AI Disrupting Healthcare. It’s happening. Are you ready?

| June 22, 2018

article image
Why Healthcare needs disrupting by AI. 1. We need better, more individualised predictions. Currently treating ‘the average’ rather than the Individual. 2. Many of the AI-enabled technologies are not in Health. E.g. voice, robotics. 3. Rise in costs in unsustainable. Inflation-adjusted spending up 40% in 10 years. cost per patient predicted to double in the next 40 years.

Spotlight

Sumo Logic

"Sumo Logic helps the world's leading companies analyze and make sense of their log data. Our SaaS-based log analytics platform analyzes TBs of data in real-time, surfacing the most important insights, so you can focus on what matters - delivering high quality software and a great user experience. More than 700 customers around the globe (including IBM, Houzz, Kaiser Permanente, and Microsoft) use Sumo Logic to get real-time insights for their DevOps, IT Ops and compliance teams."

OTHER ARTICLES

How data analytics and IoT are driving insurtech growth

Article | March 17, 2020

Technology is driving change in every industry and region around the world and insurance is no different. The financial services sector is a good example of how digitally disruptive technologies such as artificial intelligence, Big Data and mobile-first banking experiences have paved the way for innovative fintechs.The insurance industry is no different. According to a report by Accenture titled The Rise of Insurtech: How Young Startups and a Mature Industry Can Bring Out the Best in One Another, for example, there is a growing recognition that the insurance industry will ultimately see the greatest benefit and the highest levels of disruption - from this global upsurge in innovation”.

Read More

MAKING IOT DATA MEANINGFUL WITH AI-POWERED COGNITIVE COMPUTING

Article | April 1, 2020

Today, the world is all about industry 4.0 and the technologies brought in by it. From Artificial Intelligence (AI) to Big Data Analytics, all technologies are transforming one or the other industries in some ways. AI-powered Cognitive Computing is one such technology that provides high scale automation with ubiquitous connectivity. More so, it is redefining how IoT technology operates.The need for Cognitive computing in the IoT emerges from the significance of information in present-day business. In the brilliant IoT settings of things to come. Everybody from new AI services companies to undertakings to use the information to settle on choices utilizing realities instead of impulses.Cognitive computing uses information and reacts to changes inside it to decide on better options. It is based on explicit gaining from past encounters, contrasted and a standard-based choice framework

Read More

Do You Know the Differences Between Business Analytics and Data Analytics?

Article | May 19, 2021

There are some fundamental differences between Business Analytics and Data Analytics, though both hold their own importance. For example, to discover patterns and observations that are ultimately used to make informed organizational decisions, Data Analytics includes analyzing datasets. On the other hand, to make realistic, data-driven business decisions, Business Analytics focuses on evaluating different kinds of information and making improvements based on those decisions. In this blog, we discuss in more detail their individual benefits and areas of expertise. Data Analytics vs. Business Analytics attracts a lot of interest from budding analysts; we will take multiple factors into account and help explain the difference between data analyst and business analyst.

Read More

Can you really trust Amazon Product Recommendation?

Article | January 28, 2021

Since the internet became popular, the way we purchase things has evolved from a simple process to a more complicated process. Unlike traditional shopping, it is not possible to experience the products first-hand when purchasing online. Not only this, but there are more options or variants in a single product than ever before, which makes it more challenging to decide. To not make a bad investment, the consumer has to rely heavily on the customer reviews posted by people who are using the product. However, sorting through relevant reviews at multiple eCommerce platforms of different products and then comparing them to choose can work too much. To provide a solution to this problem, Amazon has come up with sentiment analysis using product review data. Amazon performs sentiment analysis on product review data with Artificial Intelligence technology to develop the best suitable products for the customer. This technology enables Amazon to create products that are most likely to be ideal for the customer. A consumer wants to search for only relevant and useful reviews when deciding on a product. A rating system is an excellent way to determine the quality and efficiency of a product. However, it still cannot provide complete information about the product as ratings can be biased. Textual detailed reviews are necessary to improve the consumer experience and in helping them make informed choices. Consumer experience is a vital tool to understand the customer's behavior and increase sales. Amazon has come up with a unique way to make things easier for their customers. They do not promote products that look similar to the other customer's search history. Instead, they recommend products that are similar to the product a user is searching for. This way, they guide the customer using the correlation between the products. To understand this concept better, we must understand how Amazon's recommendation algorithm has upgraded with time. The history of Amazon's recommendation algorithm Before Amazon started a sentiment analysis of customer product reviews using machine learning, they used the same collaborative filtering to make recommendations. Collaborative filtering is the most used way to recommend products online. Earlier, people used user-based collaborative filtering, which was not suitable as there were many uncounted factors. Researchers at Amazon came up with a better way to recommend products that depend on the correlation between products instead of similarities between customers. In user-based collaborative filtering, a customer would be shown recommendations based on people's purchase history with similar search history. In item-to-item collaborative filtering, people are shown recommendations of similar products to their recent purchase history. For example, if a person bought a mobile phone, he will be shown hints of that phone's accessories. Amazon's Personalization team found that using purchase history at a product level can provide better recommendations. This way of filtering also offered a better computational advantage. User-based collaborative filtering requires analyzing several users that have similar shopping history. This process is time-consuming as there are several demographic factors to consider, such as location, gender, age, etc. Also, a customer's shopping history can change in a day. To keep the data relevant, you would have to update the index storing the shopping history daily. However, item-to-item collaborative filtering is easy to maintain as only a tiny subset of the website's customers purchase a specific product. Computing a list of individuals who bought a particular item is much easier than analyzing all the site's customers for similar shopping history. However, there is a proper science between calculating the relatedness of a product. You cannot merely count the number of times a person bought two items together, as that would not make accurate recommendations. Amazon research uses a relatedness metric to come up with recommendations. If a person purchased an item X, then the item Y will only be related to the person if purchasers of item X are more likely to buy item Y. If users who purchased the item X are more likely to purchase the item Y, then only it is considered to be an accurate recommendation. Conclusion In order to provide a good recommendation to a customer, you must show products that have a higher chance of being relevant. There are countless products on Amazon's marketplace, and the customer will not go through several of them to figure out the best one. Eventually, the customer will become frustrated with thousands of options and choose to try a different platform. So Amazon has to develop a unique and efficient way to recommend the products that work better than its competition. User-based collaborative filtering was working fine until the competition increased. As the product listing has increased in the marketplace, you cannot merely rely on previous working algorithms. There are more filters and factors to consider than there were before. Item-to-item collaborative filtering is much more efficient as it automatically filters out products that are likely to be purchased. This limits the factors that require analysis to provide useful recommendations. Amazon has grown into the biggest marketplace in the industry as customers trust and rely on its service. They frequently make changes to fit the recent trends and provide the best customer experience possible.

Read More

Spotlight

Sumo Logic

"Sumo Logic helps the world's leading companies analyze and make sense of their log data. Our SaaS-based log analytics platform analyzes TBs of data in real-time, surfacing the most important insights, so you can focus on what matters - delivering high quality software and a great user experience. More than 700 customers around the globe (including IBM, Houzz, Kaiser Permanente, and Microsoft) use Sumo Logic to get real-time insights for their DevOps, IT Ops and compliance teams."

Events