline algorithms using a variety of models based on embeddings and cross-attention.

The suggested query-efficient correlation clustering approach can only use the Cross-Attention (CA) model and functions within budgeted clustering limits. Using the k-nearest neighbor graph (kNN), spectral clustering is applied to accomplish this. By using embedding-based similarity to query the CA model for each vertex’s k-nearest neighbors, this graph is created.

The evaluation involves the calculation of precision and recall. Precision quantifies the percentage of similar pairs among all co-clustered pairs, while recall measures the percentage of co-clustered similar pairs among all similar pairs. 

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Training robots to perform various manipulation behaviors has been made possible by imitation learning from human demonstrations. One popular method involves having human operators teleoperate with robot arms through various control interfaces, producing multiple demonstrations of robots performing different manipulation tasks, and then using the data to train the robots to perform these tasks independently. More recent efforts have attempted to scale this paradigm by gathering more data with a larger group of human operators over a wider range of functions. These works have demonstrated that imitation learning on large, diverse datasets can yield impressive performance, allowing robots to generalize toward new objects and unseen tasks.

This implies that gathering substantial and rich datasets is a crucial first step in creating broadly proficient robots. But this achievement is only possible with expensive and time-consuming human work. Look at a robot mimic case study where the agent’s job is to move a coke can from one bin to another. Although there is just one scene, one item, and one robot in this straightforward job, a sizable dataset of 200 demos was needed to attain a respectable success rate of 73.3%. Larger datasets, including tens of thousands of demos, have been required for recent attempts to expand to settings with various sceneries and items. For instance, it shows that challenges with minor changes in objects and goals may be generalized using a dataset of over 20,000 trajectories. 

Figure 1: Researchers provide a data production system that, by repurposing human demonstrations to make them useful in new contexts, can generate vast, diversified datasets from a small number of human demos. They use MimicGen to provide data for a variety of items, robot gear, and scene setups.

Several human operators, months, kitchens, and robotic arms are all involved in the roughly 1.5-year data-collecting effort from RT-1 to create rules that can successfully rearrange, clean, and recover things in a few kitchens with a 97% success rate. However, the number of years required to gather enough data to implement such a system in real-world kitchens still needs to be discovered. They ask, “To what extent does this data comprise distinct manipulation behaviors?” These datasets may include comparable alteration techniques used in various settings or circumstances. When grasping a cup, for instance, human operators may exhibit very comparable robot trajectories independent of the mug’s placement on a countertop. 

Adapting these trajectories to various situations can help produce a variety of. Although promising, the application of these approaches is limited due to their assumptions regarding certain tasks and algorithms. Rather, they want to create a universal system that can be easily included in current imitation learning processes and enhance various activities’ performance. In this research, they offer a unique data-gathering technique that automatically generates massive datasets across many scenarios using a small selection of human examples. Their technique, MimicGen, splits up a limited number of human demonstrations into pieces focused on objects. 

It then chooses one of the human demonstrations, spatially alters each object-centric part, stitches them together, and directs the robot to follow this new route to gather a recent demonstration in a new scenario with varied object postures. Despite being straightforward, they discovered that this technique is quite good at producing sizable datasets from various scenarios. The datasets may be used to imitate learning to train competent agents. 

Their contributions include the following: 

• Researchers from NVIDIA and UT Austin present MimicGen, a technology that uses new situation adaptation to create vast, diversified datasets from a limited number of human demos. 

• They show that MimicGen can provide high-quality data across various scene configurations, object instances, and robot arms—all of which are not included in the original demos—to train skilled agents through imitation learning (see Fig. 1). Pick-and-place, insertion, and interfacing with articulated objects are just a few examples of the many long-horizon and high-precision activities that MimicGen is extensively suited to and that call for distinct manipulation abilities. Using only 200 source human demos, they produced 50K+ additional demonstrations for 18 jobs spanning two simulators and a real robot arm. 

• Their method performs comparably to the alternative of gathering more human demonstrations; this raises significant concerns about when it is necessary to request additional data from a human. Using MimicGen to generate an equal amount of synthetic data (e.g., 200 demos generated from 10 human vs. 200 human demos) results in comparable agent performance.

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The biggest advancement in the field of Artificial Intelligence is the introduction of Large Language Models (LLMs). These Natural Language Processing (NLP) based models handle large and complicated datasets, which causes them to face a unique challenge in the finance industry. The fields of financial text summarisation, stock price prediction, financial report production, news sentiment analysis, and financial event extraction have all seen advancements in traditional financial NLP models. 

As the volume and complexity of financial data keep rising, LLMs encounter a number of challenges, including the lack of human-labeled data, the lack of expertise particular to finance, the difficulty of multitasking, the constraints of numerical computing, and the incapacity to handle real-time information. LLMs like GPT-4 are renowned for their strong dialogue abilities, command comprehension, and capacity for following directions. 

However, in industries like the Chinese financial market, LLMs lack an in-depth understanding of the financial industry, which makes the development of open-source Chinese financial LLMs that are suitable for a range of user types and situational settings important. To address the issue, a team of researchers has introduced DISC-FinLLM, a comprehensive approach for creating Chinese financial LLMs.

The main aim of this method is to provide the LLMs with the skill by which they gain the ability to generate and comprehend financial text, have multi-turn conversations about financial issues, and assist financial modeling and knowledge-enhanced systems through plugin functionality. The team has also developed a supervised instruction dataset known as DISC-FIN-SFT. This dataset’s primary categories are as follows.

Financial Consulting Instructions: These instructions have been developed from online financial forums and financial Q&A datasets. They aim to answer inquiries and offer guidance on financial matters.

Financial Task Instructions: These instructions are meant to help with a variety of financial chores. They are drawn from both self-constructed and available NLP datasets.

Instructions on Financial Computing: The solutions to financial statistical, computational, and modeling issues are the main subject of these instructions.

Retrieval- enhanced Instructions: These instructions make knowledge retrieval easier. They have been constructed from financial texts and include created questions, retrieved references, and generated answers.

The team has shared that the DISC-FIN-SFT instruction dataset is the basis for the construction of DISC-FinLLM, which has been built using a Multiple Experts Fine-tuning Framework (MEFF). Four distinct Low-rank adaptation (LoRA) modules have been trained using four different dataset segments. Financial multi-round dialogues, financial NLP jobs, financial computations, and retrieval question responses are just a few of the financial scenarios that these modules are made to accommodate. This enables the system to offer various services to relevant user groups, like students, developers, and financial professionals. In this particular version, the foundation of DISC-FinLLM is Baichuan-13B, a general domain LLM for the Chinese language.

The researchers have conducted multiple assessment benchmarks for evaluating DISC-FinLLM’s. The experimental results have shown that DISC-FinLLM performs better than the base foundation model in all downstream tasks. A closer look reveals the benefits of the MEFF architecture, which makes it possible for the model to perform well in a range of financial scenarios and jobs.

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Emotional intelligence is a historically placed cornerstone within the vast mosaic of human qualities. Emotional understanding is the ability to recognize and correctly process emotional data and then use that data to guide logical and analytical processes like problem resolution and behavioral management. Reflexes, perception, cognition, and behavior all give rise to emotions, and various internal and external factors can influence these components. Self-monitoring, Social Cognitive theory, and the importance of positive emotions indicate that emotion control can influence human problem-solving skills. Because of the wide-ranging effects it has on people, emotion regulation theory has been used in fields as diverse as education and health.

New research by CAS, Microsoft, William & Mary, Beijing Normal University, and HKUST investigate the connection between EQ and sophisticated AI models. Emerging large language models (LLMs) have exhibited impressive performance across various tasks, including reasoning, natural language processing and generation, and STEM problem-solving, making them one of the most promising research endeavors toward artificial general intelligence. By allowing GPT-4 to carry out several difficult tasks devised by humans, a recent study suggested that LLMs show remarkable potential toward AGI. However, it is still unknown whether LLMs can interpret psychological emotional impulses, a fundamental benefit of humans that helps them improve their problem-solving abilities. Using in-context learning methods, several academics have made huge strides in various areas. However, given the differences in their capacities, not all LLMs will benefit equally from the currently available methods. While recent research has shown evidence that LLMs can recognize and process emotional cues, this study has not assessed whether or not LLMs’ emotional intelligence plays a significant impact in improving their performance.

This new work takes the first step in investigating LLMs’ potential to comprehend and exploit emotional stimuli. Emotional cues associated with hope, self-assurance, and peer approval have been proven to have a positive effect in previous psychological research. Real-world applications of this phenomenon include uplifting language to improve academic performance and increase physical well-being. The researchers took inspiration from these psychological processes and presented EmotionPrompt, a simple yet powerful method for investigating LLMs’ emotional intelligence. In particular, they designed 11 statements as psychological phrases to be used as follow-up prompts for LLMs to elicit an emotional response. 

Both deterministic and generative tasks, which together encompass a wide range of difficulty levels, are used in their extensive investigations. They performed trials with several LLMs, such as FlanT5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4, on 24 Instruction Induction tasks and 21 curated BIG-Bench tasks, all of which are deterministic and can be evaluated with common metrics. They performed a human study with 106 participants to judge the quality of generating tasks utilizing both vanilla and emotional prompts based on GPT-4, as these activities do not lend themselves to traditional and automatic evaluation. Their human study shows that emotional prompts significantly boost the performance of generative tasks (with an average improvement of 10.9% in performance, truthfulness, and responsibility metrics). On the other hand, the standard experiments show that LLMs possess emotional intelligence and can be enhanced by emotional stimuli. 

The researchers also analyzed why EmotionPrompt is helpful for LLMs by assessing the effects of emotional stimuli on the final outputs through input attention. The findings show that gradients in LLMs benefit from emotional stimuli by giving them bigger weights, which benefits the outcomes by improving the representation of the original prompts. To learn more about how model size and temperature affect EmotionPrompt’s efficacy, they conducted an ablation study. 

Finally, they examined how using many emotional cues together affects performance and found that doing so can significantly improve outcomes. Based on the findings, EP02 is the best stimulus in Instruction Induction, outperforming the poorest stimulus by 6.06 percent, whereas EP06 is the greatest stimulus in BIG-Bench. It’s important to remember that several factors, such as task complexity, task type, and the metrics used, might affect a stimulus’s performance.

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to join our 32k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

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