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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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    The Covid-19 pandemic may have created economic uncertainty, but it’s a testament to the incredible excitement surrounding AI innovation that investments in the space largely weathered the storm: Just 7 percent of investments decreased, and 16 percent were temporarily suspended in 2020, while 47 percent remained unchanged and 30 percent were set to increase.

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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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