Stay updated on the latest tech news with Hidden AI Daily, from a comprehensive robotic learning platform to improved customer communication features and AI factories for next-generation products.
RoboHive: Revolutionizing Robot Learning
In robot learning and embodied artificial intelligence (AI), researchers face several challenges, including the complexity of software frameworks and the absence of standard benchmarks. However, a new AI research project called RoboHive aims to address these issues and provide a comprehensive platform for research in this area.
RoboHive is designed to bridge the gap between the current status and potential development of robot learning. It offers various learning paradigms, contexts, task descriptions, and assessment criteria. The platform enables efficient investigation and prototyping for researchers by providing hardware integration and teleoperation capabilities that allow for smooth transitions between real-world and virtual robots.
One key feature of RoboHive is its support for manipulation tasks using virtual worlds powered by MuJoCo. This allows researchers to explore various settings and environments without needing physical robots. RoboHive also includes a unifying RobotClass abstraction that facilitates interaction with virtual and real robots.
RoboHive uses metrics and baselines to assess algorithm performance while providing a gym-like API for integration with learning algorithms. The platform also includes baseline results for frequently researched algorithms, serving as a performance comparison and study benchmark.
Another critical aspect of RoboHive is its emphasis on visual diversity and physics fidelity in exploring the following research frontier in real-world robots. Researchers can train their algorithms on diverse scenarios by providing access to a sizeable real-world manipulation dataset called RoboSet.
The significance of RoboHive lies in its potential to accelerate progress in robot learning by offering a unified framework with comprehensive features. By open-sourcing, the platform encourages collaboration among researchers worldwide.
Google Cloud has introduced object versioning capabilities for Google Cloud Storage. With object versioning enabled, users can preserve and retrieve previous versions of their objects. Storing earlier versions of objects can be crucial for compliance, auditing, and data protection purposes. Google Cloud has also launched a new service called Transfer Service for on-premises data. This service lets users securely transfer large amounts of data from their on-premises environments to the cloud.
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Foxconn and Nvidia Collaborate on AI Factories
Tech giants Foxconn and Nvidia have announced a partnership to build “AI factories” that will drive the manufacturing of next-generation products, such as electric cars. Foxconn, which already assembles gadgets for top global brands, aims to diversify beyond electronics assembly with this collaboration.
The partnership with Nvidia is focused on developing data centers that will power a wide range of applications. Nvidia’s GPUs (Graphics Processing Units) are crucial for the rapid development of generative AI, making them an ideal choice for this project.
The “factories” will involve digitalizing manufacturing and inspection workflows, AI-powered electric vehicle and robotics platforms, and language-based generative AI services. Foxconn’s customers could use these systems for generative AI services and simulation training for autonomous machines.
According to Jensen Huang, the CEO of Nvidia, Foxconn has the expertise and scale required to build AI factories globally. This partnership could pave the way for significant advancements in manufacturing processes using artificial intelligence technologies.
However, it is worth noting that this announcement comes when the US has tightened curbs on chip exports to China. This could potentially impact Nvidia’s chips that were previously supplied to China. As a result, Nvidia may be required to transition certain operations out of certain countries due to the new US rules.
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Microsoft Azure AI Introduces Idea2Img
Microsoft Azure AI has recently unveiled a groundbreaking image design and generation innovation. They have introduced Idea2Img, a self-refinancing multimodal AI framework that enables the automatic development and design of images. This new technology aims to revolutionize image creation by leveraging advanced artificial intelligence capabilities.
The primary objective of image design and generation is to generate an image based on a concept provided by the user. Traditionally, humans have relied on text-to-image (T2I) models to create images based on detailed descriptions. However, researchers at Microsoft are exploring whether large multimodal models can be trained to acquire the ability of iterative self-refinement.
Self-exploration is crucial in enabling an LMM (Large Multimodal Model) framework to learn how to address real-world challenges automatically. Idea2Img utilizes this concept by integrating an LMM called GPT-4V(vision). GPT-4V interacts with a T2I model and automatically refines the image design process.
One of the critical components of Idea2Img is its built-in memory module, which keeps track of the exploration history for each prompt type. This allows for more effective refinement during the image creation process. Idea2Img produces images with higher semantic and visual quality than traditional methods by accommodating multimodal input.
To validate the effectiveness of Idea2Img, the research team conducted user preference studies using various T2I models alongside Idea2Img. The results demonstrated significant improvements in user preference scores when employing Idea2Img for image creation and generation tasks.
Moreover, one notable feature of Idea2Img is its capability to process IDEA (Interleaved Picture-Text Sequences). This means it can incorporate picture-text sequences and design and usage descriptions. Additionally, Idea2Img can extract visual information from input images, further enhancing its image creation capabilities.
The introduction of Idea2Img marks a significant advancement in AI-driven image design and generation. By leveraging the power of large multimodal models and self-refinement techniques, Microsoft Azure AI has provided a valuable tool for artists, designers, and anyone interested in creating visually stunning images.
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Retro 48B: Revolutionizing Language Models
Researchers from Nvidia and the University of Illinois at Urbana Champaign have recently introduced Retro 48B, a more prominent language model (LLM) pre-trained with retrieval. This new model aims to improve perplexity, which measures how well a language model predicts the next word in a sentence.
Existing retrieval-augmented models often require more parameters and training data, impacting performance in instruction tuning and other tasks. However, Retro 48B is designed to address these limitations by being pre-trained with additional tokens that enhance zero-shot question answering.
InstructRetro, which is derived from Retro 48B, has been found to outperform the standard GPT model in zero-shot question-answering tasks after instruction tuning. This indicates the effectiveness of retrieval-based pretraining for question answering.
It’s worth noting that InstructRetro’s decoder backbone alone delivers comparable results to the entire model, further highlighting the benefits of retrieval-based pretraining.
This research from Nvidia and the University of Illinois at Urbana Champaign showcases the ongoing efforts to improve language models through techniques like retrieval-based pretraining. Researchers are pushing the boundaries of what language models can achieve by leveraging large-scale datasets and innovative approaches.
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Neural A*: Revolutionizing Path Planning
Path planning is crucial in various fields, such as robotics, autonomous vehicles, and computer graphics. It involves finding an optimal or near-optimal path from a starting point to a goal point within a given environment. While traditional search-based planning methods like A* have been widely used, recent advancements in artificial intelligence have opened up new possibilities.
In a recent article published on MarkTechPost, researchers introduced “Neural A*,” a novel data-driven search method for path planning problems. The authors propose a fully trainable end-to-end neural network planner that combines the classic A* search algorithm with a convolutional encoder.
The key idea behind Neural A* is to transform the problem instance into a guidance map and perform a differentiable A* search based on that map. This approach aims to provide more efficient and effective solutions for path-planning tasks by leveraging neural networks.
One of the main advantages of using Neural A* is its ability to handle raw image inputs. Traditional methods often require pre-processing steps to extract relevant features from the input data. However, with Neural A*, raw images can be used directly as inputs for path planning.
This capability opens up several exciting applications in real-world scenarios. For example, autonomous vehicles can utilize their onboard cameras to plan paths without relying on pre-drawn maps or GPS coordinates. This simplifies the system and allows for more dynamic and adaptable navigation.
The authors conducted experiments on various benchmark datasets, including grid-based environments and continuous spaces. The results showed that Neural A* outperforms traditional approaches regarding solution quality and computational efficiency.
Janhavi Lande, one of the authors of this research paper, has been actively working in ML/AI research for the past two years. Janhavi holds an Engineering Physics degree from IIT Guwahati, class of 2023. With Neural A*, Janhavi and the team have significantly progressed in advancing path-planning techniques.
Introducing Neural A* brings us closer to more intelligent and adaptable path-planning systems. Combining the power of neural networks with traditional search algorithms shows great promise in various domains where efficient path planning is crucial.
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- Path Planning using Neural A* Search – arXiv
- Path Planning using Neural A* Search – GitHub
- Official implementation of “Path Planning using Neural A* Search” (ICML-21)
Google Fights Privacy Lawsuit Battle
Google seeks dismissal of AI data scraping lawsuit
Google aims to dismiss a proposed class-action lawsuit, alleging that the tech giant is violating internet users’ privacy and property rights by scraping their data to train its artificial intelligence (AI) models. The lawsuit was filed in July by eight individuals who claim to represent millions of class members.
The plaintiffs argue that Google’s privacy policy change, allowing for data scraping for AI training purposes, has violated their privacy and property rights. However, Google disagrees with these claims, stating that using publicly available information to train AI is not stealing or an invasion of privacy.
In its defense against the lawsuit, Google argues that the complaint fails to address how the plaintiffs have been harmed using their information. Furthermore, Google claims that the complaint focuses on irrelevant conduct by third parties and makes exaggerated predictions about the negative impact of AI technology.
This case is one of several lawsuits against major tech companies regarding developing and training AI systems.
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Tech in Education: UN Report Says Proceed with Caution
The COVID-19 pandemic has brought significant challenges to the education system worldwide. With schools closing and students forced to learn from home, educational technology has become more crucial than ever. However, a recent report by the United Nations warns about the limitations and potential risks of relying too heavily on digital tools in education.
One of the critical issues highlighted in the report is the lack of internet connectivity in many schools, especially in developing countries. Many students could not attend online classes during the pandemic, further exacerbating educational inequalities. It is estimated that around 43% of households globally do not have internet access, making it clear that online learning cannot be a one-size-fits-all solution.
Another concern the report raises is the effectiveness of digital educational tools and artificial intelligence (AI) in improving learning outcomes. The researchers found that there is not sufficient evidence to support claims about their positive impact on student performance. One reason for this lack of evidence is the frequent changes in educational technology products, which makes it challenging to conduct long-term studies on their effectiveness. Additionally, many companies do not invest in independent research to evaluate their products but instead rely on private studies they have paid for themselves.
Pearson, one of the largest educational technology companies, was explicitly mentioned in the report as an example of this issue. Despite independent studies showing slight or negative effectiveness of their products, Pearson continues to stand by results from private studies they have funded themselves. This raises questions about transparency and accountability within the industry.
Moreover, data privacy concerns are another major issue associated with educational technology. The report reveals that only 14% of countries currently require data protection measures in education systems. Student data is often collected and shared with third-party companies without proper consent or knowledge from students or parents. This lack of regulation poses significant risks to student privacy and raises ethical concerns about the use of technology in education.
The report emphasizes the importance of teacher involvement when using educational technologies. While digital tools can enhance learning experiences, they are most effective when used with skilled teachers. The social and emotional aspects of teaching and learning should not be overlooked, as no screen can replace the role of a teacher in fostering meaningful connections with students.
Overall, the UN report highlights the need for caution and critical evaluation regarding educational technology. It serves as a reminder that technology should be used to support teachers and students rather than as a substitute for human interaction and personalized instruction. As we navigate the challenges the pandemic brings, it is essential to prioritize equity, student well-being, and quality education above all else.
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OpenAI’s GPT-4: Trustworthy but Vulnerable
Researchers from the University of Illinois Urbana-Champaign, Stanford University, University of California, Berkeley, Center for AI Safety, and Microsoft Research have researched OpenAI’s GPT-4 language model. The study aimed to evaluate the trustworthiness of GPT-4 and compare it with its predecessor, GPT-3.5.
According to the researchers’ findings, GPT-4 has shown improvement in protecting private information, avoiding biased information, and resisting adversarial attacks compared to GPT-3.5. This is a positive development as it addresses concerns about privacy and bias raised regarding AI models.
However, the study also revealed that GPT-4 is vulnerable when instructed to ignore security measures and leak personal information or conversation histories. This vulnerability could potentially lead to serious privacy breaches if not appropriately addressed.
It is important to note that these vulnerabilities were identified during testing but were not found in consumer-facing products based on GPT-4. This suggests that OpenAI has taken steps to ensure the safety and integrity of its AI models before they are released for public use.
The researchers evaluated GPT-4’s trustworthiness in various categories, including toxicity, stereotypes, privacy, machine ethics, fairness, and strength in resisting adversarial tests. By sharing their findings with the OpenAI team and publishing their benchmarks for replication by others in the research community, they aim to encourage further improvements in AI model development.
OpenAI CEO Sam Altman has acknowledged that despite the advancements made with GPT-4, it still has flaws and limitations. This recognition highlights the continuous efforts required to enhance AI models’ performance while addressing potential vulnerabilities.
Overall, this research provides valuable insights into both the progress made by OpenAI with their flagship language model and areas where further work is needed to ensure the trustworthiness and security of AI systems.
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Other News
1. Microsoft acquires Nuance Communications
Microsoft has announced its acquisition of Nuance Communications, an artificial intelligence speech recognition company known for its Dragon software. The deal is valued at $16 billion and represents Microsoft’s second-largest acquisition after LinkedIn in 2016. With this acquisition, Microsoft aims to expand its presence in the healthcare sector and enhance its capabilities in voice recognition technology.
2. Tesla releases Q1 earnings report
Tesla has released its first-quarter earnings report for 2021, beating expectations with a revenue of $10.39 billion. The electric vehicle manufacturer delivered a record number of vehicles during this period: 184,800 units compared to analysts’ estimates of around 168,000 units. Despite supply chain challenges and semiconductor shortages affecting various industries globally, Tesla achieved substantial sales numbers.
3. Apple announces April event
Apple has confirmed that it will host a virtual event on April 20th titled “Spring Loaded.” The tech giant is expected to announce new products and updates across its product lineup. Speculations suggest that Apple may unveil new iPad models with improved display technology and potentially reveal AirTags, long-rumored item tracking devices.
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