The Biggest Trends in Research Platforms We’ve Seen This Year

The next decade will see a massive shift in the way researchers conduct research. Generative AI will create new forms of creative content, chatbots will interact with online shoppers, and AI will accelerate the R&D cycle. These trends will impact all aspects of research and development. However, there are a few things you should know now before you make any changes. Listed below are the top trends in research and development.

TikTok is a business tool in 2020 and 2021

It’s not hard to see why TikTok would be a valuable business tool in 2020 and 2021. The number one social media app driving consumer spending has surpassed Tinder and Facebook. Its 884.9 million active monthly users will spend $2.3 billion in 2020 and 2021, nearly three times the amount of the expenditure made by Tinder’s users. What’s more, TikTok reaches 25 percent of female Gen Z users aged 18-24, while only 17.9% of males get that number.

Its emergence from a small app has made it the go-to social media tool for brands. It’s easy to target the right audience and engage with their community of users. Unlike Instagram, TikTok has a unique algorithm that rewards consistency and valuable content. For business owners, exposure is everything. The more you can reach your target audience, the higher your chances of attracting new customers, retaining existing ones, and boosting brand awareness.

Generative AI creates new forms of creative content.

Generative AI is a technique that uses existing content to generate new, plausible content. The MIT Technology Review described generative AI as one of the most promising advances in AI over the past decade. It works by giving computers the information they need to learn to create new content by mixing available data effectively. Generative AI techniques include generative adversarial networks, transformers, and variational autoencoders.

Unlike traditional artificial intelligence, generative AI is self-learning and doesn’t require human intervention. This means that it can produce artifacts in any quantity, including videos. Generative AI is also more likely to have authentic pieces by learning from its training data. It can also protect a person’s identity so that a generative AI avatar can be used for work and interviews. It also allows machine learning algorithms to understand abstract concepts without bias or partiality.

Chatbots interact with online shoppers.

Retail chatbots usually integrate a GUI into the chat window and are designed to allow consumers to make purchases. However, they can also be used to gather consumer information and preferences. These bots should be hosted on messaging platforms like Facebook, Twitter, and Line. Since most consumers use these platforms more often than social networks, they are an excellent way to connect with customers. In addition to interacting with customers on multiple media, chatbots can also resolve customer issues through artificial intelligence.

One example of a chatbot that can provide recommendations and product reviews is Sephora Assistant, introduced in 2017. It aims to bridge the gap between in-app curation and in-store purchasing. This chatbot has grown from its origins as a research tool to a product curation assistant. It has since expanded its role to offer personalized product recommendations and encourage in-store purchases. As retailers consider other potential applications for chatbots in retail, they should explore their capabilities and how they might help their business.

AI accelerates R&D cycles.

Artificial intelligence (AI) is a powerful technology for solving problems and accelerating innovation processes. Among the many applications of AI is the resolution of a critical research problem. Each year, around two million scientific articles are published, generating vast volumes of data, making it difficult to sort through the information and find relevant data. AI helps researchers overcome these challenges by quickly identifying patterns and associations in the data and analyzing them through research platforms.

Analytics and AI have become a key innovation strategies for various industries. It has become a technical foundation for innovation and is accelerating the development of a range of technological advancements. By merging physical and cyberspace, AI and analytics can be a critical part of an organization’s innovation strategy. AI-driven innovation will help the company accelerate its R&D cycle and develop a broad array of innovative products and services.

Next-generation computing

The adoption of next-generation computing is growing exponentially, with the global market forecasted to reach US$785.1 billion by 2030. Several prominent players in the market are focused on optimizing operations and improving overall efficiency. Segmentation for the market focuses on Type, Enterprise Size, Offering, End-User, and Region. For more information about next-generation computing, check out the following infographic.

Cloud-native platforms are built for speed and elasticity, allowing organizations to react to rapid digital change. They improve upon the lift-and-shift approach to cloud computing, which fails to leverage the benefits of cloud computing and adds unnecessary complexity to maintenance. Composable applications are built using modular business components to facilitate reuse. Decision intelligence is a practical approach to improving organizational decision-making, leveraging analytics and intelligence to refine decisions.