Robots have seen widespread adoption across multiple industries, and now, they’re moving into the home. Consumer robots like dishwashers and robotic vacuums have been available for decades, but now they’re becoming more advanced, resembling commercial bots. One of the best places to find these robots is in the kitchen.
Generally speaking, automation is ideal for repetitive, predictable tasks, which doesn’t describe much in the kitchen. Preparing food often involves some amount of nuance and reacting on the fly to different variables. This unpredictability is why kitchen robots haven’t seen much use. That is, until now. Experts predict there will be 482.8 million smart homes by 2025. The home automation market is booming, and robotic kitchens are a natural extension of these systems. Automated food preparation is already more prevalent than many may realize. Restaurant Robots Like in most segments, the commercial kitchen space has embraced robotics before home kitchens have. As more restaurants implement robotics, the same technology will become more accessible and affordable. In turn, home kitchen alternatives will become more popular. The simplest restaurant robots resemble vending machines. For instance, food robot company Chowbotics makes a robotic salad dispenser called Sally. Customers use a touchscreen to order a personalized salad, then the robot prepares and serves it within seconds. When the pandemic limited interactions between people, robots like this saw skyrocketing demand. Other restaurant robots are more advanced. Miso Robotics’ Flippy is a robotic fry cook, able to prepare 19 different menu items autonomously, including burgers and hashbrowns. Flippy can cook meat to order, clean the grill and has seen work in multiple establishments from Dodger’s Stadium to White Castle. Similarly, some bars have started employing Makr Shakr, a robotic bartender. Makr Shakr can manage more than 150 bottles of spirits, creating virtually infinite drink combinations. As these examples highlight, kitchen robots have seen rising adoption across the restaurant industry. As technology naturally flows from commercial to consumer segments, similar systems are starting to emerge in home kitchens. Semi-Autonomous Kitchen Appliances The most common instances of robotics in home kitchens are less flashy than a robot bartender. As the smart home movement has gained momentum, semi-autonomous IoT technologies have appeared throughout the kitchen. These robots may only automate a few smaller tasks, but even marginal improvements save home cooks plenty of time. Some of today’s grills come with WiFi compatibility, for example. “Alpha Connect” from Grilla Grills connects to users’ phones, sending automatic updates about cooking times and food temperatures. These updates, along with automated temperature adjustments, ensure that even when users are away, they can cook their food precisely to their liking. Other connected appliances can automate tasks based on user schedules. For example, consumers can set smart coffee machines to start brewing at a specific time of day. When part of a larger IoT ecosystem, they could begin as soon as a smart speaker plays an alarm to wake users up. There are plenty of other semi-autonomous appliances like automatic soap dispensers, pan stirrers and toasters on the market. While these may not resemble most people’s idea of a robot, they represent a growing automated presence in home kitchens. Kitchen Cobots Of course, there are more advanced consumer kitchen robots available today, too. Collaborative robots, or cobots, have seen extensive use in industrial settings, and now they’re coming to the kitchen. These robots serve as cooking assistants, performing various tasks to help people prepare meals in their homes. At the 2021 Consumer Electronics Show (CES), Samsung unveiled the Bot Handy, a robotic helper for various household chores. Bot Handy can put dishes away, grab items from the pantry, pour drinks, set the table and more. While it can’t prepare an entire meal for someone, it can help streamline the process by automating these tasks. Bot Handy isn’t on the market just yet, but it could signal what’s to come in the next few years. There are other cobots that can make things easier in the kitchen and are already available, too. At the same conference, Samsung revealed the JetBot 90 AI+, a robotic vacuum that can differentiate between objects better than older alternatives. JetBotAI+ and similar technologies can clean up around the kitchen as people cook. Full Robotic Kitchens While kitchen cobots have yet to see widespread adoption, robotics companies have already gone a step further. In late 2020, Moley Robotics unveiled the first robotic kitchen, an autonomous system that can automate virtually every part of the cooking process, from prep to dishwashing. It’s not just a concept, either. The Moley Kitchen is already available for sale. The Moley Kitchen costs more than $248,000, making it inaccessible for most home cooks, but it’s a landmark achievement. The system can prepare more than 5,000 dishes completely autonomously. Users with one of these robots in their home wouldn’t have to do any cooking if they didn’t want to. Samsung revealed a similar system in early 2019. While not yet available, the Bot Chef features two robotic arms that hang from the ceiling and help with various tasks. It can add spices, stir pots, chop vegetables, clean dishes and more. It can’t handle as much as the Moley Kitchen, but it does automate many kitchen tasks and can learn new skills over time. The Future of Kitchen Robots As technology advances, systems like this will be able to do more and do it for less. Full robotic kitchens like the Bot Chef and Moley Kitchen could become the norm in restaurants before moving into the consumer space. They’ll first populate the kitchens of wealthier consumers, then, as they become more affordable, slowly become commonplace. Robotic chefs won’t likely replace human cooks entirely. People enjoy cooking, so as these robots appear in more kitchens, they’ll likely work as assistants more than chefs. This trend is already starting to take place. Semi-autonomous kitchen appliances are already commonplace, so cobots are next. Within the next few years, it may not be uncommon to see robotic assistants in home kitchens. As their functionality grows, more people will want one, leading to exponential growth. Kitchens Are Becoming Increasingly Automated People spend a lot of time in the kitchen, so it’s only natural that kitchen automation would take off. As the average home features an increasing number of automated features, kitchen robotics are becoming more commonplace. Kitchen robots can be a tremendous help, from saving busy users time to preparing restaurant-grade dishes for food enthusiasts. While fully automated kitchens may seem like something out of the future, they may be closer than many people think.
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Adoption of artificial intelligence (AI)/machine learning (ML) is going to disrupt healthcare services delivered, whether by Government or private hospitals and clinics. It will build better patients care and satisfaction and lower cost of delivery. It will also help in handling acute shortage of trained manpower in healthcare settings. This will also make healthcare services more accessible to rural regions.
Patients interact with the hospital for appointment booking, consultations, admissions, laboratory diagnostics, discharge, etc. In recent years several hospitals both private and publicly funded have adopted digital tools for automating the workflow and patient records. Hospitals have deployed electronic systems for appointments, admissions, diagnostics/tests, billings, insurance claims, pharmacies, etc. Record linkage connecting all these records for a particular patient remains a concern. AI can enhance and augment patient interactions and workflow from primary care to the Emergency Room. It can be useful in EMS or paramedic service. AI-enabled symptom checkers can screen patients even before the patient visits the clinic or hospital. Chatbots can answer common questions regarding insurance eligibility, co-payments, etc and can help in appointment booking, Screening chatbots can collect patient information and symptoms and can identify the correct specialty where the patient appointment can be given. This will help in reducing the waiting time at the hospital and bring patient satisfaction. John Hopkins School of Medicine has used deep learning (a sub-branch of AI) to improve critical care in ophthalmology. A few years ago, researchers from Stanford University demonstrated that ML models can identify heart arrhythmias from ECG better than experts. ML can build prediction models using Electronic Health Records (EHRs). EHRs are clinical data about the patient and have details of medical history, diagnosis, laboratory test results, medications prescribed, treatment plans, etc. EHR datasets are complex and heterogeneous. ML techniques of boosting and logistic regression can be used to predict serious conditions like heart disease etc. The ML models can be trained on the previous EHR records which contain such serious diseases as heart failure for which we want predictions. To make it usable in real-life, ML algorithms have to be trained separately and exhaustively for each disease and medical condition. This will require a huge amount of data to train and validate, which is luckily available due to the widespread adoption of EHRs. Training the model is a costly and time-consuming process. The trained models need to be approved by the drug regulators before putting into actual practice. If the model has to be used across different countries, retraining and approval from country-specific regulators like FDA in the US or EMA for Europe will be required. AI can also be used in risk stratification. It can classify the patients into high-risk and low-risk groups. In Emergency Room situations, it can help to predict the likelihood of mortality or admissions to ICU. There are research studies done to compare an artificial neural network (ANN) based prediction model and Acute Physiology and Chronic Health Evaluation II (Apache II) model for predicting ICU mortality using the same physiological variables. It was found that ANN-based prediction and classification model performance matched and in some aspects better than APACHE II. For real-life use, the models have to be trusted by patients as well as doctors. Many health care providers are hesitant to adopt these models as they have no visibility of how the diagnosis decision has been made. These ML algorithms, particularly Deep Learning ones, behave as a black box. Which essentially means that you run it on your data, and you get results. You do not know the logic and reasoning behind your results. This creates a low confidence situation for patients and doctors. However, in recent years, a lot of research is getting done to make it transparent and explainable. Besides clinical systems, AI/ML models can also help reduce the administrative workload of healthcare providers. AI can be used to auto-generate prescriptions from patient-doctor interactions. The auto-generated prescriptions can be run through doctors for approval and can be incorporated into the clinical workflow. These systems can also help in optimising hospital resources of beds, medical equipment, etc. The models can be built cost-effectively as there are many high-quality open source systems and libraries available. For Deep Learning, TensorFlow and PyTorch can be used. For Natural Language Processing open-source libraries like AllenNLP or StanfordNLP can be used. The models will improve themselves and become better and more accurate the more they are used. From the IT side, implementing and keeping them up to date will require building what is broadly known as MLOps. The organisation has to build continuous delivery and automation pipelines. Open source workflow orchestration software like Apache Airflow can be used for this. These models are going to use the Personal Identification Information (PII), so ways to address privacy concerns must be implemented. Security also remains to be of paramount importance in these systems. Defense-in-depth principles must be used for building multi-layered secured architectures. These AI algorithms are not going to replace the job of health caregivers, rather they are going to lessen their administrative and documentation burden which will allow them to focus more on patient care. One of the core reasons is algorithms do not understand trade-offs and the practice of patient care is an art and there are almost always trade-offs involved. Also, AI only gives the probability, it does not give certainty. AI can show optimal/relevant choices and a physician can select one of them. The transformation of healthcare with adoption of these AI/ML models certainly is on the way. Handwriting character recognition has become a preferred subject of analysis due to increased digital technologies in all sectors. Handwriting character recognition refers to the computer's ability to notice and interpret intelligible handwriting input from touch screens, images, paper documents, and different sources. Handwriting characters stay advanced since different people have different handwriting styles. The use of neural networks to recognise handwriting characters is more efficient and robust than other computing techniques.
There are two basic styles of handwriting recognition systems – online and offline. Each variety is enforced to progressively learn based on the user’s feedback while performing offline learning on data in parallel. Many strategies are used for online and offline handwriting recognition fields, like statistical strategies, structural strategies, neural networks and syntactic strategies. Some recognition systems identify strokes, and others apply recognition on a single character or entire words. Neural Network-based Handwritten Character Recognition system with feature extraction. Character Recognition Algorithms The algorithms used in character recognition can be divided into three categories: Image Pre-processing, Feature Extraction, and Classification. They are normally used in sequence – image pre-processing helps make feature extraction a smoother process, while feature extraction is necessary for correct classification. Image preprocessing Image pre-processing is crucial in the recognition pipeline for correct character prediction. These methods typically include noise removal, image segmentation, cropping, scaling, and more. The recognition system first accepts a scanned image as an input. The images can be in JPG or BMT format. Digital capture and conversion of an image often introduces noise, making it hard to identify a part of the object of interest. Considering the problem of character recognition, we want to reduce as much noise as possible while preserving the characters' strokes since they are important for correct classification. Segmentation In the segmentation stage, a sequence of characters is segmented into a sub-image of an individual character. Each character is resized into 30×20 pixels. Classification and Recognition This stage is the decision making stage of the recognition system. The classifier contains two hidden layers, using a log sigmoid activation function to train the algorithm. Feature extraction The features of input data are the measurable properties of observations used to analyse or classify these instances of data. The task of feature extraction is to identify relevant features that discriminate the instances that are independent of each other. Neural Network System for Continuous Handwritten Word Recognition A continuous handwritten word recognition method is derived when the word is segmented into triplets (containing three letters). Two subsequent triplets have two common letters. The biggest challenge for recognition systems is to perform operations on a continuous word. In this, each word is subdivided into triplets, each containing three letters. Two neighbour triplets always contain two common letters, which represent the overlapping between letters. This kind of overlapping results is a higher recognition rate. Before the intelligent data capture software was available, the only option to digitize printed paper documents was to manually re-type the text. Not only was this massively time consuming, but it also came with typing errors. Intelligent document processing software is often used as a hidden or silent technology, powering many well-known systems and services in our daily life. It’s used in data entry automation, indexing documents for search engines, automatic number plate recognition, and assisting blind and visually impaired people. Challenges in Handwriting Recognition Huge variability and ambiguity of strokes from person to person The handwriting style of a person also varies from time to time and is inconsistent Poor quality of the source document/image due to degradation over time Text in printed documents sit in a straight line, whereas humans need not write a line of text in a straight line on white paper Cursive handwriting makes separation and recognition of characters challenging Text in handwriting can have variable rotation to the right, which is in contrast to the printed text, where all the text sits up straight Collecting a well-labeled dataset to learn is not cheap compared to synthetic data Use Cases of Handwriting Recognition Healthcare and pharmaceuticals Patient prescription digitization is a major pain point in the healthcare/pharmaceutical industry. Another area where handwritten text detection has a key impact is patient enrollment and form digitization. By adding handwriting recognition to their toolkit of services, hospitals/pharmaceuticals can significantly improve user experience. Insurance A large insurance industry receives more than 20 million documents a day and a delay in processing the claim can impact the company terribly. The claims document can contain various different handwriting styles and pure manual automation of processing claims is going to completely slow down the pipeline Banking People write cheques regularly, and cheques play a major role in most non-cash transactions. In many developing countries, the present cheque processing procedure requires a bank employee to read and manually enter the information present on a cheque and also verify the entries like signature and date. As a large number of cheques have to be processed every day in a bank, a handwriting text recognition system can save costs and hours of human work. Online Libraries Huge amounts of historical knowledge are being digitized by uploading the image scans to access the entire world. Handwriting recognition plays a key role in bringing alive the medieval and 20th-century documents, postcards, research studies etc. Although there have been significant developments in technology that help better recognize handwritten text, Handwritten Text Recognition (HTR) is far from a solved problem than OCR. It hence is not yet extensively employed in the industry. Nevertheless, with the pace of technology evolution and the introduction of models like transformers, we can expect intelligent data capture software models to become commonplace soon. |
Christine WrightChristine Wright is an experienced freelance writer who has mainly written in articles in field of technology. ArchivesCategories |