GitHub and OpenAI presented a new code-generating tool, Copilot, that is now a part of Visual Studio Code that is autocompleting code snippets. Copilot is based on Codex that is a product of GPT-3, presented a year ago. It seems like the hype around GPT-3 still is not going to evaporate, and we decided to delve into details step-by-step. Check it out. GPT-3 stands for Generative Pre-trained Transformer 3, and it is the third version of the language model that Open AI released in May 2020. It is generative, as GPT-3 can generate long sentences of the unique text as the output. Notice that most neural networks are capable only of spitting out yes or no answers or simple sentences. Pre-trained means that the language model has not been built with any special domain knowledge, but it can complete domain-specific tasks like translation. Thus, GPT-3 is the most innovative language model that has ever existed. Ok, but what is Transformer, then? Simply put, it is the neural network’s architecture developed by Google’s scientists in 2017, and it uses a self-attention mechanism that is a good fit for language understanding. Given that the attention mechanism enabled a breakthrough in the NLP domain in 2015, Transformer became a ground for GPT-1 and Google’s BERT, another great language model. In essence, attention is a function that calculates the probability of the next word appearing, surrounded by the other ones. By the way, we have developed an explainer for BERT. Wait, but what makes GPT-3 so unique? GPT-3 language model has 175 billion parameters, i.e., values that a neural network is optimizing during the training (compare with 1,5 billion parameters of GPT-2). Thus, this language model has excellent potential for automatization across various industries — from customer service to documentation generation. You could play around with the beta of GPT-3 Playground by yourself. How can I use GPT-3 for my applications? As of July 2021, you can join the waitlist since the company can offer a private beta version of its API under the LmaS basis (language-model-as-a-service). Here are the examples that you might have already heard of — GPT-3 is writing stunning fiction. Gwern, author of the gwern.net who is experimenting both with GPT-2 and GPT-3, states that “GPT-3, however, is not merely a quantitative tweak yielding “GPT-2 but better” — it is qualitatively different.” The beauty of GPT-3 for text generation is that you need to train anything in a usual way. Instead, it would be best to write the prompts for GPT-3 to teach it anything you want. Sharif Shameem used GPT-3 for debuild, a platform that generates code as per request. You could type the request like “create a watermelon-style button” and grab your code to use for an app. You could even use GPT-3 to generate substantial business guidelines, as @zebulgar did. Let us look under the hood and define the nuts and bolts of GPT-3. Larger models are learning efficiently from in-context information To put it bluntly, GPT-3 calculates how likely some word can appear in the text given the other one in this text. It is known as the conditional probability of words. For example, the word chair in the sentences: “Margaret is arranging a garage sale... Maybe we could buy that old ___ “ is much more likely to appear than, let us say, an elephant. That means the probability of a word chair occurring in the prompted text is higher than the probability of an elephant. GPT-3 uses some form of data compression while consuming millions of sample texts to convert the words into vectors, i.e., numeric representations. Later, the language model is unpacking the compressed text in human-friendly sentences. Thus, compressing and decompressing text develops the model’s accuracy while calculating the conditional probability of words. Dataset used to train GPT-3 Since GPT-3 is high-performing in the “few-shot” settings, it can respond in a way consistent with a given example piece of text that has never been exposed before. Thus, it only needs a few examples to produce a relevant response, as it has already been trained on lots of text samples. Check out the research paper for more technical details: Language Models are Few-Shot Learners. The few-shot model needs only a few examples to produce a relevant response, as it has already been trained on lots of text samples. The scheme illustrates the mechanics of English to French translation. After the training, when the language model’s conditional probability as accurate as possible, it can predict the next word while given an input word, sentence, or a fragment as a prompt. Speaking formally, prediction of the next word relates to the natural language inference. In essence, GPT-3 is a text predictor — its output is a statistically plausible response to the given input, grounded on the data it was trained before. However, some critiques arguing that GPT-3 is not the best AI system for question answering and text summarizing. GPT-3 is mediocre compared to the SOTA (state-of-the-art) methods per each NLP task separately, but it is much more general than any previous system, and the upcoming ones will be resembling GPT-3. In general, GPT-3 can perform NLP tasks after a few prompts are given. It demonstrated high performance under the few-shot settings in the following tasks: GPT-3 demonstrated a perplexity of 20,5 (defines how well a probability language model predicts a sample) under the zero-shot circumstances on the Penn Tree Bank (PTB). The closest rival, BERT-Large-CAS, boasts of 31,3. GPT-3 is a leader in Language Modelling on Penn Tree Bank with a perplexity of 20.5 GPT-3 also demonstrates 86,4% accuracy (an 18% increase from previous SOTA models) in the few-shot settings while performing the LAMBADA dataset test. For this test, the model predicts the last word in the sentence, requiring “reading” of the whole paragraph. Important notice: GPT-3 demonstrated these results thanks to the fill-in-the-blank examples like: George bought some baseball equipment, a ball, a glove, and a_____. →” Moreover, researchers report about 79,3% accuracy while picking the best ending of a story while on the HellaSwag dataset in the few-shot settings. And it demonstrated 87,7% accuracy on the StoryCloze 2016 dataset (which is still “4.1% lower than the fine-tuned SOTA using a BERT based model”). … or testing broad factual knowledge with GPT-3. As per the GPT-3 research paper, it was tested on Natural Questions, WebQuestions, and TriviaQA datasets, and the results are the following: GPT-3 in the few-shot settings outperforms fine-tuned SOTA models only on the TriviaQA dataset As for translation, supervised SOTA neural machine translation (NMT) models are the clear leaders in this domain. However, GPT-3 reflects its strength as an English LM, mainly when translating into English. Researchers also state that “GPT-3 significantly outperforms prior unsupervised NMT work when translating into English but underperforms when translating in the other direction.” In general, across all three language models tested (English in combinations with French, German, and Romanian), there is a smooth upward trend with model capacity: Winograd-style tasks are classical NLP tasks, determining word pronoun referring in the sentence when it is grammatically ambiguous but semantically unambiguous for a human. Fine-tuned methods have recently reached human-like performance on the Winograd dataset but still lag behind the more complex Winogrande dataset. GPT-3 results are the following: “On Winograd GPT-3 achieves 88.3%, 89.7%, and 88.6% in the zero-shot, one-shot, and few-shot settings, showing no clear in-context learning but in all cases achieving strong results just a few points below state-of-the-art and estimated human performance. ” As for physical or scientific reasoning, GPT-3 is not outperforming fine-tuned SOTA methods: GPT-3 is not that good at arithmetic still, since the results are the following: However, when it comes to the news article generation, human detection of GPT-3 written news (few-shot settings) is close to chance — 52% of mean accuracy. Well, even the Open AI CEO Sam Altman tweeted that GPT-3 is overhyped, and here is what the researchers themselves state: GPT-3 is not good at text synthesis — while the overall quality of the generated text is high, it starts repeating itself at the document level or when it goes to the long passages. It is also lagging at the domain of the discrete language tasks, having difficulty within “common sense physics”. Thus, it is hard for GPT-3 to answer the question: “If I put cheese into the fridge, will it melt?” GPT-3 has some notable gaps in reading comprehension and comparison tasks. Tasks that empirically benefit from bidirectionally are also areas of improvement for GPT-3. It may include the following: “fill-in-the-blank tasks, tasks that involve looking back and comparing two pieces of content, or tasks that require re-reading or carefully considering a long passage and then generating a very short answer,” as researchers state. Models like GPT-3 have a lot of skills and become “overqualified” for some specific tasks. Moreover, it is the computing-power hungry model: “training the GPT-3v175B consumed several thousand petaflop/s-days of compute during pre-training, compared to tens of petaflop/s-days for a 1.5B parameter GPT-2 model”, as researchers state. Since the model was trained on the content that humans generated on the internet, there are still troubles referring to bias, fairness, and representation. Thus, GPT-3 can generate prejudiced or stereotyped content. But you may already read a lot about it online, or you can check it out in the research paper. The authors are dwelling on it pretty well. GPT-3 is a glimpse of the bright future in NLP, helping to generate code, meaningful pieces of texts, translation, and doing well with different tasks. Also, it has its limitations and ethical issues like generating biased fragments of text. All in all, we are witnessing something interesting, as it always used to be in NLP. Clap for this blog and give some more inspiration to us. Check out more of our posts on NLP: Text Preprocessing for NLP and Machine Learning Tasks Biggest Open Problems in Natural Language Processing A Comprehensive Guide to Natural Language Generation NLP vs. NLU: from Understanding a Language to Its Processing
Imagine a classroom where each lesson seems tailor-made for you, the course curriculum adapts to your pace, the materials are selected according to your interests, and even your questions! Sounds futuristic? With the integration of AI power into traditional learning management systems (LMS), it’s already becoming a reality. With AI, an LMS becomes a full-fledged learning tool, offering exceptional learning experiences that even underachievers would be amazed by. Learn more about smart and incredibly personalized AI-based learning management systems. While traditional LMS can be compared to a classroom, where students communicate with a teacher, an AI-driven one is an individual tutor for each student. This digital tutor is always available, offers tailored learning resources for each student's unique needs, and corrects mistakes in assignments swiftly. Let’s see the contrast between traditional and AI-driven LMS in more detail: All the capacities of smart digital education are possible thanks to decision trees and neural networks integrated into AI-driven LMS: Teaching efficiency AI-driven LMS provides teachers with useful tools that simplify everyday tasks. This lets them focus more on improving teaching methods and developing customized learning paths for each student. Data-Driven Learning How do teachers analyze student performance in traditional education? Check their assignments and activities during lessons. It takes a lot of time, limits individual approaches to each student, and lacks real-time insights. Let’s see, how a data-driven approach offered by AI-powered LMS can tackle this challenge. Intelligent Course Management The old-school approach had educators wait for occasional student feedback and guess if it was too easy, too challenging, or just boring. With an AI-empowering LMS, timely feedback is now possible for teachers. This allows them to refine their course materials according to the needs of current students, not next semester. Deep learning models and recurrent neural networks track and analyze students’ interaction with the platform, helping to understand real engagement and comprehension rates. Advanced Natural Language Processing (NLP) algorithms can analyze student’s feedback and mark the content as engaging or boring, too difficult or too simple, etc. Let’s see how it can work in practice! Imagine that students often replay a specific video fragment. Perhaps, it’s because the explanation is not clear enough. What does AI do? Streamlined Administrative Routine As per McKinsey research, teachers work about 50 hours per week, spending only 49% of their time in direct interaction with students. Technology can help teachers reallocate 20-30% of their time for supporting students, instead of doing routine tasks: 1. AI-Driven Learning Solutions Developing all kinds of AI-driven solutions for educational institutions, EdTech companies, and internal training systems for businesses: 2. Data-Driven Education 3. Workflow Automation 4. Hi-Tech Learning Experience
In San Francisco, a city known for tech innovation, the OpenAI Developer Conference was a major event for the AI world. This conference brought together experts, developers, and technology leaders. Leading the event were Sam Altman, the CEO of OpenAI known for pushing boundaries in AI research, and Satya Nadella, the CEO of Microsoft, whose company has been a key player in advancing AI technology. OpenAI, under Altman's leadership, has been at the forefront of AI development, sparking curiosity and anticipation in the tech community about its next moves. We at SciForce have been closely monitoring OpenAI's trajectory, intrigued by the next steps of their advancements in the broader tech landscape. The conference was much more than just showing off new technology; it was a place for sharing big ideas about the future of AI. One of the main attractions was the unveiling of GPT-4 Turbo, a new development by OpenAI. The event was crucial for looking at how AI is growing and how it might change technology as we know it. GPT-4 Turbo sets a new benchmark with its ability to handle up to 128,000 context tokens. This technical enhancement marks a significant leap from previous models, allowing the AI to process and retain information over longer conversations or data sets. Reflecting on this enhancement, Sam Altman noted, "GPT-4 supported up to 8K and in some cases up to 32K context length, but we know that isn't enough for many of you and what you want to do. GPT-4 Turbo, supports up to 128,000 context tokens. That's 300 pages of a standard book, 16 times longer than our 8k context." GPT-4 Turbo enhances accuracy over long contexts, offering more precise AI responses for complex interactions. Key features include JSON Mode for valid responses, improved function calling with multi-function capabilities, and reproducible outputs using a seed parameter, enhancing control and consistency in AI interactions. At the OpenAI Developer Conference, new text-to-speech and image recognition technologies were revealed, marking major AI advancements.
Since 2015, Sciforce has been an active contributor to the OHDSI scientific community. Our medical team is consistently at the forefront of OHDSI events, sharing groundbreaking research and driving advancements in health data harmonization that empower better health decisions and elevate care standards. The fall event was no exception: from October 20 to 22 Polina Talapova, Denys Kaduk, and Lucy Kadets were delegated as our company representatives to the Global OHDSI Symposium with more than 440 of our global collaborators together held in East Brunswick, New Jersey, USA. The symposium affords unique opportunities to dive deeper into the OMOP common data model standards and tools, multinational and multi-center collaborative research strategies, and insight into completed large-scale multinational research projects. Our Medical Team Lead, Polina Talapova presented the topic “Mapping of Critical Care EHR Flowsheet data to the OMOP CDM via SSSOM" during a lightning talk session. She emphasized the significance of mapping metadata generation and storage for producing trustworthy evidence In turn, Denys and Lucy participated in the OHDSI Collaborator Showcase, where they successfully presented a prototype and poster detailing the Jackalope Plus AI-enhanced solution. This tool streamlines the creation, visualization, and management of mappings, reducing manual effort and ensuring precision in capturing details from real-world health data. Our colleagues had an opportunity to meet in person with leading OHDSI researchers such as George Hripcsak, Patrick Ryan, Marc Suchard, Andrew Willams, Rimma Belenkaya, Paul Nagi, Mui Van Zandt, Christian Reich, Anna Ostropolets, Martijn Schuemie, Dmytro Dymshyts, Dani Prieto-Alhambra, Juan M. Banda, Seng Chan You, Kimmo Porkka, Alexander Davydov, Aleh Zhuk, among other distinguished individuals. The event was truly transformative and rewarding, expanding participants’ minds and horizons. The SciForce team is profoundly grateful to the OHDSI community for the opportunity to be a part of this fantastic journey!
In such an evolving Medical field, the synthesis of reliable and insightful data is a basis for making innovation and progress possible. With its global adoption, the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been an important tool for advancing drug safety monitoring and healthcare outcome prediction. However, it has a gap in the representation of toxic substances and environmental exposures, an aspect central to deepening our understanding of their impacts on human health. Meanwhile, the Toxin Vocabulary – our revolutionary solution, is designed to improve the representation of toxic substances within the OMOP CDM, enabling better analysis of the complex interplay between environmental factors and health outcomes. In this article, we will tell you about the approaches and collaborative efforts that powered the creation of our Toxin Vocabulary. The Vocabulary aims to empower researchers, healthcare professionals, and organizations to get deeper insights regarding environmental exposures and human health outcomes. Let’s explore what insights are possible with our Toxin Vocabulary! Now, let’s get into more detail about the current approach and the problems researchers face. The OMOP CDM was established as an open community data standard, created to harmonize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence. It was widely adopted by researchers and healthcare organizations around the globe. That’s how OMOP CDM simplified drug safety monitoring, comparative effectiveness research, clinical trial design, and healthcare outcome prediction. However, the representation of toxic substances and environmental exposures within the OMOP CDM has been a crucial need in environmental epidemiology. At the same time, environmental epidemiology focuses on investigating the impacts of exposure to toxic substances on human health, considering both short-term and long-term effects. To support such studies, Geographic Information Systems (GIS) have been utilized to analyze the spatial distribution of exposures and assess their potential health consequences. While recent efforts have aimed to integrate GIS data with the OMOP CDM, insufficient standards have hindered the comprehensive evaluation of environmental exposures and their associated health risks. So, that is how we came up with the idea of solving this issue and developing a hierarchical Toxin Vocabulary as a solution to improve the representation of environmental exposomes within the OMOP CDM. This standardized terminology has been developed through a systematic review of toxicological literature, analysis of open toxin databases, and consultation with experts in the field. By synthesizing the most relevant and up-to-date toxin terminology, our Vocabulary aims to facilitate environmental exposure assessment, support toxicological and epidemiological research, and enable the integration of GIS-related data into the OMOP CDM. The journey of the Toxin Vocabulary development needed a systematic approach containing a comprehensive review of toxicological literature, analysis of open-source toxin databases, and consultation with domain experts. These steps were essential in synthesizing a thorough and accurate representation of toxic substances within the OMOP CDM. As we already mentioned before, firstly, we conducted a systematic review of toxicological literature to identify relevant terms and classifications associated with various toxins and their impact on human health. By examining a variety of research papers, regulatory documents, and many different authoritative sources, we reached a comprehensive understanding of the diverse range of toxins and their associated semantic attributes. And, simultaneously with the literature review, we performed an analysis of open-source toxin databases. A primary resource that stood out in terms of comprehensiveness and reliability was the Toxin and Toxin Target Database (T3DB). T3DB provided us with a vast repository of toxin terminology, including descriptions of over 3,000 toxins with 41,602 synonyms. This database encompassed a wide range of toxins, including pollutants, pesticides, drugs, and food toxins, and provided extensive metadata fields for each toxin record (ToxCard), such as chemical properties, toxicity values, molecular and cellular interactions, and medical details. The process of integration required using the information obtained from the literature review and the T3DB to develop the Toxin Vocabulary. Also, it involved automatically uploading the source data to the PostgreSQL database using Python. Afterward, we extracted essential metadata, established cross-term connections, and performed a semi-automated mapping of selected terms to the OMOP Vocabulary standards. To ensure compatibility and seamless integration with the existing OMOP CDM standard vocabularies, the Toxin Vocabulary was mapped to relevant terminologies, including SNOMED CT, RxNorm, and RxNorm Extension. During the mapping process, we needed to associate corresponding concepts from the Toxin Vocabulary with the appropriate standard concepts within the OMOP CDM. This created the link between toxin terms and established clinical concepts, enabling us to do a comprehensive analysis and integration of environmental exposures with other healthcare-related data. As unique vocabulary identifiers, we used CAS codes due to their alignment with GIS data and the CAS Registry, one of the largest registries encompassing around 204 million organic substances. For toxins without CAS codes, unique T3DB codes were assigned, ensuring proper identification and classification. We have seamlessly incorporated the Toxin Vocabulary into OMOP instance by methodically organizing the information in preliminary stages, following the standard OHDSI contribution process, and ensuring each piece of data is accurately placed and interconnected for optimal use. These staging tables were instrumental in incorporating the Toxin Vocabulary's semantic and syntactic aspects. In this way, we ensured the compatibility of the system with the existing OMOP CDM framework. In the picture below you can see how our vocabulary works, our decisions, and their subsequent impact on the OMOP CDM structure Our Toxin Vocabulary represents a hierarchical and expansive representation of toxic substances within the OMOP CDM. It has over 79,377 internal relationships and maps the complex interconnections between toxins, cellular structures, relevant diseases, biological processes, and more, offering researchers an unprecedented level of detail in their analysis. But the Vocabulary's strength doesn't end here. The integration with standardized vocabularies such as SNOMED CT and RxNorm strengthens its capabilities, creating a symbiotic relationship. Such a synergy is the foundation for a more deep and detailed exploration of the exposome and can offer better insights and create a more complex understanding of the toxin-health outcome dynamics. Furthermore, it opens up new sights in drug safety monitoring, clinical trial design, and health outcome predictions, empowering researchers and healthcare professionals to harness rich, GIS-related data for advancing toxicoepidemiological research. The Toxin Vocabulary is not just a tool – it's a gateway to a future with an insightful understanding of environmental impacts on health. In the world of open science, innovative approaches and tools play a key role. The Medical Team of our company, Sciforce, is truly proud to contribute to this development, focusing on OHDSI vocabularies. Our Vocabulary was first presented at the OHDSI GIS Working Group. And, after the validation of the vocabulary is completed, we would be happy to present it publicly at the Global OHDSI Symposium (New Jersey, USA) on October 20, 2023, and officially integrate it into the OHDSI ecosystem! This opens up new opportunities in the fields of Geographic Epidemiology and Toxicoepidemiology. We are truly happy to introduce our enhanced integration of the Toxin Vocabulary, setting a new standard for healthcare analytics and research.
Microservices architecture has absolutely changed the approach of developers to building large and complex systems reliably. And, over the last few years, they are rapidly gaining popularity. According to the research conducted by Statista in 2021, 85% of companies utilize microservices. Well, one of the technical goals of building the application is making it scalable and secure, and microservices allow us to do this, simultaneously making the final product fault-tolerant. So, by breaking down applications into smaller, independently deployable services, microservices offer more flexibility and scalability than traditional monolithic architectures. Using this type of architecture, developers can deploy features that prevent cascading failures. Basically, there are 2 most popular approaches for implementation architecture using microservices: orchestration and choreography. And choosing the right one can be challenging. So, in this article, we would like to compare choreography and orchestration microservices architectures and discuss the projects those types of architecture are more suitable for. When it comes to microservices architecture, there are two common approaches to service coordination: orchestration and choreography. The picture above perfectly illustrates the key differences between those two approaches, and we would like to explain to you how each of them works: In this approach, a central orchestrator acts as the “brain,” or logic component, that assigns tasks to the microservices and manages and controls their interaction. In this case, the orchestrator is responsible for routing requests, coordinating service interactions, and ensuring that services are invoked in the correct order. Basically, here, the orchestrator is a central point of control and can enforce rules across the entire system. So, what are scenarios when using orchestration is more beneficial? This approach is an excellent decision when: Not suitable for large projects The first disadvantage is that the controller needs to communicate directly with each service and, after that- wait for the response of each service. The consequences here are the following: first of all, when the interactions are occurring across the network, invocations may take longer and can be impacted by downstream network and service availability. The second point is that this system can work okay in small projects, but everything can fall apart when we are talking about hundreds or even thousands of microservices. In such a case, you are creating a distributed monolithic application that will be too slow to function well. Tight coupling In the orchestration approach, microservices are highly dependent upon each other: basically, when they are synchronous, every service should respond to requests, thus, if a failure occurs - the whole process will be stopped. Moreover, if we are talking about microservices in an enterprise environment, hundreds or even thousands of microservices are attached to a single function. Therefore, this method won’t fulfill the demands of your business. Reliance on RESTful APIs Orchestration also relies on RESTful APIs, and the problem that occurs here is that RESTful APIs and orchestration can’t scale. RESTful APIs are usually created as tightly coupled services. This means that using such services increases the tight coupling of the architecture of your application. Moreover, if you would like to build new functionality, remember that it will cost a lot and have a high impact on the API. In this approach, there is no central orchestrator, and the situation is quite the opposite here: each microservice is responsible for its own behavior and coordination with other services. Services communicate with each other through events and messages without the need for a central point of control. Here, each service can react to events as they happen and can trigger events that other services can react to. To automatically apply this approach and ensure things go smoothly, you can try choreography tools like Kafka, Amazon SQS, and RabbitMQ. They all are Event Brokers, which is the main tool for this approach. A service mesh like Istio and runtime system DAPR is also used for the choreography approach. So, when should you use choreography? This approach is an excellent decision in the following cases: Avoiding the creation of a single point of failure or bottleneck is important for you. You need microservices to be autonomic and independent. You want to simplify the process of adding or removing services from the system without disrupting the overall flow of communication. Now, let’s discuss the benefits of choreography that are solving the problems that occur with orchestration: Loose service coupling for agility and fault tolerance Well, adding and removing services is much simpler using a choreographed microservices architecture. Basically, here you will only need to connect/disconnect the microservice to the appropriate channel in the event broker. So, using loose service coupling, the existing logic will not fall apart when you add or remove microservices. And this results in less development churn and flux. Moreover, because of the independence of each service, when one application fails, the whole system will work while the issue is rectified as choreography isolates microservices. Also, it is not required to have a built-in error handling system in case of failure of the network, as this responsibility lies on the event broker. Faster, more agile development As clients' requirements are higher every day and the market grows constantly, the speed of development and modifying the app is crucial. So, the development teams are impacted by changes to other services, and it is a common barrier to achieving agility. But, here, choreographed microservices enable development teams to focus on their key services and operate more independently. So, the services are easily shared between teams once they are created. This allows us to save labor, time, and resources. More consistent, efficient applications During the creation of microservices with specific functions, you can create a more modular codebase. In this case, each microservice by itself has its business function, and together, microservices perform a business process. Thus, your system will be consistent, and it will be easy to modify and create services because you can reuse your microservices and tap into code that’s already been proven to perform a given function. So, what are the different and similar features between microservices and orchestration? Let us briefly summarise this: Both approaches involve service coordination and communication and can be used to implement complex workflows and business processes and build scalable and maintainable microservices architectures. When deciding between orchestration and choreography, it's important to consider the specific needs of your project. Here are some of the most important factors to consider:
The usage of Artificial Intelligence in the world of Education Technologies is totally changing the learning approach, offering truly innovative solutions that transform the experience of students and teachers. AI-based solutions are bringing the education industry to a new level by enhancing the learning experience, making it more personalized, interactive, and efficient. Through the last few years, AI has started gaining popularity in many fields, and the EdTech industry is not an exception: according to the report of Global Market Insights, AI in Education Market size reached USD 4 billion in 2022 and is projected to expand at over 10% CAGR from 2023 to 2032, owing to the growing inclination towards personalized learning. In this article, we analyze the most important trends of the EdTech industry in 2023, discuss the main advantages and disadvantages of AI in EdTech, the impact of Chat GPT on education, and successful cases of how top companies use AI on their platforms. Before we discuss specific cases, pros, and cons of AI in EdTech, let's first explore some trends in this field to be able to adjust to customer needs and requirements and outstand in the highly competitive market. One of the most revolutionary trends in educational technology is AI in personalized learning. There are many great general options for obtaining new skills or knowledge on the internet. However, they often do not comply with the expectations and needs of customers in terms of personalization. Here comes AI to solve this problem: AI can tailor educational content and experiences to suit the unique abilities and learning styles of individual students. This is a big shift from traditional teaching methods that often overlook individual learners' needs and capabilities, making education more enjoyable and accessible. This revolution has been made possible through the use of adaptive learning algorithms and intelligent tutoring systems. Basically, adaptive learning algorithms can adjust the difficulty, pace, and type of content based on the learner's individual performance. These systems take into account the strengths, weaknesses, and even the interest areas of a student to provide a learning pathway that keeps a person engaged and contributes to academic growth. Intelligent tutoring systems serve as personal tutors to students, providing personalized support and feedback. These systems use AI to spot a point where a student might be struggling and to provide detailed guidance on those specific areas. The main advantage of those systems is in their ability to deliver feedback in real-time. Personalized learning with AI is already showing promising results. Studies have shown that adaptive learning technology can increase student engagement and improve retention rates. Moreover, it allows students to learn at their own pace, reducing stress and making learning a more enjoyable experience. With AI becoming increasingly sophisticated, the potential for truly personalized learning experiences is growing exponentially, promising a future where every learner can access a tailored education that fully unlocks their potential. Here you can see a few free examples to experience personalized learning: EdApp, Schoox, WalkMe, Raven360, ExplainEverything. In simple words, learning analytics is the process of collection, processing, and analysis of data about students and their success. This process is performed to optimize learning and the environments in which it occurs. Learning analytics uses AI algorithms to understand how students are learning, what they are struggling with, and how their learning path can be made easier. This tool helps educators understand which teaching methods and content types are most effective for them. Artificial Intelligence can even predict students' future performance based on their current learning patterns and provide recommendations on how to enhance their experience. For instance, if a student consistently struggles with problems in Physics classes, AI can identify the pattern and suggest additional, customized practice in this area. On the other hand, if an entire class is struggling with a specific concept, this could indicate an issue with the methods of teaching. Learning analytics has tight bounds with personalized learning: AI enables the creation of personalized learning paths by providing insights into the learning patterns of each student. Virtual and augmented reality (VR and AR) technologies create interactive learning environments that significantly enhance the educational experience, especially for kids, as their attention often drops in seconds. VR and AR allow for dynamic and interesting interactions, making education more engaging and, thus, more effective. Augmented and virtual reality can transport students to any location: forest, ocean, countryside, or any other place just from their classroom or home. These technologies can provide simulations that make difficult and abstract concepts more accessible and easy to understand. For instance, students can explore the structure of a DNA molecule or learn history by walking through different places. A great example of those technologies was the Google Expeditions program, which allowed students to explore plants' and animals' anatomy, and hundreds of destinations, including museums, monuments, and even underwater exploration. It is worth mentioning that AR applications can bring interactive content into the real world, thus, making learning more engaging and interesting and allowing students to understand and memorize the information better. The use of AI in VR and AR in education is still in the early stages, but the potential of those technologies to transform the education industry is enormous. With the success of Chat-GPT and other AI-based chatbots, using intelligent chatbots has become a growing trend in the Education Technology field. AI-driven chatbots are great virtual assistants that can respond to students' questions instantly, providing 24/7 assistance, facilitating the learning process, and even helping manage administrative tasks such as scheduling and reminders. AI-based chatbots can perform a wide range of functions: from answering questions and providing explanations of complex concepts to offering personalized study tips and reminders. For example, a student can ask the chatbot to explain a particular rule, formula, or the meaning of a term. The chatbot, using its NLP capabilities, can understand the question, search its knowledge database, and provide a clear and helpful response. Furthermore, as we mentioned before, AI chatbots can offer 24/7 instant support and fill the gap when human teachers are unavailable. Chatbots are capable of providing instant feedback on assignments, recommending different resources for study, etc. Also AI-chatbots can also provide a platform to help students who hesitate to ask questions in a regular class. The usage of NLP as a subfield of AI in intelligent chatbots is set to redefine the landscape of learner support in education. By providing responsive, personalized, and accessible support, AI chatbots are reshaping how we engage with and facilitate learning, opening exciting new possibilities for Education Technology. Well, using Chat GPT in the education industry has its own pros and cons. Let’s start with the main advantages: Disadvantages of using Chat GPT in EdTech: Better Student Performance: The tools powered by AI can assist students in explaining complex concepts, thus, enhancing academic achievements and increasing graduation rates. Increased Efficiency: Automation of grading assignments and other administrative tasks saves up a lot of valuable time for teachers. This enables tutors to concentrate on teaching more and offer personalized support to students who may need it. Cost-effectiveness: AI-driven solutions can be more cost-effective than traditional educational approaches, especially in scenarios of remote or distance learning where physical resources might be limited and not that accessible. Greater accessibility: AI-based solutions can offer broader access to education for students who study remotely, enabling them to learn from the best lecturers and use educational resources from anywhere in the world. Adaptive Learning Platforms: Leading EdTech companies are using AI algorithms to create adaptive learning platforms that tailor and customize content and instructions to individual students' abilities and learning styles. Intelligent Tutoring Systems (ITS): EdTech companies also use intelligent tutoring systems that allow to simulate the experience of one-on-one tutoring by providing immediate feedback, clarifying doubts, and offering assistance based on each student's needs. Gamification and Learning Apps: To make studying more enjoyable and students more concentrated, many EdTech companies are integrating educational games into their platforms. By using gamification, we are changing the attitude of students towards studying: from a chore, it becomes an interactive and engaging experience, which can enhance motivation. For example, Duolingo and other companies use AI to create gamified learning experiences. Coursera: Coursera, one of the most popular online learning platforms in the world, uses AI to provide personalized course recommendations to learners. The system analyzes a user's past behavior, course history, and interactions on the platform to suggest the most relevant courses. Also, the platform uses an AI-powered grading system that identifies common mistakes and provides feedback to help students improve their understanding. Knewton: Knewton uses AI to provide personalized learning experiences to students by analyzing a student's performance and adjusting course material. Students receive personalized lesson plans, content recommendations, and study strategies. This allows them to learn more effectively at their own pace. Content Technologies, Inc. (CTI): CTI uses AI to create customizable textbooks that adjust to the needs of individuals. By using machine learning and natural language processing, they can transform any standard textbook into an interactive and adaptive learning resource. Duolingo: Duolingo, one of the most popular language-learning platforms, uses AI to personalize the process of language learning for its users. Its AI algorithms analyze user's learning patterns and choose the difficulty level of exercises accordingly. Duolingo also uses AI-driven chatbots for interactive language practice. Quizlet: is a popular online learning platform that uses gamification and AI to enhance student engagement and learning. The AI algorithms analyze student performance to recommend personalized study materials and games, catering to different learning styles and ensuring continuous learning challenges and support. These use cases show how AI is revolutionizing education and learning experiences today. By using the potential of AI, these platforms have successfully improved engagement, enhanced learning outcomes, and personalized education on a global scale. To sum up, the integration of AI in the EdTech industry can absolutely revolutionize the way we learn and teach. AI-based solutions prove that they can transform the learning process and make it more personalized, interactive, and efficient. In this article, we explored the biggest trends in AI in EdTech, such as personalized learning, learning analytics, virtual and augmented reality, and intelligent chatbots. By applying those technologies in the educational system, developed products will meet the expectations of the customers and solve their challenges. If you would like to implement AI in your education platform and unlock new opportunities in the industry, do not hesitate to contact us!
In the time of a fast-developing world, which is full of innovative decisions, many sectors shine with potential, and one of them is digital health. Members of our medical team, who were privileged attendees at events such as the MedInfo 2023 congress, European OHDSI and APAC symposiums, are truly happy to share their exclusive insights from those conferences with you in this article. So, welcome to a future of healthcare — vibrant, digital, and full of exclusivity. In such a transformative era, technology and healthcare are uniting to absolutely change the way healthcare services are delivered and how patients experience them. They're creating new ways to treat patients, care for them, and handle information. These fresh concepts show us how much digital health is progressing. Though the trends we see in 2023 were present in the past few years, however, in this year, they are more advanced and modified. Let's take a closer look at a few of the important digital healthcare trends in 2023: The healthcare industry is shifting from generic solutions in favor of personalized ones. Digital health products are more focused now on addressing the specific needs and dealing with specific issues, preferences, and histories of individual patients, ensuring both personal and efficient care. The medical industry is departing from the approach of merely diagnosing and prescribing the treatment. It is more focused now on the prevention of diseases and their consequential effects. By using AI and ML, we can process the data and make predictions about the health either of a patient or population. Also, it is important to mention that technologies such as digital therapeutics and real-time patient monitoring are enhancing patient outcomes and engagement. When different health solutions are available for the medical field, it's important for them to work together, and data standardization is a key here. The patient data can come from various sources: insurance systems, electronic health records, surveys, clinical trials, etc. This data needs to be put into a common and more standardized format to make sense of it all. Organizations leading in standardization are working on structuring the data better, which allows decision-makers to get evidence-based insights. This knowledge is crucial for data-driven decisions, not just in the medical treatment aspects but also in managing resources like finances and personnel. Remote healthcare solutions, from video communications to telemedicine consultations, are absolutely changing what accessibility in healthcare means. Regardless of geographical location, patients can connect with healthcare professionals and get consultations while healthcare quality stays at a high level. This also allows doctors to gather for counseling sessions and create recommendations on laboratory test results. In conclusion, the current trends in digital health products show how the approaches in the industry are changing: the mobile apps for remote vitals monitoring are being implemented, and telepath platforms are becoming more insightful with the features they have. For example, matching patients to doctors, analytics, health assistant bots and E-prescriptions. These trends not only show the present condition of digital health but also provide an insight into the future perspective of the industry. Right now, we're on the edge of new opportunities, and some really cool ideas are leading the way and changing how we think about healthcare. The innovative solutions now aim not only to respond to current needs but also to address the challenges that can arise in the future. Let's explore them: Beyond the simplistic step counters and basic heart rate monitors, the development of wearables is a great decision in medical tech. These devices continually track physiological parameters, such as heart and respiratory rate, urinary bladder fullness, blood oxygen level, glucose level, and even stress markers, painting a picture of a patient's health in real-time. There is a consistent flow of health data from wearables, so AI-powered platforms step in to analyze a lot of information. Such predictive analytics can detect irregularities, sending early alerts to users or medical professionals and, therefore, preventing potential health crises. Leveraging no-code ecosystems, innovators are designing digital health tools that address specific patient needs, especially in the realm of mental and behavioral health. These tools do not offer generic advice; they create practical advice based on evidence. By integrating AI, digital therapeutic platforms can now provide personalized regimens that adjust in real time based on patient feedback and take into account a huge variety of factors (e.g. COVID-19, epilepsy, schizophrenia, child age). Ensuring that health data remains in the right hands is crucial. Many platforms now grant access based on robust digital identity verification, bolstering security and ensuring data privacy compliance with acts such as HIPPA and GDPR. Secure digital platforms are built to recognize participants like doctors, managers, and patients. Such recognition is made possible through the use of blockchain technology – old, but still effective one. In essence, the latest ideas in digital health innovation aren't just about advanced tech but also about precision and trust. These innovations show a promising trajectory where technology elevates patient care, data security, and personalized treatment to a new level, simplifying the work for doctors and saving their time. The digital health landscape, fueled by rapid technological advancements and a surge in patient-centric initiatives, has presented a slew of novel opportunities and pathways. As healthcare continues to integrate with technology, several potential trajectories are crystallizing. The integration of AI-powered analytics into Electronic Health Record (EHR) software is a game changer. Such integration can bring promising results by assisting clinicians in making informed decisions based on patterns and previous case studies. Such tools as AI-operated symptom checkers and AI-enhanced medical alarms/alerts are designed to supplement healthcare professionals, and they can quickly assess initial symptoms, promptly address the needs of critical patients, and send alerts for urgent conditions such as seizures or hypo-/hyperglycemia in patients at home. This allows to ensure that patients receive timely and appropriate care, thereby optimizing the healthcare process. More than ever, there's a pressing need to develop platforms enabling collaboration and system management, including community-driven approach in setting standards. They're designed for seamless patient care, interoperability, real-time data analytics, and personalized treatment protocols. . Such platforms would not only enable efficient communication and data sharing among healthcare providers, but also empower patients and healthcare decision-makers with data-driven insights and remote monitoring capabilities. Implementing such opportunities will not only level up patient care but also create a progressive path for the global healthcare industry. While the future ahead may be challenging, it's equally rich with solutions, where digitalization is playing a crucial role. At the conferences and events, we gained firsthand insights into how leading organizations within different communities are managing the challenges and realizing opportunities in digital health: Synthetic Health Data Creation: To address concerns of privacy and data availability, the generation of synthetic health data steps into the game and becomes a trend. This enables more extensive testing and research without compromising patient privacy. Using NLP: Many companies use Natural Language Processing (NLP) to convert, process, and structure unstructured data. This process is truly helpful in mapping, ETL operations, phenotyping, and more. Community Management: The communities are constantly growing, and management of member interactions and contributions is crucial. Many organizations rely on metrics to track and analyze contributors' activities, creating a more engaged and productive ecosystem. Cost-Effectiveness: A significant interest within the community arose around evaluating the cost-effectiveness of healthcare solutions. This ensures that medical interventions are beneficial and optimally utilized, be it in terms of resources or a doctor's time. Data Standardization is highly important due to the variety of medical data types. Leading organizations prioritize the alignment of these diverse datasets to enable more comprehensive analytics and well-informed decision-making. For example, the OMOP Common Data Model structures the data into specialized tables, fostering efficient big-data operations and analytics. Observational Studies: The extraction of evidence-based knowledge is highly important for research purposes. One approach to achieve this is through observational studies on vast datasets. To support this, there's a rising trend in developing tools specifically tailored for surveys and research. Ontology Platforms: There is a need to create web resources that aggregate different mappings and terminological standards, making them more interoperable for other participants of the research. This simplifies data-sharing and collaboration. We feel truly excited and hopeful about the progress in digital health! Our Medical team is happy to contribute to the expansive digital health community and be able to share the insights gleaned from the events we’ve visited. Our journey together with different experiences and knowledge shows the transformative phase we're in. Patient-centered approaches, innovations in remote healthcare solutions, and the importance of data standardization forms the 2023 digital health landscape, and it's vast and dynamic. Let's lead the way in digital health's future together!
Last year, when Open AI introduced to the world ChatGPT-3, it absolutely revolutionized the field of artificial intelligence and changed the lifestyle of millions of people. This generative AI tool can create a broad answer to almost any question and is considered to be one of the best chatbots that have ever been created. Right now, generative AI is the newest form of artificial intelligence, and it's making a huge impact in every industry, especially in EdTech. However, many companies are presenting their solutions and creating the illusion that generative AI can solve everything. At the same time, many experts are raising concerns regarding the social and employment implications of generative AI, stating that new tools can destabilize our society. When we are talking about the Education field, there are many different opinions on AI: some teachers have rejected AI in education, having concerns about its impact on the quality of the study process, and other instructors compare the use of chatGPT to the use of calculators in school. So, let's explore the technology behind the term generative AI. Generative AI is a type of AI system that can create text, images, or other forms of media in response to various prompts. The most common approach is basically providing the text as an input prompt. For example, Large Language Models (LLMs) such as ChatGPT and GPT-4 respond to textual prompts by producing corresponding text. Such systems definitely have contributed to the popularity of generative AI, and increased interest in the application of such tools in education and other industries. However, the spectrum of GenAI goes far beyond text-based inputs – generative AI can handle a variety of input types such as image, voice input, etc. In the pictures below, you can see how GenAI works and what are the types of Generative AI based on data: Let us provide you with examples of different types of input and output data: An effective remedy: A few approaches can be used to solve the issue of factually incorrect information that GenAI creates. And the most common and effective ones are post-processing fact-checking and implementation of the feedback system. Basically, the post-processing fact-check is the process when the model generates an answer, and a separate fact-checking system verifies the information. This system can, for example, cross-reference the generated text with a database of verified facts. The feedback system enables users to flag incorrect or misleading information. And, afterward, this feedback can be used to improve the future responses of the model. Without internet access, a standard LLM engine lacks knowledge of unfamiliar topics, resulting in false or no responses. An effective remedy: The model can be integrated with a search engine, or users can enhance the generated answers by giving the model additional sources for responses (for example, books or articles). Also, the model can be tuned for any particular case. For example, Open AI has recently published a tutorial on how to customise a model for an application for your particular purposes. AI in EdTech often requires collecting and analyzing vast amounts of student data. Ensuring the privacy and security of this data is crucial to prevent breaches and misuse. Training data used in LLMs do not include any confidential information like biometrical data, medical personal data, banking data, etc. However, because of the fact that LLM training requires a large amount of data, the process of content filtering needs to be done automatically, and privacy filtering algorithms can be imperfect. So, it is possible that some sensitive or confidential data can leak. An effective remedy: Data anonymisation or de-identification can effectively solve that problem. These processes aim to remove personally identifiable information from datasets, allowing for data analysis without compromising privacy. There are many effective types of data anonymization methods, like deletion of direct and indirect identifiers, pseudonymization/tokenization, data masking, introducing statistical noise, data aggregation, and synthetic data usage. Even in the case when training data was sorted out, and inappropriate content was filtered before training, LLM can still generate harmful content. Some conversational AI models like ChatGPT use training techniques involving humans to prevent certain types of content from being generated, but it still happens occasionally. Thus, there is a need in the development of appropriate content filters to avoid harmful and inappropriate content generation. An effective remedy: The problem with AI bias can be solved at early stages by testing data and algorithms and using best practices to collect the data. To address the issue of bias in AI systems, we usually start by thoroughly understanding the algorithm and data to assess high-risk areas of unfairness, examining the training dataset for representativeness, and conducting subpopulation analysis. Also, it is important to monitor the model over time and establish a comprehensive debiasing strategy that encompasses technical, operational, and organizational actions. Also, one more effective way to fix this issue is using human feedback during fine-tuning. Some examples of using such an approach are InstructGPT and Reinforcement Learning with Human Feedback (RLHF). Even though the implementation of GenAI in Edtech might bring some challenges, the ability to change how students interact with learning resources overcomes these drawbacks. It is important to understand that any technology has its imperfections, however, as we demonstrated earlier, such difficulties can be solved. Thus, we all should bear in mind that AI in EdTech is not only about intelligent responses, it's about creating a secure and unbiased educational environment for every student. Although, at this moment, genAI has some flaws, advantages are overtaking the disadvantages in many cases. For example, one of the biggest strengths of LLM models is their capacity to produce grammatically and syntactically accurate language when given existing text. LLM models acquire this skill through training with properly written English texts. During output generation, each word is chosen based on its likelihood, considering the previous context and the model's learned knowledge, which heavily relies on the grammar and syntax from the training data. Such a capacity allows people to use generative AI in EdTech in the following cases: Users can ask LLMs to summarise long texts while keeping the meaning and main details. This is a very helpful way of extracting important information from complex texts. Also, LLM can simplify information that is too hard to understand. Generative AI can help in creating course materials. For example, quizzes, exercises, explanations, tests, and summaries, benefit teachers who need diverse content. LLM models are capable of comparing two texts and detecting their differences and similarities. Researchers can ask AI to analyze two research papers and highlight variations in conclusions, and approaches or set any other relevant criteria. LLMs can be used to improve clarity and correct mistakes in any trained language. Moreover, besides the rephrasing, we can ask AI to compare both versions of the text and provide explanations for the changes made. This allows students to gain insights and learn from their own mistakes. Generative AI has a great capability for the translation and explanation of terms. In many cases, AI can be better than regular dictionaries and translators as it takes into account the context of the words. Also, it can cover a lot of idioms and phrases in English. Based on the description, we can use LLMs to create different exercises and tests for students. Here everything depends on the desired type of exercise and how well-detailed the description is. You can even ask the tool you are using to provide an explanation for the correct answers to the exercise or test. However, we must remember that the LLM's output may not be perfect, and corrections might be needed. GenAI tools can become extremely useful for designing and structuring course materials like syllabi, lesson plans, and individual assignments. They can also personalize the content of the course to match individual students' knowledge gaps, skills, and learning styles by practicing problematic areas and creating interactive exercises. LLM models can be used as general-purpose language classifiers. This means that they can classify input text into a variety of predefined categories. For example, we can ask it to tell us what language a text is written in, the emotion/sentiment of the text, its writing tone, etc. This list does not fully cover all possible applications of generative AI in education. Some of these use cases may not be suitable for all types of data and tasks. To achieve the best results, we suggest you get insights from successful implementations, find effective strategies, and tailor them to your specific case. However, to ensure the efficiency of generative AI applications, lecturers should ensure that the following principles for the effective and ethical use of AI are well-explained to students. Even though AI is gaining popularity everywhere, it's crucial to understand not only the advantages of its usage but also the limitations and potential risks. By using genAI's language capabilities, the EdTech sector can truly benefit from improved content generation, summarization, translation, and explanations. However, we must be extremely cautious because genAI might produce incorrect or nonsensical information. In order to optimize the benefits of this technology, integrating generative AI with expert systems can ensure the delivery of accurate and reliable knowledge, which will definitely positively change the educational experience for both: students and lecturers. Our conversation doesn't end here. We are truly eager to learn from you too! How are you integrating GenAI into your educational responsibilities? Share your insights and experiences in the comments below!