#16 AI Research News Updates

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👉 Twitter has recently released Qurious, a new in-house product that allows internal business customers to ask inquiries in natural language. The product consists of a web app and a Slack chatbot connected to BigQuery and Data QnA APIs. The Slack chatbot was created with node.js and the Express Framework, based on a Google Data QnA reference implementation. They are then offered real-time analytics without having to construct dashboards.

👉 Amazon Researchers Propose ‘ALLIE’: A Novel Framework to Address the Challenges of Active Learning on Large-Scale Imbalanced Graphs. Amazon researchers offer an Active Learning-based technique for Large-scale ImbalancEd graphs (ALLIE), which combines the principle of AL on graphs with reinforcement learning for accurate and efficient node categorization. Using several uncertainty measures as criteria, ALLIE may successfully pick informative unlabeled samples for labeling. Furthermore, the method prioritizes the categorization of less confident and “under-represented” samples.

👉 Researchers from Prince Sultan University in Riyadh recently published a paper that looks at the topic of real-time parking monitoring and data gathering using UAVs, assessing parking occupancy over time using deep learning for improved management and resource allocation. The contribution of this research is the creation of an AI-based solution that analyses parking occupancy using aerial photos of automobiles and combines YoloV3 object detection with DeepSort object tracker. 

👉 Meet ‘CodeGen’: An AI Model That Turns Simple Natural Language Requests Into Executable Code. The large-scale language model, CodeGen, which converts simple English prompts into executable code, is the first step toward this objective. The person doesn’t write any code; instead, (s)he describes what (s)he wants the code to perform in normal language, and the computer does the rest.

👉 MIT Researchers Discovered Hidden Magnetic Properties In Multi-Layered Electronic Material By Analyzing Polarized Neutrons Using Machine Learning. The MIT-led team is already thinking about broadening the scope of their research. The machine learning framework is easily adaptable to various challenges, such as the superconducting proximity effect, which is of significant interest in quantum computing.

👉 The Token-Dropping Approach Used By ML Researchers From Google and NYU Reduces BERT Pretraining Time And Cost By 25%. In a research paper, researchers from Google, New York University, and the University of Maryland recommend a simple but effective “token dropping” method that drastically reduces the pretraining cost of transformer models like BERT while maintaining downstream fine-tuning performance.

👉 Oracle Releases MySQL HeatWave ML That Adds Powerful Machine Learning Capabilities to MySQL Applications. Recently, Oracle released MySQL HeatWave, the only MySQL cloud database service that supports in-database machine learning (ML). It automates the ML lifecycle and saves all trained models in the MySQL database, removing the need to migrate data or models to a machine learning tool or service. This decreases application complexity, saves costs, and increases data and model security. It produces a model with the best algorithm, features, and hyper-parameters for a specific data collection and application. 

👉 Stanford Researchers Have Developed a Machine Learning-Based Algorithm To Detect Autism in Brain “Fingerprints”. Stanford researchers have created an algorithm that can tell if someone has autism by analyzing brain images. Inspired by current breakthroughs in artificial intelligence (AI), the unique system also accurately predicts the degree of autism symptoms in individuals. The algorithm might lead to faster diagnosis, more tailored therapy, and a better understanding of the brain’s roots in autism with further refinement.

👉 IBM Researchers Showcase Their Non-Von Neumann AI Hardware Breakthrough in Neuromorphic Computing That Can Help Create Machines To Recognize Objects Just Like Humans. The researchers employ a PCM memtransistive synapse, which combines memristors, a nonvolatile electronic memory element, and transistors into a single low-power device. This shows a non-Von Neumann in-memory computing architecture that offers various powerful cognitive frameworks for ML applications, such as short-term spike-timing-dependent plasticity and probabilistic Hopfield Neural Networks.

👉 Researchers from U Texas and Apple Propose a Novel Transformer-Based Architecture for Global Multi-Object Tracking. Apple researchers recently published a paper demonstrating how to express worldwide surveillance as a few layers in a deep network. Because the network generates trajectories directly, it avoids both pairwise association and graph-based optimization. The researchers demonstrate how detectors can be enhanced with transformer layers to become combined detectors and trackers.

👉 Researchers from MIT CSAIL Introduce ‘Privid’: an AI Tool, Build on Differential Privacy, to Guarantee Privacy in Video Footage from Surveillance Cameras. The system, dubbed “Privid,” allows analysts to input video data searches and then adds a tiny amount of noise (additional data) to the result to ensure that no one can be identified. The method is based on a formal notion of privacy known as “differential privacy,” which permits without having access to aggregate statistics about private data disclosing individually identifying information.

👉 Meta AI researchers have recently released Mephisto. It is a new platform to collect, share, and iterate on the most promising approaches to collecting training datasets for AI models. Researchers can exchange unique collecting strategies with Mephisto in a reusable and iterable format. It also allows them to change out components and quickly locate the exact annotations required, minimizing the barrier to custom task creation.

👉 Nvidia AI Demonstrates Insanely Fast Neural Rendering Model Called ‘NeRF’ That Turns 2D Photos into 3D Objects in Seconds. Neural Radiance Field, or NeRF, is a novel technique that includes training AI algorithms to create 3D things from two-dimensional photographs. NeRF can “fill in the gaps” by interpolating what the 2D pictures failed to capture. It’s a clever method that might lead to advancements in various sectors, including video games and self-driving cars. NVIDIA has now created a new NeRF technology — the firm says it is the quickest to date — that takes only seconds to train and build a 3D scene. The resulting approach, named Instant NeRF, is the fastest NeRF technology to date, with speedups of up to 1,000x in some circumstances. The model can create the final 3D scene in tens of milliseconds after only a few seconds of training on a few dozen still photographs — including data about the camera angles they were taken from.

👉 Google Docs Now Auto-Generate Short Summaries Using Machine Learning. Google announced a new feature enabling Google Docs to generate ideas automatically when they are available. The team employs a machine learning (ML) model to understand document text and provide a one- to two-sentence natural language description of the material.

👉 This Singapore-Based AI Startup Has Developed a No-Code MLOps Platform That Allows Companies and Teams to Build Breakthrough AI Capabilities. Datature, a Singapore-based startup, has developed a no-code end-to-end MLOps platform that allows businesses to build breakthrough AI capabilities. Without writing a single line of code, Datature’s comprehensive suite of solutions enables teams to annotate, augment, train, and deploy computer vision models. It allows teams to develop ground truths, perform transfer learning, and deploy AI models quickly.

• 👉 Being Compatible With Any Programming Language And Machine Learning Framework; Flower Team Releases Flower 0.18 With Cool New Updates For Federated Learning. The new version includes Compatibility with Jupyter Notebook / Google Colab has been improved in the Virtual Client Engine; A new Jupyter Notebook code sample is now available; Properties owned by clients (feature preview) To allow server-side strategies to query client properties, clients can implement a new method to get properties.

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