1. Synthesize data to protect privacy
Currently, as AI technology has grown exponentially and has become more advanced, its limitations remain. For example, some industries lack enough real data to train AI models, or compliance and privacy have become a pain point for technological development in some industries. Companies are beginning to deploy SyntheTIc data, artificially generated data by computers that can be used as a substitute for real data sets collected from the real world.
At this stage, image synthesis technology and video synthesis technology based on GANs are developing rapidly, but efficient and feasible table data synthesis technology is still in its infancy. Overall, advantages such as data anonymization, privacy compliance, and correction of algorithmic bias make synthetic data technology a key element in attracting companies across industries.
Second, the chip chase
With the continuous progress of AI technology, the application in various industries is accelerated. Whether it’s cloud data centers or smart edge devices like cameras, demand for compute-intensive specialized hardware is exploding.
Due to space and energy constraints, large chips are not suitable for many everyday AI application scenarios. With this in mind, more and more companies are developing AI chips that can be used in low-power devices such as automotive sensors, cameras, and automated factory robots.
3. AI-powered content review
In the United States, the number of people playing video games is at an all-time high. Seventy-six percent of teens under the age of 18 are addicted to video games, leading parents to worry that their children are at high risk of being exposed to inappropriate or hateful information.
With the fervor of the metaverse and the rapid development of the online gaming ecosystem, harmful information has spread from social media to new frontiers, namely online games and virtual worlds.
Online gaming is a harsh environment, full of hate speech, cyberbullying, and intentional quitting. A study by the Anti-Defamation League found that up to 80 percent of players in the more popular multiplayer games have experienced harassment.
Some startups are looking at leveraging AI technology for content moderation. Spectrum Labs claims that its NLP platform can reduce content moderation efforts for audio and text by 50 percent and improve detection of harmful information by a factor of 10.
It is impossible to achieve perfect content moderation with AI technology. Online users are able to constantly adapt to the censorship rules and evade the platform’s censorship. However, breakthroughs in key areas such as NLP and deep learning-based image classification, as well as multiple rounds of financing for AI startups with content review as their business direction, indicate to some extent that AI review will be one of the future directions.
4. Deepfakes detection
Deepfakes can not only create extremely realistic images, but also generate “fake” sounds and videos.
Using AI learning algorithms, Deepfakes’ technology has become more sophisticated and the effect is very realistic. The sheer number of publicly available videos and recordings on the web and their easy availability make it a lot easier to train AI algorithms and deepfakes. The researchers say it’s difficult for people to distinguish AI-forged portraits, objects and videos from the real thing.
Fake news and fake news spawned by deepfakes is a big problem. For consumers. Deepfakes also have the potential to be a tool for phishing and extortion scams.
In response to growing cybersecurity threats, some tech companies have begun experimenting with various solutions, including device-side authentication software and APIs, blockchain, and more. Last year, researchers at Meta claimed that they could not only determine whether an image is fake, but also dissect AI models used for deepfakes. But deepfakes will continue to evolve and become ubiquitous, and people will need to find new ways to destroy them.
5. Low-code/zero-code development
Algorithms that translate natural language commands into computer code, especially for citizen developers, represent a new wave of software development.
Currently automatic programming is still in its infancy. But technological advancements in the field have prompted some startups to expand zero-code/low-code solutions to enable non-technical users to participate in data science projects, close the skills gap, and speed up production cycles.
6. The rise of multimodal AI
Multimodal AI is breaking down the barriers of a single sense, using a common AI model technology to conceptualize and make predictions about the semantic information contained in multiple types of data.
Multimodal AI is moving from the lab to the real world. For example, Google is using multimodal AI to improve the search experience. In the future, if a user uploads a photo of hiking boots with the text “Can I hike Mt. Fuji in these boots?”, search engines will recognize the uploaded image and mine from text, image and video data Information on Mount Fuji on the web and connecting these trivial pieces of information to provide a pertinent answer.
7. AI for AI
As the application of AI technology expands, enterprises are looking for solutions that completely change the existing data management model and turn to an “AI first” strategy. easy to say, hard to do. From collecting data and running data quality checks to developing models and monitoring post-production performance, moving a project from raw data to production-ready is a multi-step process.
An end-to-end machine learning company that combines multiple steps in the AI lifecycle management process into one SaaS offering will be an excellent choice for businesses looking to build AI systems quickly and efficiently.
No-code and low-code trends are also extending to machine learning platforms to bridge the AI skills gap, and plug-and-play capabilities incentivize non-experts to get involved in AI projects. To this end, in 2021, Databricks, the world’s second most valuable AI unicorn company, acquired 8080 Labs, a supplier of low-code tools.
“AI for AI” has also become an area of growth as most established players begin to deploy Auto ML (Automated Machine Learning) capabilities. Companies are beginning to use AI technology to automate various aspects of the AI development process, such as data quality checks or parts of model development.
Continuous differentiation, the future has come, “AI for AI” will usher in a broader space for development.