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According to WEKA's 2023 Global Trends in AI Report, 69% of organizations now have AI projects up and running, and 28% are using AI across their whole business. This shows a big move from just trying out AI to making it a key part of how companies operate and succeed. However, this is just the beginning as the major point is not to have only AI but to have it work to your benefit. Organizations have to address various challenges such as the collection of data, hiring the right skills, and fitting AI into their existing system. This guide serves both new companies and big businesses. It gives you clear examples and direct advice on how to work around these problems. We will discuss what specific things you can do to make the most of AI, whether you want to improve your processes, give better customer service, or make better business decisions. We could help you not only to use AI but to make the best use of it to lead the competition in your area. Artificial Intelligence (AI) and Machine Learning (ML) are two modern technologies that are restructuring the way businesses work. The AI study by 451 Research data has revealed that most companies start using AI/ML not just to cut expenses but to generate revenue as well. They are using AI/ML to revamp their profit systems, sharpen their sales strategies, and enhance their product and service offerings. This demonstrates the change of viewpoint, AI/ML becoming a driver of business growth and not just a hands-on tool. For integrating AI in your business to be effective, you need to have clear goals and plans of implementation. We have put together a short guide to get you started in a smart direction for your business. 1. Identify Objectives The first step in your AI integration is clearly stating your goals. This can be: 2. Assess Your Current Setup It's important to note that about 80% of AI projects don't move past the testing phase or lab setup. This often happens because standardizing the way models are built, trained, deployed, and monitored can be tough. AI projects usually need a lot of resources, which makes them challenging to manage and set up. However, this doesn't mean small businesses can't use AI. With the right approach, even smaller companies can make AI work for them, effectively bringing it into their operations. Computational Resources AI models, especially those using machine learning or deep learning, need a lot of computing power to work well to process large datasets. This is important for training the AI, doing calculations, and handling user queries in real-time. Small businesses that don't have massive infrastructure can choose cloud computing services like AWS, Google Cloud, or Microsoft Azure. They have the necessary hardware and can adjust your performance to your needs. Data Quality and Quantity AI requires access to a lot of clean and organized data that is essential for training AI to identify patterns, make correct predictions, and answer questions. Collecting and preparing this kind of high-quality, error-free data in large amounts can be difficult, often taking up to 80% of the time from the start of the project to its deployment. For businesses that don’t have massive amounts of structured data, the solutions can be as follows: Expertise Effective AI implementation requires a strong team capable of creating algorithms, analyzing data, and training models. It involves complex math and statistics and advanced software skills like programming in Python or R, using machine learning frameworks (e.g. TensorFlow or PyTorch), and applying data visualization tools. For businesses that can't afford to gather and maintain a professional AI team, the solution is to partner with niche companies that focus on AI development services, like SciForce. Specialized service providers have the necessary technical skills and business experience that allow them to create tailored AI solutions for your needs. Integration Integrating AI into existing business operations requires planning to ensure smooth incorporation with current software and workflows, avoiding significant disruptions. Challenges include resolving compatibility, ensuring data synchronization, and maintaining workflow efficiency as AI features are introduced. To overcome integration challenges, choose AI solutions with easy compatibility with standard business software, focusing on those with APIs and SDKs for seamless integration. Prefer AI platforms with plug-and-play features for CRM and ERP systems. SciForce offers integration services, specializing in AI solutions that integrate effortlessly with existing software, hardware, and operations with zero disruptions. Ongoing Maintenance and Updates Before engaging in the implementation of AI solutions in the company, remember that AI systems need regular updates, including consistent data stream and software improvements. This helps AI adapt, learn from new inputs, and stay secure against threats. Creating AI from scratch, you will need a permanent internal team to maintain it. If you opt for an out-of-the-box solution, the vendor will deliver automatic updates. Partnering with SciForce, you receive managed AI services with our professionals handling the maintenance and updates of your system. 3. Choose Your AI Tools and Technologies With a variety of AI/ML tools available in the market, it’s hard to choose the one that will suit your needs, especially if it’s your first AI project. Here we asked our ML experts to share top tools they use in their everyday work. Databases AI\ML can’t exist without databases that are the foundation for data handling, training, and analysis. SciForce top choice is Qdrant, a specialized vector database, that excels in this role by offering flexibility, high performance, and secure hosting options. It's particularly useful for creating AI assistants using organizational data. Machine Learning Here is our top choice of the tools that allow us to easier AI model management and deployment. Speech Processing Frameworks These tools help our team to refine voice recognition and teach computers to understand human language better. Large Language Models There are lots of tools for working with LLMs, but many of them are complex inside and not straightforward. Yet, our team picked some tools for you that simplify working with LLMs: Data Science Our Data Science team considers the DoWhy library a valuable tool for causal analysis. It helps to analyze and work with data in more depth, focusing on the cause-and-effect connections between different elements: 4. Start Small and Scale Gradually Begin with small AI projects to see what works best for your business. Learn from these projects and gradually implement more complex AI solutions. - Be focused Start with a small, well-defined AI project that addresses a specific business need or pain point. This could be automating a single task or improving a specific process. Define clear, achievable objectives for your initial AI project. This helps in measuring success and learning from the experience. - Gather a cross-functional team Assemble a team with diverse skills, including members from the relevant business unit, IT, and the ones with specific AI skills you need. This ensures the project benefits from different perspectives. You can also turn to a service provider with relevant expertise. - Use Available Data Begin with the data you already have. This approach helps in understanding the quality and availability of your data for AI applications. In case you lack data, consider using public datasets or purchasing ones. - Scale Based on Learnings Once you have the first results, review them and plan your next steps. To achieve your first goals, you can plan to expand the scope of AI within your business. - Build on Success Use the success of your initial projects to encourage the wider use of AI in your organization. Share what worked and what you learned to get support from key decision-makers. - Monitor and Adjust In managing AI initiatives, it's critical to regularly assess their impact and adapt as needed. Define key performance indicators (KPIs) relevant to each project, such as process efficiency or customer engagement metrics. Employ analytics tools for ongoing monitoring, ensuring continuous alignment with business goals. Read on to learn how to assess AI performance within your business. To make the most of AI for your business, it's essential to measure its impact using Key Performance Indicators (KPIs). These indicators help track AI performance and guide improvements, ensuring that AI efforts are delivering clear results and driving your business forward. 1. Defining Success Metrics To benefit from AI in your business, it's crucial to pick the right Key Performance Indicators (KPIs). These should align with your main business objectives and clearly show how your AI projects are performing: 1. Align with Business Goals Start by reviewing your business objectives. Whether it's growth, efficiency, or customer engagement, ensure your KPIs are directly linked to these goals. 2. Identify AI Impact Areas Pinpoint where AI is expected to make a difference. Is it streamlining operations, enhancing customer experiences, or boosting sales? 3. Choose Quantifiable Metrics Select metrics that offer clear quantification. This might include numerical targets, percentages, or specific performance benchmarks. 4. Ensure Relevance and Realism KPIs should be both relevant to the AI technology being used and realistic in terms of achievable outcomes. 5. Plan for Continuous Review Set up a schedule for regular KPI reviews to adapt and refine your metrics as needed, based on evolving business needs and AI capabilities. Baseline Measurement and Goal Setting Record key performance metrics before integrating AI to serve as a reference point. This helps in directly measuring AI's effect on your business, such as tracking improvements in customer service response times and satisfaction scores. Once you have a baseline, set realistic goals for what you want to achieve with AI. These should be challenging but achievable, tailored to the AI technology you're using and the areas you aim to enhance. Regular Monitoring and Reporting Regularly checking KPIs and keeping up with consistent reports is essential. This ongoing effort makes sure AI efforts stay in line with business targets, enabling quick changes based on real results and feedback. 1. Reporting Schedule Establish a fixed schedule for reports, such as monthly or quarterly, to consistently assess KPI trends and impacts. 2. Revenue Monitoring Monitor revenue shifts, especially those related to AI projects, to measure their direct impact on sales. 3. Operational Costs Comparison Analyze operational expenses before and after AI adoption to evaluate financial savings or efficiencies gained. 4. Customer Satisfaction Tracking Regularly survey customer satisfaction, noting changes that correlate with AI implementations, to assess AI's effect on service quality. ROI Analysis of AI Projects Determining the Return on Investment (ROI) of any project is essential for smart investment in technology. Here’s a concise guide to calculating ROI for AI projects: 1. Cost-Benefit Analysis List all expenses for your AI project, such as development costs, software and hardware purchases, maintenance fees, and training for your team. Then, determine the financial benefits the AI project brings, such as increased revenue and cost savings. 2. ROI Calculation Determine the financial advantages your AI project brings, including any increase in sales or cost reductions. Calculate the net benefits by subtracting the total costs from these gains. Then, find the ROI: 3. Ongoing Evaluation Continuously revise your ROI analysis to include any new data on costs or benefits. This keeps your assessment accurate and helps adjust your AI approach as necessary. Future Growth Opportunities Use the success of your current AI projects as a springboard for more growth and innovation. By looking at how these projects have improved your business, you can plan new ways to use AI for even better results: Expanding AI Use Search for parts of your business that haven't yet benefited from AI, using your previous successes as a guide. For example, if AI has already enhanced your customer service, you might also apply it to make your supply chain more efficient. Building on Success Review your best-performing AI projects to see why they succeeded. Plan to apply these effective strategies more broadly or deepen their impact for even better results. Staying Ahead with AI Keep an eye on the latest in AI and machine learning to spot technologies that could address your current needs or open new growth opportunities. Use the insights from your AI projects to make smart, data-informed choices about where to focus your AI efforts next. AI transforms business operations by enhancing efficiency and intelligence. It upgrades product quality, personalizes services, and streamlines inventory with predictive analytics. Crucial for maintaining a competitive edge, AI optimizes customer experiences and enables quick adaptation to market trends, ensuring businesses lead in their sectors. Computer Vision Computer Vision (CV) empowers computers to interpret and understand visual data, allowing them to make informed decisions and take actions based on what they "see." By automating tasks that require visual inspection and analysis, businesses can increase accuracy, reduce costs, and open up new opportunities for growth and customer engagement. - Quality Control in Manufacturing Computer Vision (CV) streamlines the inspection process by quickly and accurately identifying product flaws, surpassing manual checks. This ensures customers receive only top-quality products. - Retail Customer Analytics CV analyzes store videos to gain insights into how customers shop, what they prefer, and how they move around. Retailers can use this data to tailor marketing efforts and arrange stores in ways that increase sales and improve shopping experiences. - Automated Inventory Management CV helps manage inventory by using visual recognition to keep track of stock levels, making the restocking process automatic and reducing the need for manual stock checks. This increases operational efficiency, keeps stock at ideal levels, and avoids overstocking or running out of items. Case: EyeAI – Space Optimization & Queue Management System Leveraging Computer Vision, we created EyeAI – SciForce custom video analytics product for space optimization and queue management. It doesn’t require purchasing additional hardware or complex integrations – you can immediately use it even with one camera in your space. - Customer Movement Tracking: Our system observes how shoppers move and what they buy, allowing us to personalize offers, and improve their shopping journey. - Store Layout Optimization: We use insights to arrange stores more intuitively, placing popular items along common paths to encourage purchases. - Traffic Monitoring: By tracking shopper numbers and behavior, we adjust staffing and marketing to better match customer flow. - Checkout Efficiency: We analyze line lengths and times, adjusting staff to reduce waits and streamline checkout. - Identifying Traffic Zones: We pinpoint high and low-traffic areas to optimize product placement and store design, enhancing the overall shopping experience. Targeted for HoReCa, retail, public security, healthcare sectors, it analyzes customer behavior and movements and gives insights of space optimization for better security and customer service. Natural Language Processing Natural Language Processing (NLP) allows computers to handle and make sense of human language, letting them respond appropriately to text and spoken words. This automation of language-related tasks helps businesses improve accuracy, cut down on costs, and create new ways to grow and connect with customers. Customer Service Chatbots NLP enables chatbots to answer customer questions instantly and accurately, improving satisfaction by cutting down wait times. This technology helps businesses expand their customer service without significantly increasing costs. Sentiment Analysis for Market Research NLP examines customer opinions in feedback, social media, and reviews to gauge feelings towards products or services. These insights guide better marketing, product development, and customer service strategies. Automated Document Processing NLP automates the handling of large amounts of text data, from emails to contracts. It simplifies tasks like extracting information, organizing data, and summarizing documents, making processes faster and reducing human errors. Case: Recommendation and Classification System for Online Learning Platform We improved a top European online learning platform using advanced AI to make the user experience even better. Knowing that personalized recommendations are key (like how 80% of Netflix and 60% of YouTube views come from them), our client wanted a powerful system to recommend and categorize courses for each user's tastes. The goal was to make users more engaged and loyal to the platform. We needed to enhance how users experience the platform and introduce a new feature that automatically sorts new courses based on what users like. We approached this project with several steps: - Gathering Data: First, we set up a system to collect and organize the data we needed. - Building a Recommendation System: We created a system that suggests courses to users based on their preferences, using techniques that understand natural language and content similarities. - Creating a Classification System: We developed a way to continually classify new courses so they could be recommended accurately. - Integrating Systems: We smoothly added these new systems into the platform, making sure users get personalized course suggestions. The platform now automatically personalizes content for each user, making learning more tailored and engaging. Engagement went up by 18%, and the value users get from the platform increased by 13%. Adopting AI and ML is about setting bold goals, upgrading tech, smart resource use, accessing top data, building an expert team, and aiming for continuous improvement. It isn't just about successful competition — it's about being a trendsetter. Here at SciForce, we combine AI innovations and practical solutions, delivering clear business results. Contact us for a free consultation.
We are excited to announce that we are launching a new section today at Sciforce — “Top AI news of the month.” We decided to create this column to keep our valued clients aware of the latest news and technologies in the AI world. So, here, we will share some interesting information with you about SciTech! Well, today we will discuss Top-5 NLP news in December:
As soon as you start working on a data science task you realize the dependence of your results on the data quality. The initial step — data preparation — of any data science project sets the basis for the effective performance of any sophisticated algorithm. In textual data science tasks, this means that any raw text needs to be carefully preprocessed before the algorithm can digest it. In the most general terms, we take some predetermined body of text and perform upon it some basic analysis and transformations, in order to be left with artifacts that will be much more useful for a more meaningful analytic task afterward. The preprocessing usually consists of several steps that depend on a given task and the text but can be roughly categorized into segmentation, cleaning, normalization, annotation, and analysis.
The NLP domain reports great advances to the extent that a number of problems, such as part-of-speech tagging, are considered to be fully solved. At the same time, such tasks as text summarization or machine dialog systems are notoriously hard to crack and remain open for the past decades. However, if we look deeper into such tasks we’ll see that the problems behind them are rather similar and fall into two groups:
We write a lot about open problems in Natural Language Processing. We complain a lot when working on NLP projects. We pick on inaccuracies and blatant errors of different models. But what we need to admit is that NLP has already changed and new models have solved the problems that may still linger in our memory. One of such drastic developments is the launch of Google’s Bidirectional Encoder Representations from Transformers, or BERT model — the model that is called the best NLP model ever based on its superior performance over a wide variety of tasks. When Google researchers presented a deep bidirectional Transformer model that addresses 11 NLP tasks and surpassed even human performance in the challenging area of question answering, it was seen as a game-changer in NLP/NLU.
Natural language processing (NLP) is a field of Artificial Intelligence that tries to establish human-like communication with computers. Although it can boast significant success, computers still struggle with comprehending many facets of language, such as pragmatics, that are difficult to characterize formally. Moreover, most of the success is achieved in popular languages like English or other languages that have text corpora of hundreds of millions of words. But we should understand that these are only about 20 languages from approximately 7,000 languages in the world. The majority of human languages are in dire need of tools and resources to overcome the resource barrier such that NLP can deliver more widespread benefits. They are called low-resource languages languages, or languages lacking large monolingual or parallel corpora and/or manually crafted linguistic resources sufficient for building statistical NLP applications. It might look like we need only a dozen of languages to do fine in the world, so why bother with minor or extinct languages? However, building NLP applications for such languages can at the same time reinforce the ties between the world and ensure its diversity. _Transfer of annotations_ (such as POS tags, syntactic or semantic features) via cross-lingual bridges (e.g., word or phrase alignments). However, training such models with cross-lingual transfer learning usually requires linguistic knowledge and resources about the relation between the source language and the target language. Recent developments, though, offer techniques that do not require ancillary resources such as parallel corpora. In Kim et al. (2017), for instance, a cross-lingual model utilizes a common BLSTM that enables knowledge transfer from other languages, and private BLSTMs for language-specific representations without exploiting any linguistic knowledge between the source language and the target language. The cross-lingual model is trained with language-adversarial training and bidirectional language modeling to represent language-general information and preserve the information about a specific target language. _Transfer of models_ refers to training a model in a resource-rich language and applying it in a resource-poor language in zero-shot or one-shot learning. Zero-shot learning refers to training a model in one domain and assuming it generalizes more or less out-of-the-box in a low-resource domain. One-shot learning is a similar approach that uses a very limited number of examples from a low-resource domain to adapt the model trained in the rich-resource domain. This approach is particularly popular in machine translation where the weights collected for a rich-resource language pair are transferred to low-resource pairs. An example of such an approach is a model by Zoph et al. (2016). A “parent” model is trained in a high-resource language pair (French to English) and some of the trained weights are reused as the initialization for a “child” model which is further trained on a specific low-resource language pair (Hansa, Turkish, and Uzbek into English). A similar approach was explored by Nguyen and Chiang (2017) where the parent language pair is also low-resource but it was related to the child language pair. _Joint Multilingual or “Polyglot” Learning_ converts data in all languages to a shared representation (e.g., phones or multilingual word vectors) and trains a single model on a mix of datasets in all languages, to enable parameter sharing where possible. This approach is closely related to recent efforts to train a cross-lingual Transformer language model trained on 100 most popular languages and cross-lingual sentence embeddings. The latter approach learns joint multilingual sentence representations for 93 languages, belonging to more than 30 different language families and written in 28 scripts. With the help of a single BiLSTM encoder with a shared BPE vocabulary for all languages, which is coupled with an auxiliary decoder and trained on parallel corpora, the approach allows learning a classifier on top of the resulting sentence embeddings using English annotated data only and transfer it to any of the 93 languages without any modification. Drawing a conclusion, we can once more say that the actual reason many specialists work on NLP problems is to build systems that break down barriers. Given the potential impact on mankind, building systems for low-resource languages is one of the most important areas to work on. There are already a lot of promising approaches dealing with low-data settings that may include low-resource languages, dialects, sociolects, and domains, but notwithstanding the pursuit to find linguistic universalities, there is still no universal solution to cover all the languages in the world.
Integration and interdisciplinarity are the cornerstones of modern science and industry. One of the examples of recent attempts to combine everything is the integration of computer vision and natural language processing (NLP). Both these fields are one of the most actively developing machine learning research areas. Yet, until recently, they have been treated as separate areas without many ways to benefit from each other. It is now, with the expansion of multimedia, researchers have started exploring the possibilities of applying both approaches to achieve one result. The most natural way for humans is to extract and analyze information from diverse sources. This conforms to the theory of semiotics (Greenlee 1978) — the study of the relations between signs and their meanings at different levels. Semiotics studies the relationship between signs and meaning, the formal relations between signs (roughly equivalent to syntax), and the way humans interpret signs depending on the context (pragmatics in linguistic theory). If we consider purely visual signs, then this leads to the conclusion that semiotics can also be approached by computer vision, extracting interesting signs for natural language processing to realize the corresponding meanings. Malik summarizes Computer Vision tasks in 3Rs (Malik et al. 2016): reconstruction, recognition, and reorganization. Reconstruction refers to the estimation of a 3D scene that gave rise to a particular visual image by incorporating information from multiple views, shading, texture, or direct depth sensors. The process results in a 3D model, such as point clouds or depth images. Recognition involves assigning labels to objects in the image. For 2D objects, examples of recognition are handwriting or face recognition, and 3D tasks tackle such problems as object recognition from point clouds which assists in robotics manipulation. Reorganization means bottom-up vision when raw pixels are segmented into groups that represent the structure of an image. Low-level vision tasks include edge, contour, and corner detection, while high-level tasks involve semantic segmentation, which partially overlaps with recognition tasks. It is recognition that is most closely connected to language because it has an output that can be interpreted as words. For example, objects can be represented by nouns, activities by verbs, and object attributes by adjectives. In this sense, vision and language are connected by means of semantic representations (Gardenfors 2014; Gupta 2009). NLP tasks are more diverse as compared to Computer Vision and range from syntax, including morphology and compositionality, semantics as a study of meaning, including relations between words, phrases, sentences, and discourses, to pragmatics, a study of shades of meaning, at the level of natural communication. Some complex tasks in NLP include machine translation, dialog interface, information extraction, and summarization. It is believed that switching from images to words is the closest to machine translation. Still, such “translation” between low-level pixels or contours of an image and a high-level description in words or sentences — the task known as Bridging the Semantic Gap (Zhao and Grosky 2002) — remains a wide gap to cross. The integration of vision and language was not going smoothly in a top-down deliberate manner, so researchers came up with a set of principles. Integrated techniques were rather developed bottom-up, as some pioneers identified certain rather specific and narrow problems, attempted multiple solutions, and found a satisfactory outcome. The new trajectory started with the understanding that most present-day files are multimedia and that they contain interrelated images, videos, and natural language texts. For example, a typical news article contains a written by a journalist and a photo related to the news content. Furthermore, there may be a clip video that contains a reporter or a snapshot of the scene where the event in the news occurred. Language and visual data provide two sets of information that are combined into a single story, making the basis for appropriate and unambiguous communication. This understanding gave rise to multiple applications of an integrated approach to visual and textual content not only in working with multimedia files, but also in the fields of robotics, visual translations, and distributional semantics. The multimedia-related tasks for NLP and computer vision fall into three main categories: visual properties description, visual description, and visual retrieval. Visual properties description: A step beyond classification, the descriptive approach summarizes object properties by assigning attributes. Such attributes may be both binary values for easily recognizable properties or relative attributes describing a property with the help of a learning-to-rank framework. The key is that the attributes will provide a set of contexts as a knowledge source for recognizing a specific object by its properties. The attribute words become an intermediate representation that helps bridge the semantic gap between the visual space and the label space. Visual description: in real life, the task of visual description is to provide image or video capturing. It is believed that sentences would provide a more informative description of an image than a bag of unordered words. To generate a sentence that would describe an image, a certain amount of low-level visual information should be extracted that would provide the basic information “who did what to whom, and where and how they did it”. From the part-of-speech perspective, the quadruplets of “Nouns, Verbs, Scenes, Prepositions” can represent meaning extracted from visual detectors. Visual modules extract objects that are either a subject or an object in the sentence. Then a Hidden Markov Model is used to decode the most probable sentence from a finite set of quadruplets along with some corpus-guided priors for verb and scene (preposition) predictions. The meaning is represented using objects (nouns), visual attributes (adjectives), and spatial relationships (prepositions). Then the sentence is generated with the help of the phrase fusion technique using web-scale n-grams for determining probabilities. Visual retrieval: Content-based Image Retrieval (CBIR) is another field in multimedia that utilizes language in the form of query strings or concepts. As a rule, images are indexed by low-level vision features like color, shape, and texture. CBIR systems try to annotate an image region with a word, similar to semantic segmentation, so the keyword tags are close to human interpretation. CBIR systems use keywords to describe an image for image retrieval but visual attributes describe an image for image understanding. Nevertheless, visual attributes provide a suitable middle layer for CBIR with an adaptation to the target domain. Robotics Vision: Robots need to perceive their surroundings from more than one way of interaction. Similar to humans processing perceptual inputs by using their knowledge about things in the form of words, phrases, and sentences, robots also need to integrate their perceived picture with the language to obtain the relevant knowledge about objects, scenes, actions, or events in the real world, make sense of them and perform a corresponding action. For example, if an object is far away, a human operator may verbally request an action to reach a clearer viewpoint. Robotics Vision tasks relate to how a robot can perform sequences of actions on objects to manipulate the real-world environment using hardware sensors like depth cameras or motion cameras and having a verbalized image of their surroundings to respond to verbal commands. Situated Language: Robots use languages to describe the physical world and understand their environment. Moreover, spoken language and natural gestures are more convenient ways of interacting with a robot for a human being, if the robot is trained to understand this mode of interaction. From the human point of view, this is a more natural way of interaction. Therefore, a robot should be able to perceive and transform the information from its contextual perception into a language using semantic structures. The most well-known approach to representing meaning is Semantic Parsing, which transforms words into logical predicates. SP tries to map a natural language sentence to a corresponding meaning representation that can be a logical form like λ-calculus using Combinatorial Categorical Grammar (CCG) as rules to compositionally construct a parse tree. Early Multimodal Distributional Semantics Models: The idea lying behind Distributional Semantics Models is that words in similar contexts should have similar meaning, therefore, word meaning can be recovered from co-occurrence statistics between words and contexts in which they appear. This approach is believed to be beneficial in computer vision and natural language processing as image embedding and word embedding. DSMs are applied to jointly model semantics based on both visual features like colors, shape, or texture and textual features like words. The common pipeline is to map visual data to words and apply distributional semantics models like LSA or topic models on top of them. Visual attributes can approximate the linguistic features for a distributional semantics model. Neural* *Multimodal Distributional Semantics Models: Neural models have surpassed many traditional methods in both vision and language by learning better-distributed representation from the data. For instance, Multimodal Deep Boltzmann Machines can model joint visual and textual features better than topic models. In addition, neural models can model some cognitively plausible phenomena such as attention and memory. For attention, an image can initially give an image embedding representation using CNNs and RNNs. An LSTM network can be placed on top and act like a state machine that simultaneously generates outputs, such as image captions, or looks at relevant regions of interest in an image one at a time. For memory, commonsense knowledge is integrated into visual question-answering If combined, two tasks can solve a number of long-standing problems in multiple fields, including:
As artificial intelligence progresses and technology becomes more sophisticated, we expect existing concepts to embrace this change — or change themselves. Similarly, in the domain of computer-aided processing of natural languages, shall the concept of natural language processing give way to natural language understanding? Or is the relation between the two concepts subtler and more complicated than merely the linear progress of a technology? In this post, we’ll scrutinize the concepts of NLP and NLU and their niches in AI-related technology. Importantly, though sometimes used interchangeably, they are actually two different concepts that have some overlap. First of all, they both deal with the relationship between natural language and artificial intelligence. They both attempt to make sense of unstructured data, like language, as opposed to structured data like statistics, actions, etc. However, NLP and NLU are opposites of a lot of other data mining techniques. Source: https://nlp.stanford.edu/~wcmac/papers/20140716-UNLU.pdf NLP is an already well-established, decades-old field operating at the cross-section of computer science, artificial intelligence, and increasingly data mining. The ultimate of NLP is to read, decipher, understand, and make sense of human languages by machines, taking certain tasks off the humans and allowing for a machine to handle them instead. Common real-world examples of such tasks are online chatbots, text summarizers, auto-generated keyword tabs, as well as tools analyzing the sentiment of a given text. NLP in its broadest sense can refer to a wide range of tools, such as speech recognition, natural language recognition, and natural language generation. Yet, the most common tasks of NLP are historically:
No matter what technology domain you are looking at, the unanimous expectation of the next year is that natural language processing will take the leading role in this domain advancement. Be it Business Intelligence, FinTech, or Healthcare, NLP seems to become a field-shaping technology. In 2018, businesses used NLP techniques in several areas:
When it comes to choosing the right book, you become immediately overwhelmed with the abundance of possibilities: should you choose a classic for a solid base or a fresh-from-the-oven book for the newest trends? What level to stick to? Will a beginner’s guide be too easy? In this review, we have collected our Top 10 NLP and Text Analysis Books of all time, ranging from beginners to experts. by Steven Bird, Ewan Klein and Edward Loper. It is so popular, that every top seems to have it listed. Well, it is a timeless classic that provides an introduction to NLP using the Python and its NLTK library. Target readers: Beginners in NLP, computational linguists and AI developers Why it is good: The book is very practice-oriented: you won’t be introduced to complex theories behind, just plenty of code and concepts to start experimenting right away. Where to find: Target readers: Beginners in natural language processing with no required knowledge of linguistics or statistics Why it is good: Though rather old, this book gives a strong foundation in linguistics and statistical methods and to better understand the newer methods and encodings. Where to find: Target readers: Beginners in natural language and speech processing Why it is good: The book provides a solid foundational knowledge as it introduces linguistics, computer science and statistics at comprehensive depth. Where to find: Target readers: Linguists as well as researchers in informatics, artificial intelligence, language engineering, and cognitive science. Why it is good: It is an academic edition, meaning that it theory-oriented and provides deeper understanding of major concepts that their functioning. Where to find: Target readers: Practitioners at least slightly familiar with R. Why it is good: It is quite new; therefore it has a practical and modern feel to the demonstrations and provides examples of real text mining problems. Where to find: Target readers: Software developers and industry practitioners who are already familiar with neural networks. Why it is good: The book offers a thorough overview of state-of-the-art neural network models that may be useful for NLP. Where to find: Target readers: Software developers who want to familiarize themselves with enterprise-grade NLP tools for work projects. Why it is good: This book offers first-hand insights into Apache-based NLP a cofounder of the Apache Mahout project. Besides, it is a rare book having Java code examples. Where to find: Target readers: Advanced undergraduate and graduate students in computational linguistics and computer science, as well as academic and industrial researchers. Why it is good: First of all, it is a 2018 edition, so it reviews the real state of the art. Besides, it provides deep and fundamental knowledge of deep learning far beyond practical applications. Where to find: Target readers: Software developers in Python who are interested in applying natural language processing and machine learning to their software development toolkit. Why it is good: This practical book presents a data scientist’s perspective on building language-aware products with applied machine learning techniques. Where to find: Target readers: Software developers with at least minor previous experience in machine learning. Why it is good: The book gives a comprehensive overview of the most recent developments in machine learning starting from simple linear regression and progressing to deep neural networks — and it all on two most popular libraries: Scikit-Learn and TensorFlow. Where to find: