Meet TRACE: A New AI Approach for Accurate 3D Human Pose and Shape Estimation with Global Coordinate Tracking

Many areas can benefit from and use the recent advances in estimating 3D human pose and shape (HPS). However, most approaches only consider a single frame at a time, estimating human positions relative to the camera. Furthermore, these techniques do not follow individuals and cannot retrieve their worldwide travel paths. The problem is compounded in most hand-held videos since they are shot with a jittery, shaky camera. 

To solve these problems, researchers from the Harbin Institute of Technology, Explore Academy of, Max Planck Institute for Intelligent Systems, and implement novel end-to-end reasoning about persons in situations using a 5D representation (space, time, and identity). The proposed TRACE technique has various innovative architectural features. Most notably, it employs two novels, “Maps,” to reason about people’s 3D motion in time and space, both from the camera’s perspective and the world’s perspective. With the help of a second memory module, it is possible to keep tabs on individuals even after lengthy absences. TRACE recovers 3D human models in global coordinates from moving cameras in a single step and simultaneously tracks their movements. 

They had the objective of reconstructing each person’s global coordinates, 3D position, shape, identity, and motion simultaneously. To do this, TRACE first extracts temporal information before using a dedicated brain network to decode each sub-task. First, TRACE uses two parallel axes to encode the video and motion into separate feature maps, one for the temporal picture (F’i) and one for the motion (Oi). Using these features, the Detection and Tracking sub-trees execute multi-subject tracking to reconstruct the 3D human motion in camera coordinates.
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The estimated 3D Motion Offset map shows the relative movement of each subject in space between two frames. An innovative memory unit extracts subject identities and constructs human trajectories in camera coordinates using estimated 3D detections and 3D motion offsets. The novel’s World branch then calculates a world motion map to estimate the subjects’ trajectories in global coordinates.

The absence of real-world data for training and evaluating global human trajectory estimates persists even with a robust 5D representation. However, compiling global human trajectory and camera postures for dynamic camera movies of natural environments (DC videos) is challenging. Therefore, the team simulated camera motions to transform wild films acquired by stationary cameras into DC videos and generate a new dataset called DynaCam.

The team tested TRACE using the DynaCam dataset and two multi-person in-the-wild benchmarks. When it comes to 3DPW, TRACE provides results that are SOTA. On MuPoTS-3D, TRACE achieves better results at tracking humans under long-term occlusion than earlier 3D-representation-based approaches and tracking-by-detection methods. Findings show that TRACE outperforms GLAMR on DynaCam when it comes to calculating the overall 3D trajectory of a human from DC videos.

The team suggests investigating explicit camera motion estimation using training data such as BEDLAM, which includes complicated human motion, 3D scenes, and camera motions in the future. 

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6 AI-Powered Features Transforming Gmail into an Efficient Email Solution

Google’s Gmail has been at the forefront of harnessing the power of artificial intelligence (AI) to enhance user experience. With a history of integrating AI into its platform, Gmail continues to evolve, offering a range of features that simplify email management and streamline communication. This article explores six AI-powered capabilities that make Gmail an indispensable tool for users worldwide.

1. “Help me write”:

Gmail’s latest addition, the “Help me write” feature, empowers users to compose emails effortlessly. Accessible through the Workspace Labs program, this feature generates complete email drafts based on simple prompts. Users can refine, customize, and tailor their emails according to their preferences by leveraging generative AI language models. Additionally, the tool can extract details from previous conversations, providing contextual assistance.
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2. Smart Compose:

Smart Compose revolutionizes email composition by suggesting wording options while users type. Operating on Tensor Processing Units (TPUs), this hybrid language generation model enables users to incorporate suggested phrases and sentences into their drafts with a single tap of the “Tab” button. Besides improving efficiency, Smart Compose also aids language learners by exposing them to new English, Spanish, French, and Italian phrases.

3. Smart Reply:

Gmail’s Smart Reply feature accelerates email communication by offering up to three contextually relevant responses to received messages. Powered by advanced machine learning techniques, including deep neural networks, Smart Reply presents nuanced options beyond simple “Yes” or “No” answers. Users can swiftly select and send a suitable response, saving time and effort. Smart Reply adapts to the user’s communication style, enhancing personalization.

4. Tabbed Inbox:

Gmail’s Tabbed Inbox feature intelligently categorizes incoming emails into five tabs: Primary, Promotions, Social, Updates, and Forums. Using a combination of neural network-based machine learning and heuristic algorithms, Gmail accurately assigns emails to the appropriate tab, ensuring a clutter-free inbox. Users can customize the tabs based on their preferences, and the system learns from aggregated and anonymized data to maintain privacy.

5. Summary Cards:

Summary Cards simplify information extraction from email messages, particularly when users only require specific details. By employing heuristic and machine learning algorithms, Gmail automatically identifies relevant content within emails, such as flight itineraries or online purchase summaries. Instead of scrolling through lengthy messages, users are presented with concise information cards containing necessary details at the top of their emails.

6. Nudging:

Nudging helps users stay on top of their email communications by providing reminders to reply to or follow up on important messages. Leveraging machine learning models, Nudging detects unanswered emails and predicts which ones users would typically respond to. After a few days, the system returns these messages to the top of the inbox, reminding users to act. Nudging also extends to outgoing messages, prompting users to send follow-ups if no response is received within a specified time frame.

Google’s ongoing commitment to integrating AI technologies into Gmail has transformed the email experience for millions of users. From the intuitive “Help me write” feature to the time-saving Smart Compose and Smart Reply functionalities, Gmail’s AI-powered capabilities optimize efficiency and assist users in various email-related tasks. The Tabbed Inbox and Summary Cards enhance organization and facilitate quick access to essential information. Finally, Nudging ensures that important emails are noticed, fostering better communication and productivity. As Gmail continues to innovate and evolve, users can expect further advancements that revolutionize their email management experience.

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Friendship Ended with Single Modality – Now Multi-Modality is My Best Friend: CoDi is an AI Model that can Achieve Any-to-Any Generation via Composable Diffusion

Generative AI is a term we hear almost every day now. I even don’t remember how many papers I’ve read and summarized about generative AI here. They are impressive, what they do seems unreal and magical, and they can be used in many applications. We can generate images, videos, audio, and more by just using text prompts.

The significant progress made in generative AI models in recent years has enabled use cases that were deemed impossible not so long ago. It started with text-to-image models, and once it was seen that they produced incredibly nice results. After that, the demand for AI models capable of handling multiple modalities has increased.

Recently, there is a surging demand for models that can take any combination of inputs (e.g., text + audio) and generate various combinations of modal outputs (e.g., video + audio) has increased. Several models have been proposed to tackle this, but these models have limitations regarding real-world applications involving multiple modalities that coexist and interact. 
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While it’s possible to chain together modality-specific generative models in a multi-step process, the generation power of each step remains inherently limited, resulting in a cumbersome and slow approach. Additionally, independently generated unimodal streams may lack consistency and alignment when combined, making post-processing synchronization challenging.

Training a model to handle any mixture of input modalities and flexibly generate any combination of outputs presents significant computational and data requirements. The number of possible input-output combinations scales exponentially, while aligned training data for many groups of modalities is scarce or non-existent. 

Let us meet with CoDi, which is proposed to tackle this challenge. CoDi is a novel neural architecture that enables the simultaneous processing and generation of arbitrary combinations of modalities. 

Overview of CoDi. Source:

CoDi proposes aligning multiple modalities in both the input conditioning and generation diffusion steps. Additionally, it introduces a “Bridging Alignment” strategy for contrastive learning, enabling it to efficiently model the exponential number of input-output combinations with a linear number of training objectives.

The key innovation of CoDi lies in its ability to handle any-to-any generation by leveraging a combination of latent diffusion models (LDMs), multimodal conditioning mechanisms, and cross-attention modules. By training separate LDMs for each modality and projecting input modalities into a shared feature space, CoDi can generate any modality or combination of modalities without direct training for such settings. 

The development of CoDi requires comprehensive model design and training on diverse data resources. First, the training starts with a latent diffusion model (LDM) for each modality, such as text, image, video, and audio. These models can be trained independently in parallel, ensuring exceptional single-modality generation quality using modality-specific training data. For conditional cross-modality generation, where images are generated using audio+language prompts, the input modalities are projected into a shared feature space, and the output LDM attends to the combination of input features. This multimodal conditioning mechanism prepares the diffusion model to handle any modality or combination of modalities without direct training for such settings.

Overview of CoDi model. Source:

In the second stage of training, CoDi handles many-to-many generation strategies involving the simultaneous generation of arbitrary combinations of output modalities. This is achieved by adding a cross-attention module to each diffuser and an environment encoder to project the latent variable of different LDMs into a shared latent space. This seamless generation capability allows CoDi to generate any group of modalities without training on all possible generation combinations, reducing the number of training objectives from exponential to linear.

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20+ Best AI Tools For Startups (2023)

Workplace creativity, analysis, and decision-making are all being revolutionized by AI. Today, artificial intelligence capabilities present a tremendous opportunity for businesses to hasten expansion and better control internal processes. Artificial intelligence applications are vast, ranging from automation and predictive analytics to personalization and content development. Here is a rundown of the best artificial intelligence tools that can give young businesses a leg up and speed up their expansion.

Boost your advertising and social media game with – the ultimate Artificial Intelligence solution. Say goodbye to hours of creative work and hello to the high-converting ad and social media posts generated in mere seconds. Maximize your success and minimize your effort with today.

OpenAI’s DALLE 2 is a cutting-edge AI art generator that creates unique and creative visuals from a single text input. Its AI model was trained on a huge dataset of images and textual descriptions to produce detailed and visually attractive images in response to written requests. Startups can use DALLE 2 to create images in advertisements and on their websites and social media pages. Businesses can save time and money by not manually sourcing or creating graphics from the start, thanks to this method of generating different images from text. 
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Using artificial intelligence, Otter.AI empowers users with real-time transcriptions of meeting notes that are shareable, searchable, accessible, and secure. Get a meeting assistant that records audio, writes notes, automatically captures slides, and generates summaries.

Notion is aiming to increase its user base through the utilization of its advanced AI technology. Their latest feature, Notion AI, is a robust generative AI tool that assists users with tasks like note summarization, identifying action items in meetings, and creating and modifying text. Notion AI streamlines workflows by automating tedious tasks, providing suggestions, and templates to users, ultimately simplifying and improving the user experience.

Motion is a clever tool that uses AI to create daily schedules that account for your meetings, tasks, and projects. Say goodbye to the hassle of planning and hello to a more productive life.

With its outstanding content production features, Jasper, an advanced AI content generator, is making waves in the creative industry. Jasper, considered the best in its area, aids new businesses in producing high-quality content across multiple media with minimal time and effort investment. The tool’s efficiency stems from recognizing human writing patterns, which facilitates groups’ rapid production of interesting content. To stay ahead of the curve, entrepreneurs may use Jasper as an AI-powered companion to help them write better copy for landing pages and product descriptions and more intriguing and engaging social media posts.

Lavender, a real-time AI Email Coach, is widely regarded as a game-changer in the sales industry, helping thousands of SDRs, AEs, and managers improve their email response rates and productivity. Competitive sales environments make effective communication skills crucial to success. Startups may capitalize on the competition by using Lavender to boost their email response rate and forge deeper relationships with prospective customers.

Speak is a speech-to-text software driven by artificial intelligence that makes it simple for academics and marketers to transform linguistic data into useful insights without custom programming. Startups can acquire an edge and strengthen customer relationships by transcribing user interviews, sales conversations, and product reviews. In addition, they can examine rivals’ material to spot trends in keywords and topics and use this information to their advantage. In addition, marketing groups can utilize speech-to-text transcription to make videos and audio recordings more accessible and generate written material that is search engine optimization (SEO) friendly and can be used in various contexts.  

Recently, GitHub released an AI tool called GitHub Copilot, which can translate natural language questions into code recommendations in dozens of languages. This artificial intelligence (AI) tool was trained on billions of lines of code using OpenAI Codex to detect patterns in the code and make real-time, in-editor suggestions of code that implement full functionalities. A startup’s code quality, issue fixes, and feature deliveries can all benefit greatly from using GitHub Copilot. Moreover, GitHub Copilot enables developers to be more productive and efficient by handling the mundane aspects of coding so that they can concentrate on the bigger picture.

For faster hiring across all industries and geographies, businesses can turn to Olivia, a conversational recruiting tool developed by Paradox. This AI-powered conversational interface may be used for candidate screening, FAQs, interview scheduling, and new hire onboarding. With Olivia, entrepreneurs may locate qualified people for even the most technical positions and reclaim the hours spent on administrative activities.

Lumen5 is a marketing team-focused video production platform that allows for developing high-quality videos with zero technical requirements. Lumen5 uses Machine Learning to automate video editing, allowing users to quickly and easily produce high-quality videos. Startups can quickly and easily create high-quality films for social media, advertising, and thought leadership with the help of the platform’s built-in media library, which provides access to millions of stock footage, photographs, and music tracks. In addition, AI can help firms swiftly convert blog entries to videos or Zoom recordings into interesting snippets for other marketing channels.

Spellbook is an artificial intelligence (AI) tool that leverages OpenAI’s GPT-3 to review and recommend language for your contracts without you having to leave the comfort of a Word document. It was trained on billions of lines of legal text. This artificial intelligence tool can be used by startups in drafting and reviewing agreements and external contracts to identify aggressive words, list missing clauses and definitions, and red flag flags. Spellbook can also generate new clauses and recommend common topics of negotiation based on the agreement’s context.

Grammarly is an AI-powered writing app that flags and corrects grammar errors as you type. A machine learning algorithm trained on a massive dataset of documents containing known faults drives the system. Enter your content (or copy and paste it) into Grammarly, and the program will check it for mistakes. Furthermore, the program “reads” the mood of your work and makes suggestions accordingly. You can choose to consider the recommendations or not. As an AI tool, Grammarly automates a process that previously required human intervention (in this case, proofreading). Use an AI writing checker like Grammarly, and you’ll save yourself a ton of time.

Chatbots are one of the most well-known uses of artificial intelligence. Computer programs called “chatbots” attempt to pass as humans in online conversations. They process user input using NLP algorithms that enable them to respond appropriately. From assisting customers to promoting products, chatbots have many potential applications. Chatbots on websites and mobile apps have increased in recent years to provide constant help to customers. Whether answering basic questions or solving complex problems, chatbots are up to the challenge. In addition, businesses can use them to make suggestions to customers, such as offering related items or services.

Keeping track of customer support inquiries can take time and effort, especially for smaller organizations. Zendesk is an artificial intelligence (AI)-powered platform for managing customer assistance. Zendesk goes above and beyond the capabilities of chatbots by discovering trends and patterns in customer service inquiries. Useful metrics are automatically gathered, such as typical response times and most often encountered issues. It also finds the most popular articles in your knowledge base so you can prioritize linking to them. An intuitive dashboard displays all this information for a bird’s-eye view of your customer service.

Timely is an AI-powered calendar app that will revolutionize how you schedule your day. It integrates with your regular software to make tracking time easier for your business. Track your team’s efficiency, identify time-consuming tasks, and understand how your company spends its resources. Timely is a fantastic tool for increasing the effectiveness and efficiency of your team. You can see how your staff spends their time in real-time and adjust workflows accordingly.

If you own an online store, you understand the ongoing threat of fraud. Companies lose billions of dollars annually to credit card fraud, which can also hurt your reputation. Through the analysis of client behavior patterns, fraud can be prevented with the help of AI. Machine learning algorithms are used by businesses like aiReflex to sift through client data in search of signs of fraud. It would be impractical and time-consuming to inspect every transaction manually. However, this can be automated with the help of AI, which will keep an eye on all of your financial dealings and flag anything that looks fishy. Your company will be safe from fraudulent activity if you take this precaution.

Murf is an artificial intelligence–powered text-to-speech tool. It has a wide range of applications, from speech generation for corporate training to use in audiobook and podcast production. It is a highly flexible tool that may also be used for voiceovers in promotional videos or infomercials. Murf is a wonderful option if you need to generate a speech but don’t have the funds to hire a professional voice actor. Choosing a realistic-sounding voice from their more than 120 options in 20 languages is easy. Their studio is easy to use, and you may incorporate audio, video, and still photographs into your production. As a bonus, you have complete command over the rate, pitch, and intonation of your recording, allowing you to mimic the performance of a trained voice actor.

OpenAI’s ChatGPT is a massive language model built on the GPT-3.5 framework. It can produce logical and appropriate answers to various inquiries because it has been trained on large text data. Because ChatGPT can automate customer care and support, it has helped startups provide 24/7 help without hiring a huge customer service department. For instance, the Indian food delivery firm Swiggy has used ChatGPT to enhance customer service and shorten response times, resulting in happier and more loyal customers.

Google’s Bard uses the Language Model for Dialogue Applications (LaMDA) as an artificially intelligent chatbot and content-generating tool. Its sophisticated communication abilities have been of great use to new businesses. New companies have used Bard to improve their software development, content creation, and customer service. For example, virtual assistant startup Robin AI has implemented Bard to boost customer service and answer quality. Startups can now provide more tailored and interesting user experiences because of Bard’s intelligent and context-aware dialogue production, increasing customer satisfaction and revenue.

Small business owners and founders often need persuasive presentations to win over investors and new clientele. Create great presentations without spending hours in PowerPoint or Slides by using The software will automatically generate engaging slides from the data you provide, like text and graphics. Over 60 editable slide templates and multiple presentation layouts are available on Try it out and see if it helps you make a better impression.

If you want to reach millennials and other young people with short attention spans, you need to have a presence on TikTok and Instagram. Dumme is a useful tool for extracting key moments from longer videos and podcasts to make shorts (short videos to share on social media). You may use Dumme to pick the best moments from any video or audio you post to use them in short. It will automatically create a short video with a title, description, and captions suitable for sharing online. Making a short video for sharing on social media can be done without spending hours in front of a computer.

The Open AI-backed firm Cohere Generate created the language AI platform. It helps organizations and startups save time and effort in creating large-scale, personalized text content. It employs NLP and machine learning algorithms to develop content that fits with the brand’s voice and tone. Use this tool to boost your startup’s online visibility, expand your reach, and strengthen your content marketing strategy.

Synthesia is a cutting-edge video synthesis platform that has been a huge boon to the video production efforts of new businesses. It uses artificial intelligence to eliminate the need for costly and time-consuming video shoots by fusing a human performer’s facial emotions and lip movements with the audio. To improve their advertising campaigns, product presentations, and customer onboarding procedures, startups may use Synthesia to create tailored video content at scale. For instance, entrepreneurs can produce multilingual, locally adapted videos or dynamic video ads with little to no more work. Synthesia gives young companies the tools to reach more people at a lower cost per unit while still delivering high-quality content.

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Meet Seal: An AI Framework that Pursues ‘Segment Any Point Cloud Sequences’ by Leveraging 2D Vision Foundation Models for Self-Supervised Learning on Large-Scale 3D Point Clouds

Large Language Models (LLMs) have taken the Artificial Intelligence community by storm. Their recent impact and incredible performance display have helped contribute to a wide range of industries such as healthcare, finance, entertainment, etc. The well-known LLMs like GPT-3.5, GPT 4, DALLE 2, and BERT, also known as the foundation models, perform extraordinary tasks and ease our lives by generating unique content given just a short natural language prompt. 

Recent vision foundation models (VFMs) like SAM, X-Decoder, and SEEM have made many advancements in computer vision. Although VFMs have made tremendous progress in 2D perception tasks, 3D VFM research still needs to be improved. Researchers have suggested that expanding current 2D VFMs for 3D perception tasks is required. One crucial 3D perception task is the segmentation of point clouds captured by LiDAR sensors, which is essential for the safe operation of autonomous vehicles.

Existing point cloud segmentation techniques mainly rely on sizable datasets that have been annotated for training; however, labeling point clouds is time-consuming and difficult. To overcome all the challenges, a team of researchers has introduced Seal, a framework that uses vision foundation models for segmenting diverse automotive point cloud sequences. Inspired by cross-modal representation learning, Seal gathers semantically rich knowledge from VFMs to support self-supervised representation learning on automotive point clouds. The main idea is to develop high-quality contrastive samples for cross-modal representation learning using a 2D-3D relationship between LiDAR and camera sensors.
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Seal possesses three key properties: scalability, consistency, and generalizability.

Scalability – Seal makes use of VFMs by simply converting them into point clouds, doing away with the necessity for 2D or 3D annotations during the pretraining phase. Due to its scalability, it manages vast amounts of data, which even helps eliminates the time-consuming need for human annotation.

Consistency: The architecture enforces spatial and temporal links at both the camera-to-LiDAR and point-to-segment stages. Seal enables efficient cross-modal representation learning by capturing the cross-modal interactions between vision, i.e., camera and LiDAR sensors which help in making sure that the learned representations incorporate pertinent and coherent data from both modalities.

Generalizability: Seal enables knowledge transfer to downstream applications involving various point cloud datasets. It generalizes and handles datasets with different resolutions, sizes, degrees of cleanliness, contamination levels, actual data, and artificial data.

Some of the key contributions mentioned by the team are –

The proposed framework Seal is a scalable, reliable, and generalizable framework created to capture semantic-aware spatial and temporal consistency.

It allows the extraction of useful features from automobile point cloud sequences.

The authors have stated that this study is the first to use 2D vision foundation models for self-supervised representation learning on a significant scale of 3D point clouds.

Across 11 different point cloud datasets with various data configurations, SEAL has performed better than earlier methods in both linear probing and fine-tuning for downstream applications.

For evaluation, the team has performed tests on eleven distinct point cloud datasets to assess Seal’s performance. The outcomes demonstrated Seal’s superiority to the existing approaches. On the nuScenes dataset, Seal achieved a remarkable mean Intersection over Union (mIoU) of 45.0% after linear probing. This performance surpassed random initialization by 36.9% mIoU and outperformed previous SOTA methods by 6.1% mIoU. Seal also portrayed significant performance gains in twenty different few-shot fine-tuning tasks across all eleven tested point cloud datasets.

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Researchers From Max Plank Propose MIME: A Generative AI Model that Takes 3D Human Motion Capture and Generates Plausible 3D Scenes that are Consistent with the Motion

Humans are always interacting with their surroundings. They move about a space, touch things, sit on chairs, or sleep on beds. These interactions detail how the scene is set up and where the objects are. A mime is a performer who uses their comprehension of such relationships to create a rich, imaginative, 3D environment with nothing more than their body movements. Can they teach a computer to mimic human actions and make the appropriate 3D scene? Numerous fields, including architecture, gaming, virtual reality, and the synthesis of synthetic data, might benefit from this technique. For instance, there are substantial datasets of 3D human motion, such as AMASS, but these datasets seldom include details on the 3D setting in which they were collected. 

Could they create believable 3D sceneries for all the motions using AMASS? If so, they could make training data with realistic human-scene interaction using AMASS. They developed a novel technique called MIME (Mining Interaction and Movement to infer 3D Environments), which creates believable interior 3D scenes based on 3D human motion to respond to such inquiries. What makes it possible? The fundamental assumptions are as follows: (1) Human motion across space denotes the absence of items, essentially defining areas of the picture devoid of furniture. Additionally, this limits the kind and location of 3D objects when in touch with the scene; for instance, a sitting person must be seated on a chair, sofa, bed, etc. 

Figure 1: Estimating 3D scenes from human movement. They recreate realistic 3D settings in which the motion may have occurred given 3D human motion (left), such as that obtained from motion capture or body-worn sensors. Their generative model is able to generate several realistic scenarios (right) with proper human-scene interaction that take into account the locations and postures of the person.

Researchers from the Max Planck Institute for Intelligent Systems in Germany and Adobe created MIME, a transformer-based auto-regressive 3D scene generation technique, to give these intuitions some tangible form. Given an empty floor plan and a human motion sequence, MIME predicts the furniture that will come into contact with the human. Additionally, it foresees believable items that do not come into touch with people but fit in with other objects and adhere to the free-space restrictions brought on by the motions of people. They partition the motion into contact and non-contact snippets to condition the 3D scene creation for human motion. They estimate potential contact poses using POSA. The non-contact postures project the foot vertices onto the ground plane to establish the room’s free space, which they record as 2D floor maps. 
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The contact vertices predicted by POSA create 3D bounding boxes that reflect the contact postures and associated 3D human body models. The objects that satisfy the contact and free-space criteria are expected autoregressively use this data as input to the transformer; see Fig. 1. They expanded the large-scale synthetic scene dataset 3D-FRONT to create a new dataset named 3D-FRONT HUMAN to train MIME. They automatically add people to the 3D scenarios, including non-contact people (a series of walking motions and people standing) and contact people (people sitting, touching, and lying). To do this, they use static contact poses from RenderPeople scans and motion sequences from AMASS. 

MIME creates a realistic 3D scene layout for the input motion at inference time, represented as 3D bounding boxes. They choose 3D models from the 3D-FUTURE collection based on this arrangement; then, they fine-tune their 3D placement based on geometric restrictions between the human positions and the scene. Their method produces a 3D set that supports human touch and motion while placing convincing objects in free space, unlike pure 3D scene creation systems like ATISS. Their approach permits the development of items not in contact with the person, anticipating the complete scene instead of individual objects, in contrast to Pose2Room, a recent pose-conditioned generative model. They show that their approach works without any adjustments on genuine motion sequences that have been recorded, like PROX-D. 

In conclusion, they contribute the following: 

• A brand-new motion-conditioned generative model for 3D room scenes that auto-regressively creates things that come into contact with people while avoiding occupying motion-defined vacant space. 

• A brand-new 3D scene dataset made up of interacting people and people in free space was created by filling 3D FRONT with motion data from AMASS and static contact/standing poses from RenderPeople.

The code is available on GitHub along with a video demo. They also have a video explanation of their approach.

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The Backpack That Solves ChatGPT’s Bias: Backpack Language Models Are Alternative AI Methods for Transformers

AI language models are becoming an essential part of our lives. We have been using Google for decades to access information, but now, we are slowly switching to ChatGPT. It provides concise answers, clear explanations, and it is usually quicker to find the information we seek. 

These models learn from the data we produced over the years. As a result, we transferred our biases to the AI models, and this is a topic of debate in the domain. One particular bias that has gained attention is the gender bias in pronoun distributions, where models tend to prefer gendered pronouns such as “he” or “she” based on the context. 

Addressing this gender bias is crucial for ensuring fair and inclusive language generation. For example, if you start the sentence “The CEO believes that…”, the model continues with he, and if you replace the CEO with the nurse, the next token becomes she. This example serves as an interesting case study to examine biases and explore methods to mitigate them.
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It turns out that the context plays a crucial role in shaping these biases. By replacing CEO with a profession stereotypically associated with a different gender, we can actually flip the observed bias. But here’s the challenge: achieving consistent debiasing across all the different contexts where CEO appears is no easy task. We want interventions that work reliably and predictably, regardless of the specific situation. After all, interpretability and control are key when it comes to understanding and improving language models. Unfortunately, the current Transformer models, while impressive in their performance, don’t quite meet these criteria. Their contextual representations introduce all sorts of complex and nonlinear effects that depend on the context at hand.

So, how can we overcome these challenges? How can we tackle the bias we introduced in large language models? Should we improve transformers, or should we come up with new structures? The answer is Backpack Language Models.

Backpack LM tackles the challenge of debiasing pronoun distributions by leveraging non-contextual representations known as sense vectors. These vectors capture different aspects of a word’s meaning and its role in diverse contexts, giving words multiple personalities.

Overview of Backpack LM. Source:

In Backpack LMs, predictions are log-linear combinations of non-contextual representations, referred to as sense vectors. Each word in the vocabulary is represented by multiple sense vectors, encoding distinct learned aspects of the word’s potential roles in different contexts. 

These sense vectors specialize and can be predictively useful in specific contexts. The weighted sum of sense vectors for words in a sequence forms the Backpack representation of each word, with the weights determined by a contextualization function that operates on the entire sequence. By leveraging these sense vectors, Backpack models enable precise interventions that behave predictably across all contexts. 

This means that we can make non-contextual changes to the model that consistently influences its behavior. Compared to Transformer models, Backpack models offer a more transparent and manageable interface. They provide precise interventions that are easier to understand and control. Moreover, Backpack models don’t compromise on performance either. In fact, they achieve results on par with Transformers while offering enhanced interpretability. 

Example of sense vectors. Source:

Sense vectors in Backpack models encode rich notions of word meaning, outperforming word embeddings of state-of-the-art Transformer models on lexical similarity tasks. Additionally, interventions on sense vectors, such as reducing gender bias in professional words, demonstrate the control mechanism offered by Backpack models. By downscaling the sense vector associated with gender bias, significant reductions in contextual prediction disparities can be achieved in limited settings.

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Prompt Engineering – Complete Guide

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In the realm of natural language processing (NLP), Prompt engineering has emerged as a powerful technique to enhance the performance and adaptability of language models. By carefully designing prompts, we can shape the behavior and output of these models to achieve specific tasks or generate targeted responses. In this comprehensive guide, we will explore the concept of prompt engineering, its significance, and delve into various techniques and use cases. From basic prompt formatting to advanced strategies like N-shot prompting and self-consistency, we will provide insights and examples to help you harness the true potential of prompt engineering.

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What is Prompt Engineering?

Prompt engineering involves crafting precise and context-specific instructions or queries, known as prompts, to elicit desired responses from language models. These prompts provide guidance to the model and help shape its behavior and output. By leveraging prompt engineering techniques, we can enhance model performance, achieve better control over generated output, and address limitations associated with open-ended language generation.

Why Prompt Engineering?

Prompt engineering plays a crucial role in fine-tuning language models for specific applications, improving their accuracy, and ensuring more reliable results. Language models, such as GPT-3, have shown impressive capabilities in generating human-like text. However, without proper guidance, these models may produce responses that are either irrelevant, biased, or lack coherence. Prompt engineering allows us to steer these models towards desired behaviors and produce outputs that align with our intentions.

Few Standard Definitions:

Before diving deeper into prompt engineering, let’s establish some standard definitions:

Label: The specific category or task we want the language model to focus on, such as sentiment analysis, summarization, or question-answering.

Logic: The underlying rules, constraints, or instructions that guide the language model’s behavior within the given prompt.

Model Parameters (LLM Parameters): Refers to the specific settings or configurations of the language model, including temperature, top-k, and top-p sampling, that influence the generation process.

Basic Prompts and Prompt Formatting

When designing prompts, it’s essential to understand the basic structures and formatting techniques. Prompts often consist of instructions and placeholders that guide the model’s response. For example, in sentiment analysis, a prompt might include a placeholder for the text to be analyzed along with instructions such as “Analyze the sentiment of the following text: .” By providing clear and specific instructions, we can guide the model’s focus and produce more accurate results.

Elements of a Prompt:

A well-designed prompt should include several key elements:

Context: Providing relevant background or context to ensure the model understands the task or query.

Task Specification: Clearly defining the task or objective the model should focus on, such as generating a summary or answering a specific question.

Constraints: Including any limitations or constraints to guide the model’s behavior, such as word count restrictions or specific content requirements.

General Tips for Designing Prompts:

To optimize the effectiveness of prompts, consider the following tips

Be Specific: Clearly define the desired output and provide precise instructions to guide the model’s response.Keep it Concise: Avoid overly long prompts that may confuse the model. Focus on essential instructions and information.Be Contextually Aware: Incorporate relevant context into the prompt to ensure the model understands the desired task or query.Test and Iterate: Experiment with different prompt designs and evaluate the model’s responses to refine and improve the prompt over time.

Prompt Engineering Use Cases

Prompt engineering can be applied to various NLP tasks. Let’s explore some common use cases:

With well-crafted prompts, language models can extract specific information from given texts. For example, by providing a prompt like “Extract the names of all characters mentioned in the text,” the model can generate a list of character names, enabling efficient information extraction.

Prompt: “Extract the names of all characters mentioned in the text.”Example Text: “In the novel ‘Pride and Prejudice,’ Elizabeth Bennet, Mr. Darcy, and Jane Bennet are prominent characters.”Output: [“Elizabeth Bennet”, “Mr. Darcy”, “Jane Bennet”]

Text Summarization

Prompts can guide language models to generate concise and accurate summaries of longer texts. By providing an instruction like “Summarize the following passage in 3-4 sentences,” we can obtain concise summaries that capture the essential information.

Prompt: “Summarize the following passage in 3-4 sentences.”Example Text: “Scientists have discovered a new species of orchid in the rainforests of South America. This orchid, named Orchidaceae novus, boasts vibrant purple petals and a unique fragrance. Its discovery brings new insights into the rich biodiversity of the region.”Output: “A new species of orchid, Orchidaceae novus, has been found in the South American rainforests. This vibrant purple orchid with a unique fragrance adds to the region’s biodiversity.”

Question Answering

Through carefully constructed prompts, language models can excel at question-answering tasks. For instance, by framing a prompt like “Answer the following question: [question],” the model can generate relevant and accurate responses.

Prompt: “Answer the following question: Who won the 2020 Nobel Prize in Physics?”Example Question: “Who won the 2020 Nobel Prize in Physics?”Output: “The 2020 Nobel Prize in Physics was awarded to Roger Penrose, Reinhard Genzel, and Andrea Ghez for their groundbreaking discoveries on black holes.”

Code Generation

Prompt engineering can assist in generating code snippets or programming solutions. By providing a clear task specification and relevant context, language models can generate code that aligns with the desired functionality.

Prompt: “Generate a Python code snippet to calculate the factorial of a given number.”Example Output:def factorial(n):if n == 0 or n == 1:return 1else:return n * factorial(n-1)number = int(input(“Enter a number: “))result = factorial(number)print(“The factorial of”, number, “is”, result)

Text Classification

Prompts can guide language models to perform text classification tasks, such as sentiment analysis or topic categorization. By providing specific instructions and context, models can accurately classify texts into predefined categories.

Prompt: “Classify the following review as positive or negative.”Example Text: “The movie had incredible acting, breathtaking cinematography, and a captivating storyline that kept me on the edge of my seat.”Output: Positive

Prompt Engineering Techniques

To further enhance the capabilities of prompt engineering, several advanced techniques can be employed:

N-shot Prompting:

N-shot prompting involves fine-tuning models with limited or no labeled data for a specific task. By providing a small number of labeled examples, language models can learn to generalize and perform the task accurately. N-shot prompting encompasses zero-shot and few-shot prompting approaches.

Zero-shot Prompting:

In zero-shot prompting, models are trained to perform tasks they haven’t been explicitly trained on. Instead, the prompt provides a clear task specification without any labeled examples. For example:

Prompt: “Translate the following English sentence to French.”
English Sentence: “I love to travel and explore new cultures.”
Output: “J’aime voyager et découvrir de nouvelles cultures.”
Few-shot Prompting:
In few-shot prompting, models are trained with a small number of labeled examples to perform a specific task. This approach allows models to leverage a limited amount of labeled data to learn and generalize. For example:
Prompt: “Classify the sentiment of the following customer reviews as positive or negative.”
Example Reviews:
“The product exceeded my expectations. I highly recommend it!”
“I was extremely disappointed with the quality. Avoid this product.”

Chain-of-Thought (CoT) Prompting

CoT prompting involves breaking down complex tasks into a sequence of simpler questions or steps. By guiding the model through a coherent chain of prompts, we can ensure context-aware responses and improve the overall quality of the generated text.

“Identify the main theme of the given text.”
“Provide three supporting arguments that highlight this theme.”
“Summarize the text in a single sentence.”
Example Text:
“The advancement of technology has revolutionized various industries, leading to increased efficiency and productivity. It has transformed the way we communicate, works, and access information.”
Main Theme: “The advancement of technology and its impact on industries.”
Supporting Arguments:
Increased efficiency and productivity
Transformation of communication, work, and information access
Revolutionizing various industries
Summary: “Technology’s advancements have revolutionized industries, enhancing efficiency and transforming communication, work, and information access.”

Generated Knowledge Prompting

Generated knowledge prompting involves leveraging external knowledge bases or generated content to enhance the model’s responses. By incorporating relevant information into prompts, models can provide detailed and accurate answers or generate content based on acquired knowledge.

Prompt: “Based on your understanding of historical events, provide a brief explanation of the causes of World War II.”
Generated Knowledge:
“The main causes of World War II include territorial disputes, economic instability, the rise of totalitarian regimes, and the failure of international diplomacy.”
“The causes of World War II were influenced by territorial disputes, economic instability, the rise of totalitarian regimes, and the failure of international diplomacy.”


Self-consistency techniques focus on maintaining consistency and coherence in language model responses. By comparing generated outputs and ensuring they align with previously generated content or instructions, we can improve the overall quality and coherence of model responses.

“Generate a story beginning with the following sentence:”
“Continue the story from the previous prompt, ensuring consistency and coherence.”
“Conclude the story in a meaningful and satisfying way.”
Prompt: “Generate a story beginning with the following sentence: ‘Once upon a time in a small village…'”
Output: “Once upon a time in a small village, there lived a young girl named Emma who possessed a magical power.”
Prompt: “Continue the story from the previous prompt, ensuring consistency and coherence.”
Output: “Emma’s magical power allowed her to communicate with animals, and she used this gift to help her community and protect the village from harm.”
Prompt: “Conclude the story in a meaningful and satisfying way.”
Output: “As the years went by, Emma’s reputation as a guardian of the village grew, and her selflessness and bravery became legendary.”

These examples demonstrate how prompt engineering techniques like N-shot prompting, CoT prompting, generated knowledge prompting, and self-consistency can be applied to guide language models and produce more accurate, contextually appropriate, and coherent responses. By leveraging these techniques, we can enhance the performance and control of language models in various NLP tasks.


Prompt engineering is a powerful approach to shape and optimize the behavior of language models. By carefully designing prompts, we can influence the output and achieve more precise, reliable, and contextually appropriate results. Through techniques like N-shot prompting, CoT prompting, and self-consistency, we can further enhance model performance and control over generated output. By embracing prompt engineering, we can harness the full potential of language models and unlock new possibilities in natural language processing.

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How to Become a Successful Machine Learning Engineer

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Machine learning has been one of the hottest fields in technology in recent years, and machine learning engineers are in high demand. In this guide, we will explore more about the role – a machine learning engineer, what skills are required to become one, the job opportunities available in this field, and the salaries you can expect.

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What is a Machine Learning Engineer?

Defining a Machine Learning Engineer Role

Machine learning engineers are professionals who design, build, and deploy machine learning systems. This involves developing algorithms and predictive models that enable machines to analyze and make decisions based on large data sets.

What Skills Do You Need to Become a Machine Learning Engineer?

To become a machine learning engineer, you need a strong foundation in computer science and data science, as well as advanced knowledge of machine learning algorithms and models. You also need to be familiar with big data and data structures, and have strong programming skills in languages such as Python, R, and Java.

How Does a Machine Learning Engineer Differ from a Data Scientist or a Software Engineer?

While there are similarities between machine learning engineers, data scientists, and software engineers, each role has its own unique set of skills and responsibilities. Data scientists focus on analyzing data and developing insights, while machine learning engineers are responsible for creating and deploying predictive models based on that data. Software engineers focus on designing and building software systems.

Reasons to Become a Machine Learning Engineer

Lifetime Career Opportunities

Machine learning is a growing field, and demand for machine learning engineers is expected to increase in the coming years. This means that there will be plenty of job opportunities for those with the right skills and experience.

High Average Salary

Machine learning engineers are in high demand, and as a result, can command high salaries. The average salary for a machine learning engineer is around $120,000 per year.

Global Market Growth

The global market for machine learning is expected to grow significantly in the coming years, driven by demand from a range of industries including healthcare, finance, and e-commerce.

How to Become a Machine Learning Engineer

Get a Solid Foundation in Computer Science

To become a successful machine learning engineer, you need a solid foundation in computer science. This includes knowledge of programming languages, data structures, and algorithms.

Master Data Science Skills

Machine learning is a key part of data science, so it’s important to have a good understanding of data analysis. This includes knowledge of statistical methods and tools, as well as experience working with large data sets.

Learn Machine Learning Algorithms and Models

Machine learning engineers need to have a strong understanding of machine learning algorithms and models. This includes both supervised and unsupervised learning techniques, as well as neural networks and deep learning.

The Importance of Data Science Teams

Machine learning engineers often work as part of data science teams, alongside data scientists and other professionals. This allows them to contribute to data pipeline design, and to develop machine learning models that are aligned with the overall goals of the organization.

How to Deploy Machine Learning Models in Production

Deploying machine learning models in production is a key part of the machine learning engineer’s role. This involves ensuring that models are performing as expected, and that they are integrated into the organization’s overall technology stack.

Common Responsibility of a Machine Learning Engineer

Machine learning engineers are responsible for designing and implementing machine learning projects, as well as working with data scientists and other stakeholders to ensure that models are optimized for performance and accuracy.

Skills Required to Excel as a Machine Learning Engineer 

Advanced Knowledge of Machine Learning Algorithms

Machine learning engineers need to have a strong understanding of advanced machine learning algorithms, such as neural networks and deep learning. They also need to have experience with predictive models, as well as the ability to build custom models based on specific business needs.

Strong Programming Skills and Knowledge of Programming Languages

Machine learning engineers need strong programming skills, particularly in languages such as Python, R, and Java. They also need to have experience with data manipulation and analysis tools such as Pandas and NumPy.

Familiarity with Big Data and Data Structures

Machine learning engineers need to be familiar with big data and data structures, and have the ability to work with large data sets. They also need to have experience with distributed computing tools such as Hadoop and Spark.

Data Scientist

Data scientists focus on analyzing data and developing insights that can be used to inform business decisions. They often work as part of data science teams alongside machine learning engineers.

Artificial Intelligence Expert

Artificial intelligence experts focus on developing AI systems that can learn and make decisions based on data. This involves machine learning but also includes other areas such as natural language processing and computer vision.

Deep Learning Engineer

Deep learning engineers specialize in creating and optimizing deep learning models, which are a type of machine learning algorithm that uses neural networks to learn from data.

Average Salary of a Machine Learning Engineer

The average salary for a machine learning engineer is around $120,000 per year. However, this can vary depending on a range of factors, such as location and level of experience.

Factors Affecting the Salary of a Machine Learning Engineer

The salary of a machine learning engineer depends on a range of factors, including their level of experience, the size and type of organization they work for, and the specific technologies and tools they are working with.

The average salary for a machine learning engineer is higher than the average salary for other positions in the tech industry, such as software engineers and data analysts. This is due to the high demand for machine-learning skills.


To become a successful machine learning engineer, you need a strong foundation in computer science and data science, as well as advanced knowledge of machine learning algorithms and models. You also need to be familiar with big data and data structures and have strong programming skills in languages such as Python, R, and Java. Additionally, being a good communicator and team player is essential for working as part of a data science team.

Final Takeaways on Pursuing a Career in Machine Learning and Artificial Intelligence

Machine learning and artificial intelligence are growing fields, with plenty of job opportunities and high salaries available to those with the right skills and experience. If you have an interest in technology and a passion for solving complex problems, then pursuing a career as a machine learning engineer could be the perfect choice.

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Meet AIAgent: A Web-based AutomateGPT that Needs No API Keys and is Powered by GPT4

AIAgent is a powerful web-based app that gives users the power to create customized AI agents for their specific tasks and goals. The application works by breaking down goals into smaller tasks and completing them individually. The app’s benefits include the ability to run multiple AI agents simultaneously and democratize access to cutting-edge technology. 

AI agents allow users to instruct AI to do tasks for them, for example, searching for competitors for a product and writing a report on the findings or writing an entire application instead of just code snippets.

With GPT-4 capabilities and internet access, AIAgent is perfect for automating blog writing with SEO optimization, researching podcast topics, and more. It does not require an API key to work with and has a clean and simple user interface, making working with AI agents all the more effortless.
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AIAgent also has the ability to read and write files, thereby streamlining the user’s document workflow. It also has features like inline code blocks with syntax highlighting and seamless collaboration with third-party platforms.

The current version of the tool offers a free tier for users to utilize the GPT-3.5 model. However, in order to access the GPT-4 model, users will need to pay a monthly fee.

Use cases

AIAgent is perfect for automating blog content research and writing, ensuring that SEO optimization remains a top priority.

Users can use the tool to create a well-defined posting schedule for Twitter, enabling them to consistently engage with their audience and share valuable content regularly.

AIAgent has internet access making it a valuable resource for researching podcast topics. It can retrieve key information from various online sources to enrich the podcast.

The tool can be used in the field of marketing by learning strategies from a seasoned specialist. It can access and analyze articles and expert opinions from marketing professionals to gain insights into successful marketing techniques.

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AIAgent is powered by the GPT-4 model, which incorporates the latest advancements in natural language processing and understanding.

Users can use the tool without API keys, providing a seamless and hassle-free experience.

The simple and clean User Interface (UI) ensures users can navigate and interact with the system effortlessly.

The tool has Internet access enabling it to leverage online resources and retrieve real-time information.

Individuals also have the ability to fully customize and modify tasks according to their specific needs and preferences. 


In conclusion, AIAgent is a powerful web-based application that enables users to create customized AI agents for various tasks. Its advanced GPT-4 model and internet access offer benefits such as automating blog writing, researching podcast topics, and learning marketing strategies. AIAgent’s user-friendly interface, lack of API key requirement, and ability to run multiple AI agents simultaneously make it a game-changer in the field of AI tools, positioning it as a strong competitor to similar platforms like ChatGPT, AutoGPT, and AgentGPT.

Check Out The Project. Don’t forget to join our 24k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more. If you have any questions regarding the above article or if we missed anything, feel free to email us at [email protected]

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