Oct 6, 2020

Fractal forges ahead in Asian AI market

AI
Technology
investment
Asia
Paddy Smith
2 min
Digital transformation
Fractal subsidiary Theremin.ai stocks coffers for Indian and Asian capital market investment strategy expansion...

New York-based Fractal has announced further funding for its Theremin.ai subsidiary, bolstering the company’s drive to dominate Asian investment strategy.

The new funding, from OLMO Capital, will be used to hire fresh talent and build the company’s algorithmic investment product, which focuses on quantitative investment strategies for the Indian and Asian capital markets.

Fractal’s portfolio now includes Theremin.ai, an investment decision-making platform, Qure.ai, which focuses on radiology diagnostics, Cuddle.ai, a strategic decision-making aid, and Eugenie.ai, which picks out anomalies in high-velocity data.

"Theremin.ai's focus is to generate high-value alpha investment strategies and hence there is a need for large datasets, exhaustive research, and specialized talent. This round of financing will help us to continue investing in these areas," said Hemant Kothavade, founder and CEO of Theremin.ai. 

Fractal co-founder, group chief executive and vice chairman Srikanth Velamakanni said, "We set up Theremin.ai to test whether our algorithms could find signals in a nearly perfect capital markets context and we are encouraged by the results. We are excited about two of our AI product businesses (Qure.ai and Theremin.ai) raising external equity financing during this extraordinary year. It validates our approach of finding great entrepreneurs within and outside Fractal and building AI businesses with them while staying consistent with our mission of powering human decisions with AI."

"There is a tremendous potential to use AI to make better informed and high-performance Investment strategies"

OLMO Capital chairman Gulu Mirchandani, said, "We are very excited to partner with Theremin.ai, who are transforming the way investment decisions are being made today. We believe not much innovation has happened in this regard in the Indian and Asian capital markets, and there is a tremendous potential to use AI to make better informed and high-performance Investment strategies. Theremin.ai has the right team and capabilities to capitalize on the situation, and we are glad to partner with them to help accelerate their work in this space.”

Satish Raman, chief strategy officer at Fractal said, "Fractal's Ideas2Business initiative is focused on fostering cutting-edge ideas and innovation that help in creating new and market-ready AI-based digital products, platforms and solutions. Our spin-outs of Theremin.ai and Qure.ai are testaments to the success of this program. Cuddle.ai and Eugenie.ai are two other AI products that we believe will soon take the same route, even while we have many others in the early stages, which we will bring to the market."

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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