Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! The metaverse has captivated our collective imagination. The exponential development in internet-connected devices and virtual content is preparing the metaverse for general acceptance, requiring businesses to go beyond traditional approaches to create metaverse content. However, next-generation technologies such as the metaverse, which employs artificial intelligence (AI) and machine learning (ML), rely on enormous datasets to function effectively.

Companies are building software that uses AI to monitor people's behavior and interpret their emotions and body language in real life, virtually and even in the metaverse. But to develop that AI, they need fake data, and startups are stepping in to supply it. Synthetic data companies are providing millions of images, videos and sometimes audio data samples that have been generated for the sole purpose of training or improving AI models that could become part of our everyday lives in controversial forms of AI such as facial recognition, emotion AI and other algorithmic systems used to keep track of people's behavior. While in the past companies building computer vision-based AI often relied on publicly available datasets, now AI developers are looking to customized synthetic data to "address more and more domain-specific problems that have zero data you can actually access," said Ofir Zuk, co-founder and CEO of synthetic data company Datagen. Synthetic data companies including Datagen, Mindtech and Synthesis AI represent a corner of an increasingly compartmentalized AI industry.

Synthesis AI, a startup developing a platform that generates synthetic data to train AI systems, today announced that it raised $17 million in a Series A funding round led by 468 Capital with participation from Sorenson Ventures and Strawberry Creek Ventures, Bee Partners, PJC, iRobot Ventures, Boom Capital and Kubera Venture Capital. CEO and cofounder Yashar Behzadi says that the proceeds will be put toward product R&D, growing the company's team, and expanding research -- particularly in the area of mixed real and synthetic data. Synthetic data, or data that's created artificially rather than captured from the real world, is coming into wider use in data science as the demand for AI systems grows. The benefits are obvious: While collecting real-world data to develop an AI system is costly and labor-intensive, a theoretically infinite amount of synthetic data can be generated to fit any criteria. For example, a developer could use synthetic images of cars and other vehicles to develop a system that can differentiate between makes and models.

Synthetic datasets are increasingly being used to train AI models. These promise greater privacy and less bias, but are not without their drawbacks. Synthetic datasets are becoming increasingly popular for training artificial intelligence models. Proponents of this computer-generated data say it protects personal information and reduces the chances of bias emerging in AI systems. But for many, concerns over privacy and accuracy remain.

C3 AI and Snowflake are partnering to give Snowflake customers access to C3 AI's development tools and enterprise applications, including AI-driven CRM, predictive maintenance, supply network optimization, and fraud detection apps, the companies announced. Billed as a way to "deliver next-generation enterprise AI applications at scale," the partnership will make C3 AI's suite of Integrated Development Studio (IDS) tools -- including C3 AI Data Studio, C3 AI ML Studio, C3 AI App Studio, C3 AI DevSecOps Studio, and C3 AI Marketplace -- available to Snowflake users. Customers using Snowflake's cloud-based data warehousing platform will also get access to C3 AI's AI Suite of operational and security apps based on the C3 AI model-driven architecture. Services provided by those apps include data persistence, batch and stream processing, time-series normalization, auto-scaling, data encryption, attribute and role-based access control, and AI/ML services. "The C3 AI Suite and C3 AI's prebuilt enterprise-grade models significantly speed and simplify the development of enterprise AI applications. As our customers deploy enterprise AI applications at scale, integration with C3 AI to Snowpark will accelerate the development and deployment of complex AI and machine learning use cases," Snowflake senior vice president Christian Kleinerman said in a statement.

AI has the potential to change the world in many amazing ways. But like every revolution, it requires fuel. It's long been said that "data is the oil of the information age," and that's certainly true in many ways. But while data is a less finite resource than actual oil, it does come with some challenges. People are (rightly) protective of their personal data, and there are compliance and regulatory responsibilities that must be upheld if we're using that personal data (often the most valuable kind of data) to power AI and generate predictions.

Modern enterprises are inundated with data; however, not all data is usable as is for machine learning. Though an organization may have millions of data points, it could still have data struggles that stunt machine learning. Turning to synthetic data for machine learning can boost privacy, democratize data, minimize bias in data sets and reduce costs. More broadly, real data and synthetic data tend to be used in combination. "I can't think of any project in the AI space where you wouldn't be able to get a better outcome by leveraging synthetic data," said Kjell Carlsson, principal analyst at Forrester Research.

Founded in 2019, San Francisco-based Synthesis AI has developed technology that generates vast quantities of photorealistic images and pixel-perfect labels to optimize computer vision training. "The world is exploding with cameras," says Synthesis AI CEO Yashar Behzadi. This is great news for AI startups that specialize in computer vision, a field of AI that trains computers to interpret elements from digital images and videos. Up to now, computer vision has relied heavily on supervised learning, in which humans label key attributes in an image and then teach computers to do the same. But to Behzadi, this method has some pretty major setbacks.

Google Assistant will soon be able to act as an interpreter, working as a go-between for natural conversations between initially 27 languages. Think of it as your own personal translator in your pocket. It's a pretty neat addition to the increasingly useful Google Assistant and will be rolling out over the next few weeks but I got a chance to try it out at CES in Las Vegas this week. You jump into interpretation mode by asking Google, say, "Hey Google, be my French translator." When I did that, the Google HomeHub smart display at a cafe Google had set up woke up and waited for my first words.

C3 IoT updated its Internet of Things platform with more machine learning and artificial intelligence technology, integrated more with Amazon Web Services and has more than 100 million sensors and devices under management. The tech revolution is spreading to every corner of the earth with the Internet of Things, and it's enabling data analytics and automation in ways never before imagined in business. Version 7 of C3 IoT's platform will help the company expand into new verticals. As noted previously, C3 IoT initially gained traction with utilities and then moved into new verticals. Houman Behzadi, chief product officer at C3 IoT, said the company is actively working on implementations at oil and gas, health care, and financial services companies.