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  • New voice technology advancements are here to stay. They will only continue to grow in popularity, making now the perfect time to get ahead of the curve and start creating innovative voice experiences for drivers. As car manufacturers integrate speech recognition into their cars, this opens up a new world of possibilities for the technology and its users.

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    It’s clear that food AI will have a huge influence on how we eat. From fast food chains’ drive towards more customizable menus to a slew of new, innovative restaurants, there are countless opportunities for technology to simplify our eating experiences and improve the quality of our food. With the advancement of artificial intelligence and machine learning algorithms, we can expect intelligent food AI to positively impact our health and the overall ecological impact of our food system.

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    In summary, semantic segmentation is an important sector of deep learning algorithms leveraged to supercharge advancements in computer vision. Semantic segmentation will continue to advance in many of these related subcategories, object detection, classification, and localization.

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    Overall, an effective speech recognition system should be easy to set up and use in various situations while achieving accurate results with little frustration on the user’s part.

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    Building smart home data requires a set of processes that make sure in the end that the machine learning algorithm is working and processing data without any disruption.

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    The insurance industry has been traditionally conservative with technology advances and hesitant to adopt new technologies. However, times are changing, and artificial intelligence (AI) is gaining much attention from insurance companies, who are starting to realize the important role that AI can play in their operations.

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    Data collection is the process of gathering, analyzing, and, measuring accurate data from diverse systems to use for business process decision-making, speech projects, and research.

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    Banking isn’t what it used to be. Most of us need fast, efficient, flawless banking services that are hassle-free and, most importantly, reliable. It only makes sense to shift to digital banking channels that can provide these things. As it turns out, artificial intelligence (AI) and machine learning (ML) powered virtual assistants can do precisely that.

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    Have you ever had to translate important emails into another language? If so, you’ll find it frustrating knowing that someone’s email answering service cannot translate your emails for you quickly. This can be particularly frustrating if communication is key for any organization.

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    The terms chatbot and virtual assistants are used for creating conversations using automation capability with a human touch. With autonomous resolution, chatbots and virtual assistants accelerate employee and customer experience as well.

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    Often regarded as one of the sub-domain of text classification, an oversimplified version of document classification means tagging the docs and setting them right into predefined categories – for the purpose of easy maintenance and efficient discovery.

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    Hey Siri, can you search me for a good blog post that enlists the top Conversational AI trends. Or, Alexa, can you simply play me a song that takes my mind off the mundane daily tasks. Well, these aren’t just rhetorics but standard drawing-room discussions that validate the overall impact of a concept called Conversational AI.

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    OCR or Optical Character Recognition is a fun way to read and understand documents. But why does it even make sense? Let’s find out. But before we proceed, we need to wrap our head around a less common machine learning term: RPA (Robotic Process Automation).

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    The hard truth is that the quality of your collected training data determines the quality of your speech recognition model or even the device. Therefore, it is necessary to connect with experienced data vendors to help you sail through the process without a lot of effort, especially when training a model or the concerned algorithms requires the collection, annotation, and other skillful strategies.

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    The ability infused into the machines— making them capable of interacting in the most humane ways possible— has a different kind of high to it. Yet, the question remains, how does conversational AI work in real-time and what kind of technology is powering its very existence.

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    As the name suggests, synthetic data is the data that is artificially generated rather than being created by actual events. In marketing, social media, healthcare, finance, and security, synthetic data helps build more innovative solutions.

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    When we talk about Optical Character Recognition (OCR), it is a field of Artificial Intelligence (AI) that is specifically related to computer vision and pattern recognition. OCR refers to the process of extracting information from multiple data formats like images, pdf, handwritten notes, and scanned documents and converting them into digital format for further processing.

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    The driver monitoring system is an advanced safety feature that utilizes a camera mounting on the dashboard to monitor the driver’s alertness and drowsiness. In case the driver is getting sleepy and distracted driver monitoring system generates an alert and recommends taking a break.

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    Natural Language Processing is a subfield of Artificial Intelligence capable of breaking down human language and feeding the tenets of the same to the intelligent models. Have you planned to use NLP as your model training technology? Read on to know the challenges and solutions to fix them.

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    On top of that Conversational AI constantly learns from previous experiences using machine learning datasets to offer real-time insight and excellent customer service. Also, Conversational AI not only manually understands and responds to our queries but also can be connected to other AI technologies such as search and vision to fast-track the process.

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    Image recognition is the ability of software to identify objects, places, people, and actions in images. Using machine learning datasets, enterprises can use image recognition to identify and classify objects into several categories.

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    Artificial Intelligence makes machines smarter, period! Yet, the way they do it is as different and intriguing as the concerned vertical. For instance, the likes of Natural Language Processing come in handy if you were to design and develop witty chatbots and digital assistants. Similarly, if you want to make the insurance sector more transparent and accommodative toward the users, Computer Vision is the AI subdomain that you must focus on.

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    Can machines detect emotions by simply scanning the face? The good news is that they can. And the bad news is that the market still has a long way to go before turning mainstream. Yet, the roadblocks and adoption challenges aren’t stopping the AI evangelists from putting ‘Emotion Detection’ on the AI map—quite aggressively.

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    Computer Vision isn’t as widespread as other AI applications like Natural Language Processing. Yet, it is slowly coming up the ranks, making 2022 an exciting year for larger-scale adoption. Here are some of the trendy computer vision potentials (mostly the domains) that are expected to be better explored by businesses in 2022.

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    Enterprises across the world are transitioning from paper-based documents to digital data processing. But, what is OCR? How does it work? And in which business process can it be utilized to leverage its benefits? Let’s dig into this article into what benefits OCR brings to the table.

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    The answer is Automatic Speech Recognition (ASR). It is a huge step to transform the spoken word into written form. Automatic Speech Recognition (ASR) is a trend that is set to make noise in 2022. And the rise in the growth of voice assistants is due to in-built voice assistant smartphones and smart voice devices like Alexa.

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    Are you searching for the brains behind the best Artificial Intelligence models? Well, bow down to the Data Annotators. Even though data annotation takes center stage in preparing resources relevant to every AI-driven vertical, we shall explore the concept and learn more about the labeling protagonists from the perspective of Healthcare AI.

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    And don’t you find it fascinating if shoppers pay the bill at check-out by just representing a face, not any card or wallet? Facial recognition allows retailers to analyze shoppers’ moods and preferences based on their past purchases.

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    With the rising digital payments being made across the globe how can financial organizations ensure maximum sales conversion and payment acceptance, as well as minimize risk exposure? Sounds alarming? In the finance industry that is highly reliant on data processing and information maintaining a marginal edge and understanding the natural nuance of customers to provide on-time resolution requires AI-related technology.

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    Drones are a viable tool for data collection and provide real-time information. Using data analytics enable inspecting bridges, mining, and weather forecast easier.

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    Call Center sentiment analysis is the processing of data by identifying the natural nuance of customer context and analyzing data to make customer service more empathetic.

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    Well, the first reason doesn’t need any validation. Machine learning projects require algorithms, data procurement, high-quality annotation, and other complex aspects taken good care of.

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    As a branch of Artificial Intelligence, NLP is all about making machines responsive to human language. Coming to the tech aspect of it, NLP, quite appropriately, uses computer science, linguistics, algorithms, and overall language structure to make the machines intelligent. The proactive and intuitive machines, whenever built, can extract, analyze, and understand the true meaning and context from speech and even text.

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    This is where Medical Image Annotation has a role to play as it efficiently imparts requisite knowledge to the AI-powered Medical diagnostic setups for furthering the presence of accurate computer vision, as the underlying model development technology.

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    Artificial Intelligence doesn’t need to be a grim topic to discuss. Replete with possibilities to become the most transformative tool in the years to come, AI is fast shaping into an assistive resource instead of staying on course as an overwhelming tech.

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    Are you aware of the technicalities involved in making Machine Learning models holistic, intuitive, and impactful? If not, you first need to understand how each process is broadly segregated into three phases, i.e., Fun, Functionality, and Finesse. While the ‘Finesse’ concerns training ML algorithms to perfection by first developing complex programs using relevant programming languages, the ‘Fun’ part is all about making the customers happy by offering them the perceptive and intelligent fun product.

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    Imagine waking up one fine day and seeing all your kitchen containers market in black, blinding you towards what’s inside. And then, finding sugar cubes for your tea will be a challenge. Provided, you can find the tea first.

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    Data annotation is simply the process of labeling information so that machines can use it. It is especially useful for supervised machine learning (ML), where the system relies on labeled datasets to process, understand, and learn from input patterns to arrive at desired outputs.

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    Data labeling isn’t all that difficult, said no organization ever! But despite the challenges along the way, not many understand the exacting nature of the tasks at hand. Labeling data sets, especially to make them suitable for AI and Machine learning models, is something that requires years of experience and hands-on credibility. And to top it all, data labeling isn’t a one-dimensional approach and varies depending on the type of model in the works.

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    Acquiring data for speech projects is simplified when you take a systematic approach. Read our exclusive post on data acquisition for speech projects and get clarity.

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    In simple words, text annotation is all about labelling specific documents, digital files, and even the associated content. Once these resources are tagged or labelled, they become understandable and can be deployed by the machine learning algorithms to train the models to perfection.

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    Today we have selected Vatsal Ghiya to take his interview. Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.

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    Financial services have metamorphosed over time. The surge in mobile payments, personal banking solutions, better credit monitoring, and other financial patterns further ensures that the realm concerning monetary inclusions isn’t what it was a few years back. In 2021, it isn’t just about the ‘Fin’ or Finance but all ‘FinTech’ with disruptive Financial Technologies making their presence felt to change the customer experience, modus operandi for relevant organizations, or the entire fiscal arena to be exact.

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    Despite the timely ascension of the automotive industry, the vertical leaves a lot of scope for incremental improvements. Starting from lowering traffic accidents to improving vehicle manufacturing and resource deployment, Artificial Intelligence seems like the most probable solution to get things moving skywards.

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    Artificial Intelligence seems more like marketing jargon these days. Every company, startup, or business you know now promotes its products and services with the term ‘AI-powered’ as its USP. True to this, artificial intelligence sure seems to be inevitable nowadays. If you notice, almost everything you have around you is powered by AI. From the recommendation engines on Netflix and algorithms in dating apps to some of the most complex entities in the healthcare sector that help in oncology, artificial intelligence is at the fulcrum of everything today.

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    Machine learning has probably the most mixed definitions and interpretations in the world. What arrived as a buzzword a few years ago continues to baffle a lot of people thanks to the way it’s been portrayed and presented.

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    Artificial Intelligence (AI) is ambitious and immensely beneficial for the advancement of humankind. In a space like healthcare, especially, artificial intelligence is bringing about remarkable changes in the ways we approach the diagnosis of diseases, their treatments, patient care, and patient monitoring. Not to forget the research and development involved in the development of new drugs, newer ways to discover concerns and underlying conditions, and more.

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    Healthcare, as a vertical, was never static. But then, it hasn’t been this dynamic ever, with the confluence of disparate medical insights, making us stare inanimately at piles of unstructured data. To be honest, the gargantuan volume of data isn’t even an issue anymore. It’s a reality, which even exceeded the 2,000 Exabyte mark by the end of 2020.

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    Artificial intelligence is the technology that empowers machines to mimic human behaviors. It is all about teaching machines how to learn and think autonomously and use results to react and respond accordingly.

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    Every time your GPS navigation system asks you to take a detour to avoid traffic, realize that such precise analysis and results come after several hundreds of hours of training. Whenever your Google Lens app accurately identifies an object or a product, understand that thousands after thousands of images have been processed by its AI (Artificial Intelligence) module for exact identification.

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    4 Basic Things To Know About Data De-identification, With data generation happening at the rate of 2.5 quintillion bytes every day, we as internet users generated almost 1.7MB every single second in 2020.

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    Now that the entire planet is online and connected, we are collectively generating immeasurable quantities of data. An industry, a business, market segment, or any other entity would view data as a single unit. Still, as far as individuals are concerned, data is better referred to as our digital footprint.

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    Quality data translates to success stories while poor data quality makes for a good case study. Some of the most impactful case studies on AI functionality have stemmed from a lack of quality datasets. While companies are all excited and ambitious about their AI ventures and products, the excitement doesn’t reflect on data collection and training practices. With more focus on output than training, several businesses end up delaying their time to market, losing funding, or even pulling down their shutters for eternity.

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    A process to annotate or tag generated data, this allows machine learning and artificial intelligence algorithms to efficiently identify each data type and decide what to learn from it and what to do with it. The more well-defined or labeled each data set is, the better the algorithms can process it for optimized results.

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    Alexa, is there a sushi place near me? Oftentimes, we often ask open-ended questions to our virtual assistants. Asking questions like these to fellow humans is understandable considering this is how we are used to speaking and interacting. However, asking a very casual question colloquially to a machine that hardly has any grasp of language and conversational intricacies doesn’t make any sense right?

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    Well, behind every such surprising incident, there are concepts in action like artificial intelligence, machine learning, and most importantly, NLP (Natural Language Processing). One of the biggest breakthroughs of our recent times is NLP, where machines are gradually evolving to understand how humans talk, emote, comprehend, respond, analyze and even mimic human conversations and sentiment-driven behaviors. This concept has been highly influential in the development of chatbots, text-to-speech tools, voice recognition, virtual assistants, and more.

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    Despite being a concept introduced in the 1950s, Artificial Intelligence (AI) did not become a household name until a couple of years back. The evolution of AI has been gradual and it has taken almost 6 decades to offer the insane features and functionalities it does today. All this has been immensely possible due to the simultaneous evolution of hardware peripherals, tech infrastructures, allied concepts like cloud computing, data storage and processing systems (Big Data and analytics), the penetration and commercialization of the internet, and more. Everything together has led to this amazing phase of tech timeline, where AI and Machine Learning (ML) are not just powering innovations but becoming inevitable concepts to live without as well.

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    Every AI system needs massive volumes of quality data to train and deliver accurate results. Now, there are two keywords in this sentence - massive volumes and quality data. Let’s discuss both individually.

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    All conversations and discussions so far on the deployment of artificial intelligence for business and operations purposes have only been superficial. Some talk about the benefits of implementing them while others discuss how an AI module can increase productivity by 40%. But we hardly address the real challenges involved in incorporating them for our business purposes.

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    It is hard to imagine fighting a global pandemic without technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The exponential rise of Covid-19 cases around the world left many health infrastructures paralyzed. However, institutions, governments, and organizations were able to fight back with the help of advanced technologies. Artificial intelligence and machine learning, once seen as a luxury for elevated lifestyles and productivity, have become life-saving agents in combating Covid thanks to their innumerable applications.

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    Pain is experienced more intensely among certain groups of people. Studies have shown that individuals from minority and underprivileged groups tend to experience more physical pain than the general population due to stress, overall health, and other factors.

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    Before you even plan to procure the data, one of the most important considerations in determining how much you should spend on your AI training data. In this article, we will give you insights to develop an effective budget for AI training data.

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    Shaip is an online platform that focuses on healthcare AI data solutions and offers licensed healthcare data designed to help construct AI models. It provides text-based patient medical records and claims data, audio such as physician recordings or patient/doctor conversations, and images and video in the form of X-rays, CT scans, and MRI results.

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    Data is one of the most important elements in developing an AI algorithm. Remember that just because data is being generated faster than ever before doesn’t mean the right data is easy to come by. Low-quality, biased, or incorrectly annotated data can (at best) add another step. These extra steps will slow you down because the data science and development teams must work through these on the way to a functional application.

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    Much has been made about the potential for artificial intelligence to transform the healthcare industry, and for good reason. Sophisticated AI platforms are fueled by data, and healthcare organizations have that in abundance. So why has the industry lagged behind others in terms of AI adoption? That’s a multifaceted question with many possible answers. All of them, however, will undoubtedly highlight one obstacle in particular: large amounts of unstructured data.

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    However, what appears simple is tedious to develop and deploy like any other complex AI system. Before your device could recognize the image that you capture and the Machine Learning (ML) modules could process it, a data annotator or a team of them would have spent thousands of hours annotating data to make them understandable by machines.

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    In this special guest feature, Vatsal Ghiya, CEO and co-founder of Shaip, explores the three factors that he believes will allow data-driven AI to reach its full potential in the future: the talent and resources necessary to construct innovative algorithms, an immense amount of data to accurately train those algorithms, and ample processing power to effectively mine that data. Vatsal is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. Shaip enables the on-demand scaling of its platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.

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    Processes in Artificial Intelligence (AI) systems are evolutionary. Unlike other products, services, or systems in the market, AI models don’t offer instant use cases or immediately 100% accurate results. The results evolve with more processing of relevant and quality data. It’s like how a baby learns to talk or how a musician starts by learning the first five major chords and then builds on them. Achievements are not unlocked overnight, but training happens consistently for excellence.

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    Whenever we talk about Artificial Intelligence (AI) and Machine Learning (ML), what we instantly imagine are powerful tech companies, convenient and futuristic solutions, fancy self-driving cars, and basically everything that is aesthetically, creatively, and intellectually pleasing. What hardly gets projected to people is the real world behind all the conveniences and lifestyle experiences offered by AI.

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    An exclusive interview where Utsav, Business Head - Shaip interacts with Sunil, Executive Editor, My Startup to brief him on how Shaip enhances human life by solving the problems of the future with its Conversational AI and Healthcare AI offerings. He further states how AI, ML is set to revolutionize the way we do business and how Shaip will contribute to the development of next-generation technologies.

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    Artificial Intelligence (AI) is making our lifestyles better through better movie recommendations, restaurant suggestions, resolving conflicts through chatbots, and more. The power, potential, and capabilities of AI are increasingly being put to good use across industries and in areas that nobody probably thought of. In fact, AI is being explored and implemented in areas such as healthcare, retail, banking, criminal justice, surveillance, hiring, fixing wage gaps, and more.

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    We’ve all seen what happens when AI development goes awry. Consider Amazon’s attempt to create an AI recruiting system, which was a great way to scan résumés and identify the most qualified candidates — provided those candidates were male.

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    The healthcare industry was put to the test last year due to the pandemic, and a lot of innovation shone through—from new drugs and medical devices to supply-chain breakthroughs and better collaboration processes. Business leaders from all areas of the industry found new ways to accelerate growth to support the common good and generate critical revenue.

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    We’ve seen them in films, we’ve read about them in books and we’ve experienced them in real life. As sci-fi as it may seem, We have to face the facts – facial recognition is here to stay. The tech is evolving at a dynamic rate and with the diverse use cases that are popping up across industries, the wide range of developments of facial recognition simply appear to be inevitable and infinite.

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    Multilingual chatbots are transforming the business world. Chatbots have come a long way since their early stages, where they’d provide simple one-word answers. A chatbot can now chat fluently in dozens of languages, allowing businesses to expand into a wider global marketplace.

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    Healthcare is often thought of as an industry on the cutting edge of technological innovation. That’s true in many ways, but the healthcare space is also highly regulated by sweeping legislation such as GDPR and HIPAA, along with many more local guidelines and restrictions.

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    A 2018 report revealed that we generated close to 2.5 quintillion bytes of data every single day. Contrary to popular belief, not all the data we generate can be processed for insights.

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    Artificial intelligence is getting smarter by the day. Today, powerful machine learning algorithms are within reach of normal businesses, and algorithms requiring processing power that would once have been reserved for massive mainframes can now be deployed on affordable cloud servers.

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